yash_kulkarni.ipynb

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  "metadata": {
    "colab": {
      "name": "Tutorial_1.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [
        "Ap1lyTOOYjCa"
      ]
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.1"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "2zIID9dcYjBz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "pd.options.display.max_rows = 8"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1jv7imUwYjBy",
        "colab_type": "text"
      },
      "source": [
        "<!--<img width=700px; src=\"../img/logoUPSayPlusCDS_990.png\"> -->\n",
        "\n",
        "<p style=\"margin-top: 3em; margin-bottom: 2em;\"><b><big><big><big><big>Introduction to Pandas</big></big></big></big></b></p>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tZcDbYpSYjB2",
        "colab_type": "text"
      },
      "source": [
        "# 1. Let's start with a showcase\n",
        "\n",
        "#### Case 1: titanic survival data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ur89Vvj-gmMT",
        "colab_type": "code",
        "outputId": "7406652a-6e75-4008-9470-0bd302d34eb4",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-e209c96e-95d5-4ead-bb38-115116095ab2\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-e209c96e-95d5-4ead-bb38-115116095ab2\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving titanic.csv to titanic.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hWtdpE-agnoO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import io\n",
        "titanic_data = pd.read_csv(io.BytesIO(uploaded['titanic.csv']))\n",
        "df = titanic_data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bBqWTAXfYjB5",
        "colab_type": "code",
        "outputId": "a9959956-4f30-4a32-9702-9555f15d1e83",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "0            1         0       3  ...   7.2500   NaN         S\n",
              "1            2         1       1  ...  71.2833   C85         C\n",
              "2            3         1       3  ...   7.9250   NaN         S\n",
              "3            4         1       1  ...  53.1000  C123         S\n",
              "4            5         0       3  ...   8.0500   NaN         S\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CoB9vZ8EYjB7",
        "colab_type": "text"
      },
      "source": [
        "Starting from reading this dataset, to answering questions about this data in a few lines of code:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4knS-J80YjB8",
        "colab_type": "text"
      },
      "source": [
        "**What is the age distribution of the passengers?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AtaeqCdmYjB8",
        "colab_type": "code",
        "outputId": "39da95e5-b4e4-4534-f20f-dbf752df128f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "df['Age'].hist()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9fffee55c0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 148
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKpxFL0mFs+glqXD/H93kvRdEufYeAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mAg6BRgFYjB-",
        "colab_type": "text"
      },
      "source": [
        "**How does the survival rate of the passengers differ between sexes?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PiIjOlF4YjB_",
        "colab_type": "code",
        "outputId": "3d26a987-4dd8-41ef-db77-2f7fa0ce10cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        }
      },
      "source": [
        "df.groupby('Sex')[['Survived']].aggregate(lambda x: x.sum() / len(x))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Survived</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>female</th>\n",
              "      <td>0.742038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>0.188908</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Survived\n",
              "Sex             \n",
              "female  0.742038\n",
              "male    0.188908"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B6FrpNdcYjCC",
        "colab_type": "text"
      },
      "source": [
        "**Or how does it differ between the different classes?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qb7C-fp5YjCC",
        "colab_type": "code",
        "outputId": "4fd994f8-2322-4e2a-a7e5-8ef4e6922c0c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "df.groupby('Pclass')['Survived'].aggregate(lambda x: x.sum() / len(x)).plot(kind='bar')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9fffe16fd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 150
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8Z4FX9ta/CHwkyR3M/cpJgH8Afi3Ju4FrDLsuFb6JSS17pqqu7V9IOn2iwEeAN1bVI0lu\nB64HqKq3JXkdcDPwcJLrquqeJPt7a1NJfqmqPjvE70G6IJ6560rzWeCWJC8GSPKiBfZcBfxnkmcz\nd+ZOb+/Lqmp/Ve0EZoG1SX4QOFZVfwR8ErhiP4VQlxbP3HVFqapDSd4H/G2S7wBfBm6ft+03gf3M\nBXw/c7EH+GDvBdMAnwEeAd4N/HySbzP3kbO/t+TfhNSBL6hKUoO8LCNJDTLuktQg4y5JDTLuktQg\n4y5JDTLuktQg4y5JDfpfVeqzbSvdtiYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EO9Vn3_OYjCF",
        "colab_type": "text"
      },
      "source": [
        "All the needed functionality for the above examples will be explained throughout this tutorial."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "59AnXyrJYjCF",
        "colab_type": "text"
      },
      "source": [
        "#### Case 2: air quality measurement timeseries"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "slideshow": {
          "slide_type": "subslide"
        },
        "id": "YGL70tM_YjCH",
        "colab_type": "text"
      },
      "source": [
        "AirBase (The European Air quality dataBase): hourly measurements of all air quality monitoring stations from Europe\n",
        "\n",
        "Starting from these hourly data for different stations:\n",
        "\n",
        "data = pd.read_csv('data/20000101_20161231-NO2.csv', sep=';', skiprows=[1], na_values=['n/d'], index_col=0, parse_dates=True)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "43ba97f2-c287-477f-cf90-16bd28857bd5",
        "id": "Q8QBVDZTcy1L",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-5d9a88a1-3b66-4263-bdb7-817fc73f474c\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-5d9a88a1-3b66-4263-bdb7-817fc73f474c\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving 20000101_20161231-NO2.csv to 20000101_20161231-NO2 (1).csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ut9tDSrHcy1H",
        "colab": {}
      },
      "source": [
        "import io\n",
        "airpoll_data = pd.read_csv(io.BytesIO(uploaded['20000101_20161231-NO2.csv']), sep=';', skiprows=[1], na_values=['n/d'], index_col=0, parse_dates=True)\n",
        "data = airpoll_data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4pDfUgAUYjCL",
        "colab_type": "code",
        "outputId": "23b4e609-a3d7-461f-f36d-858be3a00329",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>BASCH</th>\n",
              "      <th>BONAP</th>\n",
              "      <th>PA18</th>\n",
              "      <th>VERS</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>timestamp</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2000-01-01 01:00:00</th>\n",
              "      <td>108.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>65.0</td>\n",
              "      <td>47.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-01 02:00:00</th>\n",
              "      <td>104.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>42.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-01 03:00:00</th>\n",
              "      <td>97.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>34.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-01 04:00:00</th>\n",
              "      <td>77.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>57.0</td>\n",
              "      <td>29.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-01 05:00:00</th>\n",
              "      <td>79.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>28.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     BASCH  BONAP  PA18  VERS\n",
              "timestamp                                    \n",
              "2000-01-01 01:00:00  108.0    NaN  65.0  47.0\n",
              "2000-01-01 02:00:00  104.0   60.0  77.0  42.0\n",
              "2000-01-01 03:00:00   97.0   58.0  73.0  34.0\n",
              "2000-01-01 04:00:00   77.0   52.0  57.0  29.0\n",
              "2000-01-01 05:00:00   79.0   52.0  64.0  28.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "id": "7sCtnYqDYjCO",
        "colab_type": "text"
      },
      "source": [
        "to answering questions about this data in a few lines of code:\n",
        "\n",
        "**Does the air pollution show a decreasing trend over the years?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D7VCkpeYYjCP",
        "colab_type": "code",
        "outputId": "87bf36e2-4421-40a8-9a15-7aa1b09fe80a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "data['1999':].resample('M').mean().plot(ylim=[0,120])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9fffddc630>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 154
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ESsT3tw/FsGxKBVr6NSYcTmBatUc4NevHJN1IsFpyM3pCiEkvFz4O03lKpgfZ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\nZPQua9Y4+nhaZjnX6o2Gfk9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t53JalriiQqXMkHLWbCGs5tTFV1wNRVEFUZg2bRpisRhU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\nDbwNlNiW5ANdAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zALIMK1RYjCS",
        "colab_type": "code",
        "outputId": "4251cce9-5a3a-43b1-e8b7-cd02940c5b61",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "data['1999':].resample('A').mean().plot(ylim=[0,100])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9fffd215f8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 155
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kvB2AUOeMShilhkP3K059BStdMBkGUnMVR9VRxMd+h0vuhwMr1fl7Jr0Lvae4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\nY6AAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "id": "DRdtUgjvYjCV",
        "colab_type": "text"
      },
      "source": [
        "**What is the difference in diurnal profile between weekdays and weekend?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xKCdXnoaYjCW",
        "colab_type": "code",
        "outputId": "50952c18-30d3-499e-f4e9-c432c65e5ce3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "data['weekday'] = data.index.weekday\n",
        "data['weekend'] = data['weekday'].isin([5, 6])\n",
        "data_weekend = data.groupby(['weekend', data.index.hour])['BASCH'].mean().unstack(level=0)\n",
        "data_weekend.plot()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9fffcba7f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 156
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Z6ITWcmFUdDDdGlehcgU7T+43pByBRSPANxRGrAA3CzbREwJ6fghftYbfXoaB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\nzErOid7MrOT+BTPbCyqxznZSAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Iun4NNtYjCZ",
        "colab_type": "text"
      },
      "source": [
        "We will come back to these example, and build them up step by step."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ap1lyTOOYjCa",
        "colab_type": "text"
      },
      "source": [
        "# 2. Pandas: data analysis in python\n",
        "\n",
        "For data-intensive work in Python the [Pandas](http://pandas.pydata.org) library has become essential.\n",
        "\n",
        "What is `pandas`?\n",
        "\n",
        "* Pandas can be thought of as *NumPy arrays with labels* for rows and columns, and better support for heterogeneous data types, but it's also much, much more than that.\n",
        "* Pandas can also be thought of as `R`'s `data.frame` in Python.\n",
        "* Powerful for working with missing data, working with time series data, for reading and writing your data, for reshaping, grouping, merging your data, ...\n",
        "\n",
        "It's documentation: http://pandas.pydata.org/pandas-docs/stable/\n",
        "\n",
        "\n",
        "** When do you need pandas? **\n",
        "\n",
        "When working with **tabular or structured data** (like R dataframe, SQL table, Excel spreadsheet, ...):\n",
        "\n",
        "- Import data\n",
        "- Clean up messy data\n",
        "- Explore data, gain insight into data\n",
        "- Process and prepare your data for analysis\n",
        "- Analyse your data (together with scikit-learn, statsmodels, ...)\n",
        "\n",
        "<div class=\"alert alert-warning\">\n",
        "<b>ATTENTION!</b>: <br><br>\n",
        "\n",
        "Pandas is great for working with heterogeneous and tabular 1D/2D data, but not all types of data fit in such structures!\n",
        "<ul>\n",
        "<li>When working with array data (e.g. images, numerical algorithms): just stick with numpy</li>\n",
        "<li>When working with multidimensional labeled data (e.g. climate data): have a look at [xarray](http://xarray.pydata.org/en/stable/)</li>\n",
        "</ul>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rIXEwWkNYjCb",
        "colab_type": "text"
      },
      "source": [
        "# 2. The pandas data structures: `DataFrame` and `Series`\n",
        "\n",
        "A `DataFrame` is a **tablular data structure** (multi-dimensional object to hold labeled data) comprised of rows and columns, akin to a spreadsheet, database table, or R's data.frame object. You can think of it as multiple Series object which share the same index.\n",
        "\n",
        "\n",
        "<img align=\"left\" width=50% src=\"img/schema-dataframe.svg\">"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "id": "j2zGbqA7YjCe",
        "colab_type": "text"
      },
      "source": [
        "### Attributes of the DataFrame\n",
        "\n",
        "A DataFrame has besides a `index` attribute, also a `columns` attribute:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "2cusxi6CYjCb",
        "colab_type": "code",
        "outputId": "52410567-439c-4030-dc50-f50b908ad8c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "df "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</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>887</th>\n",
              "      <td>888</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Graham, Miss. Margaret Edith</td>\n",
              "      <td>female</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>112053</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>B42</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>889</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>W./C. 6607</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>890</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Behr, Mr. Karl Howell</td>\n",
              "      <td>male</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>111369</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>C148</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>891</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Dooley, Mr. Patrick</td>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>370376</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "0              1         0       3  ...   7.2500   NaN         S\n",
              "1              2         1       1  ...  71.2833   C85         C\n",
              "2              3         1       3  ...   7.9250   NaN         S\n",
              "3              4         1       1  ...  53.1000  C123         S\n",
              "..           ...       ...     ...  ...      ...   ...       ...\n",
              "887          888         1       1  ...  30.0000   B42         S\n",
              "888          889         0       3  ...  23.4500   NaN         S\n",
              "889          890         1       1  ...  30.0000  C148         C\n",
              "890          891         0       3  ...   7.7500   NaN         Q\n",
              "\n",
              "[891 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YfLIvmkkYjCf",
        "colab_type": "code",
        "outputId": "ef987c2d-6b0d-4b83-d0a3-73ebb5f7d604",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df.index"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RangeIndex(start=0, stop=891, step=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 158
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-CrsBUunYjCh",
        "colab_type": "code",
        "outputId": "d499080f-fc83-40d3-ba69-9000ef8bb47a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
              "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "id": "Zi8yzECEYjCk",
        "colab_type": "text"
      },
      "source": [
        "To check the data types of the different columns:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oevJtqXaYjCo",
        "colab_type": "code",
        "outputId": "1441190d-045b-49e5-ef43-335c72e3935b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      int64\n",
              "Survived         int64\n",
              "Pclass           int64\n",
              "Name            object\n",
              "                ...   \n",
              "Ticket          object\n",
              "Fare           float64\n",
              "Cabin           object\n",
              "Embarked        object\n",
              "Length: 12, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "id": "_c26K61uYjCs",
        "colab_type": "text"
      },
      "source": [
        "An overview of that information can be given with the `info()` method:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vDCqXsjIYjCt",
        "colab_type": "code",
        "outputId": "27fd40a0-6e70-43e0-9cc0-93daf8257301",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 12 columns):\n",
            "PassengerId    891 non-null int64\n",
            "Survived       891 non-null int64\n",
            "Pclass         891 non-null int64\n",
            "Name           891 non-null object\n",
            "Sex            891 non-null object\n",
            "Age            714 non-null float64\n",
            "SibSp          891 non-null int64\n",
            "Parch          891 non-null int64\n",
            "Ticket         891 non-null object\n",
            "Fare           891 non-null float64\n",
            "Cabin          204 non-null object\n",
            "Embarked       889 non-null object\n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 83.6+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "id": "Fxs5iB25YjCv",
        "colab_type": "text"
      },
      "source": [
        "Also a DataFrame has a `values` attribute, but attention: when you have heterogeneous data, all values will be upcasted:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "piM9UHDqYjCw",
        "colab_type": "code",
        "outputId": "49aff271-4fec-4cab-afaf-8b4548872d7b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "df.values"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 0, 3, ..., 7.25, nan, 'S'],\n",
              "       [2, 1, 1, ..., 71.2833, 'C85', 'C'],\n",
              "       [3, 1, 3, ..., 7.925, nan, 'S'],\n",
              "       ...,\n",
              "       [889, 0, 3, ..., 23.45, nan, 'S'],\n",
              "       [890, 1, 1, ..., 30.0, 'C148', 'C'],\n",
              "       [891, 0, 3, ..., 7.75, nan, 'Q']], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "id": "nH0b-vMFYjCy",
        "colab_type": "text"
      },
      "source": [
        "Apart from importing your data from an external source (text file, excel, database, ..), one of the most common ways of creating a dataframe is from a dictionary of arrays or lists.\n",
        "\n",
        "Note that in the IPython notebook, the dataframe will display in a rich HTML view:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ep-RO4AsYjCy",
        "colab_type": "code",
        "outputId": "4aedf408-2d59-4988-dcbd-f24cc83bc103",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "data = {'country': ['Belgium', 'France', 'Germany', 'Netherlands', 'United Kingdom'],\n",
        "        'population': [11.3, 64.3, 81.3, 16.9, 64.9],\n",
        "        'area': [30510, 671308, 357050, 41526, 244820],\n",
        "        'capital': ['Brussels', 'Paris', 'Berlin', 'Amsterdam', 'London']}\n",
        "df_countries = pd.DataFrame(data)\n",
        "df_countries"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>country</th>\n",
              "      <th>population</th>\n",
              "      <th>area</th>\n",
              "      <th>capital</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Belgium</td>\n",
              "      <td>11.3</td>\n",
              "      <td>30510</td>\n",
              "      <td>Brussels</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>France</td>\n",
              "      <td>64.3</td>\n",
              "      <td>671308</td>\n",
              "      <td>Paris</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Germany</td>\n",
              "      <td>81.3</td>\n",
              "      <td>357050</td>\n",
              "      <td>Berlin</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Netherlands</td>\n",
              "      <td>16.9</td>\n",
              "      <td>41526</td>\n",
              "      <td>Amsterdam</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>64.9</td>\n",
              "      <td>244820</td>\n",
              "      <td>London</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          country  population    area    capital\n",
              "0         Belgium        11.3   30510   Brussels\n",
              "1          France        64.3  671308      Paris\n",
              "2         Germany        81.3  357050     Berlin\n",
              "3     Netherlands        16.9   41526  Amsterdam\n",
              "4  United Kingdom        64.9  244820     London"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "id": "UIpkHQ7eYjC1",
        "colab_type": "text"
      },
      "source": [
        "### One-dimensional data: `Series` (a column of a DataFrame)\n",
        "\n",
        "A Series is a basic holder for **one-dimensional labeled data**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KreOQPiNYjC2",
        "colab_type": "code",
        "outputId": "e4820bf9-7953-4f35-9b76-f605b552b28a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "df['Age']"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      22.0\n",
              "1      38.0\n",
              "2      26.0\n",
              "3      35.0\n",
              "       ... \n",
              "887    19.0\n",
              "888     NaN\n",
              "889    26.0\n",
              "890    32.0\n",
              "Name: Age, Length: 891, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s3Q3IjJqYjC4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "age = df['Age']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "id": "VCN8iF-1YjC6",
        "colab_type": "text"
      },
      "source": [
        "### Attributes of a Series: `index` and `values`\n",
        "\n",
        "The Series has also an `index` and `values` attribute, but no `columns`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c76EVuG1YjC8",
        "colab_type": "code",
        "outputId": "cd7f27f4-7a63-4b9e-d5fd-244f16b0b6f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "age.index"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RangeIndex(start=0, stop=891, step=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 166
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PITu1GNIYjC_",
        "colab_type": "text"
      },
      "source": [
        "You can access the underlying numpy array representation with the `.values` attribute:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "-fg1AMWiYjDA",
        "colab_type": "code",
        "outputId": "7481c7e3-44e8-4289-8793-ed066cc07f38",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "age.values[:10]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([22., 38., 26., 35., 35., nan, 54.,  2., 27., 14.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 167
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "id": "QX1qpCSWYjDD",
        "colab_type": "text"
      },
      "source": [
        "We can access series values via the index, just like for NumPy arrays:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sTosvCKZYjDE",
        "colab_type": "code",
        "outputId": "bcebbc5b-556f-4cd6-b9f6-ff765fd074c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "age[0]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "22.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "id": "9OVIynDcYjDH",
        "colab_type": "text"
      },
      "source": [
        "Unlike the NumPy array, though, this index can be something other than integers:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U5tcembjYjDI",
        "colab_type": "code",
        "outputId": "0b1879d5-dda1-4df2-b0a1-598956ff5023",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        }
      },
      "source": [
        "df = df.set_index('Name')\n",
        "df"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Braund, Mr. Owen Harris</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cumings, Mrs. John Bradley (Florence Briggs Thayer)</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Heikkinen, Miss. Laina</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Futrelle, Mrs. Jacques Heath (Lily May Peel)</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Graham, Miss. Margaret Edith</th>\n",
              "      <td>888</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>112053</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>B42</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Johnston, Miss. Catherine Helen \"Carrie\"</th>\n",
              "      <td>889</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>W./C. 6607</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Behr, Mr. Karl Howell</th>\n",
              "      <td>890</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>male</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>111369</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>C148</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dooley, Mr. Patrick</th>\n",
              "      <td>891</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>370376</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                    PassengerId  ...  Embarked\n",
              "Name                                                             ...          \n",
              "Braund, Mr. Owen Harris                                       1  ...         S\n",
              "Cumings, Mrs. John Bradley (Florence Briggs Tha...            2  ...         C\n",
              "Heikkinen, Miss. Laina                                        3  ...         S\n",
              "Futrelle, Mrs. Jacques Heath (Lily May Peel)                  4  ...         S\n",
              "...                                                         ...  ...       ...\n",
              "Graham, Miss. Margaret Edith                                888  ...         S\n",
              "Johnston, Miss. Catherine Helen \"Carrie\"                    889  ...         S\n",
              "Behr, Mr. Karl Howell                                       890  ...         C\n",
              "Dooley, Mr. Patrick                                         891  ...         Q\n",
              "\n",
              "[891 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g5rCy_zKYjDM",
        "colab_type": "code",
        "outputId": "307af03e-fa0c-46cf-a30d-e4f8e6fad616",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "age = df['Age']\n",
        "age"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Name\n",
              "Braund, Mr. Owen Harris                                22.0\n",
              "Cumings, Mrs. John Bradley (Florence Briggs Thayer)    38.0\n",
              "Heikkinen, Miss. Laina                                 26.0\n",
              "Futrelle, Mrs. Jacques Heath (Lily May Peel)           35.0\n",
              "                                                       ... \n",
              "Graham, Miss. Margaret Edith                           19.0\n",
              "Johnston, Miss. Catherine Helen \"Carrie\"                NaN\n",
              "Behr, Mr. Karl Howell                                  26.0\n",
              "Dooley, Mr. Patrick                                    32.0\n",
              "Name: Age, Length: 891, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 170
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hwl9TlcJYjDO",
        "colab_type": "code",
        "outputId": "9fca5ed8-0d86-4a3e-91ba-5f8a487efb41",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "age['Dooley, Mr. Patrick']"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "32.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 171
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        },
        "id": "IBAYQ_lkYjDR",
        "colab_type": "text"
      },
      "source": [
        "but with the power of numpy arrays. Many things you can do with numpy arrays, can also be applied on DataFrames / Series.\n",
        "\n",
        "Eg element-wise operations:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XlHRbEVdYjDS",
        "colab_type": "code",
        "outputId": "324ac983-1095-4a17-f542-5165e1be9559",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "age * 1000"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Name\n",
              "Braund, Mr. Owen Harris                                22000.0\n",
              "Cumings, Mrs. John Bradley (Florence Briggs Thayer)    38000.0\n",
              "Heikkinen, Miss. Laina                                 26000.0\n",
              "Futrelle, Mrs. Jacques Heath (Lily May Peel)           35000.0\n",
              "                                                        ...   \n",
              "Graham, Miss. Margaret Edith                           19000.0\n",
              "Johnston, Miss. Catherine Helen \"Carrie\"                   NaN\n",
              "Behr, Mr. Karl Howell                                  26000.0\n",
              "Dooley, Mr. Patrick                                    32000.0\n",
              "Name: Age, Length: 891, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 172
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E54KHDV1YjDV",
        "colab_type": "text"
      },
      "source": [
        "A range of methods:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rU24E7YsYjDW",
        "colab_type": "code",
        "outputId": "9fc2c97c-76d3-4f44-c5f4-7cda058ecb14",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "age.mean()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "29.69911764705882"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 173
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g1olPbGxYjDY",
        "colab_type": "text"
      },
      "source": [
        "Fancy indexing, like indexing with a list or boolean indexing:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eAKgQ060YjDY",
        "colab_type": "code",
        "outputId": "fe0905ee-b3b4-4f24-ddd8-8d38fa3649ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "age[age > 70]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Name\n",
              "Goldschmidt, Mr. George B               71.0\n",
              "Connors, Mr. Patrick                    70.5\n",
              "Artagaveytia, Mr. Ramon                 71.0\n",
              "Barkworth, Mr. Algernon Henry Wilson    80.0\n",
              "Svensson, Mr. Johan                     74.0\n",
              "Name: Age, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 174
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s-SB31smYjDa",
        "colab_type": "text"
      },
      "source": [
        "But also a lot of pandas specific methods, e.g."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SVPH8uqxYjDb",
        "colab_type": "code",
        "outputId": "7db04970-de63-45e5-9634-e02834e89108",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "df['Embarked'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "S    644\n",
              "C    168\n",
              "Q     77\n",
              "Name: Embarked, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 175
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a1-ms6F1YjDc",
        "colab_type": "text"
      },
      "source": [
        "<div class=\"alert alert-success\">\n",
        "\n",
        "<b>EXERCISE</b>:\n",
        "\n",
        " <ul>\n",
        "  <li>What is the maximum Fare that was paid? And the median?</li>\n",
        "</ul>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "clear_cell": true,
        "id": "G5_8JN3zYjDd",
        "colab_type": "code",
        "outputId": "a781eb0e-fc65-42c1-ec05-8b37c1c175ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "max = 0\n",
        "\n",
        "for fare in df['Fare'] :\n",
        "  if fare>max:\n",
        "    max=fare\n",
        "    \n",
        "print(max)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "512.3292\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "clear_cell": true,
        "id": "OhX9PiYhYjDf",
        "colab_type": "code",
        "outputId": "2c9adaca-602b-4bf9-cbce-68f62c26cc7b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(df['Fare'].median())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "14.4542\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s4TXSuOuYjDh",
        "colab_type": "text"
      },
      "source": [
        "<div class=\"alert alert-success\">\n",
        "\n",
        "<b>EXERCISE</b>:\n",
        "\n",
        " <ul>\n",
        "  <li>Calculate the average survival ratio for all passengers (note: the 'Survived' column indicates whether someone survived (1) or not (0)).</li>\n",
        "</ul>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "clear_cell": true,
        "run_control": {
          "frozen": false,
          "read_only": false
        },
        "id": "rlHXFj4YYjDj",
        "colab_type": "code",
        "outputId": "0b6ecee3-55c9-47f9-ad68-f733791fe74a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(df['Survived'].value_counts()[1]/(df['Survived'].value_counts()[1]+df['Survived'].value_counts()[0]))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.3838383838383838\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rXN7WdQLDbaX",
        "colab_type": "code",
        "outputId": "d04f6eb9-6699-4193-83f3-aebaa8c3ec6f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "df['Survived'].describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    891.000000\n",
              "mean       0.383838\n",
              "std        0.486592\n",
              "min        0.000000\n",
              "25%        0.000000\n",
              "50%        0.000000\n",
              "75%        1.000000\n",
              "max        1.000000\n",
              "Name: Survived, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 179
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7NfUj_iIYjDo",
        "colab_type": "text"
      },
      "source": [
        "# 3. Data import and export"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "id": "2lYKyODpYjDp",
        "colab_type": "text"
      },
      "source": [
        "A wide range of input/output formats are natively supported by pandas:\n",
        "\n",
        "* CSV, text\n",
        "* SQL database\n",
        "* Excel\n",
        "* HDF5\n",
        "* json\n",
        "* html\n",
        "* pickle\n",
        "* sas, stata\n",
        "* (parquet)\n",
        "* ..."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "trQGoqMeYjDq",
        "colab_type": "text"
      },
      "source": [
        "Very powerful csv reader:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JM0Ce8K_YjDr",
        "colab_type": "text"
      },
      "source": [
        "Luckily, if we have a well formed csv file, we don't need many of those arguments:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dQXaCg6wYjDv",
        "colab_type": "code",
        "outputId": "b502624d-2b62-49f2-d3ee-0b19441cab65",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Braund, Mr. Owen Harris</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cumings, Mrs. John Bradley (Florence Briggs Thayer)</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Heikkinen, Miss. Laina</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Futrelle, Mrs. Jacques Heath (Lily May Peel)</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Allen, Mr. William Henry</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                    PassengerId  ...  Embarked\n",
              "Name                                                             ...          \n",
              "Braund, Mr. Owen Harris                                       1  ...         S\n",
              "Cumings, Mrs. John Bradley (Florence Briggs Tha...            2  ...         C\n",
              "Heikkinen, Miss. Laina                                        3  ...         S\n",
              "Futrelle, Mrs. Jacques Heath (Lily May Peel)                  4  ...         S\n",
              "Allen, Mr. William Henry                                      5  ...         S\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 180
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w6-zPWVCYjDy",
        "colab_type": "text"
      },
      "source": [
        "<div class=\"alert alert-success\">\n",
        "\n",
        "<b>EXERCISE</b>: Read the `data/20000101_20161231-NO2.csv` file into a DataFrame `no2`\n",
        "<br><br>\n",
        "Some aspects about the file:\n",
        " <ul>\n",
        "  <li>Which separator is used in the file?</li>\n",
        "  <li>The second row includes unit information and should be skipped (check `skiprows` keyword)</li>\n",
        "  <li>For missing values, it uses the `'n/d'` notation (check `na_values` keyword)</li>\n",
        "  <li>We want to parse the 'timestamp' column as datetimes (check the `parse_dates` keyword)</li>\n",
        "</ul>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "clear_cell": true,
        "id": "8-0w-OhLYjDz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "no2 = airpoll_data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "clear_cell": false,
        "id": "rHYt13cRYjD2",
        "colab_type": "code",
        "outputId": "61a4bf8b-2518-4e39-884d-66fdc37f3103",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        }
      },
      "source": [
        "no2"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>BASCH</th>\n",
              "      <th>BONAP</th>\n",
              "      <th>PA18</th>\n",
              "      <th>VERS</th>\n",
              "      <th>weekday</th>\n",
              "      <th>weekend</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>timestamp</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>2000-01-01 01:00:00</th>\n",
              "      <td>108.0</td>\n",
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              "      <td>True</td>\n",
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              "      <th>2000-01-01 02:00:00</th>\n",
              "      <td>104.0</td>\n",
              "      <td>60.0</td>\n",
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              "      <td>42.0</td>\n",
              "      <td>5</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-01 03:00:00</th>\n",
              "      <td>97.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>5</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-01 04:00:00</th>\n",
              "      <td>77.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>57.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>5</td>\n",
              "      <td>True</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-31 20:00:00</th>\n",
              "      <td>73.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>5</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-31 21:00:00</th>\n",
              "      <td>61.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>5</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-31 22:00:00</th>\n",
              "      <td>57.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>5</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-31 23:00:00</th>\n",
              "      <td>51.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>5</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>149039 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                     BASCH  BONAP  PA18  VERS  weekday  weekend\n",
              "timestamp                                                      \n",
              "2000-01-01 01:00:00  108.0    NaN  65.0  47.0        5     True\n",
              "2000-01-01 02:00:00  104.0   60.0  77.0  42.0        5     True\n",
              "2000-01-01 03:00:00   97.0   58.0  73.0  34.0        5     True\n",
              "2000-01-01 04:00:00   77.0   52.0  57.0  29.0        5     True\n",
              "...                    ...    ...   ...   ...      ...      ...\n",
              "2016-12-31 20:00:00   73.0   51.0  49.0  20.0        5     True\n",
              "2016-12-31 21:00:00   61.0   51.0  48.0  16.0        5     True\n",
              "2016-12-31 22:00:00   57.0   49.0  45.0  14.0        5     True\n",
              "2016-12-31 23:00:00   51.0   47.0  45.0  12.0        5     True\n",
              "\n",
              "[149039 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 182
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FZAb9znCYjD4",
        "colab_type": "text"
      },
      "source": [
        "# 4. Exploration"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "id": "RaRIIF4pYjD4",
        "colab_type": "text"
      },
      "source": [
        "Some useful methods:\n",
        "\n",
        "`head` and `tail`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "slideshow": {
          "slide_type": "-"
        },
        "id": "uShgHQHDYjD5",
        "colab_type": "code",
        "outputId": "e83c42b1-4bb6-489b-b3fd-004be7ec786a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "no2.head(3)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr>\n",
              "      <th>2000-01-01 02:00:00</th>\n",
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              "    <tr>\n",
              "      <th>2000-01-01 03:00:00</th>\n",
              "      <td>97.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>5</td>\n",
              "      <td>True</td>\n",
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            "text/plain": [
              "                     BASCH  BONAP  PA18  VERS  weekday  weekend\n",
              "timestamp                                                      \n",
              "2000-01-01 01:00:00  108.0    NaN  65.0  47.0        5     True\n",
              "2000-01-01 02:00:00  104.0   60.0  77.0  42.0        5     True\n",
              "2000-01-01 03:00:00   97.0   58.0  73.0  34.0        5     True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 183
        }
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    {
      "cell_type": "code",
      "metadata": {
        "id": "e5YR44fdYjD9",
        "colab_type": "code",
        "outputId": "8114acc5-9bb8-4e5e-d249-74586abc8a45",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "no2.tail()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>VERS</th>\n",
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              "      <th>weekend</th>\n",
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              "    <tr>\n",
              "      <th>timestamp</th>\n",
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              "      <th></th>\n",
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              "  </thead>\n",
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              "      <th>2016-12-31 19:00:00</th>\n",
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              "    <tr>\n",
              "      <th>2016-12-31 20:00:00</th>\n",
              "      <td>73.0</td>\n",
              "      <td>51.0</td>\n",
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              "      <td>5</td>\n",
              "      <td>True</td>\n",
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              "    <tr>\n",
              "      <th>2016-12-31 21:00:00</th>\n",
              "      <td>61.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>5</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-31 22:00:00</th>\n",
              "      <td>57.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>5</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-31 23:00:00</th>\n",
              "      <td>51.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>5</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     BASCH  BONAP  PA18  VERS  weekday  weekend\n",
              "timestamp                                                      \n",
              "2016-12-31 19:00:00   77.0   49.0  52.0  23.0        5     True\n",
              "2016-12-31 20:00:00   73.0   51.0  49.0  20.0        5     True\n",
              "2016-12-31 21:00:00   61.0   51.0  48.0  16.0        5     True\n",
              "2016-12-31 22:00:00   57.0   49.0  45.0  14.0        5     True\n",
              "2016-12-31 23:00:00   51.0   47.0  45.0  12.0        5     True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 184
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "id": "3vj9hDGRYjD_",
        "colab_type": "text"
      },
      "source": [
        "`info()`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yWP4nsENYjEB",
        "colab_type": "code",
        "outputId": "13b63f87-3dc0-4614-c8c0-c0775a4080f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "no2.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 149039 entries, 2000-01-01 01:00:00 to 2016-12-31 23:00:00\n",
            "Data columns (total 6 columns):\n",
            "BASCH      139949 non-null float64\n",
            "BONAP      136493 non-null float64\n",
            "PA18       142259 non-null float64\n",
            "VERS       143813 non-null float64\n",
            "weekday    149039 non-null int64\n",
            "weekend    149039 non-null bool\n",
            "dtypes: bool(1), float64(4), int64(1)\n",
            "memory usage: 12.0 MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "slideshow": {
          "slide_type": "subslide"
        },
        "id": "CvzgPubuYjED",
        "colab_type": "text"
      },
      "source": [
        "Getting some basic summary statistics about the data with `describe`:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H8ClsXvhYjEE",
        "colab_type": "code",
        "outputId": "a95a6056-9e49-4480-8715-f9f9552aad8c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "no2.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>BASCH</th>\n",
              "      <th>BONAP</th>\n",
              "      <th>PA18</th>\n",
              "      <th>VERS</th>\n",
              "      <th>weekday</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>139949.000000</td>\n",
              "      <td>136493.000000</td>\n",
              "      <td>142259.000000</td>\n",
              "      <td>143813.000000</td>\n",
              "      <td>149039.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>89.270098</td>\n",
              "      <td>64.001714</td>\n",
              "      <td>45.104211</td>\n",
              "      <td>27.613227</td>\n",
              "      <td>3.000309</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>36.772855</td>\n",
              "      <td>27.866767</td>\n",
              "      <td>23.212719</td>\n",
              "      <td>19.604953</td>\n",
              "      <td>2.000007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>63.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>87.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>112.000000</td>\n",
              "      <td>81.000000</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>358.000000</td>\n",
              "      <td>345.000000</td>\n",
              "      <td>306.000000</td>\n",
              "      <td>197.000000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               BASCH          BONAP  ...           VERS        weekday\n",
              "count  139949.000000  136493.000000  ...  143813.000000  149039.000000\n",
              "mean       89.270098      64.001714  ...      27.613227       3.000309\n",
              "std        36.772855      27.866767  ...      19.604953       2.000007\n",
              "min         4.000000       0.000000  ...       0.000000       0.000000\n",
              "25%        63.000000      44.000000  ...      13.000000       1.000000\n",
              "50%        87.000000      62.000000  ...      22.000000       3.000000\n",
              "75%       112.000000      81.000000  ...      38.000000       5.000000\n",
              "max       358.000000     345.000000  ...     197.000000       6.000000\n",
              "\n",
              "[8 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 186
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "id": "0qxOPfsVYjEI",
        "colab_type": "text"
      },
      "source": [
        "Quickly visualizing the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "slideshow": {
          "slide_type": "-"
        },
        "id": "0wvYyBGCYjEJ",
        "colab_type": "code",
        "outputId": "a4f9e46d-2917-4c55-c232-ebb1eb79d55d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "no2.plot(kind='box', ylim=[0,250])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9fffbe3278>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 187
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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66D8l6am0a6c6bTsZeCFnmZ1p2zEkXStps6TNe/bsGUYZZpZvVVVVrF27ljfffJO1a9dS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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "id": "SfydDszZYjEO",
        "colab_type": "code",
        "outputId": "29b6ea08-7129-4f1b-ef48-fb2bc2a7f17d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "no2['BASCH'].plot(kind='hist', bins=50)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9fffb60cc0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 188
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HQTzkeAnYjEQ",
        "colab_type": "text"
      },
      "source": [
        "<div class=\"alert alert-success\">\n",
        "\n",
        "<b>EXERCISE</b>: \n",
        "\n",
        " <ul>\n",
        "  <li>Plot the age distribution of the titanic passengers</li>\n",
        "</ul>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "clear_cell": true,
        "id": "ZpHXNyfuYjER",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# code here"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VpJVvI9XYjEU",
        "colab_type": "text"
      },
      "source": [
        "The default plot (when not specifying `kind`) is a line plot of all columns:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "id": "0YqxWq7hYjEV",
        "colab_type": "code",
        "outputId": "0290128d-7bde-465a-f772-2204ffc1ff7b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        }
      },
      "source": [
        "no2.plot(figsize=(12,6))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9fffc34320>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 190
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HIM/xuZIxlgqgp8QhYwAs4pzXAzjBGMsAcBWAXX6l+foa+98p5X53JQjAN2gA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\ny5ZwX5RqPz6wO2rzPdfa+VIhbZPsq3HRg+23a4jYXnU9LZYrRIERNN/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3O0n4jYTfRMKT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jn1dXi8qgJvlJZghaBcIhAAp+h5zdheMwx0XeKsz1fTt+Ef+gfYjczKvSMDv77fS5ub+7uUbPKk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\nDQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7jbMrW0oYjEa",
        "colab_type": "text"
      },
      "source": [
        "This does not say too much .."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "id": "QziH1e9AYjEa",
        "colab_type": "text"
      },
      "source": [
        "We can select part of the data (eg the latest 500 data points):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uXymm63YYjEb",
        "colab_type": "code",
        "outputId": "8ac69799-0426-46df-a2f7-d4fdf076d1a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        }
      },
      "source": [
        "no2[-500:].plot(figsize=(12,6))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9fffa61be0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 191
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XXsH27dsxffp0XHzxxVixYkXO77zzzjs49dRT8eabbzo+5wc/+EG88sor+MpX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sWWtBzYHrhOOxg1S4Qku8FLRFFwK8mlBSAxDr1cApdIAFtfCmbhhwDTXUlmWcZeMHRt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p1ceFXRcS9UU9oHRT/9UELIvvRRyw74Lk4z+Ar74RLIuwX2U0GeaFZiC5oLXWI7ta4b6L2f/M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\nNUfatAhEtuZJtldWwDT9rclWiF57LZFrrkZJJpE0jeQ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ukVzhnWhAbG2eCyRJkOuqqzIo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GjZy5elKOYkC4KALPvYHDc6lAOOAFokXNCuVxoayQwPkzx0iNiOHWjj5YlkuaoKx9KlZYdZ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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F3attgD8YjEd",
        "colab_type": "text"
      },
      "source": [
        "Or we can use some more advanced time series features -> see further in this notebook!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7s5l-i4VYjEe",
        "colab_type": "text"
      },
      "source": [
        "# 5. Selecting and filtering data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6a9IHU57YjEe",
        "colab_type": "text"
      },
      "source": [
        "<div class=\"alert alert-warning\">\n",
        "<b>ATTENTION!</b>: <br><br>\n",
        "\n",
        "One of pandas' basic features is the labeling of rows and columns, but this makes indexing also a bit more complex compared to numpy. <br><br> We now have to distuinguish between:\n",
        "\n",
        " <ul>\n",
        "  <li>selection by **label**</li>\n",
        "  <li>selection by **position**</li>\n",
        "</ul>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rZAVr7euYjEh",
        "colab_type": "text"
      },
      "source": [
        "### `df[]` provides some convenience shortcuts "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "id": "C4JGOzLsYjEj",
        "colab_type": "text"
      },
      "source": [
        "For a DataFrame, basic indexing selects the columns.\n",
        "\n",
        "Selecting a single column:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OukhWTFkDvzk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = titanic_data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h7Hc28LtYjEj",
        "colab_type": "code",
        "outputId": "836e01f3-ac50-44cb-9153-ea36c62cc309",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "df['Age']"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      22.0\n",
              "1      38.0\n",
              "2      26.0\n",
              "3      35.0\n",
              "       ... \n",
              "887    19.0\n",
              "888     NaN\n",
              "889    26.0\n",
              "890    32.0\n",
              "Name: Age, Length: 891, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 193
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "id": "YeP_A1LUYjEl",
        "colab_type": "text"
      },
      "source": [
        "or multiple columns:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0vbt6jMYYjEm",
        "colab_type": "code",
        "outputId": "0cb6c07e-2c16-40d2-8df6-1fa300f81fca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        }
      },
      "source": [
        "df[['Age', 'Fare']]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Age</th>\n",
              "      <th>Fare</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>22.0</td>\n",
              "      <td>7.2500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>38.0</td>\n",
              "      <td>71.2833</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>26.0</td>\n",
              "      <td>7.9250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>35.0</td>\n",
              "      <td>53.1000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>19.0</td>\n",
              "      <td>30.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>NaN</td>\n",
              "      <td>23.4500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>26.0</td>\n",
              "      <td>30.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>32.0</td>\n",
              "      <td>7.7500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Age     Fare\n",
              "0    22.0   7.2500\n",
              "1    38.0  71.2833\n",
              "2    26.0   7.9250\n",
              "3    35.0  53.1000\n",
              "..    ...      ...\n",
              "887  19.0  30.0000\n",
              "888   NaN  23.4500\n",
              "889  26.0  30.0000\n",
              "890  32.0   7.7500\n",
              "\n",
              "[891 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 194
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "id": "-u-wYycjYjEo",
        "colab_type": "text"
      },
      "source": [
        "But, slicing accesses the rows:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dTwga9NJYjEp",
        "colab_type": "code",
        "outputId": "4285a9e0-a399-46d8-be6b-1c0ab7563a9b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df[10:15]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
              "      <td>female</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>PP 9549</td>\n",
              "      <td>16.7000</td>\n",
              "      <td>G6</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Bonnell, Miss. Elizabeth</td>\n",
              "      <td>female</td>\n",
              "      <td>58.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>113783</td>\n",
              "      <td>26.5500</td>\n",
              "      <td>C103</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Saundercock, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5. 2151</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Andersson, Mr. Anders Johan</td>\n",
              "      <td>male</td>\n",
              "      <td>39.0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>347082</td>\n",
              "      <td>31.2750</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
              "      <td>female</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>350406</td>\n",
              "      <td>7.8542</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "10           11         1       3  ...  16.7000    G6         S\n",
              "11           12         1       1  ...  26.5500  C103         S\n",
              "12           13         0       3  ...   8.0500   NaN         S\n",
              "13           14         0       3  ...  31.2750   NaN         S\n",
              "14           15         0       3  ...   7.8542   NaN         S\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 195
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "id": "L8yNeqmnYjEr",
        "colab_type": "text"
      },
      "source": [
        "### Systematic indexing with `loc` and `iloc`\n",
        "\n",
        "When using `[]` like above, you can only select from one axis at once (rows or columns, not both). For more advanced indexing, you have some extra attributes:\n",
        "    \n",
        "* `loc`: selection by label\n",
        "* `iloc`: selection by position"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vKb6Gn5NYjEs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = df.set_index('Name')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8yD5wnWkYjEu",
        "colab_type": "code",
        "outputId": "1840ac2d-1e16-49a1-a66d-9b3833c4a6b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df.loc['Bonnell, Miss. Elizabeth', 'Fare']"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "26.55"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 197
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "WRGmdYcxYjEw",
        "colab_type": "code",
        "outputId": "9078a0de-91eb-4402-a112-5fabc086cb5a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "df.loc['Bonnell, Miss. Elizabeth':'Andersson, Mr. Anders Johan', :]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Bonnell, Miss. Elizabeth</th>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>58.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>113783</td>\n",
              "      <td>26.550</td>\n",
              "      <td>C103</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Saundercock, Mr. William Henry</th>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5. 2151</td>\n",
              "      <td>8.050</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Andersson, Mr. Anders Johan</th>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>39.0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>347082</td>\n",
              "      <td>31.275</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                PassengerId  Survived  ...  Cabin Embarked\n",
              "Name                                                   ...                \n",
              "Bonnell, Miss. Elizabeth                 12         1  ...   C103        S\n",
              "Saundercock, Mr. William Henry           13         0  ...    NaN        S\n",
              "Andersson, Mr. Anders Johan              14         0  ...    NaN        S\n",
              "\n",
              "[3 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 198
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "id": "CDPg_TyIYjEy",
        "colab_type": "text"
      },
      "source": [
        "Selecting by position with `iloc` works similar as indexing numpy arrays:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v_-hrXyjYjEz",
        "colab_type": "code",
        "outputId": "e2da3ea0-e447-4618-c74b-c41de350fe98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        }
      },
      "source": [
        "df.iloc[0:2,1:3]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Braund, Mr. Owen Harris</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cumings, Mrs. John Bradley (Florence Briggs Thayer)</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                    Survived  Pclass\n",
              "Name                                                                \n",
              "Braund, Mr. Owen Harris                                    0       3\n",
              "Cumings, Mrs. John Bradley (Florence Briggs Tha...         1       1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 199
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "id": "33S4CY2JYjE1",
        "colab_type": "text"
      },
      "source": [
        "The different indexing methods can also be used to assign data:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mVbIrFuuYjE1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.loc['Braund, Mr. Owen Harris', 'Survived'] = 100"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jkqAZN9RYjE4",
        "colab_type": "code",
        "outputId": "00dff474-32d3-4f94-9674-d29339983ad9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "    }\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>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Braund, Mr. Owen Harris</th>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cumings, Mrs. John Bradley (Florence Briggs Thayer)</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Heikkinen, Miss. Laina</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Futrelle, Mrs. Jacques Heath (Lily May Peel)</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Graham, Miss. Margaret Edith</th>\n",
              "      <td>888</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>112053</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>B42</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Johnston, Miss. Catherine Helen \"Carrie\"</th>\n",
              "      <td>889</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>W./C. 6607</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Behr, Mr. Karl Howell</th>\n",
              "      <td>890</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>male</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>111369</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>C148</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dooley, Mr. Patrick</th>\n",
              "      <td>891</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>370376</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                    PassengerId  ...  Embarked\n",
              "Name                                                             ...          \n",
              "Braund, Mr. Owen Harris                                       1  ...         S\n",
              "Cumings, Mrs. John Bradley (Florence Briggs Tha...            2  ...         C\n",
              "Heikkinen, Miss. Laina                                        3  ...         S\n",
              "Futrelle, Mrs. Jacques Heath (Lily May Peel)                  4  ...         S\n",
              "...                                                         ...  ...       ...\n",
              "Graham, Miss. Margaret Edith                                888  ...         S\n",
              "Johnston, Miss. Catherine Helen \"Carrie\"                    889  ...         S\n",
              "Behr, Mr. Karl Howell                                       890  ...         C\n",
              "Dooley, Mr. Patrick                                         891  ...         Q\n",
              "\n",
              "[891 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 201
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f8X_D4-8YjE7",
        "colab_type": "text"
      },
      "source": [
        "### Boolean indexing (filtering)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0g4IfpcpYjE8",
        "colab_type": "text"
      },
      "source": [
        "Often, you want to select rows based on a certain condition. This can be done with 'boolean indexing' (like a where clause in SQL) and comparable to numpy. \n",
        "\n",
        "The indexer (or boolean mask) should be 1-dimensional and the same length as the thing being indexed."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "run_control": {
          "frozen": false,
          "read_only": false
        },
        "id": "IshtVoBjYjE8",
        "colab_type": "code",
        "outputId": "bf4e85a0-ece9-4441-d6ff-36fd02c12e95",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df['Fare'] > 50"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Name\n",
              "Braund, Mr. Owen Harris                                False\n",
              "Cumings, Mrs. John Bradley (Florence Briggs Thayer)     True\n",
              "Heikkinen, Miss. Laina                                 False\n",
              "Futrelle, Mrs. Jacques Heath (Lily May Peel)            True\n",
              "                                                       ...  \n",
              "Graham, Miss. Margaret Edith                           False\n",
              "Johnston, Miss. Catherine Helen \"Carrie\"               False\n",
              "Behr, Mr. Karl Howell                                  False\n",
              "Dooley, Mr. Patrick                                    False\n",
              "Name: Fare, Length: 891, dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 202
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "run_control": {
          "frozen": false,
          "read_only": false
        },
        "scrolled": false,
        "id": "Cc_rqzaOYjFA",
        "colab_type": "code",
        "outputId": "ad28f2b2-d11c-475c-9b13-4d09e646aeaa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        }
      },
      "source": [
        "df[df['Fare'] > 50]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Cumings, Mrs. John Bradley (Florence Briggs Thayer)</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Futrelle, Mrs. Jacques Heath (Lily May Peel)</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>McCarthy, Mr. Timothy J</th>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>male</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>17463</td>\n",
              "      <td>51.8625</td>\n",
              "      <td>E46</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fortune, Mr. Charles Alexander</th>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>male</td>\n",
              "      <td>19.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>19950</td>\n",
              "      <td>263.0000</td>\n",
              "      <td>C23 C25 C27</td>\n",
              "      <td>S</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sage, Miss. Dorothy Edith \"Dolly\"</th>\n",
              "      <td>864</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>CA. 2343</td>\n",
              "      <td>69.5500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Roebling, Mr. Washington Augustus II</th>\n",
              "      <td>868</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>male</td>\n",
              "      <td>31.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17590</td>\n",
              "      <td>50.4958</td>\n",
              "      <td>A24</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Beckwith, Mrs. Richard Leonard (Sallie Monypeny)</th>\n",
              "      <td>872</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>47.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>11751</td>\n",
              "      <td>52.5542</td>\n",
              "      <td>D35</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)</th>\n",
              "      <td>880</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>56.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>11767</td>\n",
              "      <td>83.1583</td>\n",
              "      <td>C50</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>160 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                    PassengerId  ...  Embarked\n",
              "Name                                                             ...          \n",
              "Cumings, Mrs. John Bradley (Florence Briggs Tha...            2  ...         C\n",
              "Futrelle, Mrs. Jacques Heath (Lily May Peel)                  4  ...         S\n",
              "McCarthy, Mr. Timothy J                                       7  ...         S\n",
              "Fortune, Mr. Charles Alexander                               28  ...         S\n",
              "...                                                         ...  ...       ...\n",
              "Sage, Miss. Dorothy Edith \"Dolly\"                           864  ...         S\n",
              "Roebling, Mr. Washington Augustus II                        868  ...         S\n",
              "Beckwith, Mrs. Richard Leonard (Sallie Monypeny)            872  ...         S\n",
              "Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)               880  ...         C\n",
              "\n",
              "[160 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 203
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wYJwzI4RYjFC",
        "colab_type": "text"
      },
      "source": [
        "<div class=\"alert alert-success\">\n",
        "\n",
        "<b>EXERCISE</b>:\n",
        "\n",
        " <ul>\n",
        "  <li>Based on the titanic data set, select all rows for male passengers and calculate the mean age of those passengers. Do the same for the female passengers</li>\n",
        "</ul>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O-QuxIafYjFD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = titanic_data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "clear_cell": true,
        "id": "Nva8CTBuYjFF",
        "colab_type": "code",
        "outputId": "f8b87c08-0fb5-493f-9287-b6cfcb53e5bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df[df['Sex'] == 'male']['Age'].mean()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30.72664459161148"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 205
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "clear_cell": true,
        "id": "07SfwWaSYjFI",
        "colab_type": "code",
        "outputId": "2ae40a1a-d4e4-4621-e674-2aa67d06e196",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df[df['Sex'] == 'female']['Age'].mean()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "27.915708812260537"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 206
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vqa7r8AQYjFK",
        "colab_type": "text"
      },
      "source": [
        "<div class=\"alert alert-success\">\n",
        "\n",
        "<b>EXERCISE</b>:\n",
        "\n",
        " <ul>\n",
        "  <li>Based on the titanic data set, how many passengers older than 70 were on the Titanic?</li>\n",
        "</ul>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "syshX8h_Jayj",
        "colab_type": "code",
        "outputId": "6eb11594-da42-4904-b27e-847f29d7ee1a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df[df['Age'] > 70].shape[0]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 207
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h6UUTRujYjFN",
        "colab_type": "text"
      },
      "source": [
        "# 6. The group-by operation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LS1_F37iYjFO",
        "colab_type": "text"
      },
      "source": [
        "### Some 'theory': the groupby operation (split-apply-combine)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "run_control": {
          "frozen": false,
          "read_only": false
        },
        "id": "8KXdnaXDYjFP",
        "colab_type": "code",
        "outputId": "a8cd4979-4e3b-451c-833e-03f49fccd1d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        }
      },
      "source": [
        "df = pd.DataFrame({'key':['A','B','C','A','B','C','A','B','C'],\n",
        "                   'data': [0, 5, 10, 5, 10, 15, 10, 15, 20]})\n",
        "df"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th>0</th>\n",
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              "      <th>2</th>\n",
              "      <td>C</td>\n",
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              "      <th>3</th>\n",
              "      <td>A</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>C</td>\n",
              "      <td>15</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
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              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>B</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>C</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   key  data\n",
              "0    A     0\n",
              "1    B     5\n",
              "2    C    10\n",
              "3    A     5\n",
              "..  ..   ...\n",
              "5    C    15\n",
              "6    A    10\n",
              "7    B    15\n",
              "8    C    20\n",
              "\n",
              "[9 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 208
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-d30mzN-YjFQ",
        "colab_type": "text"
      },
      "source": [
        "### Recap: aggregating functions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qAYxf-HDYjFR",
        "colab_type": "text"
      },
      "source": [
        "When analyzing data, you often calculate summary statistics (aggregations like the mean, max, ...). As we have seen before, we can easily calculate such a statistic for a Series or column using one of the many available methods. For example:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "run_control": {
          "frozen": false,
          "read_only": false
        },
        "id": "e73WL1-rYjFT",
        "colab_type": "code",
        "outputId": "b33801d0-1c61-4d4b-9827-11a10bb3bb6e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df['data'].sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "90"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 209
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qQSGADagYjFV",
        "colab_type": "text"
      },
      "source": [
        "However, in many cases your data has certain groups in it, and in that case, you may want to calculate this statistic for each of the groups.\n",
        "\n",
        "For example, in the above dataframe `df`, there is a column 'key' which has three possible values: 'A', 'B' and 'C'. When we want to calculate the sum for each of those groups, we could do the following:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "run_control": {
          "frozen": false,
          "read_only": false
        },
        "id": "ojUwW_jcYjFW",
        "colab_type": "code",
        "outputId": "92d3303b-ef48-4618-887d-943f7413d5a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "for key in ['A', 'B', 'C']:\n",
        "    print(key, df[df['key'] == key]['data'].sum())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "A 15\n",
            "B 30\n",
            "C 45\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lITpUcJRYjFY",
        "colab_type": "text"
      },
      "source": [
        "This becomes very verbose when having multiple groups. You could make the above a bit easier by looping over the different values, but still, it is not very convenient to work with.\n",
        "\n",
        "What we did above, applying a function on different groups, is a \"groupby operation\", and pandas provides some convenient functionality for this."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V6QjSnRgYjFY",
        "colab_type": "text"
      },
      "source": [
        "### Groupby: applying functions per group"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "id": "yMnak_nTYjFZ",
        "colab_type": "text"
      },
      "source": [
        "The \"group by\" concept: we want to **apply the same function on subsets of your dataframe, based on some key to split the dataframe in subsets**\n",
        "\n",
        "This operation is also referred to as the \"split-apply-combine\" operation, involving the following steps:\n",
        "\n",
        "* **Splitting** the data into groups based on some criteria\n",
        "* **Applying** a function to each group independently\n",
        "* **Combining** the results into a data structure\n",
        "\n",
        "<img src=\"img/splitApplyCombine.png\">\n",
        "\n",
        "Similar to SQL `GROUP BY`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GUkASUZYYjFa",
        "colab_type": "text"
      },
      "source": [
        "Instead of doing the manual filtering as above\n",
        "\n",
        "\n",
        "    df[df['key'] == \"A\"].sum()\n",
        "    df[df['key'] == \"B\"].sum()\n",
        "    ...\n",
        "\n",
        "pandas provides the `groupby` method to do exactly this:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "run_control": {
          "frozen": false,
          "read_only": false
        },
        "id": "brWpZujPYjFb",
        "colab_type": "code",
        "outputId": "b0718ed8-d32d-44f2-a3de-5f9f8711de81",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "df.groupby('key').sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>data</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>key</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>A</th>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C</th>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     data\n",
              "key      \n",
              "A      15\n",
              "B      30\n",
              "C      45"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 211
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "run_control": {
          "frozen": false,
          "read_only": false
        },
        "slideshow": {
          "slide_type": "subslide"
        },
        "id": "kxBqX9gbYjFd",
        "colab_type": "code",
        "outputId": "b4126e4f-73bb-4ee9-96ee-f96fdb65a04b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "df.groupby('key').aggregate(np.sum)  # 'sum'"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>data</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>key</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>A</th>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C</th>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     data\n",
              "key      \n",
              "A      15\n",
              "B      30\n",
              "C      45"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 212
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hgc3PXzdYjFg",
        "colab_type": "text"
      },
      "source": [
        "And many more methods are available. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "run_control": {
          "frozen": false,
          "read_only": false
        },
        "id": "xLGILFvQYjFh",
        "colab_type": "code",
        "outputId": "10998719-4370-4e53-ffbe-37d9c385a786",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "df.groupby('key')['data'].sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "key\n",
              "A    15\n",
              "B    30\n",
              "C    45\n",
              "Name: data, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 213
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "id": "1j0tuUP1YjFi",
        "colab_type": "text"
      },
      "source": [
        "### Application of the groupby concept on the titanic data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NM0o34aBYjFi",
        "colab_type": "text"
      },
      "source": [
        "We go back to the titanic passengers survival data:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "InOHVMqSLgWf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = titanic_data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "run_control": {
          "frozen": false,
          "read_only": false
        },
        "id": "IOUuVzvlYjFm",
        "colab_type": "code",
        "outputId": "9cc8a126-cf91-4d53-d050-1b69fbf3e96c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "0            1         0       3  ...   7.2500   NaN         S\n",
              "1            2         1       1  ...  71.2833   C85         C\n",
              "2            3         1       3  ...   7.9250   NaN         S\n",
              "3            4         1       1  ...  53.1000  C123         S\n",
              "4            5         0       3  ...   8.0500   NaN         S\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 215
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cey0LXX2YjFn",
        "colab_type": "text"
      },
      "source": [
        "<div class=\"alert alert-success\">\n",
        "\n",
        "<b>EXERCISE</b>:\n",
        "\n",
        " <ul>\n",
        "  <li>Calculate the average age for each sex again, but now using groupby.</li>\n",
        "</ul>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "clear_cell": true,
        "run_control": {
          "frozen": false,
          "read_only": false
        },
        "id": "l6IVixKcYjFn",
        "colab_type": "code",
        "outputId": "66174a9a-335b-4c88-89e2-634dda969790",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "df.groupby('Sex')['Age'].mean()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sex\n",
              "female    27.915709\n",
              "male      30.726645\n",
              "Name: Age, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 216
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dzEQn6OFYjFp",
        "colab_type": "text"
      },
      "source": [
        "<div class=\"alert alert-success\">\n",
        "\n",
        "<b>EXERCISE</b>:\n",
        "\n",
        " <ul>\n",
        "  <li>Calculate the average survival ratio for all passengers.</li>\n",
        "</ul>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "clear_cell": true,
        "run_control": {
          "frozen": false,
          "read_only": false
        },
        "id": "_UM-Kxw2YjFq",
        "colab_type": "code",
        "outputId": "f7b74bac-e96e-4cdd-9e0b-9c16444f278c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "df.groupby('Sex')['Survived'].sum()/df['Survived'].sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sex\n",
              "female    0.681287\n",
              "male      0.318713\n",
              "Name: Survived, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 217
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "89mGoJAoYjFr",
        "colab_type": "text"
      },
      "source": [
        "<div class=\"alert alert-success\">\n",
        "\n",
        "<b>EXERCISE</b>:\n",
        "\n",
        " <ul>\n",
        "  <li>Calculate this survival ratio for all passengers younger that 25 (remember: filtering/boolean indexing).</li>\n",
        "</ul>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "clear_cell": true,
        "run_control": {
          "frozen": false,
          "read_only": false
        },
        "scrolled": true,
        "id": "IzHZr4hUYjFs",
        "colab_type": "code",
        "outputId": "0f51db0a-9ac6-4e15-eda9-b92168f4143f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "df[df['Age'] < 25].groupby('Sex')['Survived'].sum()/df[df['Age'] < 25]['Survived'].sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sex\n",
              "female    0.720339\n",
              "male      0.279661\n",
              "Name: Survived, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 218
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ywnmbxAtYjFt",
        "colab_type": "text"
      },
      "source": [
        "<div class=\"alert alert-success\">\n",
        "\n",
        "<b>EXERCISE</b>:\n",
        "\n",
        " <ul>\n",
        "  <li>What is the difference in the survival ratio between the sexes?</li>\n",
        "</ul>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "clear_cell": true,
        "run_control": {
          "frozen": false,
          "read_only": false
        },
        "id": "4G8jlBXEYjFt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Code here"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "es5p8VVAYjFv",
        "colab_type": "text"
      },
      "source": [
        "<div class=\"alert alert-success\">\n",
        "\n",
        "<b>EXERCISE</b>:\n",
        "\n",
        " <ul>\n",
        "  <li>Or how does it differ between the different classes? Make a bar plot visualizing the survival ratio for the 3 classes.</li>\n",
        "</ul>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "clear_cell": true,
        "id": "v__Q6ocgYjFv",
        "colab_type": "code",
        "outputId": "6a768e3c-3305-46e0-de83-ab7979ce0c6c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "df[df['Age'] < 25].groupby('Pclass')['Survived'].aggregate(lambda x: x.sum() / len(x)).plot(kind='bar')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9ffc52b908>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 220
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YiZu0ejfYjFy",
        "colab_type": "text"
      },
      "source": [
        "<div class=\"alert alert-success\">\n",
        "\n",
        "<b>EXERCISE</b>:\n",
        "\n",
        " <ul>\n",
        "  <li>Make a bar plot to visualize the average Fare payed by people depending on their age. The age column is devided is separate classes using the `pd.cut` function as provided below.</li>\n",
        "</ul>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "clear_cell": false,
        "run_control": {
          "frozen": false,
          "read_only": false
        },
        "id": "GeIbLBUeYjFz",
        "colab_type": "code",
        "outputId": "30f93614-f229-4012-a13a-70da5f70486a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        }
      },
      "source": [
        "df['AgeClass'] = pd.cut(df['Age'], bins=np.arange(0,90,10))\n",
        "\n",
        "df.groupby('AgeClass')['Fare'].aggregate(lambda x: x.sum() / len(x)).plot(kind='bar')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9ffc49de80>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 221
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gyeV0e+S/BFwD3K+qtvffYr1w5rVeL1M7KbqV7tNwvqsb9l3nLIvZe+afW1X1vv5yyw/0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\nXZIaYaFLUiP+P2nkdSU+GnP7AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "id": "lq6TKKtiYjF1",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "## Further reading\n",
        "\n",
        "* Pandas documentation: http://pandas.pydata.org/pandas-docs/stable/\n",
        "\n",
        "\n",
        "*  Blog\n",
        "\n",
        "  * https://tomaugspurger.github.io/modern-1.html"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yBUljlRxYjF1",
        "colab_type": "text"
      },
      "source": [
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0yPDP5BrYjF2",
        "colab_type": "text"
      },
      "source": [
        "## Make a simple logistic Regression Model\n",
        "* The model would be predicting whether a person with a certain attributes would survive or not\n",
        "* Train with 70 percent of the data and take the y column as Survived or not\n",
        "* You **CAN NOT** use any library to make the logistic regression model, you can use only numpy and pandas. The model has to be built from the scratch\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ZvAIg0Wn_Q6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = titanic_data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H8Y41_7amXWq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = df.drop(axis = 1,columns=['PassengerId','Name','Ticket','Cabin'])\n",
        "\n",
        "df.isnull().sum(axis=0).head(13)\n",
        "\n",
        "df.fillna(df.mean(), inplace=True)\n",
        "\n",
        "df['Embarked'] = df['Embarked'].fillna('S')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l7e9MM7znglI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cat = pd.get_dummies(df.select_dtypes('object'))\n",
        "\n",
        "df = pd.concat([df,cat],axis=1)\n",
        "\n",
        "df = df.drop(axis = 1,columns=['Sex','Embarked'])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8o0anfdbqgNL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y = df[['Survived']]\n",
        "df = df.drop('Survived',axis = 1)\n",
        "\n",
        "y_train = y[:int(0.7*len(y))]\n",
        "y_test = y[int(0.7*len(y)):]\n",
        "\n",
        "x_train = df[:int(0.7*len(df))]\n",
        "x_test = df[int(0.7*len(df)):]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1iJmtdWQYjF2",
        "colab_type": "code",
        "outputId": "8abbc9c2-8fa8-4ea3-fc05-72e8fe3a42f3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        }
      },
      "source": [
        "w = np.ones((len(df.columns),1))\n",
        "bais = 1\n",
        "alpha = 0.05\n",
        "\n",
        "def sig(p):\n",
        "  return(1/(1+np.exp(-p)))\n",
        "\n",
        "for i in range(x_train.shape[0]):\n",
        "  f = np.array(x_train[i:i+1])\n",
        "  y = np.dot(f,w) + bais\n",
        "  p = sig(y)\n",
        "  if y_train.iloc[i,0] == 1:\n",
        "    for j in range(len(x_train.columns)):\n",
        "      w[j][0] = w[j][0] + alpha*(f[0][j]/(1+np.exp(y)))\n",
        "    bais = bais - alpha*(1/(1+np.exp(y)))\n",
        "  else:\n",
        "    for j in range(len(x_train.columns)):\n",
        "      w[j][0] = w[j][0] + alpha*(-f[0][j]/(1+np.exp(-y)))\n",
        "    bais = bais - alpha*(-1/(1+np.exp(-y)))\n",
        "\n",
        "print(w,bais)\n",
        "  \n",
        "  "
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: RuntimeWarning: overflow encountered in exp\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:18: RuntimeWarning: overflow encountered in exp\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:19: RuntimeWarning: overflow encountered in exp\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: RuntimeWarning: overflow encountered in exp\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:15: RuntimeWarning: overflow encountered in exp\n",
            "  from ipykernel import kernelapp as app\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[[ 0.35708735]\n",
            " [-0.48945139]\n",
            " [-0.40572912]\n",
            " [ 0.3494775 ]\n",
            " [ 4.14628552]\n",
            " [ 4.38066873]\n",
            " [-2.18539728]\n",
            " [ 1.54151483]\n",
            " [ 1.22911985]\n",
            " [ 0.42463678]] [[0.80472854]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uGUL08uvYhKE",
        "colab_type": "code",
        "outputId": "374dcbaf-12ba-4024-987a-4fb38da55d80",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "source": [
        "tp=0\n",
        "fp=0\n",
        "tn=0\n",
        "fn=0\n",
        "y_pred = y_test\n",
        "\n",
        "for i in range(x_test.shape[0]):\n",
        "  f = np.array(x_test[i:i+1])\n",
        "  y = np.dot(f,w) + bais\n",
        "  p = sig(y)\n",
        "  if p > 0.5 :\n",
        "    if y_test.iloc[i,0] == 1:\n",
        "      tp+=1\n",
        "    else:\n",
        "      fp+=1\n",
        "    y_pred.iloc[i,0] = 1\n",
        "  else:\n",
        "    if y_test.iloc[i,0] == 1:\n",
        "      fn+=1\n",
        "    else:\n",
        "      tn+=1\n",
        "    y_pred.iloc[i,0] = 0\n",
        "      \n",
        "print(tp,tn,fp,fn)\n",
        "\n",
        "\n",
        "from sklearn import metrics\n",
        "y_test = np.array(y_test)\n",
        "y_pred = np.array(y_pred)\n",
        "fpr, tpr, thresholds = metrics.roc_curve(y_pred, y_test, pos_label=2)\n",
        "metrics.auc(fpr, tpr)\n"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "error",
          "ename": "IndexError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-64-1676106e8b62>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     14\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     15\u001b[0m       \u001b[0mfp\u001b[0m\u001b[0;34m+=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m     \u001b[0my_pred\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\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     18\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\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[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m    187\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    188\u001b[0m             \u001b[0mkey\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--> 189\u001b[0;31m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_setitem_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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    190\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setitem_with_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    191\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_setitem_indexer\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    165\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\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[1;32m    166\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--> 167\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_setter\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    168\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mIndexingError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    169\u001b[0m                 \u001b[0;32mpass\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/core/indexing.py\u001b[0m in \u001b[0;36m_convert_tuple\u001b[0;34m(self, key, is_setter)\u001b[0m\n\u001b[1;32m    246\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mi\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[0mndim\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    247\u001b[0m                     \u001b[0;32mraise\u001b[0m \u001b[0mIndexingError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Too many indexers'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 248\u001b[0;31m                 \u001b[0midx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert_to_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_setter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mis_setter\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    249\u001b[0m                 \u001b[0mkeyidx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    250\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyidx\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/core/indexing.py\u001b[0m in \u001b[0;36m_convert_to_indexer\u001b[0;34m(self, obj, axis, is_setter)\u001b[0m\n\u001b[1;32m   2245\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2246\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-> 2247\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_key\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\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   2248\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2249\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\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/core/indexing.py\u001b[0m in \u001b[0;36m_validate_key\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   2068\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2069\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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-> 2070\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_integer\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   2071\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\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[1;32m   2072\u001b[0m             \u001b[0;31m# a tuple should already have been caught by this point\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/core/indexing.py\u001b[0m in \u001b[0;36m_validate_integer\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   2137\u001b[0m         \u001b[0mlen_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\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[0m_get_axis\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2138\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mlen_axis\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mlen_axis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2139\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"single positional indexer is out-of-bounds\"\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   2140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2141\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtup\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;31mIndexError\u001b[0m: single positional indexer is out-of-bounds"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o2yTeXci8LHE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "outputId": "98a8481e-3c3d-44b5-e0fe-010cfff9366e"
      },
      "source": [
        "df_ = pd.DataFrame(columns=['Survived'])\n",
        "df_"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Survived]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "poONIGzyYjF3",
        "colab_type": "text"
      },
      "source": [
        "## Make a linear Regression model\n",
        "* The model would be predicting The age of a given passenger using all the other known attributes of the person\n",
        "* Train with 70 percent of the data and take the y column as Age\n",
        "* You **CAN NOT** use any library to make the logistic regression model, you can use only numpy and pandas. The model has to be built from the scratch\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Ttfb5xLuXR3g",
        "colab": {}
      },
      "source": [
        "df = titanic_data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "AwNjMvByXR3z",
        "colab": {}
      },
      "source": [
        "df = df.drop(axis = 1,columns=['PassengerId','Name','Ticket','Cabin'])\n",
        "\n",
        "df.isnull().sum(axis=0).head(13)\n",
        "\n",
        "df.fillna(df.mean(), inplace=True)\n",
        "\n",
        "df['Embarked'] = df['Embarked'].fillna('S')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ax6zS9w8XR37",
        "colab": {}
      },
      "source": [
        "cat = pd.get_dummies(df.select_dtypes('object'))\n",
        "\n",
        "df = pd.concat([df,cat],axis=1)\n",
        "\n",
        "df = df.drop(axis = 1,columns=['Sex','Embarked'])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "QoCX47k-XR4A",
        "colab": {}
      },
      "source": [
        "y = df[['Age']]\n",
        "df = df.drop('Age',axis = 1)\n",
        "\n",
        "y_train = y[:int(0.7*len(y))]\n",
        "y_test = y[int(0.7*len(y)):]\n",
        "\n",
        "x_train = df[:int(0.7*len(df))]\n",
        "x_test = df[int(0.7*len(df)):]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uSb-HXo9YjF3",
        "colab_type": "code",
        "outputId": "eca5d5ec-f863-4769-aa1e-169266afa974",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "w = np.zeros((len(x_train.columns),1))  #why dsnt wrk wt 1 brac\n",
        "alpha = 0.0005\n",
        "bias = 1\n",
        "\n",
        "for i in range(x_train.shape[0]):\n",
        "  f = np.array(x_train[i:i+1])\n",
        "  y = np.dot(f,w) + bias\n",
        "  bias = bias - alpha*(y_train.iloc[i,0] - y)\n",
        "  for j in range(len(w)):\n",
        "    w[j][0] = w[j][0] - (alpha/len(x_train))*((y_train.iloc[i,0] - y)*f[0][j])\n",
        "  \n",
        "print(w,bias)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[-0.01110297]\n",
            " [-0.05040266]\n",
            " [-0.01251222]\n",
            " [-0.01045799]\n",
            " [-1.26058768]\n",
            " [-0.00999253]\n",
            " [-0.01509561]\n",
            " [-0.00629746]\n",
            " [-0.00176834]\n",
            " [-0.01702233]] [[-14.62990815]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E7a91olsor37",
        "colab_type": "code",
        "outputId": "e1f89b5e-4512-45b3-a3a4-599528af1c20",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "total_error = 0\n",
        "for i in range(x_test.shape[0]):\n",
        "  f = np.array(x_test[i:i+1])\n",
        "  y = np.dot(f,w) + bias\n",
        "  total_error = total_error + np.absolute((y_test.iloc[i,0]-y)/y_test.iloc[i,0])\n",
        "  \n",
        "print(total_error*100/len(x_test))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[457.50733063]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xr12wAJeYjF4",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "']'# Submission rules\n",
        "* Report the AUC score of the logistic regression model and the MAPE score of the Linear Regression model on the test data\n",
        "* AUC score as .4 and MAPE as 5 percent then **name_4_95.ipynb** should be the name of your submission file \n",
        "* All submissions Are to be made as pull requests on the Business Club IIT Kharagpur's github account on the Summer assignment 1 repository (https://github.com/business-club-iit-kharagpur)=p\n",
        "'oooooooooooo,.ko0k.=9'"
      ]
    }
  ]
}