Assignment_20.ipynb

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   "source": [
    "##### Q1. List any five functions of the pandas library with execution."
   ]
  },
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     "text": [
      "Function- read_csv():\n",
      "   PassengerId  Survived  Pclass  \\\n",
      "0            1         0       3   \n",
      "1            2         1       1   \n",
      "2            3         1       3   \n",
      "3            4         1       1   \n",
      "4            5         0       3   \n",
      "\n",
      "                                                Name     Sex   Age  SibSp  \\\n",
      "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
      "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
      "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
      "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
      "4                           Allen, Mr. William Henry    male  35.0      0   \n",
      "\n",
      "   Parch            Ticket     Fare Cabin Embarked  \n",
      "0      0         A/5 21171   7.2500   NaN        S  \n",
      "1      0          PC 17599  71.2833   C85        C  \n",
      "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
      "3      0            113803  53.1000  C123        S  \n",
      "4      0            373450   8.0500   NaN        S  \n",
      "\n",
      "Function- head():\n",
      "   PassengerId  Survived  Pclass  \\\n",
      "0            1         0       3   \n",
      "1            2         1       1   \n",
      "2            3         1       3   \n",
      "3            4         1       1   \n",
      "4            5         0       3   \n",
      "\n",
      "                                                Name     Sex   Age  SibSp  \\\n",
      "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
      "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
      "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
      "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
      "4                           Allen, Mr. William Henry    male  35.0      0   \n",
      "\n",
      "   Parch            Ticket     Fare Cabin Embarked  \n",
      "0      0         A/5 21171   7.2500   NaN        S  \n",
      "1      0          PC 17599  71.2833   C85        C  \n",
      "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
      "3      0            113803  53.1000  C123        S  \n",
      "4      0            373450   8.0500   NaN        S  \n",
      "\n",
      "Function- describe():\n",
      "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
      "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
      "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
      "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
      "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
      "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
      "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
      "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
      "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
      "\n",
      "            Parch        Fare  \n",
      "count  891.000000  891.000000  \n",
      "mean     0.381594   32.204208  \n",
      "std      0.806057   49.693429  \n",
      "min      0.000000    0.000000  \n",
      "25%      0.000000    7.910400  \n",
      "50%      0.000000   14.454200  \n",
      "75%      0.000000   31.000000  \n",
      "max      6.000000  512.329200  \n",
      "\n",
      "Function- query():\n",
      "     PassengerId  Survived  Pclass  \\\n",
      "1              2         1       1   \n",
      "2              3         1       3   \n",
      "3              4         1       1   \n",
      "8              9         1       3   \n",
      "9             10         1       2   \n",
      "..           ...       ...     ...   \n",
      "875          876         1       3   \n",
      "879          880         1       1   \n",
      "880          881         1       2   \n",
      "887          888         1       1   \n",
      "889          890         1       1   \n",
      "\n",
      "                                                  Name     Sex   Age  SibSp  \\\n",
      "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
      "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
      "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
      "8    Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
      "9                  Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
      "..                                                 ...     ...   ...    ...   \n",
      "875                   Najib, Miss. Adele Kiamie \"Jane\"  female  15.0      0   \n",
      "879      Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)  female  56.0      0   \n",
      "880       Shelley, Mrs. William (Imanita Parrish Hall)  female  25.0      0   \n",
      "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
      "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
      "\n",
      "     Parch            Ticket     Fare Cabin Embarked  \n",
      "1        0          PC 17599  71.2833   C85        C  \n",
      "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
      "3        0            113803  53.1000  C123        S  \n",
      "8        2            347742  11.1333   NaN        S  \n",
      "9        0            237736  30.0708   NaN        C  \n",
      "..     ...               ...      ...   ...      ...  \n",
      "875      0              2667   7.2250   NaN        C  \n",
      "879      1             11767  83.1583   C50        C  \n",
      "880      1            230433  26.0000   NaN        S  \n",
      "887      0            112053  30.0000   B42        S  \n",
      "889      0            111369  30.0000  C148        C  \n",
      "\n",
      "[342 rows x 12 columns]\n",
      "\n",
      "Function- unique():\n",
      "[0 1]\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Answer:\n",
    "\n",
    "The five functions of pandas library are as follows:\n",
    "1. read_csv(): This function is used to read data from a CSV file and create a DataFrame.\n",
    "   Example:\n",
    "\"\"\"\n",
    "import pandas as pd\n",
    "\n",
    "data = pd.read_csv('https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv')\n",
    "print(\"Function- read_csv():\")\n",
    "print(data.head())\n",
    "\n",
    "\"\"\"\n",
    "2. head(): This function displays the first five rows of a DataFrame.\n",
    "\"\"\"\n",
    "print(\"\\nFunction- head():\")\n",
    "print(data.head())\n",
    "\n",
    "\"\"\"\n",
    "3. describe(): This function generates descriptive statistics for the numerical columns of a DataFrame.\n",
    "\"\"\"\n",
    "print(\"\\nFunction- describe():\")\n",
    "print(data.describe())\n",
    "\n",
    "\"\"\"\n",
    "4. query(): This function is used to query the DataFrame based on an expression.\n",
    "\"\"\"\n",
    "print(\"\\nFunction- query():\")\n",
    "print(data.query(\"Age > 18\" and  \"Survived == 1\"))\n",
    "\n",
    "\"\"\"\n",
    "5. unique(): This function returns the list of unique values in a column or series.\n",
    "\"\"\"\n",
    "print(\"\\nFunction- unique():\")\n",
    "print(data[\"Survived\"].unique())"
   ]
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   "cell_type": "markdown",
   "id": "f64ed759-760b-46f8-82f7-1ec65f4e765c",
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   "source": [
    "##### Q2. Given a Pandas DataFrame df with columns 'A', 'B', and 'C', write a Python function to re-index the\n",
    "##### DataFrame with a new index that starts from 1 and increments by 2 for each row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "377efd09-bf39-428b-a3fa-67d985242a11",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>50</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>60</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>30</td>\n",
       "      <td>70</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>40</td>\n",
       "      <td>80</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "    A   B    C\n",
       "1  10  50   90\n",
       "3  20  60  100\n",
       "5  30  70  110\n",
       "7  40  80  120"
      ]
     },
     "execution_count": 2,
     "metadata": {},
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    }
   ],
   "source": [
    "## Program to re-index a DataFrame using a python function.\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "def new_index(df):\n",
    "    new_index = range(1, (2 * len(df)) + 1, 2)\n",
    "    df_copy = df.copy()\n",
    "    df_copy.index = new_index\n",
    "    return df_copy\n",
    "    \n",
    "data = {'A': [10, 20, 30, 40],\n",
    "        'B': [50, 60, 70, 80],\n",
    "        'C': [90, 100, 110, 120]}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "new_index(df)"
   ]
  },
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   "cell_type": "markdown",
   "id": "bf3ab08b-e9c6-4a99-bcd0-e88aa50e5b12",
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   "source": [
    "##### Q3. You have a Pandas DataFrame df with a column named 'Values'. Write a Python function that iterates over the DataFrame and calculates the sum of the \n",
    "##### first three values in the 'Values' column. The function should print the sum to the console.\n",
    "##### For example, if the 'Values' column of df contains the values [10, 20, 30, 40, 50], your function should\n",
    "##### calculate and print the sum of the first three values, which is 60."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7e7386f3-e4a1-4057-ae30-e7019be43694",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The sum of first three values of DataFrame is 60\n"
     ]
    }
   ],
   "source": [
    "## Program to create a function to calculate the sum of first three values of Values column in a DataFrame \n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "def df_sum_first_three_values(df):\n",
    "    count = 0\n",
    "    sum = 0\n",
    "    for i in df[\"Values\"]:\n",
    "        sum += i\n",
    "        count += 1\n",
    "        if count == 3:\n",
    "            break\n",
    "    return sum\n",
    "\n",
    "data = {'Values': [10, 20, 30, 40, 50]}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "print(f\"The sum of first three values of DataFrame is {df_sum_first_three_values(df)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "500d3424-f34c-4a20-a27c-03c0d14e32ed",
   "metadata": {},
   "source": [
    "##### Q4. Given a Pandas DataFrame df with a column 'Text', write a Python function to create a new column\n",
    "##### 'Word_Count' that contains the number of words in each row of the 'Text' column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "26493012-8d4e-4966-b848-d0d0349fded9",
   "metadata": {},
   "outputs": [
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     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Text</th>\n",
       "      <th>Word_Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>This is a sample sentence.</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hello, how are you?</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Python programming is fun!</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Text  Word_Count\n",
       "0  This is a sample sentence.           5\n",
       "1         Hello, how are you?           4\n",
       "2  Python programming is fun!           4"
      ]
     },
     "execution_count": 29,
     "metadata": {},
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    }
   ],
   "source": [
    "## Program to create a function that can create a new column 'Word_Count' that contains the number of words in each row of the 'Text' column of a DataFrame.\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "def df_word_count(df):\n",
    "    l = []\n",
    "    for i in df[\"Text\"]:\n",
    "        l.append((len(i.split())))\n",
    "    df[\"Word_Count\"] = l\n",
    "    return df\n",
    "\n",
    "data = {'Text': [\"This is a sample sentence.\",\n",
    "                 \"Hello, how are you?\",\n",
    "                 \"Python programming is fun!\"]}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "df_word_count(df)"
   ]
  },
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   "cell_type": "markdown",
   "id": "c3aeea8b-e4d7-45b1-aa42-2f9dc04d1346",
   "metadata": {},
   "source": [
    "##### Q5. How are DataFrame.size() and DataFrame.shape() different?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "be783322-b3a4-410d-b250-325d32fe3141",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Function- DataFrame.size\n",
      "6\n",
      "\n",
      "Function- DataFrame.shape\n",
      "(3, 2)\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "1. DataFrame.size:\n",
    "   It returns the total number of elements in the DataFrame, which is equivalent to the total number of cells in the DataFrame.\n",
    "   The value returned by DataFrame.size is a single integer.\n",
    "2. DataFrame.shape:\n",
    "   It returns a tuple representing the dimensions of the DataFrame.\n",
    "   The value returned by DataFrame.shape is a tuple like (num_rows, num_columns).\n",
    "3. Example:\n",
    "\"\"\"\n",
    "import pandas as pd\n",
    "\n",
    "data = {'A': [1, 2, 3],\n",
    "        'B': [4, 5, 6]}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "print(\"Function- DataFrame.size\")\n",
    "print(df.size)\n",
    "print(\"\")\n",
    "print(\"Function- DataFrame.shape\")\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fb789f9-02d7-48f2-a700-1040f6c3510d",
   "metadata": {},
   "source": [
    "##### Q6. Which function of pandas do we use to read an excel file?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1e05c842-97b0-4f17-aa22-d2d2d5c850e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nAnswer:\\n\\nThe pandas function 'read_excel()' is used to read an excel file.\\n\""
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Answer:\n",
    "\n",
    "The pandas function 'read_excel()' is used to read an excel file.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25e61838-7678-41e0-aef0-288a442da968",
   "metadata": {},
   "source": [
    "##### Q7. You have a Pandas DataFrame df that contains a column named 'Email' that contains email addresses in the format 'username@domain.com'. Write a \n",
    "##### Python function that creates a new column 'Username' in df that contains only the username part of each email address.\n",
    "##### The username is the part of the email address that appears before the '@' symbol. For example, if the email address is 'john.doe@example.com',\n",
    "##### the 'Username' column should contain 'john.doe'. \n",
    "##### Your function should extract the username from each email address and store it in the new 'Username' column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "dfdd77f0-1b2b-4973-b1e8-6bb329a1f039",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        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>Email</th>\n",
       "      <th>Username</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>akshay@gmail.com</td>\n",
       "      <td>akshay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>anant@yahoo.com</td>\n",
       "      <td>anant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>john123@rediff.com</td>\n",
       "      <td>john123</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Email Username\n",
       "0    akshay@gmail.com   akshay\n",
       "1     anant@yahoo.com    anant\n",
       "2  john123@rediff.com  john123"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Program to create a function that extracts the username from each email address and store it in the new 'Username' column inside a DataFrame.\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "def extract_username(df):\n",
    "    l1 = []\n",
    "    for i in range(len(df)):\n",
    "        l1.append(df[\"Email\"][i][0:df[\"Email\"][i].find(\"@\")])\n",
    "    df[\"Username\"] = l1\n",
    "    return df\n",
    "\n",
    "email_addresses = {\n",
    "    \"Email\": [\"akshay@gmail.com\", \"anant@yahoo.com\", \"john123@rediff.com\"]\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(email_addresses)\n",
    "  \n",
    "extract_username(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "829cea89-1056-4412-861f-0ada1b226556",
   "metadata": {},
   "source": [
    "##### Q8. You have a Pandas DataFrame df with columns 'A', 'B', and 'C'. Write a Python function that selects all rows where the value in \n",
    "##### column 'A' is greater than 5 and the value in column 'B' is less than 10. The function should return a new DataFrame that contains only the selected rows.\n",
    "##### For example, if df contains the following values:\n",
    "#####   A B C\n",
    "##### 0 3 5 1\n",
    "##### 1 8 2 7\n",
    "##### 2 6 9 4\n",
    "##### 3 2 3 5\n",
    "##### 4 9 1 2\n",
    "\n",
    "##### Your function should select the following rows: A B C\n",
    "##### 1 8 2 7\n",
    "##### 4 9 1 2\n",
    "##### The function should return a new DataFrame that contains only the selected rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "0a6e7633-2cbf-4370-8b92-6fa57f33b05f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   A  B  C\n",
      "1  8  2  7\n",
      "2  6  9  4\n",
      "4  9  1  2\n"
     ]
    }
   ],
   "source": [
    "## Program to create a function that return a new DataFrame that contains only the selected rows based on a condition.\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "def selected_rows(df):\n",
    "    selected_rows = df[(df[\"A\"] > 5) & (df[\"B\"] < 10)]\n",
    "    return selected_rows\n",
    "\n",
    "data = {\n",
    "    \"A\": [3,8,6,2,9],\n",
    "    \"B\": [5,2,9,3,1],\n",
    "    \"C\": [1,7,4,5,2]\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "new_selected_rows_df = selected_rows(df)\n",
    "\n",
    "print(new_selected_rows_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31369e6b-d450-4bd5-908c-04a3ba92c5fa",
   "metadata": {},
   "source": [
    "##### Q9. Given a Pandas DataFrame df with a column 'Values', write a Python function to calculate the mean, median, and standard deviation of the \n",
    "##### values in the 'Values' column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "393a5b6f-f75a-4d1b-b568-63b40b94ad16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: 30.0\n",
      "Median: 30.0\n",
      "Standard Deviation: 15.811388300841896\n"
     ]
    }
   ],
   "source": [
    "## Program to create a function to calculate the mean, median, and standard deviation of the values in the 'Values' column of a DataFrame.\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "def df_statistics(df):\n",
    "    df_mean = df[\"Values\"].mean()\n",
    "    df_median = df[\"Values\"].median()\n",
    "    df_std = df[\"Values\"].std()\n",
    "    \n",
    "    return df_mean, df_median, df_std\n",
    "\n",
    "data = {'Values': [10, 20, 30, 40, 50]}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "Mean, Median, Std_deviation = df_statistics(df)\n",
    "\n",
    "print(\"Mean:\", Mean)\n",
    "print(\"Median:\", Median)\n",
    "print(\"Standard Deviation:\", Std_deviation)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7410e5f8-dfe6-41ae-a526-610b05ffbbd3",
   "metadata": {},
   "source": [
    "##### Q10. Given a Pandas DataFrame df with a column 'Sales' and a column 'Date', write a Python function to create a new column 'MovingAverage' \n",
    "##### that contains the moving average of the sales for the past 7 days for each row in the DataFrame. \n",
    "##### The moving average should be calculated using a window of size 7 and should include the current day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "421fd939-8261-488e-baed-1f536156de8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        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>Date</th>\n",
       "      <th>Sales</th>\n",
       "      <th>'MovingAverage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-08-01</td>\n",
       "      <td>10</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-08-02</td>\n",
       "      <td>20</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-08-03</td>\n",
       "      <td>15</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-08-04</td>\n",
       "      <td>30</td>\n",
       "      <td>18.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-08-05</td>\n",
       "      <td>25</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2023-08-06</td>\n",
       "      <td>40</td>\n",
       "      <td>23.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2023-08-07</td>\n",
       "      <td>35</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2023-08-08</td>\n",
       "      <td>50</td>\n",
       "      <td>30.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2023-08-09</td>\n",
       "      <td>45</td>\n",
       "      <td>34.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2023-08-10</td>\n",
       "      <td>60</td>\n",
       "      <td>40.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2023-08-11</td>\n",
       "      <td>55</td>\n",
       "      <td>44.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2023-08-12</td>\n",
       "      <td>70</td>\n",
       "      <td>50.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2023-08-13</td>\n",
       "      <td>65</td>\n",
       "      <td>54.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2023-08-14</td>\n",
       "      <td>80</td>\n",
       "      <td>60.714286</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Sales  'MovingAverage\n",
       "0  2023-08-01     10       10.000000\n",
       "1  2023-08-02     20       15.000000\n",
       "2  2023-08-03     15       15.000000\n",
       "3  2023-08-04     30       18.750000\n",
       "4  2023-08-05     25       20.000000\n",
       "5  2023-08-06     40       23.333333\n",
       "6  2023-08-07     35       25.000000\n",
       "7  2023-08-08     50       30.714286\n",
       "8  2023-08-09     45       34.285714\n",
       "9  2023-08-10     60       40.714286\n",
       "10 2023-08-11     55       44.285714\n",
       "11 2023-08-12     70       50.714286\n",
       "12 2023-08-13     65       54.285714\n",
       "13 2023-08-14     80       60.714286"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Program to create a function to calculate moving average of the sales for the past 7 days for each row in the DataFrame.\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "def calculate_moving_average(df):\n",
    "    l = []\n",
    "    for i in range(len(df[\"Sales\"])):\n",
    "        if i < 7:\n",
    "            l.append(df[\"Sales\"][0: i+1].mean())\n",
    "        else: \n",
    "            l.append(df[\"Sales\"][i: i-7: -1].mean())\n",
    "    df[\"'MovingAverage\"] = l\n",
    "    return df\n",
    "\n",
    "data = {\n",
    "    'Date': pd.date_range(start='2023-08-01', periods=14),\n",
    "    'Sales': [10, 20, 15, 30, 25, 40, 35, 50, 45, 60, 55, 70, 65, 80]\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "calculate_moving_average(df)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "756ccd55-38f5-47f6-b1dc-7ad81095f5c1",
   "metadata": {},
   "source": [
    "##### Q11. You have a Pandas DataFrame df with a column 'Date'. Write a Python function that creates a new column 'Weekday' in the DataFrame. \n",
    "##### The 'Weekday' column should contain the weekday name (e.g. Monday, Tuesday) corresponding to each date in the 'Date' column.\n",
    "##### For example, if df contains the following values:\n",
    "#####    Date\n",
    "##### 0 2023-01-01\n",
    "##### 1 2023-01-02\n",
    "##### 2 2023-01-03\n",
    "##### 3 2023-01-04\n",
    "##### 4 2023-01-05\n",
    "##### Your function should create the following DataFrame:\n",
    "\n",
    "#####      Date    Weekday\n",
    "##### 0 2023-01-01 Sunday\n",
    "##### 1 2023-01-02 Monday\n",
    "##### 2 2023-01-03 Tuesday\n",
    "##### 3 2023-01-04 Wednesday\n",
    "##### 4 2023-01-05 Thursday\n",
    "##### The function should return the modified DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "652c4802-2f46-4099-bf5c-97fbc9fbafa5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        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>Date</th>\n",
       "      <th>Weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-01-01</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-01-02</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-01-03</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-01-04</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-01-05</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date    Weekday\n",
       "0  2023-01-01     Sunday\n",
       "1  2023-01-02     Monday\n",
       "2  2023-01-03    Tuesday\n",
       "3  2023-01-04  Wednesday\n",
       "4  2023-01-05   Thursday"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Program to create a function that creates a new column 'Weekday' with weekday name corresponding to each row in 'Date' column in the DataFrame.\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "def df_weekday_name(df):\n",
    "    l = []\n",
    "    for i in range(len(df[\"Date\"])):\n",
    "        l.append(pd.Timestamp(df[\"Date\"][i]).day_name())\n",
    "    df[\"Weekday\"] = l\n",
    "    return df\n",
    "\n",
    "data = {\n",
    "    \"Date\": [\"2023-01-01\", \"2023-01-02\", \"2023-01-03\", \"2023-01-04\", \"2023-01-05\"]\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "df_weekday_name(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e0c4868-1afe-4812-8d44-5d66b1393c77",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### Q12. Given a Pandas DataFrame df with a column 'Date' that contains timestamps, write a Python function to select all rows where the date is \n",
    "##### between '2023-01-01' and '2023-01-31'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "8eb73bdd-9f38-40cb-9b02-4701ebdcfcf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        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>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-01-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-01-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-01-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-01-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2023-01-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2023-01-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2023-01-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2023-01-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2023-01-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2023-01-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2023-01-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2023-01-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2023-01-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2023-01-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2023-01-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2023-01-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2023-01-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2023-01-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2023-01-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2023-01-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2023-01-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2023-01-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2023-01-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2023-01-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2023-01-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2023-01-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2023-01-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2023-01-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2023-01-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2023-01-31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date\n",
       "0  2023-01-01\n",
       "1  2023-01-02\n",
       "2  2023-01-03\n",
       "3  2023-01-04\n",
       "4  2023-01-05\n",
       "5  2023-01-06\n",
       "6  2023-01-07\n",
       "7  2023-01-08\n",
       "8  2023-01-09\n",
       "9  2023-01-10\n",
       "10 2023-01-11\n",
       "11 2023-01-12\n",
       "12 2023-01-13\n",
       "13 2023-01-14\n",
       "14 2023-01-15\n",
       "15 2023-01-16\n",
       "16 2023-01-17\n",
       "17 2023-01-18\n",
       "18 2023-01-19\n",
       "19 2023-01-20\n",
       "20 2023-01-21\n",
       "21 2023-01-22\n",
       "22 2023-01-23\n",
       "23 2023-01-24\n",
       "24 2023-01-25\n",
       "25 2023-01-26\n",
       "26 2023-01-27\n",
       "27 2023-01-28\n",
       "28 2023-01-29\n",
       "29 2023-01-30\n",
       "30 2023-01-31"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Program to create a function to select all rows where the date is between '2023-01-01' and '2023-01-31' from a DataFrame.\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "def df_select_rows_between_dates(df, start_date, end_date):\n",
    "    selected_rows = df[(df[\"Date\"] >= start_date) & (df[\"Date\"] <= end_date)]\n",
    "    return selected_rows\n",
    "\n",
    "data = {\n",
    "    'Date': pd.date_range(start='2023-01-01', end='2023-02-28', freq='D'),\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "start_date = \"2023-01-01\"\n",
    "end_date = \"2023-01-31\"\n",
    "\n",
    "df_select_rows_between_dates(df, start_date, end_date)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b24db095-7660-4871-b614-67d10cd2d790",
   "metadata": {},
   "source": [
    "##### Q13. To use the basic functions of pandas, what is the first and foremost necessary library that needs to be imported?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "c4076a11-dc7a-4cf5-8903-4ff02d80a6b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nAnswer:\\n\\nWe need to import the Pandas library itself to use the basic functions of the Pandas library. Example: import pandas as pd\\n'"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Answer:\n",
    "\n",
    "We need to import the Pandas library itself to use the basic functions of the Pandas library. Example: import pandas as pd\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09f78a74-f875-49d9-be10-35a689047e77",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab101e68-388c-497a-bc6a-988ed722451e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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