day2_visualization.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "day2_visualization.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "M6bN2PgpNVbt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NJg4kR3COP_O",
        "colab_type": "code",
        "outputId": "685c0f5a-0365-441c-c89e-cc4e8fb8de35",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "pwd"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q6_aWaXbOVRv",
        "colab_type": "code",
        "outputId": "2514981d-ec49-4ca4-a921-5f845f58bca8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZlOcZjZJPAiV",
        "colab_type": "code",
        "outputId": "dc4590b1-d478-4fba-cb69-c5fd19054779",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "cd  /content/drive/My Drive/Colab Notebooks/matrix/matrix_two/dw_matrix_car"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/Colab Notebooks/matrix/matrix_two/dw_matrix_car\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JyiOynGJPKvV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pwd\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eqaXOGxAPTbk",
        "colab_type": "code",
        "outputId": "49e07955-2f49-465e-d5b5-51eb62c3d145",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        }
      },
      "source": [
        "df = pd.read_hdf('data/car.h5')\n",
        "df.shape"
      ],
      "execution_count": 0,
      "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-14-3e34ff7d18d5>\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[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_hdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'data/car.h5'\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      2\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/pytables.py\u001b[0m in \u001b[0;36mread_hdf\u001b[0;34m(path_or_buf, key, mode, **kwargs)\u001b[0m\n\u001b[1;32m    405\u001b[0m                     )\n\u001b[1;32m    406\u001b[0m             \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcandidate_only_group\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_v_pathname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 407\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mstore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mselect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mauto_close\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mauto_close\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    408\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[0mKeyError\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    409\u001b[0m         \u001b[0;31m# if there is an error, close the store\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/python3.6/dist-packages/pandas/io/pytables.py\u001b[0m in \u001b[0;36mselect\u001b[0;34m(self, key, where, start, stop, columns, iterator, chunksize, auto_close, **kwargs)\u001b[0m\n\u001b[1;32m    780\u001b[0m         )\n\u001b[1;32m    781\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 782\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mit\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    783\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    784\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mselect_as_coordinates\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[0mwhere\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop\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/usr/local/lib/python3.6/dist-packages/pandas/io/pytables.py\u001b[0m in \u001b[0;36mget_result\u001b[0;34m(self, coordinates)\u001b[0m\n\u001b[1;32m   1637\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1638\u001b[0m         \u001b[0;31m# directly return the result\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1639\u001b[0;31m         \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwhere\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   1640\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\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   1641\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/pytables.py\u001b[0m in \u001b[0;36mfunc\u001b[0;34m(_start, _stop, _where)\u001b[0m\n\u001b[1;32m    764\u001b[0m         \u001b[0;31m# function to call on iteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    765\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_start\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_stop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_where\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--> 766\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_start\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_stop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwhere\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_where\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumns\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    767\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    768\u001b[0m         \u001b[0;31m# create the iterator\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/python3.6/dist-packages/pandas/io/pytables.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, start, stop, **kwargs)\u001b[0m\n\u001b[1;32m   3204\u001b[0m             \u001b[0mblk_items\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"block{idx}_items\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mi\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   3205\u001b[0m             values = self.read_array(\n\u001b[0;32m-> 3206\u001b[0;31m                 \u001b[0;34m\"block{idx}_values\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_start\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_stop\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3207\u001b[0m             )\n\u001b[1;32m   3208\u001b[0m             \u001b[0mblk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_block\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplacement\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblk_items\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/python3.6/dist-packages/pandas/io/pytables.py\u001b[0m in \u001b[0;36mread_array\u001b[0;34m(self, key, start, stop)\u001b[0m\n\u001b[1;32m   2735\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2736\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtables\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mVLArray\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-> 2737\u001b[0;31m             \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mstop\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   2738\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   2739\u001b[0m             \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"value_type\"\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;32m/usr/local/lib/python3.6/dist-packages/tables/vlarray.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    679\u001b[0m             start, stop, step = self._process_range(\n\u001b[1;32m    680\u001b[0m                 key.start, key.stop, key.step)\n\u001b[0;32m--> 681\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep\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    682\u001b[0m         \u001b[0;31m# Try with a boolean or point selection\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    683\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\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/python3.6/dist-packages/tables/vlarray.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, start, stop, step)\u001b[0m\n\u001b[1;32m    819\u001b[0m         \u001b[0matom\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0matom\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    820\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0matom\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'size'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# it is a pseudo-atom\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 821\u001b[0;31m             \u001b[0moutlistarr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0matom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfromarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0marr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlistarr\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    822\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    823\u001b[0m             \u001b[0;31m# Convert the list to the right flavor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mtables/hdf5extension.pyx\u001b[0m in \u001b[0;36mtables.hdf5extension.VLArray._read_array\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: cannot set WRITEABLE flag to True of this array"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9u6q2g5ZQS-T",
        "colab_type": "code",
        "outputId": "4beec046-f5a5-4256-bce4-9098c3417ef4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "pwd\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/drive/My Drive/Colab Notebooks/matrix/matrix_two/dw_matrix_car'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bsWCXeSdQt6N",
        "colab_type": "code",
        "outputId": "2f153fc9-0bc4-4b2c-e935-fa9386914bd8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        }
      },
      "source": [
        "\n",
        "!pip install --upgrade tables"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tables\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ed/c3/8fd9e3bb21872f9d69eb93b3014c86479864cca94e625fd03713ccacec80/tables-3.6.1-cp36-cp36m-manylinux1_x86_64.whl (4.3MB)\n",
            "\u001b[K     |████████████████████████████████| 4.3MB 2.9MB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: numpy>=1.9.3 in /usr/local/lib/python3.6/dist-packages (from tables) (1.17.5)\n",
            "Requirement already satisfied, skipping upgrade: numexpr>=2.6.2 in /usr/local/lib/python3.6/dist-packages (from tables) (2.7.1)\n",
            "Installing collected packages: tables\n",
            "  Found existing installation: tables 3.4.4\n",
            "    Uninstalling tables-3.4.4:\n",
            "      Successfully uninstalled tables-3.4.4\n",
            "Successfully installed tables-3.6.1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "tables"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JNe1PvOrQ6xO",
        "colab_type": "code",
        "outputId": "e2426296-7b70-4fe6-ca9f-2e8c6a83359a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df = pd.read_hdf('data/car.h5')\n",
        "df.shape\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(106494, 155)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rbmDLbOHRWFT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.columns.values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ImLat76GRtv8",
        "colab_type": "code",
        "outputId": "3c5291f8-585f-4d23-ef04-5144e0907157",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "df['price_value'].hist(bins=100);"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuGTmGmDNRNcTEdsyc9EklDSprKsz1tUZ6+rMkV5Xvwxl3QOc1Ho9r7RJknqgX8LhHmBB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\n4SBJqhgOkqSK4SBJqhgOkqTK/wNyaqlsjjVAFQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xv8-QdatSHS0",
        "colab_type": "code",
        "outputId": "dd901134-6610-4f5a-8e2b-336bfcd162b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "df['price_value'].describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    106494.000000\n",
              "mean      47534.137437\n",
              "std       66308.716918\n",
              "min         400.000000\n",
              "25%       13890.000000\n",
              "50%       25500.000000\n",
              "75%       52900.000000\n",
              "max      799900.000000\n",
              "Name: price_value, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZngxMNmYSmmi",
        "colab_type": "code",
        "outputId": "939fe2a6-032a-4ec5-d426-45bee5fc45c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        }
      },
      "source": [
        "df['param_marka-pojazdu'].unique()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Dacia', 'Peugeot', 'Porsche', 'Volkswagen', 'Škoda', 'Jeep',\n",
              "       'Renault', 'Opel', 'Volvo', 'Audi', 'BMW', 'Toyota', 'Kia',\n",
              "       'Hyundai', 'Citroën', 'Nissan', 'Daewoo', 'Fiat', 'Mercedes-Benz',\n",
              "       'Mini', 'Ford', 'Chevrolet', 'Infiniti', 'Suzuki', 'Lexus', 'Seat',\n",
              "       'Mitsubishi', 'SsangYong', 'Mazda', 'Alfa Romeo', 'Saab', 'Dodge',\n",
              "       'Jaguar', 'Honda', 'Smart', 'Land Rover', 'Chrysler',\n",
              "       'Aston Martin', 'Subaru', 'Piaggio', 'Daihatsu', 'Cadillac',\n",
              "       'Lada', 'Iveco', 'Lincoln', 'Inny', 'Lancia', 'Yugo', 'Pontiac',\n",
              "       'MG', 'Scion', None, 'Hummer', 'Polonez', 'Maybach', 'Maserati',\n",
              "       'Aixam', 'Bentley', 'Lamborghini', 'Isuzu', 'Gaz', 'Rover',\n",
              "       'Wartburg', 'Mercury', 'Ferrari', 'Oldsmobile', 'Rolls-Royce',\n",
              "       'Moskwicz', 'Ligier', 'Vauxhall', 'Tesla', 'Microcar', 'Acura',\n",
              "       'Buick', 'Tarpan', 'Uaz', 'McLaren', 'GMC', 'Warszawa', 'Morgan',\n",
              "       'Żuk', 'Tata', 'Brilliance', 'Triumph', 'Chatenet', 'Zastava',\n",
              "       'Wołga', 'Austin', 'Trabant', 'LTI', 'Gonow', 'Aro', 'Syrena',\n",
              "       'Autobianchi', 'Mahindra', 'Asia', 'Grecav', 'GWM', 'Zaporożec',\n",
              "       'Plymouth', 'Talbot'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G2ctB4GZTKG6",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_BJngv8CS_PM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def group_and_barplot(feat_groupby, feat_agg='price_value' , agg_funcs=[np.mean, np.median, np.size], feat_sort = 'mean', top=50, subplots = True):\n",
        "  return(\n",
        "\n",
        "    df\n",
        "    .groupby(feat_groupby)[feat_agg]\n",
        "    .agg(agg_funcs)\n",
        "    .sort_values(by=feat_sort, ascending=False)\n",
        "    .head(top)\n",
        "\n",
        ").plot(kind='bar', figsize=(15,5), subplots=subplots)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DGc4VTWcY72b",
        "colab_type": "code",
        "outputId": "9bf01f59-76f3-46ec-9b0b-4c3c84f91ac5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        }
      },
      "source": [
        "group_and_barplot('param_kraj-pochodzenia', feat_sort='size')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f800e1f3438>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f800e19ecc0>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f800e14de80>],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Y5EmSJElSi5jkSZIkSVKLmORJkiRJUouM2MXQN9hgg5wwYcJw74YkSZIkDYvZs2f/MTPH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\nlFJKKaWUUofkIC+llFJKKaWUOuT/AfaQr7C2KUs5AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1080x360 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YYdnfdSFawkr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "'price_value'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oF0MCMhkas-C",
        "colab_type": "code",
        "outputId": "410f7d3f-9862-427d-9f73-03bf10446eb5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        }
      },
      "source": [
        "group_and_barplot('param_kolor', feat_sort='mean')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f800de64d30>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f800dd18518>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f800dcc5978>],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LFi3i+OOPZ8KECSsUR2Tmsg+I2Bi4AFgfSODszPx2RLwR+AkwGpgLHJCZz0Sx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\n",
            "text/plain": [
              "<Figure size 1080x360 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_MWsTr3-bl6a",
        "colab_type": "text"
      },
      "source": [
        "# New Section"
      ]
    }
  ]
}