AI_18_안상도.ipynb

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      "metadata": {
        "id": "BDLlPkpt9BS7"
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      "source": [
        "# 라이브러리 불러오기\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#데이터 불러오기\n",
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "metadata": {
        "id": "_633Jfh-9DLL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77
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              "      Upload widget is only available when the cell has been executed in the\n",
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              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
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              "  const steps = uploadFilesStep(inputId, outputId);\n",
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              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
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              "\n",
              "  // Wait for the user to pick the files.\n",
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              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
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              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
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              "      }\n",
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              "\n",
              "  for (const file of files) {\n",
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              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
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              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
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              "      reader.onload = (e) => {\n",
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              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
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              "    do {\n",
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              "\n",
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              "      yield {\n",
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving vgames2.csv to vgames2.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('vgames2.csv')"
      ],
      "metadata": {
        "id": "y526YcOr9cnB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#데이터\n",
        "df.head(5)"
      ],
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              "   Unnamed: 0                                            Name Platform  \\\n",
              "0           1                    Candace Kane's Candy Factory       DS   \n",
              "1           2                                  The Munchables      Wii   \n",
              "2           3  Otome wa Oanesama Boku ni Koi Shiteru Portable      PSP   \n",
              "3           4                Deal or No Deal: Special Edition       DS   \n",
              "4           5       Ben 10 Ultimate Alien: Cosmic Destruction      PS3   \n",
              "\n",
              "     Year      Genre           Publisher NA_Sales EU_Sales JP_Sales  \\\n",
              "0  2008.0     Action           Destineer     0.04        0        0   \n",
              "1  2009.0     Action  Namco Bandai Games     0.17        0        0   \n",
              "2  2010.0  Adventure           Alchemist        0        0     0.02   \n",
              "3  2010.0       Misc           Zoo Games     0.04        0        0   \n",
              "4  2010.0   Platform         D3Publisher     0.12     0.09        0   \n",
              "\n",
              "  Other_Sales  \n",
              "0           0  \n",
              "1        0.01  \n",
              "2           0  \n",
              "3           0  \n",
              "4        0.04  "
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        "id": "0xxCoOsa9tly",
        "outputId": "44912c2b-9288-4b9c-c38a-78091a78ab74"
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      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(16598, 10)"
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          "metadata": {},
          "execution_count": 5
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      "cell_type": "code",
      "source": [
        "df.info()"
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hr1slcItADM-",
        "outputId": "dfad3650-076b-40fb-f42f-24efc1f4867a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 16598 entries, 0 to 16597\n",
            "Data columns (total 10 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   Unnamed: 0   16598 non-null  int64  \n",
            " 1   Name         16598 non-null  object \n",
            " 2   Platform     16598 non-null  object \n",
            " 3   Year         16327 non-null  float64\n",
            " 4   Genre        16548 non-null  object \n",
            " 5   Publisher    16540 non-null  object \n",
            " 6   NA_Sales     16598 non-null  object \n",
            " 7   EU_Sales     16598 non-null  object \n",
            " 8   JP_Sales     16598 non-null  object \n",
            " 9   Other_Sales  16598 non-null  object \n",
            "dtypes: float64(1), int64(1), object(8)\n",
            "memory usage: 1.3+ MB\n"
          ]
        }
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    {
      "cell_type": "code",
      "source": [
        "# 데이터 정제\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VlmuSPOTAEBr",
        "outputId": "8be6203d-3949-4980-a6fc-486e467d9bfa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0       0\n",
              "Name             0\n",
              "Platform         0\n",
              "Year           271\n",
              "Genre           50\n",
              "Publisher       58\n",
              "NA_Sales         0\n",
              "EU_Sales         0\n",
              "JP_Sales         0\n",
              "Other_Sales      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#결측값을 결측값의 뒷 행의 값으로 채우기 // 좋은 방법 아니지만 최선이었음.\n",
        "df=df.fillna(method='bfill')"
      ],
      "metadata": {
        "id": "cOLA--ZcEJk1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#결측값 다시 확인 \n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kEtVOlzOyrzv",
        "outputId": "8f2b2758-2e89-405f-eb11-88fda4314f9a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0     0\n",
              "Name           0\n",
              "Platform       0\n",
              "Year           0\n",
              "Genre          0\n",
              "Publisher      0\n",
              "NA_Sales       0\n",
              "EU_Sales       0\n",
              "JP_Sales       0\n",
              "Other_Sales    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(5)"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "u4eIcRBYN78O",
        "outputId": "bcd67393-b1f8-4376-893f-8b059e76c7cb"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                             Name Platform    Year      Genre  \\\n",
              "0                    Candace Kane's Candy Factory       DS  2008.0     Action   \n",
              "1                                  The Munchables      Wii  2009.0     Action   \n",
              "2  Otome wa Oanesama Boku ni Koi Shiteru Portable      PSP  2010.0  Adventure   \n",
              "3                Deal or No Deal: Special Edition       DS  2010.0       Misc   \n",
              "4       Ben 10 Ultimate Alien: Cosmic Destruction      PS3  2010.0   Platform   \n",
              "\n",
              "            Publisher NA_Sales EU_Sales JP_Sales Other_Sales  \n",
              "0           Destineer     0.04        0        0           0  \n",
              "1  Namco Bandai Games     0.17        0        0        0.01  \n",
              "2           Alchemist        0        0     0.02           0  \n",
              "3           Zoo Games     0.04        0        0           0  \n",
              "4         D3Publisher     0.12     0.09        0        0.04  "
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Unnamed: 0 행 삭제\n",
        "df=df.drop('Unnamed: 0', axis=1)"
      ],
      "metadata": {
        "id": "c2Pk3Plt6zPj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['NA_Sales'] = df['NA_Sales'].astype(str).str.replace('K','')"
      ],
      "metadata": {
        "id": "iCceZp9e0cfQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['NA_Sales'] = df['NA_Sales'].astype(str).str.replace('M','')"
      ],
      "metadata": {
        "id": "Hq8ZxHzY6mvL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['NA_Sales']=df['NA_Sales'].astype('float')"
      ],
      "metadata": {
        "id": "BaW3-TC_Cjqu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['NA_Sales']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R4868dw5CuOz",
        "outputId": "b61d01cb-4060-4925-ddc3-e70f8c3c4d4a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        0.04\n",
              "1        0.17\n",
              "2        0.00\n",
              "3        0.04\n",
              "4        0.12\n",
              "         ... \n",
              "16593    0.15\n",
              "16594    0.01\n",
              "16595    0.44\n",
              "16596    0.05\n",
              "16597    0.00\n",
              "Name: NA_Sales, Length: 16598, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5vrHb4s0Cn5B",
        "outputId": "cc69b8b7-d380-4812-bb8e-a4273a2f6d3d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 16598 entries, 0 to 16597\n",
            "Data columns (total 9 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   Name         16598 non-null  object \n",
            " 1   Platform     16598 non-null  object \n",
            " 2   Year         16598 non-null  float64\n",
            " 3   Genre        16598 non-null  object \n",
            " 4   Publisher    16598 non-null  object \n",
            " 5   NA_Sales     16598 non-null  float64\n",
            " 6   EU_Sales     16598 non-null  object \n",
            " 7   JP_Sales     16598 non-null  object \n",
            " 8   Other_Sales  16598 non-null  object \n",
            "dtypes: float64(2), object(7)\n",
            "memory usage: 1.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 북미 판매량 중 'K'가 포함된 값 찾기\n",
        "k=df['NA_Sales'].str.contains('K')"
      ],
      "metadata": {
        "id": "rABogFBHAvGE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 'K'를 모두 ''(공백)으로 변경해줌\n",
        "df['NA_Sales'] = df['NA_Sales'].astype(str).str.replace('K','')"
      ],
      "metadata": {
        "id": "VMvIvVCzFent"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 북미 판매량 컬럼의 데이터 타입을 실수 타입으로 변경\n",
        "df['NA_Sales']=df['NA_Sales'].astype('float')"
      ],
      "metadata": {
        "id": "ZlEOZ9hPFo2I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 북미 판매량 중 'M'이 포함된 값 찾기\n",
        "m=df['NA_Sales'].str.contains('M')"
      ],
      "metadata": {
        "id": "v8SjPyyB3U4m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 'M'을 모두 ''(공백)으로 변경해줌\n",
        "df['NA_Sales'] = df['NA_Sales'].astype(str).str.replace('M','')"
      ],
      "metadata": {
        "id": "ci9nAilTF9-v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# K가 포함된 값의 인덱스 찾기\n",
        "df[k].index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9VxgD6DfBMEx",
        "outputId": "7ee27bcb-4b94-446f-c49c-f7c2ee88e0fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([   10,    45,   145,   878,  1086,  1359,  1456,  1475,  1838,\n",
              "             1922,  1964,  1971,  2673,  2924,  3262,  3328,  3498,  4471,\n",
              "             4617,  4978,  5032,  5175,  5284,  5296,  5334,  5768,  6055,\n",
              "             6086,  6115,  6150,  6153,  6203,  6545,  6595,  6694,  6929,\n",
              "             7023,  7049,  7109,  7192,  7403,  7435,  7774,  7881,  8134,\n",
              "             8189,  8235,  8239,  8579,  8689,  8842,  8872,  9020,  9257,\n",
              "             9437,  9456,  9566,  9791,  9829, 10301, 10357, 10358, 10544,\n",
              "            10823, 11563, 11992, 11996, 12107, 12158, 12240, 12332, 12410,\n",
              "            12517, 12745, 12876, 13057, 13341, 13557, 13798, 13887, 13935,\n",
              "            14180, 14292, 14347, 14410, 14446, 14630, 14757, 15088, 15107,\n",
              "            15111, 15340, 15430, 15597, 15622, 15647, 16194, 16330, 16492,\n",
              "            16578],\n",
              "           dtype='int64')"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# M이 포함된 값의 인덱스 찾기\n",
        "df[m].index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H73T2Lfi3awz",
        "outputId": "0dd6c726-ddeb-45b5-ee70-247a85def0bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([  447,   458,   577,   625,   672,   767,   955,  1046,  1358,\n",
              "             1469,  1656,  1882,  1931,  2158,  2495,  2757,  2958,  2960,\n",
              "             2997,  3078,  3145,  3251,  3832,  3861,  4010,  4070,  4111,\n",
              "             4581,  4754,  5060,  5312,  5737,  6303,  6661,  6763,  6883,\n",
              "             7260,  7499,  7597,  7710,  7884,  8058,  8156,  8632,  8860,\n",
              "             8894,  8949,  8954,  9548,  9563,  9630,  9633,  9667,  9671,\n",
              "             9718,  9821, 10007, 10020, 10224, 10337, 10653, 10680, 10903,\n",
              "            10913, 11019, 11124, 11245, 11722, 12109, 12351, 12404, 12417,\n",
              "            12462, 12600, 12698, 12738, 12872, 12945, 13246, 13615, 13710,\n",
              "            13719, 13870, 13883, 13896, 14079, 14100, 14242, 14474, 14559,\n",
              "            14839, 15149, 15385, 15481, 15708, 15986, 16152, 16169, 16408,\n",
              "            16482],\n",
              "           dtype='int64')"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# K가 포함된 인덱스(위치)들을 o라고 지정\n",
        "o=df[k].index"
      ],
      "metadata": {
        "id": "61BHae6UY4oq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# NA_Sales의 'K'가 포함된 위치에 있는 값들을 d라는 시리즈로 지정\n",
        "d=df['NA_Sales'][o]"
      ],
      "metadata": {
        "id": "gEOdpx5291-C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#시리즈 d를 리스트 형태로 만듬\n",
        "d=d.to_list()"
      ],
      "metadata": {
        "id": "nQiUbYtAAsbw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# lambda를 사용, 0.001을 곱함으로써 정상값으로 만듬\n",
        "result2 = list(map((lambda x: x * 0.001), d))"
      ],
      "metadata": {
        "id": "EMwRQig65LRq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "result2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3i69tpbt51F9",
        "outputId": "8abb83ec-f095-4c46-ef3f-a36becfc14cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              " 0.22,\n",
              " 0.31,\n",
              " 1.61,\n",
              " 0.8200000000000001,\n",
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              " 1.78,\n",
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              " 0.0,\n",
              " 0.55,\n",
              " 2.0100000000000002,\n",
              " 0.0,\n",
              " 0.1,\n",
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              " 0.77,\n",
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              " 0.34,\n",
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              " 0.13,\n",
              " 2.57,\n",
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              " 0.09,\n",
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              " 0.0,\n",
              " 0.27,\n",
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              " 0.0,\n",
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              " 0.12,\n",
              " 0.72,\n",
              " 0.47000000000000003,\n",
              " 0.32]"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['NA_Sales'][o]=result2"
      ],
      "metadata": {
        "id": "oR8QA-06Y8bJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d19ff52b-27b7-4cde-adfe-6448961a1188"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-21-018aa55fdeba>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['NA_Sales'][o]=result2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 위와 동일하므로 설명 생략\n",
        "n=df['EU_Sales'].str.contains('K')"
      ],
      "metadata": {
        "id": "FO8PSKSgBlPh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "p=df[n].index"
      ],
      "metadata": {
        "id": "-oMklWwBFKQp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['EU_Sales'] = df['EU_Sales'].astype(str).str.replace('K','')"
      ],
      "metadata": {
        "id": "wirpgqpyHY1S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['EU_Sales'] = df['EU_Sales'].astype(str).str.replace('M','')"
      ],
      "metadata": {
        "id": "lc60Fa38IPl8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "z=df['EU_Sales'][p]"
      ],
      "metadata": {
        "id": "kblSk-XiHkbQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "z=z.to_list()"
      ],
      "metadata": {
        "id": "_JiXne2wH5I1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['EU_Sales']=df['EU_Sales'].astype('float')"
      ],
      "metadata": {
        "id": "XRVT26-6INfG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "result3 = list(map((lambda x: x * 0.001), z))"
      ],
      "metadata": {
        "id": "fky_D8VZFJbU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['EU_Sales'][p]=result3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6IAomw4CIYBh",
        "outputId": "e2ba9f4c-598d-4641-dc1f-41177367718f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-32-5c2baf4f844d>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['EU_Sales'][p]=result3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "j=df['JP_Sales'].str.contains('K')"
      ],
      "metadata": {
        "id": "IX3arCdWIIOE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "r=df[j].index"
      ],
      "metadata": {
        "id": "yalP5SD_JRv7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['JP_Sales'] = df['JP_Sales'].astype(str).str.replace('K','')"
      ],
      "metadata": {
        "id": "h_8yzRsuJbEB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['JP_Sales'] = df['JP_Sales'].astype(str).str.replace('M','')"
      ],
      "metadata": {
        "id": "6z4TeM1KIqeB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "w=df['JP_Sales'][r]"
      ],
      "metadata": {
        "id": "22oVC6DIJlDe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "w=w.to_list()"
      ],
      "metadata": {
        "id": "9O7y_dFLJt0i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['JP_Sales']=df['JP_Sales'].astype('float')"
      ],
      "metadata": {
        "id": "5WJwrDVJJxfs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(type(w))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pZCSefYfK1Oh",
        "outputId": "d81f3ccb-2299-4716-960d-3156d655c0fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'list'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result4 = list(map((lambda x: x * 0.001), w))"
      ],
      "metadata": {
        "id": "UFD5TShwJzdc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['JP_Sales'][w]=result4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZyDPij2CLlzG",
        "outputId": "c80034b5-a195-4d82-b0ea-f916a63c2e39"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-42-baa5aacea802>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['JP_Sales'][w]=result4\n",
            "/usr/local/lib/python3.9/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "t=df['Other_Sales'].str.contains('K')"
      ],
      "metadata": {
        "id": "auDEbkB5Oo63"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y=df[t].index"
      ],
      "metadata": {
        "id": "d7y38AjTOww7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Other_Sales'] = df['Other_Sales'].astype(str).str.replace('K','')"
      ],
      "metadata": {
        "id": "mfRGCt7iOypI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Other_Sales'] = df['Other_Sales'].astype(str).str.replace('M','')"
      ],
      "metadata": {
        "id": "_UxZzvwjJ2PE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "tsjcus9cBqRD",
        "outputId": "d7067330-9782-4910-c931-7899ff6c3941"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           Name Platform    Year   Genre           Publisher  \\\n",
              "0  Candace Kane's Candy Factory       DS  2008.0  Action           Destineer   \n",
              "1                The Munchables      Wii  2009.0  Action  Namco Bandai Games   \n",
              "\n",
              "   NA_Sales EU_Sales JP_Sales Other_Sales  \n",
              "0      0.04        0        0           0  \n",
              "1      0.17        0        0        0.01  "
            ],
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    },
    {
      "cell_type": "code",
      "source": [
        "df['Other_Sales']=df['Other_Sales'].astype('float')"
      ],
      "metadata": {
        "id": "Mp6lDIimE3NB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "u=df['Other_Sales'][y]"
      ],
      "metadata": {
        "id": "Dg_yldyTBnUC"
      },
      "execution_count": null,
      "outputs": []
    },
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      "source": [
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      ],
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        "id": "P8XC7V2sBmgP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "result5 = list(map((lambda x: x * 0.001), u))"
      ],
      "metadata": {
        "id": "bmlm0gqKBkqZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Other_Sales'][y]=result5"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-IMHKGk_PJux",
        "outputId": "03fd7dca-7240-41ce-eea1-17e5c24c5492"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-51-09120728e5ca>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['Other_Sales'][y]=result5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터 정리 완료\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G0xkJSNPPQdc",
        "outputId": "6e0d672b-342a-4466-ae8b-7f26b20ac646"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 16598 entries, 0 to 16597\n",
            "Data columns (total 9 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   Name         16598 non-null  object \n",
            " 1   Platform     16598 non-null  object \n",
            " 2   Year         16598 non-null  float64\n",
            " 3   Genre        16598 non-null  object \n",
            " 4   Publisher    16598 non-null  object \n",
            " 5   NA_Sales     16598 non-null  float64\n",
            " 6   EU_Sales     16598 non-null  float64\n",
            " 7   JP_Sales     16598 non-null  float64\n",
            " 8   Other_Sales  16598 non-null  float64\n",
            "dtypes: float64(5), object(4)\n",
            "memory usage: 1.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.NA_Sales.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pfrd9wiWPXw1",
        "outputId": "42a15ab2-2c5a-4059-e2e6-4dbd9223f71d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    16598.000000\n",
              "mean         0.264667\n",
              "std          0.816683\n",
              "min          0.000000\n",
              "25%          0.000000\n",
              "50%          0.080000\n",
              "75%          0.240000\n",
              "max         41.490000\n",
              "Name: NA_Sales, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.EU_Sales.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SQppM6FMBf0J",
        "outputId": "d53c20f2-6958-489b-b9c4-2deb2f00619d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    16598.000000\n",
              "mean         0.146652\n",
              "std          0.505351\n",
              "min          0.000000\n",
              "25%          0.000000\n",
              "50%          0.020000\n",
              "75%          0.110000\n",
              "max         29.020000\n",
              "Name: EU_Sales, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.JP_Sales.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TkstZlFkBeJk",
        "outputId": "271efcb9-37db-4031-bbbb-41010169ce7a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    16598.000000\n",
              "mean         0.536736\n",
              "std         23.933674\n",
              "min          0.000000\n",
              "25%          0.000000\n",
              "50%          0.000000\n",
              "75%          0.040000\n",
              "max       2780.000000\n",
              "Name: JP_Sales, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.Other_Sales.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nrxcpLQIBQqv",
        "outputId": "302e281b-2c0f-48a8-8810-699cb5db78f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    16598.000000\n",
              "mean         0.048063\n",
              "std          0.188588\n",
              "min          0.000000\n",
              "25%          0.000000\n",
              "50%          0.010000\n",
              "75%          0.040000\n",
              "max         10.570000\n",
              "Name: Other_Sales, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "R2B5c77TXl8u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "MEeC3tJZXk_O",
        "outputId": "107a8287-9239-4dab-8512-a69dd3145cbc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           Name Platform    Year   Genre           Publisher  \\\n",
              "0  Candace Kane's Candy Factory       DS  2008.0  Action           Destineer   \n",
              "1                The Munchables      Wii  2009.0  Action  Namco Bandai Games   \n",
              "\n",
              "   NA_Sales  EU_Sales  JP_Sales  Other_Sales  \n",
              "0      0.04       0.0       0.0         0.00  \n",
              "1      0.17       0.0       0.0         0.01  "
            ],
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              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Platform</th>\n",
              "      <th>Year</th>\n",
              "      <th>Genre</th>\n",
              "      <th>Publisher</th>\n",
              "      <th>NA_Sales</th>\n",
              "      <th>EU_Sales</th>\n",
              "      <th>JP_Sales</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>Candace Kane's Candy Factory</td>\n",
              "      <td>DS</td>\n",
              "      <td>2008.0</td>\n",
              "      <td>Action</td>\n",
              "      <td>Destineer</td>\n",
              "      <td>0.04</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>The Munchables</td>\n",
              "      <td>Wii</td>\n",
              "      <td>2009.0</td>\n",
              "      <td>Action</td>\n",
              "      <td>Namco Bandai Games</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.0</td>\n",
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              "                                                     [key], {});\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x='Year',y='NA_Sales',hue='Genre',data=df)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "EeATPF39YvZE",
        "outputId": "66aaf15a-4ae9-49b8-d70c-e58ebeed1281"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x=df.Year);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "LAV6XoA8Xjya",
        "outputId": "2cb211d7-d219-4bd9-a6a3-d12638e1287a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Year 컬럼의 이상치 삭제\n",
        "df=df.drop(index=df[df.Year<500].index)"
      ],
      "metadata": {
        "id": "l3qfw9L0aOIo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "g = sns.relplot(x=\"Year\", y=\"NA_Sales\", kind=\"line\", data=df)\n",
        "g.fig.autofmt_xdate()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "dibIt6fEaq4n",
        "outputId": "8857462a-1140-4ef1-e02e-3370103bf1ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "g = sns.relplot(x=\"Year\", y=\"EU_Sales\", kind=\"line\", data=df)\n",
        "g.fig.autofmt_xdate()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "0m8kuju2aCz5",
        "outputId": "1d269e21-7ebb-455d-b5bb-33102db91c67"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "g = sns.relplot(x=\"Year\", y=\"JP_Sales\", kind=\"line\", data=df)\n",
        "g.fig.autofmt_xdate()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "ncCc1WUwaA4A",
        "outputId": "c4b73dce-e83d-4b20-c209-8effb1b08633"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "g = sns.relplot(x=\"Year\", y=\"Other_Sales\", kind=\"line\", data=df)\n",
        "g.fig.autofmt_xdate()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "B0y8tOeFXiy_",
        "outputId": "04ad6ab3-b2cb-409b-d885-53226bebb7ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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BmVWElVJfXghDLjQsx0XmWJVWTtlyKS0s8zFEyGgR1mgWjJJjwiLSBfwpcDHQkN2ulHphFeyqeyzHRZUd3fVjwmUcVk56GoBhCFqDNZqFo5xVoX/Hy3DYCHwIOIGXgqaZA7aj5uwJlxMTLqWFZT6GeNM+NBrNwlCOCHcopT4PWEqpB5RSvw1oL3iOWO7cPGEoLzuilBaW+RgifiWfRqNZCMrpomb5/3aLyMuBc0B75U26MEhlHMxyW6H5lOMJl9LCMh/d2F2jWVjKEeG/FpEW4H3AJ4Bm4I+qYtUFQMp2CZSYNjaJMhOFy2lhCV44Iq1FWKNZMMop1viu/+sI8ILqmHPhUM5EjXzKnTNXagvL/PPrmLBGs3DM6iKJyNtFZIv/u4jIF0VkRET2iMi0hjqa0phb3wh/7H2J4YjyWlh6mIZgu2rOs+w0Gk15lHKf+od4mRAArwMuBzYB7wU+Xh2z6hulFJkyBnzmI1Byg52M7ZY9gSOL7uGj0SwMpYiwrZTKLsr9L+ArSqkBpdRPgVj1TKtfbFeVVUCRjxeOKG3fjF3+5I4suqewRrMwlCLCroisFJEG4Gbgp3nPRapjVn3jVcvNDQEcVZoKl9fCMv81RDfx0WgWiFJWbN6P1z/CBO5VSu0FEJEbgWNVtK1uKbeAIh9DSm/qXk4Ly8noEUcazUJRSu+I74rIeqBJKTWU99Ru4LXZByJyi1LqJ1Wwse6wHXeuUQKQ0kMF5bawnMtraDSa+VGSm6SUsqcIMEqpuFJqPG/T31XUsjrGm/k2t4CEIaUXa5TTwnISCnTRnEazMMzlXrUYcw1zXnDMNT0tSynRjGwLy7lkYCh0Y3eNZqGopAjrb22JpGxnbmECvJhwKSlq5bawnIoecaTRLAyVFGFNiSTT5RVQ5FNqsUa5LSynood9ajQLQ0kiLCKGP2l5Jk7M35wLg5Q916wFP32sFE94HiJsIHq6hkazQJS6MOcCn5xln9+oiEV1jusqbKf8sUZZRErzUue1+OeXLms0mupTjjt2n4j8psw1yKgB8Hv1zl3ghNIWzeaz+GfoOXMazYJRjgj/LvAtICMioyIyJiKjVbKrbrGcubZy9xCRkrIj5jJENP81dE9hjWZhKKeVZVM1DblQsJ25N9UBPxxRgiectl3mmgVnCDocodEsECV7wn4byzeKyF/5j9eKyLXVM60+sRxV1qDOqZSaombZc8/AMESwdbWGpk4YT9uLbcKMlBOO+BTwbOD1/uNxZlms00wnZTkY8wyrexOXZxbijOPOeXySHnGkqRccV3H8/PjsOy4i5YjwdUqpdwApAL+MOVQVq+qYlOUQnGN6Wj6zOcMZ28HQC3OaCxzbdWu+8KgcNbBExMRf2heRLkB/U8skkZlfyXKWmTxh11UoJXP2uHWKmqZecFxV831QyhHhjwPfAZaJyP8FHgT+pipW1TFpyylr+nExZtJI21XMJwfD8OfY6RFHmqWO7aqa94TLyY74dxF5DK+xuwCvVErtr5pldUrKdmgJmvM6h8CMIusqVZFuSo6rKnLB0GgWC8dRODW+vlHOyHuAw8Bo9jgRWaeUOlVxq+oU22+qM9+FOWHmxu6eJzx/dERCs9TxPOHajkeULMIi8i7gA0Av4JB1yLzBn5oSsJzKeKiKmdPcHHf+r2PoEUeaOiDr+NQy5XjCfwhsU0oNVMuYesdy5zFRIx+ZWSAdV827u7NC6ekamiVPZglk+ZSzMHcaGKmWIRcCtqPmVS2XRamZww3eoto8XwPd2F2z9JlvS9eFYFZPWETe6/96DLhfRL4HpLPPK6X+sUq21R22685bHGH2aci242LM0xUW9IgjzdKnLkQYyPaMOOX/hJgo0tCuUhnYtpq3OHrM7OmmbXfOhRoTr6Cna2iWPuklEI4oZdryhwBE5A6l1LfynxORO6plWD2SceYvjgDIzMUa1jxKlvPRMWHNUmcpeMLlxIT/vMRtmiLMp59DPkrNnD6WsV3mWxktzN6fQqOpdZZC+X0pMeGXAi8DVovIx/OeagZquz1RjWFVQByzzCSQGcedfy6y6BFHmqWNN8XGJWDW9ijNUmLC54DdwB3AIX+bjZcv/EdVsqsuSVdAHLPM5Alb9vw/eIYIVo1XGmk0M2F748YX24xZKeWbug+4BG8x7rf9n78GbsBrZzkjInKriBwUkSMi8mcFnn++iDwuIraIvHrKc28RkcP+z1tK+Q/VMvY8evxOZaYktUp4wl5jd+0Ja5YulShaWghKEeG/B9qA9Uqpq5VSVwObgBbgH2Y60O+69kngpcDFwOtE5OIpu50Cfgv4+pRj2/Eq9K4DrgU+ICJtJdhbs1RCHAFQFM2OUMprWDJfsdc9hTVLnUqlhFabUkT4fwF3KaXGshuUUqPA7+PFimfiWuCIUuqYUioD3A3cnr+DUuqEUmoP09tivgT4iVJq0O9d/BPg1hLsrUkqJY4w83QNuwKFGuDdxVk6O2LB0Jkolcd1mXfl6EJQiggrVWAVSCnlMHue8Gq8SrssZ/xtpTCfY2uOSokjADPMgPNuwSoj9HphbmGIp22O9tX29IeliO26S6KSoaSYsIi8eepGEXkjcKDyJpWHiNwlIrtFZPf58+cX25yiVEocwRPIYtkRnkc1/0+eaUjNtwCsFzK2y3Ays9hm1B2OW5k2AdWmlOyIdwD/KSK/DTzmb9sJRIBXzXLsWWBt3uM1/rZSOAvcNOXY+6fupJT6DPAZgJ07d9asalRKHLMU9YQr5G4bIqRdpyLn0syM5bjE0/aSSKdaSqTt+ZfvLwSlVMydBa4TkRfiZUkAfF8pdV8J598FbBGRjXiieicTg0Jn40fA3+Qtxr2YJVwcMldxTGS8VOxoaOJPZcww9r5S3qvoOXMLRiLjkLYVGS3CFSXtz1msTHft6lHOZI2fAT8r5+RKKVtE3oknqCbwBaXUXhH5MLBbKXWviDwLb2xSG/AKEfmQUuoSpdSgiHwET8gBPqyUGizn9WuJuYrjP/z4IK6CD77iktw2r4FPkdeZpcNaqRjivYZSClkK93RLmKRl47qKtOUS1aNzK0a2QtWu8RSJcidrlI1S6vvA96dse3/e77vwQg2Fjv0C8IWqGrhAzEUcHVfx9NkROmLhSdtFKJodUcnYc/Z8esRRdUlaLsGAkLZ1+KeSWLbyKlRr/G3V9z4LxFzE8cRAnJTlMpK0Jm0XKZ7SlLYcKlQPAuhOagtBMuPQEDAZT+suAJXEcipXHFVNtAgvEHMRxwM9Xmr2uL9ok0WQouKYqeAHz2viU5FTaYrguArLcWkIXlgiPJ62i97NVYq0XZmGWdVGi/ACMRdxPNAzmvt9LDXxBZUZFuYqVpWH31NYFxFUFctxEYSgaRBP1/h9c4VwXMWe08P0jqaq9hpKKWy3Qq1jq4wW4QViLuJ4sGeMoB+PzQ9JeDHhwsdYdmWq8rLocER1sRwXhfc3sxx30h1PvTKetklaDof7xkhZ1bnwVLQ4qspoEV4gyhXH4USG7pEUV6xpBWAklSfCM4w3qmQczBtxtEQ+yUuU/E51gncLXe8MxtOETAPTMDh2vjqVgpVeoK4mWoQXCKtMT/hgrxcPvm5jBwCjUzzhYmGC2TzubAyyFLxhnyUarJkTtuNOkoqlMAliPiil6B1JEwsHaG4I0juaon+s8mEJx1U1nx+cRYvwAlFuTPhA9ximIVy9vhWYEo6geEzYdtwZFwC/9OsTvPkLj/L0meGS7NAx4eqSyDiYfqd/Eana7XmtkLQcUpZD0C9KaWoIcrB3vOKFQbarKpolVE20CC8Qs4njVA70jLKpM0ZHLIwwNSYsFPrMOq7C62Nd/IUeOtbPeNrm/ffu5WcHemc2Qumx99UmYdkE/A9GwJC6z5AYjluTOpuFAya243JyIFHR18l+F5YCWoQXgFLEMR/bcTnUN872FU2YhtDYEJi+MKemq7DtujNGwXpGU/SOpnnDdeu4eFUz//TTw3ztkZNFmwEZhu6kVm2SaTdXDBM0DeKZ+hbh3rEU0eDkGrHWaIhTA/Fp+fDzYSkNJNAivACU+4E4MZAgY7tsX9EMQEskOCkmnC0pnspsoYM9fgjihs2dfPAVl3DLjuV8c9dp/uHHhwrGInVj9+qTtBwCfjgiaBqMp+pXhDO2V3jUEJwsO4YIsXCAQz2jFQt/2bZaEs17QIvwguC65fWWzuYHb1/ZBHgiXMhLmJq54C1GFOep0yO0RYOsbYsQNA3e9cKLePP16/nF4fP8n/9+ZtprGLqJT1VxXC+XNbtWYBqCXcbC6VJjzM/wKXRHGA0FiGcczg0nK/JaaXtp5AiDFuEFwXbdstZpD/SM0R4L0dXo9YxobggyUsBDmnrOmWZqKaXYc3aYy9e05r4EIsIdO9fyv1+yjSN9Y/zxt56ie2TiS2CIYC2h27qlRrZQIx+hfjMk+sbShE2z6POtkRBHz4/nOgfOB8tdGiXLoEV4QZiLJ7x9RVNOLKeGI3LnVaV7wqcGEwwnLK5Y0zLtuedt6eJvXnUZw8kM337sTG67UWQBUFMZsoUa+SjqM1fYdRX9Y2kioeIibBpCyDQqMmUkbZe3EL6YaBFeAMqJCQ8lMvSOptm+oim3rSUSZCxlTRPdqetpM3nCT50ZAcgVf0xl+4pm1rVH6RtL57YZhg5HVBOrQLxdENJ1mKY2lrZLmrHYGA7QP56Zd6pextKesCaPctpYZpv2ZBflAJojQVw1pX8E0z3htO0WzcDYc2aYlS0NLGtuKPraHbEwA/GJMTuGCHYdemW1QsZ2pl00g2Z9pqkNxTMlNdMRERBV8M6vHDKOsySa94AW4QXBskuvljvQPUrAEDZ3Nea2tUSCAIymZv5gWk7hrlHZvsSXr54eisinIxZiYDzPE14CDbGXMvmFGlmCpsFYHYpwz2iKWLi09uXRYGDS2sRcWCptLEGL8IKQLkeEe8bY3NVIKDDxp8mJ8BTvYKonnCmyIuwtdjhcsbZ1xtfuaAyTyDgkM96toCFcEA1lFguvcmzy3ytoGiTqTIQTGXtSldxsRIImQwlrzk3us2sjS2UijBbhBcB2S2veYzkuR/rG2ZYXDwZoiXgeRH4KmWJ6TDhTxBN+6vQwAJfN5gk3erN1BuKeNyx+PrJu4lMdkhk3lyOcpZw0tZGExVA8QyJj13R5+UiivNBCVjzLPS6L7bpLpoMaLMB4I03pK7XH++NkHJcdK5snbW9u8DzhqXm8Uz3h3DiXKTx1ZpgNHVFaZxlg1hHLinCGNW3RSa+zVBLflxJJy6YxHCz4XNp2Z/QcXT/E5PiLvgpoCJo0hQM0NQRojgRn/XsvFIWq5GYjEjTpHknOuIZRjJkWqGsR7QkvAJZdWnxqYlFusifcXCAcIUzPE7YKLEZkbJf93WNcXiQrIp/sLLuB8YnFOUH3FK4GtuPOeIc0W65wPGNjuy7tsTDtsTAdsTAh02A8bXNyIMHjJ4fm7ElWkoztMpyYXiU3G5GgyeAcQxL2LEVLtYYW4QWg1IbuB3tG6WwM0dk4ebBn0DSIhsxpnvDU9hEZZ3pM+EDPKBnHLZqals/UcEQWXa9ReewZvDUDIZWZWXyGE9a0C673OQnQGg3R1BDk6XPDi96VbaYquZkQ8cpY5nIhcbUnrJlKqSu1B3rG2LaiueBzXuny5AWb/Ou96zcJmir2T50ZwRC4dHXh8+bTEDSJhcxJnrBCe8LVIOMUr6IMmgbjs1SN9Y6mZix8aAiaCMLBnrFFjen3j0+vkvvBM9186H/2zlqinA1JlIv2hDWTcF2F46pZPeGB8TR9Y+lpoYgszQ3BySlqMrnhuu0qCr3EnjPDbFnWRDRUWkyuvTE8yROeqXexZu5YM4QbArPkCqcsh/G0TThQXITB+8wMxNOcHqxsm8hScV3F+SlVcinL4asPn2T3ySHeffcTfO/p7qJd/OYakrALlIPXMlqEq0ypXmSxeHCWqU18lGLSh9dx1bQgcSJjc6h3bNbUtHw6Y6FJnjDo7IhqkLGLC4U39LO4CI+V0WmtPRrm6PlxhhOZ2XeuMOMZe1rc+4FD5xlL2bzvlq1csqqZf3vgKB+4dy/94+lpx4sIqPJDEpklVLIMWoSrTqmzrg70jE0r0shnqgh7Hmre6xSoynvm7CiuomC/iGJ0NIYmVc3picvVwWthWfhzYRqC46qii3Pnx1I0zOIF55+rORLkmXMjCx4fHhhPT4pbK6W496lzbOqMcePWLj74ikv4g5s2s697lHd+/XHuP9g3zSuOhkx6ypzKnC5xIbxW0CJcZUqddXWwZ5QtyxqLpiU1+018sh9SxeSYsONMX4zYc2aYkGlMKoGejY5YmOFEZpLwag2uPMmMk2vmXoxMgVxhx1UMxDMzxoOnEg4sfHzYdlzODCVpaphIwXvqzAinBhPcdsUqb+FNhJdeupKP33kV69qj/H8/OcTf/vDApCygSNBkMJ4pq7NcRouwJp9SZl1ZjsuR8+NFF+XAK9iwXUXCXzUXZFJCeiFP+Kkzw+xY2TSp+m42OhpDuIrc7etMk501cye/mXsxCgnPeMrGLWGNYSrNDUEG45kFiw8PjKexncmhiP9+8iytkSDP39o1ad9VrRH+329czluevYFHjw/y2V8eyz2XK9woo5dEqdlItYIW4SqTzVqYiWPn41iOKhoPhonS5eyHUfC83yxTO7UNJzKcGEiUlJqWT7Zgo9+PC4vUb3/bxSRpzewJGwjJAhkSg4n0tH4TpdIeC3G0Pz6pP0g1UEpxYiBBU8PEYvDZoSS7Tw7xsstWFrzbMw3h1des4ZaLl/PQsYFc6TyUnyVRal5+raBFuMrMlA+a5emzXpvJmUR4asGGyORFv6njXLLnLKVII5/2bMGGnyFhiJ4zV2lsx501Y8abNzc9hts7kiZaRigiH0OEloYge84Mc3YoWTQrYb6MJm0SGWdS9sb/7DlHwBBuvXTFjMfeuLWLtO3y8PGB3LZyQxLlTjZfbLQIVxmvAU7xD4RSip8f7GPb8iY6phRp5NOSLV3OS37Pj9tOLdR46swI0ZDJRcsKL/QVozNbsOF7wuaU19HMH6tA/H4qwYAwNiUvPJGxSdkzN8JxleITPzvMN3efLiiyoYBBWzTMwd5RDveNVeVve2Y4MWnhcDxl89P9vTx/axdts5RS71jZzLKmMPcf7Mtty4YkZusiCN6dpz2HcM1iokW4ysxWqHH0fJxTgwlu3rFsxvMUCkfkx2qnNu/Zc2aYy1a3lO0RNEeCBAzJZUiIoEccVZhCC25TCZoGCWuyCJfSY/cHz/Tw4329fO3hk3ztkVMFhdg0hM5YmHPDKfacqWxVXTLjcH4sTSw8IcI/3tdD2na57YpVsx5viHDj1i6ePD3MUF5aXUPApGdk9iyJpVhYpEW4yszWvOe+/b0ETeF5W7qK70R+OML7YopMTh3z2lh6v/eNpugeSXF5GalpWQwR2mOhyeEIe+l9sGsZe0q13JG+6XPVDJmeptY7miISLB6K6BlJ8cVfHefqda285OLl3LP7NHfvOl1wXxGhI+a1Ln3s5FBJXmYp9IwmMf3MB/A+o997uptLVzUXTb+cyo1bu3AV/PJwf25bNGQyMJ6eNSSx1Jr3gBbhqjOTJ2w5Lg8cOs/1mzponKXhdUPQJBQwJjxhkUlf5PwV4Wzhx6Wryhdh8BbnBv1whGHoPOFKk1+okcjY/Mm3n+KLvzpReF/fa7Ycl6GEVVSEXaX42H2HCBjCu164hT94wUW8aMcyvv7oKe7ZXViIwcuaCJkGj50YorcET3MmCqWlPXxsgL6xNLddubrk86zviLGxM8YDhyaHJBSzhySWWskyaBGuOjOly+w6MchY2uaF22cORWTJH/gpTM6IyF8Rzq4kr2qNzMnm9sZwroLJENGN3StMPGPnCjUO9Ixhu4pfHjlf0MvLzpvLVskVa4Tz3T3d7D03yu88bxOdjWEMEd75gi3ctK2Lrz58kv98/EzB48C7wLf6BR3zSWEbjHv55flOx71PnWN5c5hrN7SXda6btnZxqHd8Un+JkGkwFJ+58q9Qvnyto0W4ylh28XaF9+3voz0a4qq1bSWdq6UhmLcwN7mLWr7HfW4kRUcsRMMMt64z0RHzquaUUv7Y+5l9iyN943U5F61aJC03l56299woAPG0w64Tg5P2M5BcvLZ/PE2wSGraueEkX37oBDvXt3Fz3gXdNIT33LyV52/p5Iu/PsF/PXm2qE0B06AjFuZw39ichFgpxcmBOLG8HiWHe8fY1z3KKy5fVfbaxPO3diF4Zc5ZGoLmrCJsu8UbI9UqWoSrTDFPeCiRYffJQV6wvavkD2hzXumy4E1ggOkrwj0jKVa0lN8MO0tnY4i07RLPOLOOOFJK0TOaZLxCMcULgXTeqJ+950bY3BWjLRrk53kZAQDBgMFYykYpRd9ouuCMNsdVfOy+wwRN4Z0vuGiap2wawntv2cYNmzv4/IPH+e6ec0XtMg2hPRrmUO8YZ4bKE+LRpM142pl04b93zzkiQZMX7Vhe1rkAOhvDXLa6ZVIps7dY6cz4eVyKoTMtwlVEKYXtFo4JP3DoPK6CF24v/QPaEglMyhPORr8cpSZ9+bpHkqxqmVsoAvKbu6f980rRcteU5ZJIOwwuQoOYxUKp0sYPFSORsTENIWO7HOod47LVrdy4tctbIMvLgAiaQjztEM94wlPoc/Q/T51jf/codz1vc9EUR9MQ/uTF27huYzuf/sUxvvir40XtNw1vwe5gzxhnh0ovkJialjYwnubBw/28aMeykgd8TuXGbV2cG0lxuG980vbEDNkcS61aDrQIVxXHVUVnXd23v5ctyxpZ1x4tvEMB8pv4iIDjTLxO9h4smXEYSljz8oQnmrt7wqpQRVN/soIyFL9wPOHu4RQHe0bndKzluLm+z4f7xrAcxSWrmnnBtmV+bHgiIyCbpjYUzxQc4Hp6KMFXHz7JdRvbecG2mbNrAqbBn966nZdcsoL/fOIs773nSY73xwvumxXiA72jdM/S8xe89pSepz4hwp978DgAryghLa0YN2zuJGDIpJAEQHKG0FfGLjxnsZbRIlxFHFV4keDY+XFODCS4uczbtOZIkLTtkrIcDJEJTzivSVC249TK+Yiw7wlnMyRm6ik8krQIB0ws3656x3UVJwfj9I1mJpXWloqdt3CUjQdfvLKZjZ0x1rdH+fmBiZBENk3t3EhyWlaE4yr++aeHCQcM3nHT9DBEIYKmwTtfcBF/9fKLGU5avPeeJ/nPx88UvIU3DaEjGmZ/zyg9swhxz0gK05hIS/vl4fM8eKSf1127jpXzuCNrDAd41oZ2fnH4fM7GsGkyMkMrz6XWvAe0CFeVYvGp+w70ETCE52/pLOt8LVNKl7Mx4fzS6GxmxHw+/O3Z/hF5zd2L1WsMxf35YUKuuVA9M5K0SFkuQVPoK7PFIkwu1Nh7boR17VGaI0FEhBdsX8bB3rFpEyeSGWfaIut/P3mWg71j/O6Nm2mLlTfQ89qN7fzL667mWRva+eKvT/CX//U0vQX+L6YhtEVC7O0e5dxQkvG0zVjKYixlMer/jCQszgwlcsNoh+IZ/vX+o2xd3shvXr2mqA2DiTQDce9nKJ5hJGkxnrJJZOxJoZKbtnUxnLByE8PDQYPBGRbnvLx8LcKTEJFbReSgiBwRkT8r8HxYRL7pP/+IiGzwt28QkaSIPOn//Fu1ba00ToGcRctxuf9gH9dtbJ+UT1kK+VVzhkhuwcLNe51sVdF8POFQwKCpIZArXS424sh2XMYzFiHTIGAYJVV0LXVODyWIBE0awwFODyXKXgiy/EINx1Xs7x7jklUTnfNu9DMC8hfoCt2A9I2m+Pqjp7huY3vBC7lSipGkNWP3u5ZIkD9/6Xbec/MWjp2P865vPMF9+3un7RcwDdqjIQ72jrH7xCCPnRzisZNDPHFymCdODvHUmSHAE2ylFJ/4+WHStst7XrS1qEc6nMzQGfMW3i5e1czmZTHWtEXoaArRGA5Msn3n+nZiIZP7/ZzhoGmQspyiMe1SR4nVElUdeS8iJvBJ4BbgDLBLRO5VSu3L2+1twJBS6iIRuRP4O+C1/nNHlVJXVtPGauIUGDnkVSfZZYciYHr/CCfPE85ybiRFc0NgzoshWTryquaEwl59wnJQystdDQcMBuIZNnTG5vW6tUwiYzM4nqE9FkLEy04ZTmRm7PkxlbTlYCAc74+TtBwuySuo6WwMc/maFu4/eJ7XX7sOEcldePP57INeq8e7nrepYBhiNGXT4HuMIdPIVVtORUS4ecdyLlvdwj/99BAfu+8wR86P8zvP3TRJyAKmMW34bCHuO9DHrhND/M5zN7K2rfBaR9p2EIGLljcWHc8U7B2jbzRNSyRIKGBww0WdPHi4n5Q1cUeQyDi0RKb7kGnbnZQmtxSotid8LXBEKXVMKZUB7gZun7LP7cCX/d+/Ddws5Y5mrVHsAgtz9x3opTUa5Op1peUG55PfSc2QiTit405UYHWPJOcVisjS2RiePGGjgFeVyFsgCQcMxlLWkkwRKpXe0RSmORH7jARNTpWZU5ttYbn3nNflLt8TBnjBtmX0jKbY71c9Bk1jUsOeR48P8vCxQe581jqWNRe+27Ecl+0rm7luUzst0QB9Y6kZ49fLmhv461dexiuvXMV393Tzke/tm1ZGPRt9Yyk++8tjXLqquehinKsUoymLS1a2zDgfb1VrZHJIYmsXScvh0eNeHrVAwTaf2WykJeYIV12EVwP5NZNn/G0F91FK2cAI0OE/t1FEnhCRB0TkeVW2teJMHb09krTYdWKIm7Yum9MtU344QkRw/Tlz+YsRPSOpeYUisuSXLkNhT3gwkcmlJWWFqdwv71IhV5IbnvAqo6EAwwlrxnlwU0la3t9q77lRVjQ3TPMwn725g1DAmLRAlyVlOXz6F0dZ2x7l9isLC108bdPe6N3WR0MBLl3dyjXr2xBR9MdTM6amve25m3jHTRfxxKkh/ve395Qc81ZK8YmfHcFVij+8eWvRmOxQIsO69tisMezGcICWaDD3Wbp0dQudjaFcSCIUMBkqMHcum4201Hy4Wl6Y6wbWKaWuAt4LfF1Epo2eEJG7RGS3iOw+f/78tJMsJmnbnfSBeOCQt8p7c4llylOJhkwChjCS1+JQqYkmQZbjcn4sXRkRbgwznLQmYpgFPGFvUW6yR5Oo08q5QiW54OXyltLdK0sy7ZUs7z03wsWrpn2ciYYCPHtTBw8e6Z8mmPfsPk3fWJrfv3Fz0XaWKdtm/ZS0x9ZoiGvWt7NjeTMJy2Ywni4aL7710hV86LZL6R9P875vPcWBElLxfvBMD0+eHua3n7OxaGpkImMTC5ls6CgtJXN9ezS30GuI8PwtXTx+apiRpEVDwCg4uLSU3t21SLVF+CywNu/xGn9bwX1EJAC0AANKqbRSagBAKfUYcBTYOvUFlFKfUUrtVErt7OqaOVdyock4zqScxfsO9LK5KzbnuKmI5GbNZXH9wgHTEHpHUyhgRQXCEdkMicF4xhtxNMUTTlnTCwjCAZOBOizaKFSSm6UxHOTcSLKk/hpKKVK2Q89IitGUzaUFRBi8kMR42p5Uxnx6KMF3njjLC7ct47LVhRszpSyHWChQMI5sGMKK1gjXbexgdVuUgfF00buWK9e28tE7rqAhaPIX33maXxwq7tx0jyT5wq+Oc9XaVm69pHDDdscfy7V9ZTOBGXoh59MWDREOGrkL0fO2dOG4isdODhIwDdK2O63XxlINhVU7gr0L2CIiG/HE9k7g9VP2uRd4C/AQ8GrgZ0opJSJdwKBSyhGRTcAW4BhLCMtWuST77pEkx87HefvzNs7rnM0NgUnzthQTVULdvke2qiKe8ETBxqqWBixn8ge8UIyxIWDUZdHGaMorye1sDKOU4vFTw1yyqpmGoJmbjDwYzxSN0Wax/VFXe7s97/KSIl3urlzbSqtfxnzD5k6UUvzb/UcJBw3e+pwNRc8fz9hcvLJ5xtvxoGlw0bJGljWHOdQ9xkA8TWskNM3DX9sW5R/uuIK/+f5+Pvrjg/z8YB/hoIkhXkxWxFuFOHJ+nIAhvPvmLUVfdyiZZsvyprKygQxDWNce5UjfOB2xMJu6YjSFAzxzdjRXZZrMOJPmJ9pLVISr6gn7Md53Aj8C9gP3KKX2isiHReQ2f7fPAx0icgQv7JBNY3s+sEdEnsRbsPs9pdTkDic1Tn6j9WPnveqki1fOrb1klpZIcFI7P1epXJOgXI7wHLun5TO1dHnqrfGonyaXT8A0sJ36K9o4O5TMxb4fOzXEB/9nL199+GTu+VgoUNICneW4CLDv3Cht0WDRsJFpCDdu6WL3Ca+M+YFD59lzdoS3PHsDrUUmU1iOS8AwSs7UaG4IctX6NjZ3NTKczDBWoPdHSyTIX7/yUl5+2Up6x9KcGohzvD/O0fNxDveOcbB3DKXgD1+0tWj2xGjSoj0anlMZfVeTd07XbyR18apmnvEXNA2RabF4t0BK6FKg6rkcSqnvA9+fsu39eb+ngDsKHPcfwH9U275qkt9B7eRAHENgbfv8BLIlEszV0mcnLmccl5gZoHvEa/rd3DD/P2tnnidsikzzMgYTmYJd2hRe+tBcO7jVGinLoXfU60qnlOJrvvj+4JluXnXVajobwzQETQbiacZS1ozeXrY5/jPnRrl4VcuMHusLti/jv586x4/29XDvU+fYsqyRF19cfD7bWMpi87LGshZ8TUNY2x6lozHE4d5x+se9tLD8eHPQNPi9GzeXfM58vBJtxbYVTQXLrmcjHDBZ2dLA+bEMLZEgl65u4ZHjgwyMp2kImgwnM6xqm/g+aU9YMw3LcXLpMicGEqxsicyYmlMKk2PC3mpwtklQ90iKla0NFVkdbgwHCJkGA+MZDGNyJzXHVYwmbcKB6R8fU6Suijb6RidKch86NsDR83Fef+06lIJv5k2tCBjGrAt0lutyfjxN/3i6aDw4y6bOGOvao3z1oZOMJi3+4KaLigqs4yoQWNY0tzBUNBTg8jUtXLyyiZGkNady7KmkbYeRZIYdK5vmdUHOT1fLxsKfPjvitbWckiFhOxOpmksJLcJVJH/45smBOOtLXBmeiZZIkHhmomLIct1c856ekRQrZ4lLloqI0NEYYjCe9icuT3gZSctBoQqKfUPQrJuOao6rODWYoCkcwHEVX3vkFKtbI7xm51pecskKfrK/Nye8jeEA50aSM3ZXS1sOh3u8u5ip+cFTERFesG0ZCnjZZStnHNg6lrZY0xqdFB8tFxFheUuEnRvasJU7r3FH4ymbpOVw1bo2Oud4YcjS1BDMpatt6IgRC5k8c3YE0/BCZGl74oKRXoJ9I0CLcNVw/UUYQ7zG3N0jKTZ0zL+aLL9/hMJrCAOeYPSOpipSqJGlPRaifzyDyMTrgJduVOyjHg4YjCXtJbtSnc9gPI3tKgKmwS8Pn+f0YII3XLcO0xBes3MtpgjfePQUkC3bhcHxdNHzJTIOh/rGiIVN1rXP/lm49dIVvGbnWt50/fqi+yilcFw15ykqU2lq8AqJYuEAA/F0wSKdmRiMpwmaws717UXj1+WSTVczjWxceCJtLt9rnzrsdqmgRbhK2Hkly6cHEyioiCecbZSS9VSyYYL+cU8wVrZWxhMGb3FuIOsJ53XwGU5YhMzCt5jid3erVtHGaMrK3TJXU+i9bmkJosEAtuPy9UdPsbEzxnMu8no1tMdCvPzyldx/qC83iaIx7C3QFROuRMbhYM8YF69sLsljawwHeNP164nOUIY7nrZZ1tRAJFS5GHxD0OSy1S2sam2gfzxd0vvsuIr+8TRdTWGuXNdaUXvy09UuXdXC2eEkg/EMBpMX5/KH3WYp9yKyGCytIuslhKsmevyeHPC+pJX0hEeSNi2RIJa/IpxNT6tUOAK8NLXBY5lpvSMG4xnCwZmv34m0XXaDotmwHZenTg3nCg1c5TUbioRMYqEArZEgy5rD84qJK6UYSlgc7h0jaTl0xML8aG8P3SMp/urlOyZlhPzm1Wv44TM9/PsjJ/mzl+4gHDDpH/fGyIcDJgFTCPllx6Zf1HFuJMWLi+TTzoWU7bBmnou9hTANYevyZprCQQ70jNEYDhSN7XpDSDNsWdbI2vZoxSvW8tPVLvXjwnvPjXDN+jaGEhar/Q4AmQLNez76o4OMJC2uXNta8c9jpdCecJXIn/p6YiBOKGCwvAICmV+6DNnFCCqanpalIxbCchTxtJ3zuDN+3+DsCvqRvnG+9OsTkzyOcJGy0vkynrZxlKI9FqY9FqazMUwsFMBxvDzdfd2jHO4bm7OHPJay2HNmhKdOD2H4I+Eztsvdu06xbXkTz5oyrLIlEuT2K1fxq6MDHD3vxXrbomHSlmI4YdE3mubUYJLDvePsOzfKnrPDwOzx4FJJZGxao6Hc3VE1WNka4ep1bdiuyrWezLafHPR/H097IreuI1a1kuFsutrGzhiRoMnTZ0cIB0yGE1busze1oXvfWIpfHR3gmXOjvPHzjzJShc9kJdCecJXIn/p6cjDBurZoRRYNmqeIcLasuHskRdCUXKVbJcjmfg4mMjRHgrju9DDDtx47za+PDnDT1q5cJWBDwGCoCotz/eNpAlPuN01DMA3PQ4uFTM4Np0hmXHasbC55oSplOZwYiHNuKEkkFKCzceJi+cO9PfSPZ3jPzVsLCswrr1zNd/d087WHT/KBV1yCaUjRW/FTAwlCAYPNXcUX2UrFcb2L41XLy28EVS4t0SDP3tyB63oTVlzlZeW4fjw6aBpVT0nMT1e7eFVzbnHOcV3StktD0CRjO5Oa32cncrzx+nXcs+sMr//cw3ztbdeV3X+52mhPuEo4arInXIl4MEBTQwBDJhbmLMcLe3SPJFnREqloQ+sO/8Oa7SvsKMVYysbwLy8py+Gxk14/2d3+v0CurLSSRRtKKXpH08RmiDWK772OpiyeODU0a1w6mXE43j/Ow8cG6B9L09kYpjGvBWjKcvjW7tNcvrqFK9a2TrIl623HwgF+4+rV7D45xIHumfss7O0eZfuKpqJ9H0olZTkMxtNs7mqkNbpwt9iGIQRNg3DApCFoEg0FaGoILlhO+IqWCLbrxYVPDyVz/SOy6wPZhXDw/kb3HzzP9hVNvOSSFXzmzddwuG+c1332YfpnWDxdDLQIVwnbX8gaSVoMJ6yKxIPB+5A1NUzMmst2UOsZSVWkXDmfdr9go398Ii48GJ8o0njs5JDvhRjsPjm5mNFrN1g5EY5nHCzbLan3QGskhKs8+0am5Cwr5fUAfvrMMI8cH+D0YJLWSIiWSGiap/vdPd0MJy3eOCU7YTDh3YpnhfgVl6+iNRKcVEU3zf60zfHzcS5ZOb9QxEgyQ8ZxuWZ9O+s7q3f7X4s0hgMYIrlwzt5zo4iIF6aaEoI6MRDn1GCCm/zZezdtW8YXf+tZnBiI89pPP1RwkshioUW4Sti2wkA4MeCVK1fKEwYvJJGLCbteTPjcSIoVFVyUA2iPhhDINXf3ijSs3KLcr48O0NwQ4OWXrWR/9yjjeSvVhkjBUti5UmzYZTEawwEaAiZPnBqibzRFxnY5N5TkkWODPHFqmPG0Q3s0RFt0et8E8ETzPx4/w871bezIE85ExqYxHGBjZ4zhpOeJNQRN7ti5hj1nR3jqzHBBe/b3jKKAS4o035ktju24ivPjKVqiQXZuaKNlAT3gWsE0hM7GECtbG2gIGjxzdoSwH/pypnRQu//geUxDeM7mickjz7moky+/9Vp6RlK89tMPTRsjtVjomHCVyBZqnPRFuFKeMEBLQyCXoqaU12AmY7sVXZQDL6zQEg0yEM+g8IQpW8efsV12nRjk+Vs6uXZjB//x+FmeOj2cS+HySnkzrKvQ/7t3NEW0zLSnbIOdp8+N5BZsmsLBWaeOOK7iG4+eYjxt84br1k/ansg47NzQRiwUYDCRYTxl09gQ4NZLVvKdJ87yuV8e4wXbprcq3ePHMLctb5r23EA8nSsNF/EuYA0Bk3DQyOWZj6UttixrYk1b5ILyfqfS1dRA72iaHSuac4tzI0nLS6H03xbHVTxw6DxXr2v1KkzznIHrNnXwlbddx2994VFe8+mH+Mbbr2dtGRPPq4EW4Sph+YnjJwYSNDcEKhq7a4kEOTmYQPC+uOfHPE+1kulpWTpiIa90WbzshKy/9sTpIZKWww0XdbJteROxsMnuk4M5EfY8FKtgD95ySVkO42k711SoHIKmQVcsjIJZ4+WW4/KzA3385+NnODeS4qatXZMq1YYSGTZ1xnKpTjtWNvPo8UEaHINQwOCN163nY/cd5ou/PlHw/Nesb5sWPx2Mp+lqDLN9ZTO265LMOIylbAbjGUaTFo5ShAIG16xvL9ii8kKjye+LcunqFr768EnG0zauC4m0k0sJ3XtuhIF4hrc9t3DHwmvWt/Hvb7+ON33eE+Kvv/16Ni7iWC4twlUimzh+ciDOhgqn7mTDESJeKlzfmJ8jXMFCjSydjWGvT7HyBjQG/eyEXx8ZoDEc4PLVLZiGcPW6Nh47OZTzlLNFG0nLmbTYNRe8RcjCt+u24/LYqSEePNxPR2OIm3csnzbfLNt2sRiJjM2P9vbwX0+eYzCeYXNXjD+7dTvXb+qYtE9TOMCaPK8pGgqwfXkT+3vG6GwMc/OO5Tznos6CwzmBabnVg/E0rdEQ21Y05bI8wgGT1miIte1RXNd7/0IBY96LefVCQ9CkqSHA1uXexXHfuRG2rmhiPG3lPiH3HzpPJGhOSynM5/I1rXzj7dfzxs8/wms//RBff/t1XLRs+l3KQqBFuEpkR5ufGkxwyxyGes5EcyTIeMpG+SlDfWNpTEPm3MBlJtpjIfad8+KZyYyXCmQ5Lo8cH+D6TR25hbKd69v45eF+jp2PT/Ie4ylr3iLcM5oiGpx8jpMDcX66v4/7D/UxnLBoagj4cdyz7FjZzC07lvHci7qKpotlbJczQwl+fWyA7+3pZjxtc/nqFt5z8xauXNs66aLpuIp4xuZZG9qnefXLWxoYiGcYSmRoiYRKzhQYSmRoiYa4ZFXxRueGIfMe2FqPLG9uYDRpEQoYPHNulB0rmxlPeaX0GdvlV0f6efbmDhqC5owTpy9e1czdd13P6z/7CK/99MN87XeumxT/Xyj0X7hKWLbLwHiGlOWyvoLxYPCmLiu8jIFwyOvetawpXJXmJR2NYcbSXsxZ4ZVN7z45SDzj5EIPQG5w6WMnB3MiHDa9oo3l8+hnYfvVWG2RECnL4ecH+/jp/l4O9Y5jGsKzNrRxy47lXL2ujbGUzc8P9vHjfb18/GdH+Mwvj/G8i7p47kWdjKVtTg0mODUY59RAgp7RFNm1sOs3tfPqq9eybUVhT2g4mWFjXhgiHxHhouWN7DoxSNp2SuqSN5zM0Bg2ZxRgTXFao0EMEbavaOKZsyO88br1JDIOhgi7TgySyDjctLW0KTtblzdxz+96Qvy6z3p5xJcWWTytFlqEq0TGcXONviu5KAcTVXNjKYtgIOS1sKxwelqWbK7wcMJiWbMXk/310QGiIZMr83JnW6MhtixrZPfJIV77rHUANAQN+sczDMW9frBz6Sk7lrJzwxv/8SeHeOjYABs6orztuRu5aWvXpCYxbbEQv3H1Gl511WoO9Izxk/29PHi4n5/s7wXAEK814vqOGM/b2sX69ihbljUVnYsGXhgiGpq54U44YHLxyhaeOD1EV8yYMfQ0mrKIBE0uXd2qQwxzpDEcIGAaXLKymbt3ncZyXJKWQzRkcv+hPtqiQS5f01ry+TZ1NXLP7z6b1332YV732Yf5ym9fy1VzmIY+V7QIVwGlvDDB6SFPhNdVePU1K8LjaYfWqKJ7JMm2FXMbHjobWRFOZGwaAlEcV/HwsQGu3dA+TUR2rm/j7l2nGUlatESCBEwD03B56swwAUNY2RJhWbNXEFFqjPz8eJqgYXB2KMlDxwa445o1vOn69TMeLyLsWNnMjpXNvP25m9jXPUpHLMTqtkhZwucqrypt58bpYYiptMdCrG+LcnY4SfuUBUSvkED576HJZWta5tV28kJHRFjWHOaiZY0ovPS/NW0RkhnYfWKIl1+2suy7wnUdUb75u9fzhs89wps+/yhffOuzZowpVxL9SagC2dHbJwYSrGiubIcrmChdHktZxDMO8Ywzozc3H7LjcsZSNrFwgGfOjjCWsrkhLxSRZeeGdhTwxKmJ6rnGcICOWNgbiDmcZPeJQR49PsiZocSkXrCFcF1F32iaWDjA/+w5R8AQXnHFqrIWOSMhk2vWt7GhM1a25zmUyLChM1Zyb4YNnTEiIW/KxmA8w2Aik+utYDsubbEQl65pmXdjf423YLy2PUrQFJ45O0JXYwOPnRrCdhU3FUgRLIU1bVG+edezWdYc5s2ff5RfH+mvsNWF0Z5wFfBGb0vFGrlPJReOSFu59LRKV8tl6cibugzwq6P9NAQNrl7XOm3fi5Y10twQ4LFTQ9O+CKYhudBBxnY50jfOmaEkV61rLSpK4xk7l7b10/293Li1i7YK9agthuMqxtM2luPSHAmUdRcTMA2uXNtGxnEJGIJpCAF/KoemsjQ1BAgFDLYtb+KZs6OEAgYPHDrPmrYIm7vmHv5b0dLA3Xddzxs/9whv/dIuPvPmndxYYnx5rmhPmMr3HHVcRcZ2ODecrHg8GMjNkEtmvOYlUJkx91aBIZ3RkEnEL7xwXMVDRwfYub69oHAaIly9vo3HTw7NWAEWChh0xMI4rmJ/d/GuZ8P+fLsf7eshbbvcfuWq+f0Hi+AqxVjKynmtK1sauGZDG1evayt74SwUMHJtH4PmzPFhzdwJmgYtkSDbVzRzrH+cE/1x9p4b5aZty+b9ni9rauDuu57N5q5G3v7l3fx0X2+FrC7MBS/CyYzD/u7Rigqxo7w4rasqW66cJWAaxMImo0mLnpEUAnMqWbYcl9GklWtPaDku45nJdfgi4k/YSLO/e5ThpMUNmzuKnnPn+nZGUzZH/GGkM9HcEGQ4keFI31jB979nJEXINPjunnNcvrqFjZ3Fu49l47eJjPeTshzStkPGdrEcl4ztedTjaZuRpMVgPOOHDdKMJC06GkNcubaVGzZ3snlZI80NQS2gNc7ypgY2dcVwFfzbL44CVMxrbY+F+Prbr2P7yiZ+72uP8YOnuyty3kJc8OEI18+zXd1qV6we33EUp4e8uvRqeMLgpamNpCwSGYeOxnDJCz2u38BGKa94YHlLmLZoiJjvvR3tG+fccHJS1kFHo1c196uj/YRMg53riy9YXLW2FUNg98nBoilf+bRHQ5wbThENBSaVjyYzDomMw77uUfrHM/x+kYm/rlKMpixsV7G8KQwCjstE20VX4TgKwxAaQl7LxZBp0BA0CJgGQcO7oOlUsaVHSzTI5q4YAUPY6+cLV7J/Sms0xNd+xytxfuc3nuAfHZfbr1xdsfNnueBFGLwBgScG41wRba3I+bzMiCRBUyo2+2sqLX7VnGW7JaenWY7LUDLDxo4Yq1ojBQsLlrc05FLrsnTEQjx9doTzR9Ncs75t2kJjPG3n+jQ0R4JsW97E7pNDk/ouFENEaIuGONw3TjRk5hYCR5PebLv/fvIcK1sa2DllpTobQrAcxdr2CGvaogvWUlFTG0RDJs2RIFuWNbK/Z6zk3OByaG4I8pW3XcfbvrSL93zzSTK2yx0711b0NfTlH68J+cB4pmJdvxxXcWYwwdoKNXIvRHMkyGjSonu0tBzhRMZmLGVx+eoWNnU1FhWsxnCA5khwUhvKjliY/vEMg/HMtFCE45fWjiQnmrhfs6GdI33jJTd2Nw2hpSHIM2dHcp3YekZTnBpMcLB3jNuuWDWpT2w2nLC8uYHrN3Vw0bL5jVXXLE1EvCrRS1Z5KX/PKZCxUwkawwG+9NZrec7mTv7k23v4+iOnKnp+LcI+YdPgzFDpre1GklZu5M9U0pbD2eFkVeLBWVoiQfrG0gwnrFnT07JiuHNDO10llDavbYsQz2uI3uH3FQ4YMi13cjiZYU1bBFdNLHDuXO8luj+e1+h9NkIBL1TwzJlh4mmboYTFj/b2EAub3LzdK/v22jmm6WoKcf2mDrYsb6p4+p9madHRGOIlly7n395wTVUbHEVCJp97y05esK2Lrzx0goxd+Ls/F7QI+zQ1BOgdTZU0JXgsZfHYySEO9ozhFljZ7x9PM1TBRu6FaIkESfje6qoimRHZHrQdsRBXr28ruQ9BeyxEwJTcAl02RHDVutZJ53BchSGwviNGZ2OIuG/Pps4YbdEgj50qLMI9Iym+tfs0h3vHJm2PhgI4ymvWPTCe5tdHB3jJxStyQjuStNjUFWPbimYtvhrA81LDATPnKFSThqDJv73pGr7+9usrWmyjY8I+Il5O57nh5IzdlBxXcbB7jFjI9BrnmGNsW940aSX9sJ8ZUOmeEfnkFxAUCkfMZwJuwDRY1RKheyRJSySUa5H53Ismx9xGUxZr26OEAgZr2qI8eWY4Vw13zfo2Hjo2MKmV5fH+cb792FkePHIeV8HXHjnJa3eu5TU71+YWxpobggwlMtx/0JsP9vLLVwLeQl1D0JixfFhz4REwDdpjIeJpm2io+nIWDpgVL7bRIpxHU0OQM0PJGRd5Tg7EGc94vW0jQZPukSQBETYva8wJ3dG+bCP36oUjmvNuvQqFI0ZTGS5b3cKyOa4Wr8hboNvQGeMfXn0FW5ZPpIhlS3FX+l54SyRI0DCwHJegn0Hx0/19HOgZRSn49uNneOzkEJGgye1XruZFO5bzrcdO841dp9l9coj33rKVNX4LyoaAyc8O9HHD5k6WNTWglGI8Y3PNuraqxdg1S5dlTWH2jacXRISrwdK0ukoYIhjizWvbUKDJ80jC4sRAIldFJiJ0RMN+JoTBev+Y4/3jNIYDFZ18PJVs/Ks1Epz24cvYLg3BQG5M+FyIhQO0RL0FukjInJZuNpayWJt3sTIMYV17hBMDCdqiXs6tIfB/v7efsbRNSyTIm65fz8suXUmjX2zyvlu2cf3GDj758yP84Tef5K03bOBll63kvgO9xDNOrjhjKJFhbVvkghzpo5md5khwxn7Rtc4FL8ID4+lJhQLNDUFODSamNXuxHJd93SM0+cMGs2SLGY72j2Oawpq2KCcGEqzvKC8EUC5ZES4UihhLW9NCJHNhbVuEvedGp8VfXb9B0eq2ybHorqYGjvXHUUoRCwe4bmMHx/vjvOH69bxox7KCt3HPuaiTHSub+fjPDvPpXxzjkeOD9I6m2La8ie0rmknbDgHTKHhR1GgAf/KzWXIr0VrjghbhgfE0r/n0w1yyqpl337wFQ7x6f1cpekdTudtj8LzbjO3SGJvujRkitEfDHOwdwwRODyV44fbqdDXL0hzx/nRTQxGOP6escx5ecJa2aIiAIdNGFI2lLFYXyDOOhEzaYyESaYdYOMBfvGxHSa/THgvxgf91MT/c28PnHzxO2nZ50/XrUUoxmrK5cq1u+6iZmdWtUQ71jS1JEb6gP9ntsRAvvWwF9x3o4//78aFcyllzQ5AT/fHc44HxNKcHk7nmMYmMTc/I5JHZpiG0R0L88kg/KcutamYEeGPdg6ZMW6jyBDJaEdEKmAar2yKMpSfyp5VS2K6adIHKZ01blKRV/qh7EeGll67k43dexe89fxM3bO5kJOX1cahmWEdTH3Q1e0MNZptaXYtc0J6wiPDHL95GPGVzz2NnSGRs/vTW7TQETWxXMTCepjUWYn/3KK2REOJ37v+Xnx1hMJHh6nWtvPZZ67jYH4kSML3hllC5cuWU5TCStGiPhSYJayhg8I93XDndE1aqog3elzU1cHJgooJuNGWzoqV4e86WSJCgaWA77pxKgVe1RljVGsHyL4Cb5tERS3PhEDQN1rdHOd4fn9bPuda5oD3hLC+/fCXvuOkiHjs5xAfu3ct42qYpHOR4f4IjvV66meW4/PN9h/jwd/fR1BDgzmet5ej5OH/6H3v4i+88zVNnhlFK5Qo+KlGokbYd4hmbTV0xhpPTq/k2dMYmhQQSGZvWyOwj3cshFg74Ock2Siksx5k2SDMf0xDWtkVylW/FGE1ZDPqNg0aSFinLmRSbH0l6ce2leHupWRyW+87HUvOGL2hPOJ9bL11BLGzyjz85xF9+52k+eNslfkmuzamBJJ/4+WEG4xnuuGYNr7t2HUHT4DevXsMP9/bwncfP8n/+6xl2rGjCUYplTeGC6TK2481pKyVUkLFdxtM2V61tozkSYCzllR0XmnOWJWE5bFle+Ymxa9ujPHN2BNtRLG+OzCryy5obON4fL/r8cCJDNGyyZU0LGdvr5DYUt/y7CIXtKLqaw/PK7tBceIQDJqvbonSPJGmNLJ0QlhbhPJ63pYtYKMDf/GA/f/ofe/jTW7fz/ae7+fG+Xta2Rfjoq69ga57INQRNXnnlal526Up+sr+Xbz92hv7xNNcWGIsylrLIOC6hgMFYyqI1GpqUZZFPxnYZTVlcta41l5a1ZXkjjx4fnLZIlsVyXMIBg9YqlG5mF+jStsPa9tkbEkVCJm2xoD+fbfJHbDCRpiXiTRnOXow6G8PQ5XkwKcshaTk0NZQ+AkmjybK6NcLpwQRKqSXz+ZFKNzRfTHbu3Kl2795d1jHxtM3uE4OT4kj7u0f50Hf3Ek87GAKvumo1r792/aylipbj8tDRATZ0xnITGRxXMZhI0xELs3V5E0FTODWY4ORAnGgoME2kLMdlJGlx5dpW2qYsSJ0eTHD0/DgdBWJeg/E0m5c1Fl0wmy/H+8eJp20uXd1a0v4D42mePjuSs1UpxUA8w7KmMNtWNOnWkZqqcaBnlP4xb7hsttXp87ZUdzpGiRS8KmhPuAA7Vjbz/151OffsPs3tV6xiu7/wNhtB0+D5ee30xlM2acdh2/JmVrU25K7Mm7oa6WryUtr6x9O0RUOYhuQE+PI1LdMEGLxFq96R1DQP01UKBVW9fV/bFqWcUFtrXnqbITCQSLOiJcLW5U266k1TVda0RekeTs2+Y42gRbgIGztj/Omt23OPlVKkbW+ckO26CJDVJFOEUMAgaHo/jqsYSqZpjYS4YkVLwfhwU0OQq9e20T2S4kjfGIYIluty+eqWXMOcqZiGsHVlE4+dGKQhaObCGWMpm5UtDVVdxCrXczUNYXVbhFMDCRzXmzi9qatxTmPvNZpyaAwH6GwMMZ62iS6BRk9ahPFCBgNxb2AmipzHKoCDi4E3rLGpIUBXU4TmSJBI0MR2PVFOpG1v4SxtM5qyEIEty5pY1RKZUXQMX6jaYyGO94+zrClM5yytJpsbgqxrj3FmKJELoViOU7Xm8fNhebOX3rapK8r6jtiSidFplj7r2mM8fmpIizCAiNwK/DNgAp9TSv3tlOfDwFeAa4AB4LVKqRP+c38OvA1wgHcrpX5UafsiQZPL17biKpVzbRXeeBxXKUIBMzfscrqgmjQB5HmujqtwXFVWq7tIyOTiVS0l77++I0rfWMpP64KmSHDGrInFIhoKcM36tpq0TVPfNEemDyeoVaoqwiJiAp8EbgHOALtE5F6l1L683d4GDCmlLhKRO4G/A14rIhcDdwKXAKuAn4rIVqVURd9VwxBvdb5CmP6o82oSMA22r2jmidNDiMClXaUL+EKjBVizGIgIGztjPHV6qOYXgatt3bXAEaXUMaVUBrgbuH3KPrcDX/Z//zZws3j3rbcDdyul0kqp48AR/3waoC0WYlWr12RoqVUIaTQLQVt0eofBWqTaIrwaOJ33+Iy/reA+SikbGAE6Sjz2gmZTZyOXrWrV2QYaTQGy3nCwxr8ftX+ZmAURuQu4C2DdunWLbM3CEgoYFR2zotHUGx2NYdJ2bceFq/0NPgvkz4de428ruI+IBIAWvAW6Uo5FKfUZpdROpdTOrq6aSMjWaDQ1gmkIa2t8JFa1RXgXsEVENopICG+h7d4p+9wLvMX//dXAz5RXxncvcKeIhEVkI7AFeLTK9mo0Gs2CUtVwhFLKFpF3Aj/CS1H7glJqr4h8GNitlLoX+DzwVRE5AgziCTX+fvcA+wAbeEelMyM0Go1msbnge0doNBrNAlFwhVCv6mg0Gs0iokVYo9FoFhEtwhqNRrOIaBHWaDSaRUSLsEaj0SwiWoQ1Go1mEdEirNFoNItIXeUJi8h54OQcDu0E+itszlK0AWrDjlqwAWrDjlqwAbQdlbChXyl169SNdSXCc0VEdiuldl7oNtSKHbVgQ63YUQs2aDuqa4MOR2g0Gs0iokVYo9FoFhEtwh6fWWwDqA0boDbsqAUboDbsqAUbQNuRT0Vt0DFhjUajWUS0J6zRaDSLiBZhjUajWUQuKBH2pzgvtg018Z7XwntRSyzm+yEinYttg//65mLbISIrFtsG//UX7L2oCUGoJiKyTUQuA1CLFAAXkWtF5K98G9zFsMG34xIRucm3YzHfi79Z7IuRiDxXRP5VRP4AFuf9EJGrROT7wB8tlg2+Hc8RkS8D/0dE2hfxvbgP+AhcWO9F3YqwiARE5PN4s+o+ISLvE5HsQNEFucqKSKuIfAL4BN7w0kXxhEXEEJFPAf8B/IWIfEREdi6kPSLSLCKfBP4FOKOUchfL2xGRq4F/BR4DXiYi/yQiVy7g6xv+F/2LwNeVUn+5UK9dwJZNwKeAnwPrgY+IyMsX8PVFRP4J+ArwZaXU2xfqtQvYsijvRd2KMN6b2KSU2gb8PtAF/IGIRBbwKvsvwI1KqeuUUp+CRfOEW4FGpdR24A14F4T3iUjjAtrzF8D1wIvz3ovFSs25FtillPoc8DtAAk+MOxfixf33vA3Yp5T6GoCIdC3SRekaYL9S6kvA+4Angf+VdViqjf8ZaASeUEp9BUBENi/SndKzWIT3oq5EWESuFpGt/sMgsFNEgkqp/XgecQxvonO1bdjhP/wHwBCRoIi8QkT+XEReJiIN1bTBt2Nj3uu0AzeISEwpdR7PIx4C3unvW5Uvv29D1H/4FeA8sExEXi0i/yAid4rIumq89hQ7XiMi7xWRG/xNjwONIrJCKdUD/AzvIv3cBbAh+xpvAV4sIn8iIj8HPg58RkS6qmWDb8f1ed8R8CairxGRtUqpIeBXwDDwGwtow/uA60Tkr0TkV8BHgS+JyDXVssG34xUi8k4Rud7ftAtYu5DvBdSJCPtf9u8Bn8Sb3HyLUuoAcB/wRn+3p4AngCtEpLXKNnzJt+FJ4CGgB3g3MA68H3iXiLRX2gbfjg0i8gPgc8C/i8jFSqkjwC+A9/q7deMJ8ZUisrLSHukUG77q27AP+CXe5O0/AA4CdwB/IiJrKvn6eXaYIvJ+4E/9TZ8WkVcAceAEcKO//QG8L9sa/7iKXZQK2PCvIvIa/0v+z3jx4A/iXRCbgDeISMWnoPuhse8BPwFeIyKN/lMp4EHgNf7jg3gTztsr7SwUsCEGoJQaxfvevBr4c+B1eJ/R36zGRUlEVorI/wD/G++O5Isi8hKl1DG872vV34t8lqwIT/mi/DHwpFLq2cB/A2/2t/8SeLaIrFJKxYEzwGogWWUb/gvvNhe8L9kHlFK3KKU+AfwlcBXQXAkbitjxiFLqZrzY1odE5GLgS8D1IrJJKWUDvXhfwOjU81XBho+IyEY8D+eDSqkXKqU+C/wV3q3oxkrYMBWllANsA96nlPpH4EN4YhcAzuFdhC7234+DwKv84yp2USpgwweAd4jIVqXUR4CLlVIPKKUGgG8Ar/TtqTQxvAvgu/zfn+9vPw88DFwmItf69p4FnqOUSi2QDSilPg7cpJT6hVIqjfcd2okXKqo0O4FfKqWe5/8N/hnIxqJ/ycK8FzmWrAgDDZD78scBy9/eDBwWkQ143l8f8Cf+c/fhiXClBLCYDS3AM/4XfFwp9S95IvUgsIzKfriydmQ9qH0ASql/wYt/vg5PdB4F/t5/7hm8uHl6AWy4BvhdvLj0l7MH+N7xCuBUhWxARN4sIjfm3e30Am0iElBKfRs4CtyCF4JIAX/t77ca2FUJL3QWG/4TeBp4vYgYSqnhvEM3A4+Inx5VQTualVJn8cpt78H7f18rIqt9oXkI7y7xn3wP+RLgVF4oqVo2XCciq7L7+ncHWa7Bc5qc+dqQZ8dNIhLG04Gv5j09ABz2f3+EKr0XxVhyIiwit4jIT4CP+rd1Ck/YtojIE8CteJ7O3cB24PPAi8RbgX0aLywxtgA2mMCXReTF/pdNibfS+iM8gRqdjw1F7LCBQeAqEblCRK4AngE2+Pb8DbBaRD4hIs/g9V4emc/tdxk2rMG7+GSPu028lKRzwOA8bRD/FvPneLHWNwCf9L9E/cBleB43eLHXNwK9SqkPAcP+LfKdwOfm6oWWacMn8Lzu5f6xN4vII8ALgc/6wjgnitjxryLSqZRKKaUSwE/xbsNfCKCU6lVK/TOeAH0B7/35O3/fBbHBPy7sC+Vu4CXA387HAy1gx+v9/19UKdUtIkF/15W+LSileir5XpSEUmrJ/AAX+W/O7Xi39F8H/th/bhvwn3n7vh/4uP/7BuAVwG8ssA1/BfwT3sXu5XhX2Nur9F58Ay/W2uS/7nfxLgw7fRvf4x+3HLgBuG0RbHinf9wNeOlhr6yADab/71bga9lteCloX8DLDPkh3q1v1H/+HuCP/N+DQNci2PDNvPfjNuBVVXwvPpH/ufS3/xHeXUALXhZRdt+mRbIhkvfZqObnImdH3j7/A7zI/32Z/29gvu9FybYuxIvM8800AMP//Q3Ap/Ke+228BZXleKvb/wzs8J97LvDt7LGLbIMAgSrb8Tbfji7/8aa8594B/I7/uyySDe/M2lChz0XWs/87vAW2V+DlmeY/34d3O/lmvPzP1/rP/TtwXT3YUKIdBt7i8I152xqBj+GFqHqBVYtow65K2DAXO4AQ3oVyHfB/8e6U2yr1OS3lp6bDESLyVry40Ef8TU8Dd/qLPOB5Mcf858fwUrHeLSJ/CHwa75ZnXossFbDhPgA1z8WWEuwI4MU7/8l/fNw/7i48cXzct2PO78c8bfjtrA3zRURuxPOk24Ajvj0W8AIRuRZyC2IfAj6qvPzTHwNv9sNFAd/2JW1DGXa4eBkYH8w79OV4dy1PAZcppc4tog1PzteGOdjxIf+wBuC38L6nTXge8RALyUIqfplXtEa8FdI/xPvybve3fwzvtvdXwNfwYm0/wFtt3YG38vpl4Pp6sGEOdnwPWO4//x48L+NZ9WBDni3PA96U9/hTeAU5vwU85m8z8Bb9vg2s9betIM87X+o2zMGOe4AN/rbbgefXiw1ztGMN3sL1V4ArK2VH2XYv1guX+Kau8//9W+Cb/u8mnrf5XP/xWjzBC9WrDWXa8SUg7D+O1psN2XMCYSZiem8A/p//+5PAu/zfdwLfqNLfY9FtqBU7asGGOdhxd7XsKPenpsMRSqls6tLHgI3iJVQ7wIhS6kH/ud/DSw+rSCpLLdpQph0JwPaPqeiKbi3YkD2nUiqtJrIIbsHLdwV4K7BDRL6L56FXJARSizbM1Y75ZKLUqg1zsOOxatlRNot9FSjjKve7wAN5j6/FK8z4PrDiQrGhVuyoERtMvNvLHwAX+dsuwstGeC6w+kKwoVbsqAUbasmOUn+WxHgjP8/WFZFv45UzpvEW3Q4rpY5eKDbUih21YINvh+Ctbn8O+A7e4t8A3m3nvPOwl4oNtWJHLdhQS3aUzGJfBcq4ukXxKuD6gXdfqDbUih21YINvx/WAi5eP/LYL1YZasaMWbKglO0r5qXijkCryB3jxpFuUV1t+odpQK3bUgg3gpcz9JfCPi2hHLdhQK3bUgg21ZMesLIlwBEzcAl/oNtSKHbVgg0ZTDywZEdZoNJp6pKZT1DQajabe0SKs0Wg0i4gWYY1Go1lEtAhrNBrNIqJFWHNB4Tf6flBEXpq37Q4R+eFi2qW5cNHZEZoLDhG5FPgWXiP6AF6z/VvVHKr9xBtZVI2ZcJoLBC3CmgsSEfl7vKZLMf/f9cCleP2hP6iU+m/x5hR+1d8HvEkYvxaRm/B61Q7htfTcikYzR7QIay5IxBu3/jiQwRvDtFcp9TXxhnM+iuclK8BVSqVEZAteG8advgh/D7hUKXV8MezX1A9LqWxZo6kYSqm4iHwTGAdeA7xCRP7Yf7oBb9zNOeBfRORKvDal+R7vo1qANZVAi7DmQsb1fwT4TaXUwfwnReSDeLPPrsBbxM6f/BtfIBs1dY7OjtBo4EfAu7INvkXkKn97C9Dt98h4E16fWo2momgR1mi8RbYgsEdE9jIxyPRTwFtE5ClgO9r71VQBvTCn0Wg0i4j2hDUajWYR0SKs0Wg0i4gWYY1Go1lEtAhrNBrNIqJFWKPRaBYRLcIajUaziGgR1mg0mkVEi7BGo9EsIv8/omKjvIX7eG0AAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 지역에"
      ],
      "metadata": {
        "id": "EokFI4iDitAo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "slang_usage = df['Genre']\n",
        "values = df['NA_Sales']"
      ],
      "metadata": {
        "id": "A-HCCB3eILI8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "slang_usage"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0CUYA2R9Jd4A",
        "outputId": "1f02c206-4e9a-4359-ae3a-e7bc4a8206e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0           Action\n",
              "1           Action\n",
              "2        Adventure\n",
              "3             Misc\n",
              "4         Platform\n",
              "           ...    \n",
              "16593     Platform\n",
              "16594       Action\n",
              "16595       Sports\n",
              "16596       Action\n",
              "16597     Fighting\n",
              "Name: Genre, Length: 16498, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.pie(values, labels=slang_usage)"
      ],
      "metadata": {
        "id": "LevqJVeGIndE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.pie(values)\n",
        "plt.legend(slang_usage)"
      ],
      "metadata": {
        "id": "jrJZiBlLIpYW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 장르의 고유 값 확인 , 데이터에는 총 12개의 장르들이 포함되어 있다.\n",
        "df['Genre'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kca2Rz4iP3-h",
        "outputId": "31a950cf-d72a-4c9c-8c1c-6589f0cbbbc5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Action', 'Adventure', 'Misc', 'Platform', 'Sports', 'Simulation',\n",
              "       'Racing', 'Role-Playing', 'Puzzle', 'Strategy', 'Fighting',\n",
              "       'Shooter'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 장르 어레이를 리스트로 변경"
      ],
      "metadata": {
        "id": "xuPMwnugg461"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a=df['Genre'].unique()"
      ],
      "metadata": {
        "id": "2L2E8pBVgv8r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a=a.tolist()"
      ],
      "metadata": {
        "id": "RHceqXUEgzfg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CmjR1Awog3Y8",
        "outputId": "6c2d064b-ec6c-4bd0-829f-7291a238402e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Action',\n",
              " 'Adventure',\n",
              " 'Misc',\n",
              " 'Platform',\n",
              " 'Sports',\n",
              " 'Simulation',\n",
              " 'Racing',\n",
              " 'Role-Playing',\n",
              " 'Puzzle',\n",
              " 'Strategy',\n",
              " 'Fighting',\n",
              " 'Shooter']"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Genre'].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gIDeATj0Qoyt",
        "outputId": "94409c7e-48bc-4b86-8634-7fde8c5e9a58"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range (1,13):\n",
        "  print(\"hello\")"
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        },
        "id": "3un0H8OxmT8E",
        "outputId": "48aedf77-2422-4b77-d8e8-c312c8124b32"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "hello\n",
            "hello\n",
            "hello\n",
            "hello\n",
            "hello\n",
            "hello\n",
            "hello\n",
            "hello\n",
            "hello\n",
            "hello\n",
            "hello\n",
            "hello\n"
          ]
        }
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      "cell_type": "code",
      "source": [
        "for i in range (1,13):\n",
        "  print(\"hello\")"
      ],
      "metadata": {
        "id": "BctetJ43mWSc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range (1,13):\n",
        "  c=[]\n",
        "  c.append(df['Genre']\n",
        "\n"
      ],
      "metadata": {
        "id": "Eo3g-BnfSQak"
      },
      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
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        "for i in a:\n",
        "  c=[]\n",
        "  c.append(df.where(df['Genre'] == 'a[i]'))"
      ],
      "metadata": {
        "id": "g-1HrcqRjduS"
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      "execution_count": null,
      "outputs": []
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              "[      Name Platform  Year Genre Publisher  NA_Sales  EU_Sales  JP_Sales  \\\n",
              " 0      NaN      NaN   NaN   NaN       NaN       NaN       NaN       NaN   \n",
              " 1      NaN      NaN   NaN   NaN       NaN       NaN       NaN       NaN   \n",
              " 2      NaN      NaN   NaN   NaN       NaN       NaN       NaN       NaN   \n",
              " 3      NaN      NaN   NaN   NaN       NaN       NaN       NaN       NaN   \n",
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              " ...    ...      ...   ...   ...       ...       ...       ...       ...   \n",
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              " 16594  NaN      NaN   NaN   NaN       NaN       NaN       NaN       NaN   \n",
              " 16595  NaN      NaN   NaN   NaN       NaN       NaN       NaN       NaN   \n",
              " 16596  NaN      NaN   NaN   NaN       NaN       NaN       NaN       NaN   \n",
              " 16597  NaN      NaN   NaN   NaN       NaN       NaN       NaN       NaN   \n",
              " \n",
              "        Other_Sales  \n",
              " 0              NaN  \n",
              " 1              NaN  \n",
              " 2              NaN  \n",
              " 3              NaN  \n",
              " 4              NaN  \n",
              " ...            ...  \n",
              " 16593          NaN  \n",
              " 16594          NaN  \n",
              " 16595          NaN  \n",
              " 16596          NaN  \n",
              " 16597          NaN  \n",
              " \n",
              " [16498 rows x 9 columns]]"
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          "metadata": {},
          "execution_count": 98
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "O4MQ5itbjRQM"
      },
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      "outputs": []
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    {
      "cell_type": "code",
      "source": [
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      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#각 장르 별로 지역 선호하는 게임 장르를 찾아보자\n",
        "daction=df.where(df['Genre'] == 'Action').dropna()"
      ],
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      "execution_count": null,
      "outputs": []
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      "execution_count": null,
      "outputs": [
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              "                                                    Name Platform    Year  \\\n",
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              "7                         Samurai Warriors: State of War      PSP  2005.0   \n",
              "9      The Penguins of Madagascar: Dr. Blowhole Retur...      PS3  2011.0   \n",
              "15                                           Mini Ninjas      PS3  2009.0   \n",
              "...                                                  ...      ...     ...   \n",
              "16575                                 Grand Theft Auto V       PC  2015.0   \n",
              "16581                                    LEGO The Hobbit     XOne  2014.0   \n",
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              "16594                        Rainbow Islands: Revolution      PSP  2005.0   \n",
              "16596                        Toukiden: The Age of Demons      PSV  2013.0   \n",
              "\n",
              "        Genre                               Publisher  NA_Sales  EU_Sales  \\\n",
              "0      Action                               Destineer      0.04      0.00   \n",
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              "7      Action                              Tecmo Koei      0.07      0.00   \n",
              "9      Action                                     THQ      0.04      0.03   \n",
              "15     Action                       Eidos Interactive      0.14      0.16   \n",
              "...       ...                                     ...       ...       ...   \n",
              "16575  Action                    Take-Two Interactive      0.36      0.64   \n",
              "16581  Action  Warner Bros. Interactive Entertainment      0.13      0.12   \n",
              "16583  Action                              Activision      0.91      0.39   \n",
              "16594  Action                       Rising Star Games      0.01      0.00   \n",
              "16596  Action                              Tecmo Koei      0.05      0.05   \n",
              "\n",
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      ],
      "metadata": {
        "id": "7X4RVyKrK4J0"
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              "                           Name Platform    Year   Genre           Publisher  \\\n",
              "0  Candace Kane's Candy Factory       DS  2008.0  Action           Destineer   \n",
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              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-37f2fd6a-4a8f-4461-a6ce-c99beeabfe85')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-37f2fd6a-4a8f-4461-a6ce-c99beeabfe85 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-37f2fd6a-4a8f-4461-a6ce-c99beeabfe85');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "daction['NA_Sales'].sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o8ekUxc9LnoH",
        "outputId": "23a67b2a-8239-42d4-e0dd-221cfce279d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "873.5500000000001"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "daction['EU_Sales'].sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M8cIOeZfMVUx",
        "outputId": "dfc4c73e-ce20-4cf5-be77-c9a3e079fe0e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "521.38"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "daction['JP_Sales'].sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X7sDSAHlJ_wY",
        "outputId": "72c75013-44f3-4498-c533-c15c61a41a54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1326.63"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "daction['Other_Sales'].sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W9Y-50vFJ-Kx",
        "outputId": "b65ad01f-73af-4843-a3d5-85197a9ade2b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "186.24"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "x = np.arange(4)\n",
        "ns = ['NA_Sales', 'EU_Sales', 'JP_Sales','Other_Sales']\n",
        "sum = [873.55, 521.38, 1326.63,186.24]\n",
        "\n",
        "colors = ['y', 'dodgerblue', 'C2','C3']\n",
        "\n",
        "plt.bar(x, sum, color=colors)\n",
        "plt.xticks(x, ns)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "id": "bg9rv9aqJ9Bs",
        "outputId": "b3f9c7af-73ef-446a-8aba-e580fe68af26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "액션 장르는 일본, 북미, 유럽, 기타 국가 순으로 판매량이 높았다."
      ],
      "metadata": {
        "id": "mTF0Q92UPpNL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dad=df.where(df['Genre'] == 'Adventure').dropna()"
      ],
      "metadata": {
        "id": "dg9HK4QfOMOz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "f1[1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "Q4K4nNdLRPWK",
        "outputId": "99de94a6-9e01-4114-aa7e-e895a5944c9d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Adventure'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dad['sum_sales']=dad['NA_Sales']+dad['EU_Sales']+dad['JP_Sales']+dad['Other_Sales']"
      ],
      "metadata": {
        "id": "mJIpHJetRSEe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dad['NA_Sales'].sum()"
      ],
      "metadata": {
        "id": "Bj6S9sMuY74Q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f95847cb-79db-493f-f67c-018366aca19e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "105.74000000000001"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dad['EU_Sales'].sum()"
      ],
      "metadata": {
        "id": "92DJ9vZhY688",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "182b8fb5-d177-4001-c0b3-e38855ee4bec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "63.73"
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dad['JP_Sales'].sum()"
      ],
      "metadata": {
        "id": "ugtY1h-NYy8x",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3c8490b9-820d-4743-92f5-42b7e97de1a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "111.86"
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dad['Other_Sales'].sum()"
      ],
      "metadata": {
        "id": "NwUXjdVJYyEg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0797f372-9f8e-496f-dad5-41d20b0ed4c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16.83"
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "x = np.arange(4)\n",
        "ns = ['NA_Sales', 'EU_Sales', 'JP_Sales','Other_Sales']\n",
        "sum = [105.74, 63.73, 111.86,16.83]\n",
        "\n",
        "colors = ['y', 'dodgerblue', 'C2','C3']\n",
        "\n",
        "plt.bar(x, sum, color=colors)\n",
        "plt.xticks(x, ns)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "L3sVcb2UHCxZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "outputId": "d29c2e52-e4d9-4dac-995c-76bdbff1a98b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['NA_Sales'].apply(lambda x: 'K' in x), 'NA_Sales'] *= 0.001"
      ],
      "metadata": {
        "id": "aqGdKSkIBcEo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "k"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iiUh9Lo3BKl6",
        "outputId": "bbc9578b-b596-46bb-e491-111eb18e1fe2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        False\n",
              "1        False\n",
              "2        False\n",
              "3        False\n",
              "4        False\n",
              "         ...  \n",
              "16593    False\n",
              "16594    False\n",
              "16595    False\n",
              "16596    False\n",
              "16597    False\n",
              "Name: NA_Sales, Length: 16598, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GM2up0HSD-TD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Km98AQ0ED8bc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "k"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "18HQBwKGD5F0",
        "outputId": "af96de31-a57f-458f-c98c-8e96efc8fd10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        False\n",
              "1        False\n",
              "2        False\n",
              "3        False\n",
              "4        False\n",
              "         ...  \n",
              "16593    False\n",
              "16594    False\n",
              "16595    False\n",
              "16596    False\n",
              "16597    False\n",
              "Name: NA_Sales, Length: 16598, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HeFBsTDLFtUN",
        "outputId": "bad0a483-6b7d-4221-d233-2ba4d3d9b315"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10         0.01\n",
              "45        60.00\n",
              "145        0.00\n",
              "878        0.00\n",
              "1086       0.00\n",
              "          ...  \n",
              "15647      0.00\n",
              "16194    120.00\n",
              "16330    720.00\n",
              "16492    470.00\n",
              "16578    320.00\n",
              "Name: NA_Sales, Length: 100, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 221
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d=df[k]['NA_Sales']"
      ],
      "metadata": {
        "id": "yqoVmvcLDWts"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(type(d))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "htiyEVnjU3iI",
        "outputId": "47bb9d6b-7c80-4e3e-cb7c-ea3702604754"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.series.Series'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d=d.values.tolist()"
      ],
      "metadata": {
        "id": "WzZzCS71U5Mh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "d.index()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "xToIh5wEVF45",
        "outputId": "79c39e76-ea13-4a58-f3d6-898b419ae490"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-224-1070a9282064>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0md\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: index expected at least 1 argument, got 0"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[d]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 327
        },
        "id": "w0a7LT77ZK6P",
        "outputId": "6eb44792-c06b-44cb-a711-5d29351b3c0d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-228-a47cb672e431>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    929\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    930\u001b[0m             \u001b[0mmaybe_callable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 931\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    932\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    933\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1151\u001b[0m                     \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Cannot index with multidimensional key\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1152\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1153\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_iterable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1154\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1155\u001b[0m             \u001b[0;31m# nested tuple slicing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_iterable\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1091\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1092\u001b[0m         \u001b[0;31m# A collection of keys\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1093\u001b[0;31m         \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1094\u001b[0m         return self.obj._reindex_with_indexers(\n\u001b[1;32m   1095\u001b[0m             \u001b[0;34m{\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_dups\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1312\u001b[0m             \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_indexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reindex_non_unique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1313\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1314\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_read_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1315\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1316\u001b[0m         if needs_i8_conversion(ax.dtype) or isinstance(\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[0;34m(self, key, indexer, axis)\u001b[0m\n\u001b[1;32m   1375\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1376\u001b[0m             \u001b[0mnot_found\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mensure_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmissing_mask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnonzero\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1377\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{not_found} not in index\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1378\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1379\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: '[0.01] not in index'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d.index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pPiZ_YlOZeT6",
        "outputId": "e7723ed2-6314-4daf-bc8e-870c91fa3a91"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([   10,    45,   145,   878,  1086,  1359,  1456,  1475,  1838,\n",
              "             1922,  1964,  1971,  2673,  2924,  3262,  3328,  3498,  4471,\n",
              "             4617,  4978,  5032,  5175,  5284,  5296,  5334,  5768,  6055,\n",
              "             6086,  6115,  6150,  6153,  6203,  6545,  6595,  6694,  6929,\n",
              "             7023,  7049,  7109,  7192,  7403,  7435,  7774,  7881,  8134,\n",
              "             8189,  8235,  8239,  8579,  8689,  8842,  8872,  9020,  9257,\n",
              "             9437,  9456,  9566,  9791,  9829, 10301, 10357, 10358, 10544,\n",
              "            10823, 11563, 11992, 11996, 12107, 12158, 12240, 12332, 12410,\n",
              "            12517, 12745, 12876, 13057, 13341, 13557, 13798, 13887, 13935,\n",
              "            14180, 14292, 14347, 14410, 14446, 14630, 14757, 15088, 15107,\n",
              "            15111, 15340, 15430, 15597, 15622, 15647, 16194, 16330, 16492,\n",
              "            16578],\n",
              "           dtype='int64')"
            ]
          },
          "metadata": {},
          "execution_count": 230
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dQsQZMi7Zo1k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "d"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fvVbQiRKVHuM",
        "outputId": "d8e80a58-1c79-4bcd-db87-e7cabad2c61b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10         0.01\n",
              "45        60.00\n",
              "145        0.00\n",
              "878        0.00\n",
              "1086       0.00\n",
              "          ...  \n",
              "15647      0.00\n",
              "16194    120.00\n",
              "16330    720.00\n",
              "16492    470.00\n",
              "16578    320.00\n",
              "Name: NA_Sales, Length: 100, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 226
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yErZn6EYVHC3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZHf4hWU3U1Uu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9QwoYSOdU0t9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['NA_Sales'].insert(0, 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "UttwX3nIEOY7",
        "outputId": "597d83ce-d742-4f98-fd9f-3e2b947490d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-136-b5310932852d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'NA_Sales'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5485\u001b[0m         ):\n\u001b[1;32m   5486\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5487\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5488\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5489\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'Series' object has no attribute 'insert'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['NA_Sales']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0fz4LE5iI4kd",
        "outputId": "f5cd5e3f-f385-47e2-955c-24f8c92dfe27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        0.04\n",
              "1        0.17\n",
              "2        0.00\n",
              "3        0.04\n",
              "4        0.12\n",
              "         ... \n",
              "16593    0.15\n",
              "16594    0.01\n",
              "16595    0.44\n",
              "16596    0.05\n",
              "16597    0.00\n",
              "Name: NA_Sales, Length: 16598, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "si=df[k]['NA_Sales']"
      ],
      "metadata": {
        "id": "DAVSfG8mIUZO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(type(si))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hBcV0VW8MPDH",
        "outputId": "80ecbafe-db43-4e35-c887-26993cfe9fc4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.series.Series'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "si.index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XM_gckT9MTsT",
        "outputId": "7c080422-048c-434f-a0a6-7730dd4ad1e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([   10,    45,   145,   878,  1086,  1359,  1456,  1475,  1838,\n",
              "             1922,  1964,  1971,  2673,  2924,  3262,  3328,  3498,  4471,\n",
              "             4617,  4978,  5032,  5175,  5284,  5296,  5334,  5768,  6055,\n",
              "             6086,  6115,  6150,  6153,  6203,  6545,  6595,  6694,  6929,\n",
              "             7023,  7049,  7109,  7192,  7403,  7435,  7774,  7881,  8134,\n",
              "             8189,  8235,  8239,  8579,  8689,  8842,  8872,  9020,  9257,\n",
              "             9437,  9456,  9566,  9791,  9829, 10301, 10357, 10358, 10544,\n",
              "            10823, 11563, 11992, 11996, 12107, 12158, 12240, 12332, 12410,\n",
              "            12517, 12745, 12876, 13057, 13341, 13557, 13798, 13887, 13935,\n",
              "            14180, 14292, 14347, 14410, 14446, 14630, 14757, 15088, 15107,\n",
              "            15111, 15340, 15430, 15597, 15622, 15647, 16194, 16330, 16492,\n",
              "            16578],\n",
              "           dtype='int64')"
            ]
          },
          "metadata": {},
          "execution_count": 175
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b.insert(si,d)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "wcB4_-A4MYDT",
        "outputId": "c4764a6a-d5ba-4716-c367-6ead97d5b6ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-195-4b304e34b10e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msibal2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: 'Series' object cannot be interpreted as an integer"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sibal2[d] = d"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "Nz8dAj7MTkLz",
        "outputId": "9c18355d-5df8-413d-cdee-b9bc248790ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-202-f32d6ced2d59>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msibal2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: list indices must be integers or slices, not Series"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sibal2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kiJleHHjZvgq",
        "outputId": "cb606bf1-0deb-4711-bda2-afba4cf11c44"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "Int64Index([   10,    45,   145,   878,  1086,  1359,  1456,  1475,  1838,\n",
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              "            15111, 15340, 15430, 15597, 15622, 15647, 16194, 16330, 16492,\n",
              "            16578],\n",
              "           dtype='int64')"
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          "execution_count": 231
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            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-182-b48284c4f806>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: 'Int64Index' object cannot be interpreted as an integer"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[k]['NA_Sales']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eTOKRx87N1LX",
        "outputId": "a1327b95-d4f2-4042-8463-6eb2e0213a14"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10       480.0\n",
              "45        60.0\n",
              "145        0.0\n",
              "878        0.0\n",
              "1086       0.0\n",
              "         ...  \n",
              "15647      0.0\n",
              "16194    120.0\n",
              "16330    720.0\n",
              "16492    470.0\n",
              "16578    320.0\n",
              "Name: NA_Sales, Length: 100, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 194
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Xa555QxAN3Uh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "si"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sjbHeo8GNF8j",
        "outputId": "22fe8d61-5656-4ef2-fb1f-aee442b39fd6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10       480.0\n",
              "45        60.0\n",
              "145        0.0\n",
              "878        0.0\n",
              "1086       0.0\n",
              "         ...  \n",
              "15647      0.0\n",
              "16194    120.0\n",
              "16330    720.0\n",
              "16492    470.0\n",
              "16578    320.0\n",
              "Name: NA_Sales, Length: 100, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 186
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(type(d))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RqL__tFkNNcv",
        "outputId": "f180f3a2-e398-42ab-a460-fd05c3887f27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'list'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d"
      ],
      "metadata": {
        "id": "EKsfBYzbNRvu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Zj3POgc8NL5q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "0Il2ZSpANKiU",
        "outputId": "354fedae-401d-4b14-8c8e-9badcf70c6e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-192-8772d94a5769>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mbal\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: index expected at least 1 argument, got 0"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mMr1wPF8Ndau"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2xoEzhO9Ncru"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "si"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HsPnetF5NakD",
        "outputId": "6025880c-73e5-43b5-d518-d2192f3655f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10       480.0\n",
              "45        60.0\n",
              "145        0.0\n",
              "878        0.0\n",
              "1086       0.0\n",
              "         ...  \n",
              "15647      0.0\n",
              "16194    120.0\n",
              "16330    720.0\n",
              "16492    470.0\n",
              "16578    320.0\n",
              "Name: NA_Sales, Length: 100, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 193
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "w4D0xOWpNgHo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bal=si.values.tolist()"
      ],
      "metadata": {
        "id": "sN6hpthuNIbR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "s=df['NA_Sales'].values.tolist()"
      ],
      "metadata": {
        "id": "YoqfXD49MkWx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "s"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wt25S6cZMoKu",
        "outputId": "0b32c75d-27fb-424a-849d-61ada2323010"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              " ...]"
            ]
          },
          "metadata": {},
          "execution_count": 180
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ykd6NmuzMnUp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sibal=df['NA_Sales']"
      ],
      "metadata": {
        "id": "LPJRMW-rKWd-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sibal2.insert(o,d)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "OvGb6pokKeOd",
        "outputId": "470e3f10-6686-4f03-d3b6-bec1e67a2c9d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-165-7ccf2309a5e2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msibal2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mo\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: 'builtin_function_or_method' object cannot be interpreted as an integer"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sibal2 = list(df.loc[:, 'NA_Sales'])"
      ],
      "metadata": {
        "id": "6go_OvAbKiZn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(type(d))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-2yzGXw5Kc1E",
        "outputId": "5d0c464f-373c-4aab-ea8e-7358133a7ec6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'list'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G05pFplrLfAD",
        "outputId": "6dc0bc20-e2e5-4ec6-ff9c-94f5df1e3faa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              " 0.72,\n",
              " 0.47000000000000003,\n",
              " 0.32]"
            ]
          },
          "metadata": {},
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tsKK_zs8MEYH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "o"
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "guYRTxMULhdC",
        "outputId": "5b49a2af-72d4-4864-e428-ae1a745ebdaa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function list.index(value, start=0, stop=9223372036854775807, /)>"
            ]
          },
          "metadata": {},
          "execution_count": 167
        }
      ]
    },
    {
      "cell_type": "code",
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      "metadata": {
        "id": "gKb7Ro6pLiiG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "eC1-t3rZLh8y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mxAWTReKLgm3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "d"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6AZKeFacLLMv",
        "outputId": "b79d26db-b3ce-4dd4-df91-543f9249bedc"
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      "execution_count": null,
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    },
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      "cell_type": "code",
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        "print(type(sibal))"
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      "metadata": {
        "colab": {
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        "id": "jN9P_phdJghF",
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      "execution_count": null,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.series.Series'>\n"
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      "cell_type": "code",
      "source": [
        "df['NA_Sales'] = df['NA_Sales'].astype(str).str.replace('K', '')\n",
        "df.loc[df['NA_Sales'].apply(lambda x: 'K' in x), 'NA_Sales'] *= 0.001"
      ],
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    {
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        "i=df[k]['NA_Sales']"
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      "outputs": []
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10         0.01\n",
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              "Name: NA_Sales, Length: 100, dtype: float64"
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              "10       480.0\n",
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        "id": "4dEFtJ666IIU",
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Unnamed: 0                                            Name Platform  \\\n",
              "0               1                    Candace Kane's Candy Factory       DS   \n",
              "1               2                                  The Munchables      Wii   \n",
              "2               3  Otome wa Oanesama Boku ni Koi Shiteru Portable      PSP   \n",
              "3               4                Deal or No Deal: Special Edition       DS   \n",
              "4               5       Ben 10 Ultimate Alien: Cosmic Destruction      PS3   \n",
              "...           ...                                             ...      ...   \n",
              "16593       16594                         Ice Age 2: The Meltdown       GC   \n",
              "16594       16595                     Rainbow Islands: Revolution      PSP   \n",
              "16595       16596                                        NBA 2K16      PS3   \n",
              "16596       16597                     Toukiden: The Age of Demons      PSV   \n",
              "16597       16598                        The King of Fighters '95       PS   \n",
              "\n",
              "         Year      Genre                    Publisher NA_Sales EU_Sales  \\\n",
              "0      2008.0     Action                    Destineer     0.04        0   \n",
              "1      2009.0     Action           Namco Bandai Games     0.17        0   \n",
              "2      2010.0  Adventure                    Alchemist        0        0   \n",
              "3      2010.0       Misc                    Zoo Games     0.04        0   \n",
              "4      2010.0   Platform                  D3Publisher     0.12     0.09   \n",
              "...       ...        ...                          ...      ...      ...   \n",
              "16593  2006.0   Platform                Vivendi Games     0.15     0.04   \n",
              "16594  2005.0     Action            Rising Star Games     0.01        0   \n",
              "16595  2015.0     Sports         Take-Two Interactive     0.44     0.19   \n",
              "16596  2013.0     Action                   Tecmo Koei     0.05     0.05   \n",
              "16597  1996.0   Fighting  Sony Computer Entertainment        0        0   \n",
              "\n",
              "      JP_Sales Other_Sales  \n",
              "0            0           0  \n",
              "1            0        0.01  \n",
              "2         0.02           0  \n",
              "3            0           0  \n",
              "4            0        0.04  \n",
              "...        ...         ...  \n",
              "16593        0        0.01  \n",
              "16594        0           0  \n",
              "16595     0.03        0.13  \n",
              "16596     0.25        0.03  \n",
              "16597     0.16        0.01  \n",
              "\n",
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              "    <tr>\n",
              "      <th>16596</th>\n",
              "      <td>16597</td>\n",
              "      <td>Toukiden: The Age of Demons</td>\n",
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              "                                                     [key], {});\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['NA_Sales']"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "D9ZaooFI5AyI",
        "outputId": "43efc35f-c31b-4d43-b2c4-d69fb43772d7"
      },
      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        nan\n",
              "1        nan\n",
              "2        nan\n",
              "3        nan\n",
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              "        ... \n",
              "16593    nan\n",
              "16594    nan\n",
              "16595    nan\n",
              "16596    nan\n",
              "16597    nan\n",
              "Name: NA_Sales, Length: 16598, dtype: object"
            ]
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          "metadata": {},
          "execution_count": 38
        }
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    {
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      "source": [
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          "height": 424
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        "id": "AGBtKC000fY8",
        "outputId": "e7ccf466-eb87-41f2-b9a4-0133d7ce5415"
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      "execution_count": null,
      "outputs": [
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              "       Unnamed: 0                                            Name Platform  \\\n",
              "0               1                    Candace Kane's Candy Factory       DS   \n",
              "1               2                                  The Munchables      Wii   \n",
              "2               3  Otome wa Oanesama Boku ni Koi Shiteru Portable      PSP   \n",
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              "4               5       Ben 10 Ultimate Alien: Cosmic Destruction      PS3   \n",
              "...           ...                                             ...      ...   \n",
              "16593       16594                         Ice Age 2: The Meltdown       GC   \n",
              "16594       16595                     Rainbow Islands: Revolution      PSP   \n",
              "16595       16596                                        NBA 2K16      PS3   \n",
              "16596       16597                     Toukiden: The Age of Demons      PSV   \n",
              "16597       16598                        The King of Fighters '95       PS   \n",
              "\n",
              "         Year      Genre                    Publisher NA_Sales EU_Sales  \\\n",
              "0      2008.0     Action                    Destineer     0.04        0   \n",
              "1      2009.0     Action           Namco Bandai Games     0.17        0   \n",
              "2      2010.0  Adventure                    Alchemist        0        0   \n",
              "3      2010.0       Misc                    Zoo Games     0.04        0   \n",
              "4      2010.0   Platform                  D3Publisher     0.12     0.09   \n",
              "...       ...        ...                          ...      ...      ...   \n",
              "16593  2006.0   Platform                Vivendi Games     0.15     0.04   \n",
              "16594  2005.0     Action            Rising Star Games     0.01        0   \n",
              "16595  2015.0     Sports         Take-Two Interactive     0.44     0.19   \n",
              "16596  2013.0     Action                   Tecmo Koei     0.05     0.05   \n",
              "16597  1996.0   Fighting  Sony Computer Entertainment        0        0   \n",
              "\n",
              "      JP_Sales Other_Sales  \n",
              "0            0           0  \n",
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              "2         0.02           0  \n",
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              "4            0        0.04  \n",
              "...        ...         ...  \n",
              "16593        0        0.01  \n",
              "16594        0           0  \n",
              "16595     0.03        0.13  \n",
              "16596     0.25        0.03  \n",
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              "Unnamed: 0                                                    10\n",
              "Name           The Penguins of Madagascar: Dr. Blowhole Retur...\n",
              "Platform                                                     PS3\n",
              "Year                                                      2011.0\n",
              "Genre                                                     Action\n",
              "Publisher                                                    THQ\n",
              "NA_Sales                                                    0.04\n",
              "EU_Sales                                                    0.03\n",
              "JP_Sales                                                       0\n",
              "Other_Sales                                                 0.01\n",
              "Name: 9, dtype: object"
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          "execution_count": 24
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              "       Unnamed: 0                                            Name Platform  \\\n",
              "10             11                                     Ford Racing       PS   \n",
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              "16194       16195                            Vin Diesel: Wheelman      PS3   \n",
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              "16578       16579                            Gex: Enter the Gecko       PS   \n",
              "\n",
              "         Year     Genre                               Publisher NA_Sales  \\\n",
              "10     2001.0    Racing                      Empire Interactive     480K   \n",
              "45     2015.0    Action                                  Capcom      60K   \n",
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              "878    2009.0  Strategy                               Aqua Plus       0K   \n",
              "1086   2013.0    Action            Konami Digital Entertainment       0K   \n",
              "...       ...       ...                                     ...      ...   \n",
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              "16492  2012.0  Fighting  Warner Bros. Interactive Entertainment     470K   \n",
              "16578  1998.0  Platform           BMG Interactive Entertainment     320K   \n",
              "\n",
              "      EU_Sales JP_Sales Other_Sales   test  \n",
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          "data": {
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              "0        False\n",
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              "4        False\n",
              "         ...  \n",
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              "Name: NA_Sales, Length: 16598, dtype: bool"
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              "10       480\n",
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              "       Unnamed: 0                                            Name Platform  \\\n",
              "0               1                    Candace Kane's Candy Factory       DS   \n",
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              "      <td>Tears to Tiara Anecdotes: The Secret of Avalon</td>\n",
              "      <td>PS3</td>\n",
              "      <td>2009.0</td>\n",
              "      <td>Strategy</td>\n",
              "      <td>Aqua Plus</td>\n",
              "      <td>0K</td>\n",
              "      <td>0</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1086</th>\n",
              "      <td>1087</td>\n",
              "      <td>Pro Evolution Soccer 2014</td>\n",
              "      <td>3DS</td>\n",
              "      <td>2013.0</td>\n",
              "      <td>Action</td>\n",
              "      <td>Konami Digital Entertainment</td>\n",
              "      <td>0K</td>\n",
              "      <td>0</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15647</th>\n",
              "      <td>15648</td>\n",
              "      <td>Championship Manager 2010</td>\n",
              "      <td>PC</td>\n",
              "      <td>2009.0</td>\n",
              "      <td>Sports</td>\n",
              "      <td>Eidos Interactive</td>\n",
              "      <td>0K</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16194</th>\n",
              "      <td>16195</td>\n",
              "      <td>Vin Diesel: Wheelman</td>\n",
              "      <td>PS3</td>\n",
              "      <td>2009.0</td>\n",
              "      <td>Racing</td>\n",
              "      <td>Ubisoft</td>\n",
              "      <td>120K</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0</td>\n",
              "      <td>0.06</td>\n",
              "      <td>120</td>\n",
              "      <td>100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16330</th>\n",
              "      <td>16331</td>\n",
              "      <td>Midway Arcade Treasures</td>\n",
              "      <td>PS2</td>\n",
              "      <td>2003.0</td>\n",
              "      <td>Misc</td>\n",
              "      <td>Midway Games</td>\n",
              "      <td>720K</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0</td>\n",
              "      <td>0.19</td>\n",
              "      <td>720</td>\n",
              "      <td>100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16492</th>\n",
              "      <td>16493</td>\n",
              "      <td>Mortal Kombat</td>\n",
              "      <td>PSV</td>\n",
              "      <td>2012.0</td>\n",
              "      <td>Fighting</td>\n",
              "      <td>Warner Bros. Interactive Entertainment</td>\n",
              "      <td>470K</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0</td>\n",
              "      <td>0.12</td>\n",
              "      <td>470</td>\n",
              "      <td>100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16578</th>\n",
              "      <td>16579</td>\n",
              "      <td>Gex: Enter the Gecko</td>\n",
              "      <td>PS</td>\n",
              "      <td>1998.0</td>\n",
              "      <td>Platform</td>\n",
              "      <td>BMG Interactive Entertainment</td>\n",
              "      <td>320K</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0</td>\n",
              "      <td>0.04</td>\n",
              "      <td>320</td>\n",
              "      <td>100000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7fcd8669-5b22-4bd0-aed3-87f3a5f2e872')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "          const element = document.querySelector('#df-7fcd8669-5b22-4bd0-aed3-87f3a5f2e872');\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
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          },
          "metadata": {},
          "execution_count": 88
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    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QyowVQ2x8TVT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[test].index"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lCpRED3s1VlH",
        "outputId": "e1315bd9-9980-4c3f-eb05-11851ee4a15f"
      },
      "execution_count": null,
      "outputs": [
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          "data": {
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              "Int64Index([   10,    45,   145,   878,  1086,  1359,  1456,  1475,  1838,\n",
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              "            10823, 11563, 11992, 11996, 12107, 12158, 12240, 12332, 12410,\n",
              "            12517, 12745, 12876, 13057, 13341, 13557, 13798, 13887, 13935,\n",
              "            14180, 14292, 14347, 14410, 14446, 14630, 14757, 15088, 15107,\n",
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              "            16578],\n",
              "           dtype='int64')"
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          "metadata": {},
          "execution_count": 34
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    {
      "cell_type": "code",
      "source": [
        "df.drop(index = df[test].index)"
      ],
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          "height": 424
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        "id": "F6jC4TtR2iGa",
        "outputId": "17c97c99-0a0b-47d1-bdbf-ea363b90734a"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Unnamed: 0                                            Name Platform  \\\n",
              "0               1                    Candace Kane's Candy Factory       DS   \n",
              "1               2                                  The Munchables      Wii   \n",
              "2               3  Otome wa Oanesama Boku ni Koi Shiteru Portable      PSP   \n",
              "3               4                Deal or No Deal: Special Edition       DS   \n",
              "4               5       Ben 10 Ultimate Alien: Cosmic Destruction      PS3   \n",
              "...           ...                                             ...      ...   \n",
              "16593       16594                         Ice Age 2: The Meltdown       GC   \n",
              "16594       16595                     Rainbow Islands: Revolution      PSP   \n",
              "16595       16596                                        NBA 2K16      PS3   \n",
              "16596       16597                     Toukiden: The Age of Demons      PSV   \n",
              "16597       16598                        The King of Fighters '95       PS   \n",
              "\n",
              "         Year      Genre                    Publisher NA_Sales EU_Sales  \\\n",
              "0      2008.0     Action                    Destineer     0.04        0   \n",
              "1      2009.0     Action           Namco Bandai Games     0.17        0   \n",
              "2      2010.0  Adventure                    Alchemist        0        0   \n",
              "3      2010.0       Misc                    Zoo Games     0.04        0   \n",
              "4      2010.0   Platform                  D3Publisher     0.12     0.09   \n",
              "...       ...        ...                          ...      ...      ...   \n",
              "16593  2006.0   Platform                Vivendi Games     0.15     0.04   \n",
              "16594  2005.0     Action            Rising Star Games     0.01        0   \n",
              "16595  2015.0     Sports         Take-Two Interactive     0.44     0.19   \n",
              "16596  2013.0     Action                   Tecmo Koei     0.05     0.05   \n",
              "16597  1996.0   Fighting  Sony Computer Entertainment        0        0   \n",
              "\n",
              "      JP_Sales Other_Sales  \n",
              "0            0           0  \n",
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              "16593        0        0.01  \n",
              "16594        0           0  \n",
              "16595     0.03        0.13  \n",
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              "      <td>Sports</td>\n",
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              "      <td>0.44</td>\n",
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              "      <th>16596</th>\n",
              "      <td>16597</td>\n",
              "      <td>Toukiden: The Age of Demons</td>\n",
              "      <td>PSV</td>\n",
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              "      <td>Action</td>\n",
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              "<p>16498 rows × 10 columns</p>\n",
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          "metadata": {},
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        "df.info()"
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        "id": "t-U86mPR8zWL",
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      },
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 100 entries, 10 to 16578\n",
            "Data columns (total 13 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   Unnamed: 0   100 non-null    int64  \n",
            " 1   Name         100 non-null    object \n",
            " 2   Platform     100 non-null    object \n",
            " 3   Year         100 non-null    float64\n",
            " 4   Genre        100 non-null    object \n",
            " 5   Publisher    100 non-null    object \n",
            " 6   NA_Sales     100 non-null    object \n",
            " 7   EU_Sales     100 non-null    object \n",
            " 8   JP_Sales     100 non-null    object \n",
            " 9   Other_Sales  100 non-null    object \n",
            " 10  test         100 non-null    object \n",
            " 11  100000       100 non-null    int64  \n",
            " 12  1            100 non-null    object \n",
            "dtypes: float64(1), int64(2), object(10)\n",
            "memory usage: 10.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['test']=df['test'].astype('float')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 327
        },
        "id": "vjKh6Dkd80Lq",
        "outputId": "627aaf36-ce9c-46a2-a9ec-e894760e2903"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-103-12e85bd248bd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'test'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'test'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'float'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m   5813\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5814\u001b[0m             \u001b[0;31m# else, only a single dtype is given\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5815\u001b[0;31m             \u001b[0mnew_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5816\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5817\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    416\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    417\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"raise\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 418\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    419\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    420\u001b[0m     def convert(\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, align_keys, ignore_failures, **kwargs)\u001b[0m\n\u001b[1;32m    325\u001b[0m                     \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    326\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 327\u001b[0;31m                     \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    328\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    329\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mignore_failures\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    589\u001b[0m         \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    590\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 591\u001b[0;31m         \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_array_safe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    592\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    593\u001b[0m         \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmaybe_coerce_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/dtypes/cast.py\u001b[0m in \u001b[0;36mastype_array_safe\u001b[0;34m(values, dtype, copy, errors)\u001b[0m\n\u001b[1;32m   1307\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1308\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1309\u001b[0;31m         \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1310\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1311\u001b[0m         \u001b[0;31m# e.g. astype_nansafe can fail on object-dtype of strings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/dtypes/cast.py\u001b[0m in \u001b[0;36mastype_array\u001b[0;34m(values, dtype, copy)\u001b[0m\n\u001b[1;32m   1255\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1256\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1257\u001b[0;31m         \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_nansafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1258\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1259\u001b[0m     \u001b[0;31m# in pandas we don't store numpy str dtypes, so convert to object\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/dtypes/cast.py\u001b[0m in \u001b[0;36mastype_nansafe\u001b[0;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[1;32m   1199\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1200\u001b[0m         \u001b[0;31m# Explicit copy, or required since NumPy can't view from / to object.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1201\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1202\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1203\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: could not convert string to float: ''"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['test'] = df.test.str.replace(r'[a-zA-Z]', '', regex=True)"
      ],
      "metadata": {
        "id": "qSVx5COw9Nmt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "yTu6qAda9Zvw",
        "outputId": "528a0d19-e614-406f-dbcd-518ae7f47e2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Unnamed: 0                                            Name Platform  \\\n",
              "10             11                                     Ford Racing       PS   \n",
              "45             46                    Resident Evil: Revelations 2      PS3   \n",
              "145           146                              TOCA Race Driver 3       PC   \n",
              "878           879  Tears to Tiara Anecdotes: The Secret of Avalon      PS3   \n",
              "1086         1087                       Pro Evolution Soccer 2014      3DS   \n",
              "...           ...                                             ...      ...   \n",
              "15647       15648                       Championship Manager 2010       PC   \n",
              "16194       16195                            Vin Diesel: Wheelman      PS3   \n",
              "16330       16331                         Midway Arcade Treasures      PS2   \n",
              "16492       16493                                   Mortal Kombat      PSV   \n",
              "16578       16579                            Gex: Enter the Gecko       PS   \n",
              "\n",
              "         Year     Genre                               Publisher NA_Sales  \\\n",
              "10     2001.0    Racing                      Empire Interactive     480K   \n",
              "45     2015.0    Action                                  Capcom      60K   \n",
              "145    2006.0    Racing                             Codemasters       0K   \n",
              "878    2009.0  Strategy                               Aqua Plus       0K   \n",
              "1086   2013.0    Action            Konami Digital Entertainment       0K   \n",
              "...       ...       ...                                     ...      ...   \n",
              "15647  2009.0    Sports                       Eidos Interactive       0K   \n",
              "16194  2009.0    Racing                                 Ubisoft     120K   \n",
              "16330  2003.0      Misc                            Midway Games     720K   \n",
              "16492  2012.0  Fighting  Warner Bros. Interactive Entertainment     470K   \n",
              "16578  1998.0  Platform           BMG Interactive Entertainment     320K   \n",
              "\n",
              "      EU_Sales JP_Sales Other_Sales test  100000  \\\n",
              "10       0.33M       0K        0.06       100000   \n",
              "45        0.08     0.16        0.03       100000   \n",
              "145       0.02        0           0       100000   \n",
              "878          0     0.04           0       100000   \n",
              "1086         0     0.19           0       100000   \n",
              "...        ...      ...         ...  ...     ...   \n",
              "15647     0.01        0           0       100000   \n",
              "16194     0.15        0        0.06       100000   \n",
              "16330     0.56        0        0.19       100000   \n",
              "16492     0.18        0        0.12       100000   \n",
              "16578     0.22        0        0.04       100000   \n",
              "\n",
              "                                                       1  \n",
              "10     480K480K480K480K480K480K480K480K480K480K480K48...  \n",
              "45     60K60K60K60K60K60K60K60K60K60K60K60K60K60K60K6...  \n",
              "145    0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K...  \n",
              "878    0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K...  \n",
              "1086   0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K...  \n",
              "...                                                  ...  \n",
              "15647  0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K...  \n",
              "16194  120K120K120K120K120K120K120K120K120K120K120K12...  \n",
              "16330  720K720K720K720K720K720K720K720K720K720K720K72...  \n",
              "16492  470K470K470K470K470K470K470K470K470K470K470K47...  \n",
              "16578  320K320K320K320K320K320K320K320K320K320K320K32...  \n",
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              "      <th>Name</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11</td>\n",
              "      <td>Ford Racing</td>\n",
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              "      <td>2001.0</td>\n",
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              "      <th>45</th>\n",
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              "      <td>Resident Evil: Revelations 2</td>\n",
              "      <td>PS3</td>\n",
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              "      <td>0.16</td>\n",
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              "      <th>145</th>\n",
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              "      <td>TOCA Race Driver 3</td>\n",
              "      <td>PC</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Racing</td>\n",
              "      <td>Codemasters</td>\n",
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              "      <td>0.02</td>\n",
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              "    <tr>\n",
              "      <th>878</th>\n",
              "      <td>879</td>\n",
              "      <td>Tears to Tiara Anecdotes: The Secret of Avalon</td>\n",
              "      <td>PS3</td>\n",
              "      <td>2009.0</td>\n",
              "      <td>Strategy</td>\n",
              "      <td>Aqua Plus</td>\n",
              "      <td>0K</td>\n",
              "      <td>0</td>\n",
              "      <td>0.04</td>\n",
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              "      <td>100000</td>\n",
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              "    <tr>\n",
              "      <th>1086</th>\n",
              "      <td>1087</td>\n",
              "      <td>Pro Evolution Soccer 2014</td>\n",
              "      <td>3DS</td>\n",
              "      <td>2013.0</td>\n",
              "      <td>Action</td>\n",
              "      <td>Konami Digital Entertainment</td>\n",
              "      <td>0K</td>\n",
              "      <td>0</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0</td>\n",
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              "      <td>100000</td>\n",
              "      <td>0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K...</td>\n",
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              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>15647</th>\n",
              "      <td>15648</td>\n",
              "      <td>Championship Manager 2010</td>\n",
              "      <td>PC</td>\n",
              "      <td>2009.0</td>\n",
              "      <td>Sports</td>\n",
              "      <td>Eidos Interactive</td>\n",
              "      <td>0K</td>\n",
              "      <td>0.01</td>\n",
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              "      <td>0</td>\n",
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              "      <td>100000</td>\n",
              "      <td>0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K0K...</td>\n",
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              "    <tr>\n",
              "      <th>16194</th>\n",
              "      <td>16195</td>\n",
              "      <td>Vin Diesel: Wheelman</td>\n",
              "      <td>PS3</td>\n",
              "      <td>2009.0</td>\n",
              "      <td>Racing</td>\n",
              "      <td>Ubisoft</td>\n",
              "      <td>120K</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0</td>\n",
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              "      <td></td>\n",
              "      <td>100000</td>\n",
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              "      <th>16330</th>\n",
              "      <td>16331</td>\n",
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              "      <td>0.56</td>\n",
              "      <td>0</td>\n",
              "      <td>0.19</td>\n",
              "      <td></td>\n",
              "      <td>100000</td>\n",
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              "      <th>16492</th>\n",
              "      <td>16493</td>\n",
              "      <td>Mortal Kombat</td>\n",
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              "      <td>2012.0</td>\n",
              "      <td>Fighting</td>\n",
              "      <td>Warner Bros. Interactive Entertainment</td>\n",
              "      <td>470K</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0</td>\n",
              "      <td>0.12</td>\n",
              "      <td></td>\n",
              "      <td>100000</td>\n",
              "      <td>470K470K470K470K470K470K470K470K470K470K470K47...</td>\n",
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              "    <tr>\n",
              "      <th>16578</th>\n",
              "      <td>16579</td>\n",
              "      <td>Gex: Enter the Gecko</td>\n",
              "      <td>PS</td>\n",
              "      <td>1998.0</td>\n",
              "      <td>Platform</td>\n",
              "      <td>BMG Interactive Entertainment</td>\n",
              "      <td>320K</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0</td>\n",
              "      <td>0.04</td>\n",
              "      <td></td>\n",
              "      <td>100000</td>\n",
              "      <td>320K320K320K320K320K320K320K320K320K320K320K32...</td>\n",
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              "  </tbody>\n",
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              "<p>100 rows × 13 columns</p>\n",
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          "metadata": {},
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      "source": [
        "df.info()"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SgmeNBG89DR7",
        "outputId": "2cc1471d-dbef-4b59-95d4-7e2e307327b3"
      },
      "execution_count": null,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 100 entries, 10 to 16578\n",
            "Data columns (total 13 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   Unnamed: 0   100 non-null    int64  \n",
            " 1   Name         100 non-null    object \n",
            " 2   Platform     100 non-null    object \n",
            " 3   Year         100 non-null    float64\n",
            " 4   Genre        100 non-null    object \n",
            " 5   Publisher    100 non-null    object \n",
            " 6   NA_Sales     100 non-null    object \n",
            " 7   EU_Sales     100 non-null    object \n",
            " 8   JP_Sales     100 non-null    object \n",
            " 9   Other_Sales  100 non-null    object \n",
            " 10  test         100 non-null    object \n",
            " 11  100000       100 non-null    int64  \n",
            " 12  1            100 non-null    object \n",
            "dtypes: float64(1), int64(2), object(10)\n",
            "memory usage: 10.9+ KB\n"
          ]
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "keuruPpN886e"
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "df.drop(columns"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GVAtaLRx2qED",
        "outputId": "f99ed364-1890-47a5-8cae-d719b17f7627"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-90-2558a8bf3736>:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  df['1'] = df[test]['NA_Sales'] * df['100000']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#NA_Sales 컬럼 내 문자타입 전부 제거\n",
        "\n",
        "df['NA_Sales'] = df.NA_Sales.str.replace(r'[a-zA-Z]', '', regex=True)\n"
      ],
      "metadata": {
        "id": "8NQ-7kLgQPhg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#EU_Sales 컬럼 내 문자타입 전부 제거\n",
        "\n",
        "df['EU_Sales'] = df.EU_Sales.str.replace(r'[a-zA-Z]', '', regex=True)"
      ],
      "metadata": {
        "id": "jAJ6qFBuQX9k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#JP_Sales 컬럼 내 문자타입 전부 제거\n",
        "\n",
        "df['JP_Sales'] = df.JP_Sales.str.replace(r'[a-zA-Z]', '', regex=True)"
      ],
      "metadata": {
        "id": "d6kfYbxkQhnr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Other_Sales 컬럼 내 문자타입 전부 제거\n",
        "\n",
        "df['Other_Sales'] = df.Other_Sales.str.replace(r'[a-zA-Z]', '', regex=True)"
      ],
      "metadata": {
        "id": "DY2qynazQvY2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#NA_Sales 컬럼 데이터 타입 정수로 변경\n",
        "df['NA_Sales'] = df['NA_Sales'].astype('float')"
      ],
      "metadata": {
        "id": "KjGg-90FN9W8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#EU_Sales 컬럼 데이터 타입 정수로 변경\n",
        "df['EU_Sales'] = df['EU_Sales'].astype('float')"
      ],
      "metadata": {
        "id": "qzgUiCvgQPP4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#JP_Sales 컬럼 데이터 타입 정수로 변경\n",
        "df['JP_Sales'] = df['JP_Sales'].astype('float')"
      ],
      "metadata": {
        "id": "1KTf1jL2QlR2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Other_Sales 컬럼 데이터 타입 정수로 변경\n",
        "df['Other_Sales'] = df['Other_Sales'].astype('float')"
      ],
      "metadata": {
        "id": "l62M3tfuQ315"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 필요없는 행 (Unnamed:0) 삭제\n",
        "df.drop('Unnamed: 0', axis = 1, inplace = True)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "W0odo5I4wu2g",
        "outputId": "8ed4f964-4317-4351-c1d3-8a908fa25b89"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                             Name Platform    Year      Genre  \\\n",
              "0                    Candace Kane's Candy Factory       DS  2008.0     Action   \n",
              "1                                  The Munchables      Wii  2009.0     Action   \n",
              "2  Otome wa Oanesama Boku ni Koi Shiteru Portable      PSP  2010.0  Adventure   \n",
              "3                Deal or No Deal: Special Edition       DS  2010.0       Misc   \n",
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              "0           Destineer      0.04      0.00      0.00         0.00  \n",
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    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x=df.NA_Sales);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "UJzn5fefSbFF",
        "outputId": "bb28500b-0550-40c5-c727-ba2674673e6d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(df.NA_Sales[df.NA_Sales > 100]);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "pkZlMdK1zZWP",
        "outputId": "4a244e1e-c516-42a9-af58-d37029e1549f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7D2KZ9l7z1Gi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IRCo8UsLz0Uw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cqjaCY0ezy1w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 각 지역별 판매량이 1보다 큰 값들은 전부 삭제해줌 (좋지 않은 방법이지만 내 최선임)"
      ],
      "metadata": {
        "id": "1L3cOfFnTpYt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(df[df.NA_Sales>11])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IsRr3rawSwTi",
        "outputId": "ba82c477-26ce-4a5d-b901-04600eccb2aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "84"
            ]
          },
          "metadata": {},
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(df[df.NA_Sales<1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YMYbnXSSS7Ln",
        "outputId": "c21341d4-0f01-4a20-91fd-d9b876b8ac4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15624"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop(index = df[df[\"NA_Sales\"] >10].index)"
      ],
      "metadata": {
        "id": "7Yckpru-TALw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(df[df.EU_Sales>10])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9-O4bVOsS5a1",
        "outputId": "e475bd99-b541-4631-d9c2-4ec3e0cf1836"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EsquapexArOs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop(index = df[df[\"EU_Sales\"] >10].index)"
      ],
      "metadata": {
        "id": "hgiY5GbMTeom"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(df[df.JP_Sales>1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RXHf_ytDSNyx",
        "outputId": "c4d7197d-db85-4fa9-bf6b-da6705b9f5fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "165"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop(index = df[df[\"JP_Sales\"] >10].index)"
      ],
      "metadata": {
        "id": "vi4ZzuahTgT0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(df[df.Other_Sales>1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zwggvYg-SFMk",
        "outputId": "0e9625b3-ca9a-442b-9c6b-b317250cd3a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "62"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop(index = df[df[\"Other_Sales\"] >10].index)"
      ],
      "metadata": {
        "id": "QhVEcr9pTcSe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EOTv5ShUT8R0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#연도 결측치 추출\n",
        "missing_year = df[df['Year'].isnull()]"
      ],
      "metadata": {
        "id": "QXvqS2YsAelh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "missing_year"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "id": "fwfDQ9tdBHe6",
        "outputId": "238e7d45-fdcf-496b-a46e-f95912f314b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Name, Platform, Year, Genre, Publisher, NA_Sales, EU_Sales, JP_Sales, Other_Sales]\n",
              "Index: []"
            ],
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              "      <div>\n",
              "<style scoped>\n",
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              "      <th>NA_Sales</th>\n",
              "      <th>EU_Sales</th>\n",
              "      <th>JP_Sales</th>\n",
              "      <th>Other_Sales</th>\n",
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              "  </tbody>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "      display:flex;\n",
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "          const element = document.querySelector('#df-160291f2-64f1-4f26-bfcc-ad0d9e71601d');\n",
              "          const dataTable =\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#연도 결측치 데이터의 Name 중복값 확인\n",
        "test1=missing_year[missing_year['Name'].duplicated()]"
      ],
      "metadata": {
        "id": "ItPudDW5BENS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "awQ9SQ8Odtzj",
        "outputId": "a55f9079-9506-4016-91d1-352d94a347c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                    Name Platform  Year  \\\n",
              "2552                                  The Golden Compass      PSP   NaN   \n",
              "4124                   WRC: FIA World Rally Championship     X360   NaN   \n",
              "4181                                           Rock Band      Wii   NaN   \n",
              "6223                                     Rock Revolution      PS3   NaN   \n",
              "7131                   WRC: FIA World Rally Championship       PC   NaN   \n",
              "8076                                      Happy Feet Two      PS3   NaN   \n",
              "8812                     Combat Elite: WWII Paratroopers       XB   NaN   \n",
              "9184                         Wii de Asobu: Metroid Prime      Wii   NaN   \n",
              "9240                        LEGO Harry Potter: Years 5-7      Wii   NaN   \n",
              "9298                                    Battle vs. Chess       PC   NaN   \n",
              "9762                        LEGO Harry Potter: Years 5-7      3DS   NaN   \n",
              "9958                          LEGO Batman: The Videogame      PSP   NaN   \n",
              "10269                                        Bejeweled 3       DS   NaN   \n",
              "10321                       LEGO Harry Potter: Years 5-7       PC   NaN   \n",
              "10346                       LEGO Harry Potter: Years 5-7      PSP   NaN   \n",
              "10534                                        Backbreaker     X360   NaN   \n",
              "10563                       LEGO Harry Potter: Years 5-7       DS   NaN   \n",
              "10776                                          Rock Band      PS3   NaN   \n",
              "11665            The Lord of the Rings: War in the North     X360   NaN   \n",
              "12088                                      NBA Live 2003       GC   NaN   \n",
              "12307                                      Port Royale 3      PS3   NaN   \n",
              "12710                                     Happy Feet Two     X360   NaN   \n",
              "12858                                          Rock Band      PS2   NaN   \n",
              "13256                             Test Drive Unlimited 2       PC   NaN   \n",
              "13357                             Test Drive Unlimited 2      PS3   NaN   \n",
              "13488                                               GRID       PC   NaN   \n",
              "13583  The Chronicles of Narnia: The Lion, The Witch ...       GC   NaN   \n",
              "14119                             All-Star Baseball 2005       XB   NaN   \n",
              "14303                     Captain America: Super Soldier      Wii   NaN   \n",
              "14436                  PDC World Championship Darts 2008       DS   NaN   \n",
              "14622              Robert Ludlum's The Bourne Conspiracy     X360   NaN   \n",
              "14941                          The Daring Game for Girls      Wii   NaN   \n",
              "15013                            Silent Hill: Homecoming     X360   NaN   \n",
              "15317                       LEGO Harry Potter: Years 5-7     X360   NaN   \n",
              "15332                                     Happy Feet Two       DS   NaN   \n",
              "15897                                       Virtua Quest       GC   NaN   \n",
              "16195                                        Singularity     X360   NaN   \n",
              "16432                                        Bejeweled 3     X360   NaN   \n",
              "\n",
              "              Genre                               Publisher NA_Sales EU_Sales  \\\n",
              "2552         Action                                    Sega     0.11     160K   \n",
              "4124         Racing                        Black Bean Games        0      0.1   \n",
              "4181           Misc                               MTV Games     1.35     0.56   \n",
              "6223           Misc                                 Unknown     0.25        0   \n",
              "7131         Racing                        Black Bean Games        0     0.01   \n",
              "8076         Action  Warner Bros. Interactive Entertainment     0.09     0.01   \n",
              "8812        Shooter                                 Unknown     0.02     0.01   \n",
              "9184        Shooter                                Nintendo        0        0   \n",
              "9240         Action  Warner Bros. Interactive Entertainment     0.76     0.47   \n",
              "9298           Misc                     TopWare Interactive        0     0.02   \n",
              "9762         Action  Warner Bros. Interactive Entertainment     0.18     0.19   \n",
              "9958         Action  Warner Bros. Interactive Entertainment     0.57     0.46   \n",
              "10269        Puzzle                                 Unknown     0.32     0.02   \n",
              "10321        Action  Warner Bros. Interactive Entertainment     0.05     0.14   \n",
              "10346        Action  Warner Bros. Interactive Entertainment      0.1     0.11   \n",
              "10534        Sports                                 Unknown     0.17        0   \n",
              "10563        Action  Warner Bros. Interactive Entertainment     0.35      0.3   \n",
              "10776          Misc                         Electronic Arts     0.99     0.41   \n",
              "11665        Action  Warner Bros. Interactive Entertainment     0.52     0.26   \n",
              "12088        Sports                         Electronic Arts     0.23     0.06   \n",
              "12307    Simulation                           Kalypso Media     0.06        0   \n",
              "12710        Action  Warner Bros. Interactive Entertainment     0.08     0.01   \n",
              "12858          Misc                         Electronic Arts     0.71     0.06   \n",
              "13256        Racing                                   Atari     0.05     0.11   \n",
              "13357        Racing                                   Atari     0.16     0.35   \n",
              "13488        Racing                             Codemasters        0     0.03   \n",
              "13583        Action              Disney Interactive Studios     0.22     0.06   \n",
              "14119        Sports                                 Unknown     0.11     0.03   \n",
              "14303        Action                                    Sega     0.05        0   \n",
              "14436        Sports                                 Unknown     0.01        0   \n",
              "14622        Action                           Vivendi Games     0.26     0.14   \n",
              "14941     Adventure                                 Unknown     0.09        0   \n",
              "15013        Action            Konami Digital Entertainment     0.25     0.16   \n",
              "15317        Action  Warner Bros. Interactive Entertainment     0.51     0.39   \n",
              "15332        Action  Warner Bros. Interactive Entertainment     0.09     0.02   \n",
              "15897  Role-Playing                                 Unknown     0.01        0   \n",
              "16195       Shooter                                 Unknown     0.24     0.05   \n",
              "16432        Puzzle                                 Unknown     0.13        0   \n",
              "\n",
              "      JP_Sales Other_Sales  \n",
              "2552         0         0.1  \n",
              "4124         0        0.02  \n",
              "4181         0         0.2  \n",
              "6223         0        0.02  \n",
              "7131         0           0  \n",
              "8076         0        0.01  \n",
              "8812         0           0  \n",
              "9184      0.02           0  \n",
              "9240         0        0.13  \n",
              "9298         0        0.01  \n",
              "9762         0        0.04  \n",
              "9958         0        0.28  \n",
              "10269        0        0.03  \n",
              "10321        0        0.03  \n",
              "10346        0        0.06  \n",
              "10534        0        0.01  \n",
              "10563        0        0.07  \n",
              "10776        0        0.22  \n",
              "11665        0        0.08  \n",
              "12088        0        0.01  \n",
              "12307        0        0.01  \n",
              "12710        0        0.01  \n",
              "12858        0        0.35  \n",
              "13256        0        0.03  \n",
              "13357     0.01        0.12  \n",
              "13488        0        0.01  \n",
              "13583        0        0.01  \n",
              "14119        0        0.01  \n",
              "14303        0           0  \n",
              "14436        0           0  \n",
              "14622        0        0.04  \n",
              "14941        0        0.01  \n",
              "15013        0        0.04  \n",
              "15317        0        0.09  \n",
              "15332        0        0.01  \n",
              "15897        0           0  \n",
              "16195        0        0.02  \n",
              "16432        0        0.01  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8c70f745-a91e-47cf-af42-8025494a76bf\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Platform</th>\n",
              "      <th>Year</th>\n",
              "      <th>Genre</th>\n",
              "      <th>Publisher</th>\n",
              "      <th>NA_Sales</th>\n",
              "      <th>EU_Sales</th>\n",
              "      <th>JP_Sales</th>\n",
              "      <th>Other_Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2552</th>\n",
              "      <td>The Golden Compass</td>\n",
              "      <td>PSP</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Action</td>\n",
              "      <td>Sega</td>\n",
              "      <td>0.11</td>\n",
              "      <td>160K</td>\n",
              "      <td>0</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4124</th>\n",
              "      <td>WRC: FIA World Rally Championship</td>\n",
              "      <td>X360</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Racing</td>\n",
              "      <td>Black Bean Games</td>\n",
              "      <td>0</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4181</th>\n",
              "      <td>Rock Band</td>\n",
              "      <td>Wii</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Misc</td>\n",
              "      <td>MTV Games</td>\n",
              "      <td>1.35</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6223</th>\n",
              "      <td>Rock Revolution</td>\n",
              "      <td>PS3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Misc</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7131</th>\n",
              "      <td>WRC: FIA World Rally Championship</td>\n",
              "      <td>PC</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Racing</td>\n",
              "      <td>Black Bean Games</td>\n",
              "      <td>0</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8076</th>\n",
              "      <td>Happy Feet Two</td>\n",
              "      <td>PS3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Action</td>\n",
              "      <td>Warner Bros. Interactive Entertainment</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8812</th>\n",
              "      <td>Combat Elite: WWII Paratroopers</td>\n",
              "      <td>XB</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Shooter</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9184</th>\n",
              "      <td>Wii de Asobu: Metroid Prime</td>\n",
              "      <td>Wii</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Shooter</td>\n",
              "      <td>Nintendo</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9240</th>\n",
              "      <td>LEGO Harry Potter: Years 5-7</td>\n",
              "      <td>Wii</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Action</td>\n",
              "      <td>Warner Bros. Interactive Entertainment</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0</td>\n",
              "      <td>0.13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9298</th>\n",
              "      <td>Battle vs. Chess</td>\n",
              "      <td>PC</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Misc</td>\n",
              "      <td>TopWare Interactive</td>\n",
              "      <td>0</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9762</th>\n",
              "      <td>LEGO Harry Potter: Years 5-7</td>\n",
              "      <td>3DS</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Action</td>\n",
              "      <td>Warner Bros. Interactive Entertainment</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9958</th>\n",
              "      <td>LEGO Batman: The Videogame</td>\n",
              "      <td>PSP</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Action</td>\n",
              "      <td>Warner Bros. Interactive Entertainment</td>\n",
              "      <td>0.57</td>\n",
              "      <td>0.46</td>\n",
              "      <td>0</td>\n",
              "      <td>0.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10269</th>\n",
              "      <td>Bejeweled 3</td>\n",
              "      <td>DS</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Puzzle</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10321</th>\n",
              "      <td>LEGO Harry Potter: Years 5-7</td>\n",
              "      <td>PC</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Action</td>\n",
              "      <td>Warner Bros. Interactive Entertainment</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10346</th>\n",
              "      <td>LEGO Harry Potter: Years 5-7</td>\n",
              "      <td>PSP</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Action</td>\n",
              "      <td>Warner Bros. Interactive Entertainment</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0</td>\n",
              "      <td>0.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10534</th>\n",
              "      <td>Backbreaker</td>\n",
              "      <td>X360</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Sports</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10563</th>\n",
              "      <td>LEGO Harry Potter: Years 5-7</td>\n",
              "      <td>DS</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Action</td>\n",
              "      <td>Warner Bros. Interactive Entertainment</td>\n",
              "      <td>0.35</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0</td>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10776</th>\n",
              "      <td>Rock Band</td>\n",
              "      <td>PS3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Misc</td>\n",
              "      <td>Electronic Arts</td>\n",
              "      <td>0.99</td>\n",
              "      <td>0.41</td>\n",
              "      <td>0</td>\n",
              "      <td>0.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11665</th>\n",
              "      <td>The Lord of the Rings: War in the North</td>\n",
              "      <td>X360</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Action</td>\n",
              "      <td>Warner Bros. Interactive Entertainment</td>\n",
              "      <td>0.52</td>\n",
              "      <td>0.26</td>\n",
              "      <td>0</td>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12088</th>\n",
              "      <td>NBA Live 2003</td>\n",
              "      <td>GC</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Sports</td>\n",
              "      <td>Electronic Arts</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12307</th>\n",
              "      <td>Port Royale 3</td>\n",
              "      <td>PS3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Simulation</td>\n",
              "      <td>Kalypso Media</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12710</th>\n",
              "      <td>Happy Feet Two</td>\n",
              "      <td>X360</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Action</td>\n",
              "      <td>Warner Bros. Interactive Entertainment</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12858</th>\n",
              "      <td>Rock Band</td>\n",
              "      <td>PS2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Misc</td>\n",
              "      <td>Electronic Arts</td>\n",
              "      <td>0.71</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0</td>\n",
              "      <td>0.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13256</th>\n",
              "      <td>Test Drive Unlimited 2</td>\n",
              "      <td>PC</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Racing</td>\n",
              "      <td>Atari</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13357</th>\n",
              "      <td>Test Drive Unlimited 2</td>\n",
              "      <td>PS3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Racing</td>\n",
              "      <td>Atari</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.35</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13488</th>\n",
              "      <td>GRID</td>\n",
              "      <td>PC</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Racing</td>\n",
              "      <td>Codemasters</td>\n",
              "      <td>0</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13583</th>\n",
              "      <td>The Chronicles of Narnia: The Lion, The Witch ...</td>\n",
              "      <td>GC</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Action</td>\n",
              "      <td>Disney Interactive Studios</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14119</th>\n",
              "      <td>All-Star Baseball 2005</td>\n",
              "      <td>XB</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Sports</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14303</th>\n",
              "      <td>Captain America: Super Soldier</td>\n",
              "      <td>Wii</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Action</td>\n",
              "      <td>Sega</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14436</th>\n",
              "      <td>PDC World Championship Darts 2008</td>\n",
              "      <td>DS</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Sports</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14622</th>\n",
              "      <td>Robert Ludlum's The Bourne Conspiracy</td>\n",
              "      <td>X360</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Action</td>\n",
              "      <td>Vivendi Games</td>\n",
              "      <td>0.26</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14941</th>\n",
              "      <td>The Daring Game for Girls</td>\n",
              "      <td>Wii</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Adventure</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15013</th>\n",
              "      <td>Silent Hill: Homecoming</td>\n",
              "      <td>X360</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Action</td>\n",
              "      <td>Konami Digital Entertainment</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15317</th>\n",
              "      <td>LEGO Harry Potter: Years 5-7</td>\n",
              "      <td>X360</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Action</td>\n",
              "      <td>Warner Bros. Interactive Entertainment</td>\n",
              "      <td>0.51</td>\n",
              "      <td>0.39</td>\n",
              "      <td>0</td>\n",
              "      <td>0.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15332</th>\n",
              "      <td>Happy Feet Two</td>\n",
              "      <td>DS</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Action</td>\n",
              "      <td>Warner Bros. Interactive Entertainment</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15897</th>\n",
              "      <td>Virtua Quest</td>\n",
              "      <td>GC</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Role-Playing</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16195</th>\n",
              "      <td>Singularity</td>\n",
              "      <td>X360</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Shooter</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16432</th>\n",
              "      <td>Bejeweled 3</td>\n",
              "      <td>X360</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Puzzle</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8c70f745-a91e-47cf-af42-8025494a76bf')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8c70f745-a91e-47cf-af42-8025494a76bf button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8c70f745-a91e-47cf-af42-8025494a76bf');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(test1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rmq6QtpCdv3S",
        "outputId": "3f8a1de1-5095-4d7c-b3cc-8a6965f12bb1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "38"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[df['Name'] == 'The Golden Compass']=df[df['Name'] == 'The Golden Compass'].fillna(2007.0\t)"
      ],
      "metadata": {
        "id": "N3itE1_QexGi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[df['Name'] == 'The Golden Compass']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "tP9UDcs-u8gV",
        "outputId": "11be2a76-f639-4b85-9471-ef0145c77049"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     Name Platform    Year   Genre Publisher NA_Sales  \\\n",
              "2056   The Golden Compass      Wii     NaN  Action      Sega     0.26   \n",
              "2552   The Golden Compass      PSP     NaN  Action      Sega     0.11   \n",
              "5034   The Golden Compass      PS3  2007.0  Action      Sega     0.14   \n",
              "5258   The Golden Compass       DS  2007.0  Action      Sega     0.28   \n",
              "10880  The Golden Compass      PS2  2007.0  Action      Sega     0.13   \n",
              "16031  The Golden Compass     X360  2007.0  Action      Sega      0.3   \n",
              "\n",
              "      EU_Sales JP_Sales Other_Sales  \n",
              "2056      0.28        0        0.07  \n",
              "2552      160K        0         0.1  \n",
              "5034       0.1        0        0.05  \n",
              "5258      0.25        0        0.06  \n",
              "10880        0        0        0.39  \n",
              "16031     0.12        0        0.04  "
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2056</th>\n",
              "      <td>The Golden Compass</td>\n",
              "      <td>Wii</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Action</td>\n",
              "      <td>Sega</td>\n",
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              "    <tr>\n",
              "      <th>2552</th>\n",
              "      <td>The Golden Compass</td>\n",
              "      <td>PSP</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Action</td>\n",
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              "      <td>160K</td>\n",
              "      <td>0</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5034</th>\n",
              "      <td>The Golden Compass</td>\n",
              "      <td>PS3</td>\n",
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              "    <tr>\n",
              "      <th>5258</th>\n",
              "      <td>The Golden Compass</td>\n",
              "      <td>DS</td>\n",
              "      <td>2007.0</td>\n",
              "      <td>Action</td>\n",
              "      <td>Sega</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0</td>\n",
              "      <td>0.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10880</th>\n",
              "      <td>The Golden Compass</td>\n",
              "      <td>PS2</td>\n",
              "      <td>2007.0</td>\n",
              "      <td>Action</td>\n",
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              "      <td>0.13</td>\n",
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              "      <td>0.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16031</th>\n",
              "      <td>The Golden Compass</td>\n",
              "      <td>X360</td>\n",
              "      <td>2007.0</td>\n",
              "      <td>Action</td>\n",
              "      <td>Sega</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0</td>\n",
              "      <td>0.04</td>\n",
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              "  </tbody>\n",
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              "</div>\n",
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              "              style=\"display:none;\">\n",
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              "\n",
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              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-248ebee5-4449-4ca0-862e-de61975c4d89 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-248ebee5-4449-4ca0-862e-de61975c4d89');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f"
      ],
      "metadata": {
        "id": "JD9Pb3g1e1Yl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#결측치 데이터 중 고유 이름 컬럼과 갯수 확인\n",
        "year = missing_year['Name'].unique()\n",
        "num = missing_year['Name'].nunique()"
      ],
      "metadata": {
        "id": "ORxcWY9oEGWa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "year"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jslkp-p9eVaR",
        "outputId": "20432f02-927a-4a5d-deec-ab71f4fb6fc3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Rock Band', \"Yu-Gi-Oh! 5D's Wheelie Breakers (JP sales)\",\n",
              "       'Jewel Link Chronicles: Mountains of Madness',\n",
              "       'Majesty 2: The Fantasy Kingdom Sim', 'Famista 64',\n",
              "       'Transworld Surf', 'Battle vs. Chess', 'Major League Baseball 2K6',\n",
              "       'Twisted Metal: Small Brawl', 'Tiger Woods PGA Tour 07',\n",
              "       'Tribes: Aerial Assault', 'Metal Gear Solid 2: Substance',\n",
              "       'Star Trek: Legacy', 'My Healthy Cooking Coach',\n",
              "       'Mega Man X Collection', 'Move Fitness', \"Flip's Twisted World\",\n",
              "       'Super Puzzle Fighter II', 'Luminous Arc 2 (JP sales)',\n",
              "       'Tomb Raider (2013)', 'Street Fighter IV',\n",
              "       'Agarest Senki: Re-appearance', 'Monster Hunter Frontier Online',\n",
              "       'Harvest Moon: Save the Homeland', 'NHL Hitz Pro',\n",
              "       'Final Fantasy XI', 'The Golden Compass', 'Shorts',\n",
              "       'World of Tanks', 'Call of Duty 3', 'Madden NFL 06',\n",
              "       'Dinotopia: The Sunstone Odyssey', 'Virtua Quest',\n",
              "       'Captain America: Super Soldier',\n",
              "       'Writing and Speaking Beautiful Japanese DS',\n",
              "       \"Disney's Chicken Little: Ace In Action\", 'Madden NFL 2002',\n",
              "       \"Maze Craze: A Game of Cops 'n Robbers\", 'Sabre Wulf',\n",
              "       'Alone in the Dark: The New Nightmare',\n",
              "       \"Disney's Cinderella: Magical Dreams\", 'Chou Soujuu Mecha MG',\n",
              "       'Donkey Kong Land III', 'Rayman Arena', 'eJay Clubworld',\n",
              "       'Danganronpa: Trigger Happy Havoc', 'Action Man-Operation Extreme',\n",
              "       'Jet Impulse', 'Football Manager 2007',\n",
              "       'The Chronicles of Narnia: The Lion, The Witch and The Wardrobe',\n",
              "       'LEGO Batman: The Videogame', 'Major League Baseball 2K8',\n",
              "       'B.L.U.E.: Legend of Water', 'wwe Smackdown vs. Raw 2006', 'TERA',\n",
              "       'Homeworld Remastered Collection',\n",
              "       \"Shonen Jump's Yu-Gi-Oh! GX Card Almanac\", 'Pac-Man Fever',\n",
              "       'Street Hoops', 'WRC: FIA World Rally Championship',\n",
              "       'Legacy of Kain: Soul Reaver', 'Namco Museum',\n",
              "       'Test Drive Unlimited 2', 'Sega Rally 2006', 'Rock Revolution',\n",
              "       'Shaun White Snowboarding',\n",
              "       'Umineko no Naku Koro ni San: Shinjitsu to Gensou no Yasoukyoku',\n",
              "       'The Lord of the Rings: War in the North', 'Pet Zombies',\n",
              "       'WCW Backstage Assault', 'Swords', 'Ghostbusters II',\n",
              "       'Bejeweled 3', 'Hangman', 'Charm Girls Club: My Fashion Show',\n",
              "       'Beyond the Labyrinth', 'Dream Trigger 3D', 'Demon Chaos',\n",
              "       'BioShock 2', 'Yakuza 4', 'Harvest Moon: The Tale of Two Towns',\n",
              "       \"Dance! It's Your Stage\", 'Circus Atari',\n",
              "       'LEGO Harry Potter: Years 5-7', 'Hitman 2: Silent Assassin',\n",
              "       'Adventure', 'Nintendo Puzzle Collection',\n",
              "       'Samurai Shodown Anthology', 'Rocksmith',\n",
              "       'Samurai Spirits: Tenkaichi Kenkakuden', 'Wheel of Fortune',\n",
              "       'Mobile Ops: The One Year War', 'Record of Agarest War Zero',\n",
              "       'Atsumare! Power Pro Kun no DS Koushien',\n",
              "       'Combat Elite: WWII Paratroopers', 'Home Run',\n",
              "       'Legacy of Ys: Books I & II', 'GRID', 'Backbreaker',\n",
              "       'Dead Island: Riptide', 'Brothers in Arms: Furious 4',\n",
              "       'Happy Feet Two', 'Fishing Derby',\n",
              "       'Unreal Championship 2: The Liandri Conflict', 'Shrek the Third',\n",
              "       'Madden NFL 2004', 'College Hoops 2K6',\n",
              "       'Aquaman: Battle for Atlantis', 'The Smurfs', 'MLB SlugFest 20-03',\n",
              "       'Drill Dozer', 'Our House Party!', 'Saint',\n",
              "       'Mountain Bike Adrenaline', 'Egg Monster Hero',\n",
              "       'Wii de Asobu: Metroid Prime', 'Payout Poker & Casino',\n",
              "       'Splatterhouse', 'Jet X20', 'Reader Rabbit 2nd Grade',\n",
              "       'Jurassic Park: The Game', 'Half-Minute Hero 2',\n",
              "       'LEGO Indiana Jones: The Original Adventures',\n",
              "       'Build-A-Bear Workshop: Friendship Valley', 'Wet', 'Port Royale 3',\n",
              "       'Sonic the Hedgehog', 'Vegas Party',\n",
              "       'Disgaea 3: Absence of Detention', 'Big Beach Sports 2',\n",
              "       'Jonah Lomu Rugby Challenge',\n",
              "       \"Cubix Robots for Everyone: Clash 'n' Bash\",\n",
              "       'Dragon Ball Z: Budokai Tenkaichi 2 (JP sales)',\n",
              "       'Def Jam: Fight for NY', 'Mario Tennis', 'Super Breakout',\n",
              "       'Robotech: Battlecry', 'Advance Wars: Days of Ruin',\n",
              "       'Sword of the Samurai',\n",
              "       'Mega Man Battle Network: Operation Shooting Star', 'Tropico 4',\n",
              "       'Star Wars Jedi Knight II: Jedi Outcast',\n",
              "       'The Legend of Zelda: The Minish Cap(weekly JP sales)',\n",
              "       'Madden NFL 07', 'DanceDanceRevolution II',\n",
              "       \"Tony Hawk's Downhill Jam\",\n",
              "       'The Dukes of Hazzard II: Daisy Dukes It Out',\n",
              "       'PDC World Championship Darts 2008',\n",
              "       'National Geographic Challenge!', 'Dead Space 3',\n",
              "       'Combat Wings: The Great Battles of WWII',\n",
              "       'All-Star Baseball 2005', 'Tom and Jerry in War of the Whiskers',\n",
              "       'FIFA Soccer 2004', 'Dragster', 'Inversion', 'Get Fit with Mel B',\n",
              "       'Alex Rider: Stormbreaker', 'Triple Play 99', 'Air-Sea Battle',\n",
              "       'The History Channel: Great Battles - Medieval',\n",
              "       'NFL GameDay 2003', 'Gun', \"Cabela's Alaskan Adventure\",\n",
              "       'NBA Live 2003', 'Saru! Get You! Million Monkeys',\n",
              "       'Godzilla: Destroy All Monsters Melee',\n",
              "       'AKB1/48: Idol to Guam de Koishitara...', 'The Hidden',\n",
              "       'Hakuouki: Shinsengumi Kitan', 'Face Racers: Photo Finish',\n",
              "       'Charm Girls Club: My Fashion Mall', 'Slot Machine',\n",
              "       'Ferrari: The Race Experience', 'Star Trek: Conquest',\n",
              "       'Fullmetal Alchemist: Brotherhood', 'WarioWare: Twisted!',\n",
              "       'Silent Hill: Homecoming',\n",
              "       'The Chronicles of Riddick: Escape from Butcher Bay',\n",
              "       'Flag Capture', \"Robert Ludlum's The Bourne Conspiracy\",\n",
              "       'NASCAR: Dirt to Daytona', 'GiFTPiA', 'NBA Street Vol. 2',\n",
              "       'Call of Duty: Black Ops', 'Otomedius Excellent',\n",
              "       'The Daring Game for Girls',\n",
              "       'Valkyria Chronicles III: Unrecorded Chronicles',\n",
              "       \"Frogger's Adventures: Temple of the Frog\", 'Trauma Team',\n",
              "       'Bikkuriman Daijiten', 'Zero: Tsukihami no Kamen',\n",
              "       \"McFarlane's Evil Prophecy\", 'NHL Slapshot', 'Custom Robo',\n",
              "       'Singularity', 'NASCAR Thunder 2003', 'Housekeeping',\n",
              "       \"Luxor: Pharaoh's Challenge\", 'Super Duper Sumos', 'Indy 500',\n",
              "       'Prinny: Can I Really Be The Hero? (US sales)',\n",
              "       'Clockwork Empires', 'RollerCoaster Tycoon',\n",
              "       'The King of Fighters: Maximum Impact - Maniax',\n",
              "       'Monster Hunter 2', 'Breakaway IV',\n",
              "       \"Tom Clancy's Rainbow Six: Critical Hour\", 'Rhythm Heaven',\n",
              "       'Suikoden III', 'Combat', 'Tornado',\n",
              "       'PES 2009: Pro Evolution Soccer', 'Teen Titans', 'Steal Princess',\n",
              "       'Drake of the 99 Dragons', 'Tube Slider',\n",
              "       'Nicktoons: Battle for Volcano Island', 'Haven: Call of the King',\n",
              "       \"James Cameron's Dark Angel\", 'NBA Starting Five',\n",
              "       'Tour de France 2011', 'Smashing Drive',\n",
              "       'Runaway: A Twist of Fate', 'Yu Yu Hakusho: Dark Tournament',\n",
              "       'Without Warning', 'Dream Dancer', 'Karate',\n",
              "       'Super Robot Wars OG Saga: Masou Kishin II - Revelation of Evil God',\n",
              "       'Freaky Flyers', 'Space Invaders', 'Madden NFL 11',\n",
              "       'Yoostar on MTV'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jgNfmwBTDvZq",
        "outputId": "9b868883-6b3e-4613-eb84-5f9f3af45f54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "233"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Year 존재하는 데이터만 확인\n",
        "yesYear = df.copy()\n",
        "yesYear = yesYear.dropna(subset=['Year'])"
      ],
      "metadata": {
        "id": "SMeDX4ggElWU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 동일 Year 존재 여부 확인\n",
        "have_Year = []\n",
        "\n",
        "for data in yesYear['Name']:\n",
        "    if data in year:\n",
        "        have_Year.append(data)"
      ],
      "metadata": {
        "id": "auPI_LsHDbOo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(have_Year)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j8QWKXHPt8Dz",
        "outputId": "579b61a7-687f-485a-9ecc-c3c0a1bd8869"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "206"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cEVsctilt92h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "yesYear"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "N63z0J7bh0uO",
        "outputId": "dbc78502-bbcb-4ccd-92f6-6a2d3be7879c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                 Name Platform    Year  \\\n",
              "0                        Candace Kane's Candy Factory       DS  2008.0   \n",
              "1                                      The Munchables      Wii  2009.0   \n",
              "2      Otome wa Oanesama Boku ni Koi Shiteru Portable      PSP  2010.0   \n",
              "3                    Deal or No Deal: Special Edition       DS  2010.0   \n",
              "4           Ben 10 Ultimate Alien: Cosmic Destruction      PS3  2010.0   \n",
              "...                                               ...      ...     ...   \n",
              "16593                         Ice Age 2: The Meltdown       GC  2006.0   \n",
              "16594                     Rainbow Islands: Revolution      PSP  2005.0   \n",
              "16595                                        NBA 2K16      PS3  2015.0   \n",
              "16596                     Toukiden: The Age of Demons      PSV  2013.0   \n",
              "16597                        The King of Fighters '95       PS  1996.0   \n",
              "\n",
              "           Genre                    Publisher NA_Sales EU_Sales JP_Sales  \\\n",
              "0         Action                    Destineer     0.04        0        0   \n",
              "1         Action           Namco Bandai Games     0.17        0        0   \n",
              "2      Adventure                    Alchemist        0        0     0.02   \n",
              "3           Misc                    Zoo Games     0.04        0        0   \n",
              "4       Platform                  D3Publisher     0.12     0.09        0   \n",
              "...          ...                          ...      ...      ...      ...   \n",
              "16593   Platform                Vivendi Games     0.15     0.04        0   \n",
              "16594     Action            Rising Star Games     0.01        0        0   \n",
              "16595     Sports         Take-Two Interactive     0.44     0.19     0.03   \n",
              "16596     Action                   Tecmo Koei     0.05     0.05     0.25   \n",
              "16597   Fighting  Sony Computer Entertainment        0        0     0.16   \n",
              "\n",
              "      Other_Sales  \n",
              "0               0  \n",
              "1            0.01  \n",
              "2               0  \n",
              "3               0  \n",
              "4            0.04  \n",
              "...           ...  \n",
              "16593        0.01  \n",
              "16594           0  \n",
              "16595        0.13  \n",
              "16596        0.03  \n",
              "16597        0.01  \n",
              "\n",
              "[16327 rows x 9 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Platform</th>\n",
              "      <th>Year</th>\n",
              "      <th>Genre</th>\n",
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              "      <th>0</th>\n",
              "      <td>Candace Kane's Candy Factory</td>\n",
              "      <td>DS</td>\n",
              "      <td>2008.0</td>\n",
              "      <td>Action</td>\n",
              "      <td>Destineer</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>The Munchables</td>\n",
              "      <td>Wii</td>\n",
              "      <td>2009.0</td>\n",
              "      <td>Action</td>\n",
              "      <td>Namco Bandai Games</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Otome wa Oanesama Boku ni Koi Shiteru Portable</td>\n",
              "      <td>PSP</td>\n",
              "      <td>2010.0</td>\n",
              "      <td>Adventure</td>\n",
              "      <td>Alchemist</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Deal or No Deal: Special Edition</td>\n",
              "      <td>DS</td>\n",
              "      <td>2010.0</td>\n",
              "      <td>Misc</td>\n",
              "      <td>Zoo Games</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Ben 10 Ultimate Alien: Cosmic Destruction</td>\n",
              "      <td>PS3</td>\n",
              "      <td>2010.0</td>\n",
              "      <td>Platform</td>\n",
              "      <td>D3Publisher</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16593</th>\n",
              "      <td>Ice Age 2: The Meltdown</td>\n",
              "      <td>GC</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>Platform</td>\n",
              "      <td>Vivendi Games</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16594</th>\n",
              "      <td>Rainbow Islands: Revolution</td>\n",
              "      <td>PSP</td>\n",
              "      <td>2005.0</td>\n",
              "      <td>Action</td>\n",
              "      <td>Rising Star Games</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16595</th>\n",
              "      <td>NBA 2K16</td>\n",
              "      <td>PS3</td>\n",
              "      <td>2015.0</td>\n",
              "      <td>Sports</td>\n",
              "      <td>Take-Two Interactive</td>\n",
              "      <td>0.44</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16596</th>\n",
              "      <td>Toukiden: The Age of Demons</td>\n",
              "      <td>PSV</td>\n",
              "      <td>2013.0</td>\n",
              "      <td>Action</td>\n",
              "      <td>Tecmo Koei</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16597</th>\n",
              "      <td>The King of Fighters '95</td>\n",
              "      <td>PS</td>\n",
              "      <td>1996.0</td>\n",
              "      <td>Fighting</td>\n",
              "      <td>Sony Computer Entertainment</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>16327 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a32bd9c3-4382-4e55-9f2a-ab5eafc48e2c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a32bd9c3-4382-4e55-9f2a-ab5eafc48e2c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a32bd9c3-4382-4e55-9f2a-ab5eafc48e2c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 31
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              "Name\n",
              "'98 Koshien                                1998.0\n",
              ".hack//G.U. Vol.1//Rebirth                 2006.0\n",
              ".hack//G.U. Vol.2//Reminisce               2006.0\n",
              ".hack//G.U. Vol.2//Reminisce (jp sales)    2006.0\n",
              ".hack//G.U. Vol.3//Redemption              2007.0\n",
              "                                            ...  \n",
              "thinkSMART: Chess for Kids                 2011.0\n",
              "uDraw Studio                               2010.0\n",
              "uDraw Studio: Instant Artist               2011.0\n",
              "wwe Smackdown vs. Raw 2006                    NaN\n",
              "¡Shin Chan Flipa en colores!               2007.0\n",
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              "0        False\n",
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              "31                                         Rock Band\n",
              "109       Yu-Gi-Oh! 5D's Wheelie Breakers (JP sales)\n",
              "273      Jewel Link Chronicles: Mountains of Madness\n",
              "358               Majesty 2: The Fantasy Kingdom Sim\n",
              "430                                       Famista 64\n",
              "                            ...                     \n",
              "16240                                  Freaky Flyers\n",
              "16275                                 Space Invaders\n",
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              "16432                                    Bejeweled 3\n",
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              "                                 Name    Year\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test['Name']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1wi3KQhNuINV",
        "outputId": "4eb99036-7f2c-46a7-9f96-0cd1c89ecb83"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "27                       World of Tanks\n",
              "182                       Madden NFL 07\n",
              "383                       Madden NFL 07\n",
              "422                     Shrek the Third\n",
              "482                     Madden NFL 2002\n",
              "                      ...              \n",
              "16031                The Golden Compass\n",
              "16056                    Space Invaders\n",
              "16250                       Teen Titans\n",
              "16351    National Geographic Challenge!\n",
              "16381                     Madden NFL 07\n",
              "Name: Name, Length: 206, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oOqMkLeGuj09"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ApNq7vbOuicj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Mvcmg91YntLB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gJsemj8ZnSRZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7tWSkdZ7nKyY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yR8Z--Qjb4i2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KSJs_58Ab2q8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Year 결측치 값 대체할 수 있는 Name 컬럼 \n",
        "have_Year=have_Year.array"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "id": "x4yVrFYwGGXM",
        "outputId": "c18aa353-6b7c-40bd-ee7f-1757adcd4853"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-30-5c094694c38a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Year 결측치 값 대체할 수 있는 Name 컬럼\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mhave_Year\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhave_Year\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'array'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UR5PseOncaqM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(have_Year)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JnUAEzRCDVn9",
        "outputId": "2b38e42b-07fd-4603-eea5-1bab877be2d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "206"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#결측치 별로 안 중요한 것 같음. 무시하도록 함."
      ],
      "metadata": {
        "id": "biAZ0uwtwwfR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Year'].fillna(0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EhcD1b1Ow0cg",
        "outputId": "c5d0356a-8595-47eb-e847-c89664eac47c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        2008.0\n",
              "1        2009.0\n",
              "2        2010.0\n",
              "3        2010.0\n",
              "4        2010.0\n",
              "          ...  \n",
              "16593    2006.0\n",
              "16594    2005.0\n",
              "16595    2015.0\n",
              "16596    2013.0\n",
              "16597    1996.0\n",
              "Name: Year, Length: 16598, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QjVXD0KBCyCw",
        "outputId": "36940189-561d-4638-e899-a2b9dccf29c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Name             0\n",
              "Platform         0\n",
              "Year           271\n",
              "Genre           50\n",
              "Publisher       58\n",
              "NA_Sales         0\n",
              "EU_Sales         0\n",
              "JP_Sales         0\n",
              "Other_Sales      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VtUYUeScC3ii"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FqgPDd46C2up"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5tTh0dNxw2Fw",
        "outputId": "7961af99-e023-4e92-a117-5ac7506f4f4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(16598, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['NA_Sales']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MyhQy9MlAHkJ",
        "outputId": "e08c942c-542c-4e91-eb4e-ab3804c76b1d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        0.04\n",
              "1        0.17\n",
              "2           0\n",
              "3        0.04\n",
              "4        0.12\n",
              "         ... \n",
              "16593    0.15\n",
              "16594    0.01\n",
              "16595    0.44\n",
              "16596    0.05\n",
              "16597       0\n",
              "Name: NA_Sales, Length: 16598, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TyLHFBpuATWk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3jPBlaBQAQNI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "b5RksRHDAM3X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Rtv5GplRALxk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oKgkQmlyAKQF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('Name')['NA_Sales'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 639
        },
        "id": "skZSQ59y83v1",
        "outputId": "c930b363-1216-4ef6-c6b0-59b9d4f019e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/groupby/generic.py\u001b[0m in \u001b[0;36marray_func\u001b[0;34m(values)\u001b[0m\n\u001b[1;32m    339\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 340\u001b[0;31m                 result = self.grouper._cython_operation(\n\u001b[0m\u001b[1;32m    341\u001b[0m                     \u001b[0;34m\"aggregate\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_count\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmin_count\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/groupby/ops.py\u001b[0m in \u001b[0;36m_cython_operation\u001b[0;34m(self, kind, values, how, axis, min_count, **kwargs)\u001b[0m\n\u001b[1;32m   1009\u001b[0m         \u001b[0mngroups\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mngroups\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1010\u001b[0;31m         return cy_op.cython_operation(\n\u001b[0m\u001b[1;32m   1011\u001b[0m             \u001b[0mvalues\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/groupby/ops.py\u001b[0m in \u001b[0;36mcython_operation\u001b[0;34m(self, values, axis, min_count, comp_ids, ngroups, **kwargs)\u001b[0m\n\u001b[1;32m    670\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 671\u001b[0;31m         return self._cython_op_ndim_compat(\n\u001b[0m\u001b[1;32m    672\u001b[0m             \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/groupby/ops.py\u001b[0m in \u001b[0;36m_cython_op_ndim_compat\u001b[0;34m(self, values, min_count, ngroups, comp_ids, mask, **kwargs)\u001b[0m\n\u001b[1;32m    501\u001b[0m             \u001b[0mvalues2d\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 502\u001b[0;31m             res = self._call_cython_op(\n\u001b[0m\u001b[1;32m    503\u001b[0m                 \u001b[0mvalues2d\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/groupby/ops.py\u001b[0m in \u001b[0;36m_call_cython_op\u001b[0;34m(self, values, min_count, ngroups, comp_ids, mask, **kwargs)\u001b[0m\n\u001b[1;32m    562\u001b[0m         \u001b[0mout_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_output_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mngroups\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 563\u001b[0;31m         \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_cython_func_and_vals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_numeric\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    564\u001b[0m         \u001b[0mout_dtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_out_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/groupby/ops.py\u001b[0m in \u001b[0;36mget_cython_func_and_vals\u001b[0;34m(self, values, is_numeric)\u001b[0m\n\u001b[1;32m    204\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 205\u001b[0;31m         \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_cython_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_numeric\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    206\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/groupby/ops.py\u001b[0m in \u001b[0;36m_get_cython_function\u001b[0;34m(cls, kind, how, dtype, is_numeric)\u001b[0m\n\u001b[1;32m    169\u001b[0m                 \u001b[0;31m# raise NotImplementedError here rather than TypeError later\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 170\u001b[0;31m                 raise NotImplementedError(\n\u001b[0m\u001b[1;32m    171\u001b[0m                     \u001b[0;34mf\"function is not implemented for this dtype: \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNotImplementedError\u001b[0m: function is not implemented for this dtype: [how->mean,dtype->object]",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/nanops.py\u001b[0m in \u001b[0;36m_ensure_numeric\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m   1602\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1603\u001b[0;31m             \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1604\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: could not convert string to float: '0.170.30.430.010.110.82'",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/nanops.py\u001b[0m in \u001b[0;36m_ensure_numeric\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m   1606\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1607\u001b[0;31m                 \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcomplex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1608\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: complex() arg is a malformed string",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-80-a67bcf17a1df>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Name'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'NA_Sales'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36mmean\u001b[0;34m(self, numeric_only)\u001b[0m\n\u001b[1;32m   1685\u001b[0m         \u001b[0mnumeric_only\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_resolve_numeric_only\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumeric_only\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1686\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1687\u001b[0;31m         result = self._cython_agg_general(\n\u001b[0m\u001b[1;32m   1688\u001b[0m             \u001b[0;34m\"mean\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1689\u001b[0m             \u001b[0malt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumeric_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnumeric_only\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/groupby/generic.py\u001b[0m in \u001b[0;36m_cython_agg_general\u001b[0;34m(self, how, alt, numeric_only, min_count)\u001b[0m\n\u001b[1;32m    350\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    351\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 352\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobjvals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    353\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    354\u001b[0m         ser = self.obj._constructor(\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/groupby/generic.py\u001b[0m in \u001b[0;36marray_func\u001b[0;34m(values)\u001b[0m\n\u001b[1;32m    346\u001b[0m                 \u001b[0;31m# try to python agg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    347\u001b[0m                 \u001b[0;31m# TODO: shouldn't min_count matter?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 348\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_agg_py_fallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mndim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    349\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    350\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36m_agg_py_fallback\u001b[0;34m(self, values, ndim, alt)\u001b[0m\n\u001b[1;32m   1396\u001b[0m         \u001b[0;31m#  should always be preserved by the implemented aggregations\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1397\u001b[0m         \u001b[0;31m# TODO: Is this exactly right; see WrappedCythonOp get_result_dtype?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1398\u001b[0;31m         \u001b[0mres_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrouper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0magg_series\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mser\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreserve_dtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1399\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1400\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCategorical\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/groupby/ops.py\u001b[0m in \u001b[0;36magg_series\u001b[0;34m(self, obj, func, preserve_dtype)\u001b[0m\n\u001b[1;32m   1058\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1059\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1060\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_aggregate_series_fast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1061\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1062\u001b[0m         \u001b[0mnpvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaybe_convert_objects\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtry_float\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/groupby/ops.py\u001b[0m in \u001b[0;36m_aggregate_series_fast\u001b[0;34m(self, obj, func)\u001b[0m\n\u001b[1;32m   1083\u001b[0m         \u001b[0mids\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mids\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1084\u001b[0m         \u001b[0msgrouper\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlibreduction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeriesGrouper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mngroups\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1085\u001b[0;31m         \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msgrouper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1086\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1087\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/_libs/reduction.pyx\u001b[0m in \u001b[0;36mpandas._libs.reduction.SeriesGrouper.get_result\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/_libs/reduction.pyx\u001b[0m in \u001b[0;36mpandas._libs.reduction._BaseGrouper._apply_to_group\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m   1687\u001b[0m         result = self._cython_agg_general(\n\u001b[1;32m   1688\u001b[0m             \u001b[0;34m\"mean\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1689\u001b[0;31m             \u001b[0malt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumeric_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnumeric_only\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1690\u001b[0m             \u001b[0mnumeric_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnumeric_only\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1691\u001b[0m         )\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mmean\u001b[0;34m(self, axis, skipna, level, numeric_only, **kwargs)\u001b[0m\n\u001b[1;32m  10749\u001b[0m         )\n\u001b[1;32m  10750\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumeric_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m> 10751\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mNDFrame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumeric_only\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m  10752\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  10753\u001b[0m         \u001b[0msetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"mean\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmean\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/nanops.py\u001b[0m in \u001b[0;36mnew_func\u001b[0;34m(values, axis, skipna, mask, **kwargs)\u001b[0m\n\u001b[1;32m    409\u001b[0m             \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    410\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 411\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mskipna\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    412\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    413\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdatetimelike\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/nanops.py\u001b[0m in \u001b[0;36mnanmean\u001b[0;34m(values, axis, skipna, mask)\u001b[0m\n\u001b[1;32m    664\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    665\u001b[0m     \u001b[0mcount\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_counts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype_count\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 666\u001b[0;31m     \u001b[0mthe_sum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_ensure_numeric\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype_sum\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    667\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    668\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthe_sum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"ndim\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/nanops.py\u001b[0m in \u001b[0;36m_ensure_numeric\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m   1608\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1609\u001b[0m                 \u001b[0;31m# e.g. \"foo\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1610\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Could not convert {x} to numeric\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1611\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1612\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: Could not convert 0.170.30.430.010.110.82 to numeric"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[340]"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GAsndnXb9Ovd",
        "outputId": "0b39ac2d-b54f-484c-f6ef-d09c694a16a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Name           Fate/Stay Night [Réalta Nua]\n",
              "Platform                                PS2\n",
              "Year                                 2007.0\n",
              "Genre                             Adventure\n",
              "Publisher                   Kadokawa Shoten\n",
              "NA_Sales                                  0\n",
              "EU_Sales                                  0\n",
              "JP_Sales                               0.17\n",
              "Other_Sales                               0\n",
              "Name: 340, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "L2voon0b98wX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gBhmBU6U95kU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "T0L1eD9e93Bk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LXDxhTLg9ajJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mhIuzzU-9XOR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zAPgvoFE9Ung"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WULCTB5p9EUD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "x7i9NVEn9CPN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "43sCHgvlw5dD",
        "outputId": "f3772736-9d65-4b41-aaf3-eeb6fbde7fec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 16598 entries, 0 to 16597\n",
            "Data columns (total 9 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   Name         16598 non-null  object \n",
            " 1   Platform     16598 non-null  object \n",
            " 2   Year         16329 non-null  float64\n",
            " 3   Genre        16548 non-null  object \n",
            " 4   Publisher    16540 non-null  object \n",
            " 5   NA_Sales     16598 non-null  object \n",
            " 6   EU_Sales     16598 non-null  object \n",
            " 7   JP_Sales     16598 non-null  object \n",
            " 8   Other_Sales  16598 non-null  object \n",
            "dtypes: float64(1), object(8)\n",
            "memory usage: 1.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#NA_Sales 컬럼 내 문자타입 전부 제거\n",
        "\n",
        "df['NA_Sales'] = df.NA_Sales.str.replace(r'[a-zA-Z]', '', regex=True)\n"
      ],
      "metadata": {
        "id": "JqPp_pc3w7rF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "\n",
        "def has_errors(inputString):\n",
        "  return bool(re.search('[^.\\d+]', inputString))"
      ],
      "metadata": {
        "id": "8BQoiIlbASKF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "I9wtZOOPBR2D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Lb80oiPJA_ue"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 이름에 섞여 있는 오류 데이터를 확인\n",
        "error_condition = df.NA_Sales.apply(has_errors)\n",
        "error_names = df[error_condition]\n",
        "error_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "id": "sGFEqwE9Ammn",
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Name, Platform, Year, Genre, Publisher, NA_Sales, EU_Sales, JP_Sales, Other_Sales]\n",
              "Index: []"
            ],
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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    {
      "cell_type": "code",
      "source": [
        "df['NA_Sales'].astype(int)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "s4z0Qd8q4b1H",
        "outputId": "878c65d0-bf07-4257-a75d-36dbdf52e374"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-128-0d49a9e66f95>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'NA_Sales'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m   5813\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5814\u001b[0m             \u001b[0;31m# else, only a single dtype is given\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5815\u001b[0;31m             \u001b[0mnew_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5816\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5817\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    416\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    417\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"raise\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 418\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    419\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    420\u001b[0m     def convert(\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, align_keys, ignore_failures, **kwargs)\u001b[0m\n\u001b[1;32m    325\u001b[0m                     \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    326\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 327\u001b[0;31m                     \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    328\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    329\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mignore_failures\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    589\u001b[0m         \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    590\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 591\u001b[0;31m         \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_array_safe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    592\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    593\u001b[0m         \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmaybe_coerce_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/dtypes/cast.py\u001b[0m in \u001b[0;36mastype_array_safe\u001b[0;34m(values, dtype, copy, errors)\u001b[0m\n\u001b[1;32m   1307\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1308\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1309\u001b[0;31m         \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1310\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1311\u001b[0m         \u001b[0;31m# e.g. astype_nansafe can fail on object-dtype of strings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/dtypes/cast.py\u001b[0m in \u001b[0;36mastype_array\u001b[0;34m(values, dtype, copy)\u001b[0m\n\u001b[1;32m   1255\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1256\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1257\u001b[0;31m         \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_nansafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1258\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1259\u001b[0m     \u001b[0;31m# in pandas we don't store numpy str dtypes, so convert to object\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/dtypes/cast.py\u001b[0m in \u001b[0;36mastype_nansafe\u001b[0;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[1;32m   1172\u001b[0m         \u001b[0;31m# work around NumPy brokenness, #1987\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1173\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0missubdtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minteger\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1174\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype_intsafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1175\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1176\u001b[0m         \u001b[0;31m# if we have a datetime/timedelta array of objects\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.astype_intsafe\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: '0.04'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.NA_Sales.sort_values()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JQPhQ5mG4kRJ",
        "outputId": "9713d6d3-238e-434f-9efd-5b424e1b3b02"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8298        0\n",
              "7892        0\n",
              "7891        0\n",
              "7889        0\n",
              "7879        0\n",
              "         ... \n",
              "6248     9.67\n",
              "10650    9.81\n",
              "14347      90\n",
              "13798      90\n",
              "13057      90\n",
              "Name: NA_Sales, Length: 16598, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "90K=df[df.NA_Sales='90K']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        },
        "id": "zms7bp_i-YS9",
        "outputId": "4364dc4a-515d-4f21-c740-4d2d2b524781"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-125-0b830424b7f0>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    90K=df[df.NA_Sales='90K']\u001b[0m\n\u001b[0m      ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mjRtNj0y_XGl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_JO59aJf_V-B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "o_cyJCPA_Us4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sx38SmTx_S3X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "less10_over100 = patients[(patients.체질량지수 < 10) | (patients.체질량지수 > 100)]"
      ],
      "metadata": {
        "id": "_qg0O2aO_IEW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OaKrQHnS-Vu7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tHRn16gv7ht1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "\n",
        "def has_errors(inputString):\n",
        "  return bool(re.search('[_!.\\d+]', inputString))"
      ],
      "metadata": {
        "id": "pc1uA3gv5Asp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BXRAaACs4XpD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lndD0SAT0xZC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-0qZT8bkw6Hz"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}