kaustubh_analytics1.ipynb

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    "kernelspec": {
      "display_name": "Python 3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/KaustubhShirsath/Summer-Assignment-1-Submissions/blob/master/kaustubh_analytics1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NyU7Lx9J_lYH",
        "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": "code",
      "metadata": {
        "id": "TYrybpbi_lYJ",
        "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": "8Z379u65BaAS",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
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              "ok": true,
              "headers": [
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                  "content-type",
                  "application/javascript"
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              "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-d454df0b-13c6-4c65-bb8b-44b62024d7af\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-d454df0b-13c6-4c65-bb8b-44b62024d7af\">\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 (1).csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qn-VVlwk_lYO",
        "colab_type": "text"
      },
      "source": [
        "# 1. Let's start with a showcase\n",
        "\n",
        "#### Case 1: titanic survival data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aE-cmoI8_lYP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import io\n",
        "df = pd.read_csv(\"titanic.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oBMu3Qfy_lYS",
        "colab_type": "code",
        "outputId": "b7eae5bb-10e0-425f-c608-ece1f74c57e2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 229
        }
      },
      "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": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qo3_vg4Z_lYV",
        "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": "SmzxdL7q_lYW",
        "colab_type": "text"
      },
      "source": [
        "**What is the age distribution of the passengers?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2s3XpAkw_lYW",
        "colab_type": "code",
        "outputId": "82e9c184-b233-4d79-c346-16f9bef11344",
        "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 0x7f60d07cb4e0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "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": "iVq3_nkb_lYZ",
        "colab_type": "text"
      },
      "source": [
        "**How does the survival rate of the passengers differ between sexes?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3m6DyLjh_lYa",
        "colab_type": "code",
        "outputId": "1c00b811-0b0f-441e-910b-e8988a7e92c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "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": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wqR796D5_lYc",
        "colab_type": "text"
      },
      "source": [
        "**Or how does it differ between the different classes?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C74QusmC_lYd",
        "colab_type": "code",
        "outputId": "af367c27-2a86-4ebb-b652-68137947ea77",
        "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 0x7f60d06e1ef0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "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": "code",
      "metadata": {
        "id": "z8FOyBFtCrNS",
        "colab_type": "code",
        "outputId": "ede3a1e4-8128-4a08-e672-7b28c91a79b8",
        "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": 41
        }
      },
      "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-cd138360-ee2c-478f-b8b1-3b33a3c07151\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-cd138360-ee2c-478f-b8b1-3b33a3c07151\">\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": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PLnGU7II_lYf",
        "colab_type": "text"
      },
      "source": [
        "All the needed functionality for the above examples will be explained throughout this tutorial."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6UcQ1_VH_lYg",
        "colab_type": "text"
      },
      "source": [
        "#### Case 2: air quality measurement timeseries"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "id": "IyaytkyO_lYh",
        "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:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nurwGQYg_lYj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = pd.read_csv('20000101_20161231-NO2.csv', sep=';', skiprows=[1], na_values=['n/d'], index_col=0, parse_dates=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QSnmuLuj_lYm",
        "colab_type": "code",
        "outputId": "fa8f4201-bae6-4f58-f544-b26dab8225e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        }
      },
      "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": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ugXUO6vj_lYo",
        "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": "DWiXK1DG_lYp",
        "colab_type": "code",
        "outputId": "05c7b066-bff4-4ddd-e6a9-8e526f29dc7a",
        "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 0x7f60cd3955f8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "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": "nbVh3SD-_lYr",
        "colab_type": "code",
        "outputId": "93b1fbc4-8679-4cc9-d496-ffcd3bd0f43c",
        "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 0x7f60cd395550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "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": {
        "id": "_8G8Hmls_lYu",
        "colab_type": "text"
      },
      "source": [
        "**What is the difference in diurnal profile between weekdays and weekend?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nkt1BLch_lYu",
        "colab_type": "code",
        "outputId": "5c380670-63c8-4e17-fb62-fa9884900cee",
        "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 0x7f60cd48fdd8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "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": "tuOpTRqV_lYy",
        "colab_type": "text"
      },
      "source": [
        "We will come back to these example, and build them up step by step."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VYX_S6vo_lYy",
        "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": "X6_Pk4JE_lYz",
        "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": "code",
      "metadata": {
        "scrolled": false,
        "id": "yDCle1qo_lY0",
        "colab_type": "code",
        "outputId": "711d4d19-8ad9-4329-a455-b9bb2b7585bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        }
      },
      "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": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GaM0An5i_lY2",
        "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": {
        "id": "D53I-oKJ_lY3",
        "colab_type": "code",
        "outputId": "6fddcaca-54ed-4e8c-fe3a-331ff7efa467",
        "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": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a8Oh42Q5_lY5",
        "colab_type": "code",
        "outputId": "7c6186d3-caac-4b23-bb86-68952d565253",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "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": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E20V71rU_lY8",
        "colab_type": "text"
      },
      "source": [
        "To check the data types of the different columns:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MGdsh3zh_lY8",
        "colab_type": "code",
        "outputId": "12a1ff69-c7c7-43eb-b263-3e021e475cc9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        }
      },
      "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": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XbPJWX6C_lZC",
        "colab_type": "text"
      },
      "source": [
        "An overview of that information can be given with the `info()` method:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JClVHkl__lZD",
        "colab_type": "code",
        "outputId": "e88e3f4f-5da9-47a1-df0e-c1b4366af97e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        }
      },
      "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": {
        "id": "mlv4Yxd5_lZF",
        "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": "w99J6u0B_lZF",
        "colab_type": "code",
        "outputId": "c987b538-c20e-4cd0-bf6e-5ca5968f5c13",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        }
      },
      "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": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mfyp31L4_lZH",
        "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": "07AX0z96_lZI",
        "colab_type": "code",
        "outputId": "31189966-3c92-4e40-8dff-c647cd8449bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "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": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pjP6z0gq_lZK",
        "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": "WaNCDqy5_lZL",
        "colab_type": "code",
        "outputId": "9dbcbdd5-5fa0-4f17-b32c-bd0dd3ce3be2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        }
      },
      "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": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rA3FpDbY_lZN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "age = df['Age']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_QcPjQ9b_lZP",
        "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": "g7H95wiR_lZQ",
        "colab_type": "code",
        "outputId": "b0dea66f-3863-4288-c78a-ad38b86e4248",
        "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": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f98W7Ptq_lZU",
        "colab_type": "text"
      },
      "source": [
        "You can access the underlying numpy array representation with the `.values` attribute:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "wiPHKKI-_lZU",
        "colab_type": "code",
        "outputId": "77b41323-fe28-49fd-9fb2-539b135594a5",
        "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": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yCOij8J__lZW",
        "colab_type": "text"
      },
      "source": [
        "We can access series values via the index, just like for NumPy arrays:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zw97B339_lZY",
        "colab_type": "code",
        "outputId": "1435898f-29eb-4384-f3e7-302b11b9fc5c",
        "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": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ukfHs46T_lZb",
        "colab_type": "text"
      },
      "source": [
        "Unlike the NumPy array, though, this index can be something other than integers:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3bTTfq9A_lZc",
        "colab_type": "code",
        "outputId": "33cf8a64-dd76-4a54-a1a6-1fbdbc380075",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "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": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uL_uphOW_lZe",
        "colab_type": "code",
        "outputId": "52b244c6-b705-4870-bf06-38d68cc51347",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "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": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FNla7XOP_lZg",
        "colab_type": "code",
        "outputId": "f607a237-4a5c-4abd-a996-9dc05f7031f2",
        "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": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v_A_ZIBw_lZh",
        "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": "jTTl5IZV_lZj",
        "colab_type": "code",
        "outputId": "b6894e2d-d45a-4f29-fc52-1b1715c8e952",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "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": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n6foYUTA_lZn",
        "colab_type": "text"
      },
      "source": [
        "A range of methods:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wnEa_Mji_lZo",
        "colab_type": "code",
        "outputId": "2a9488c0-4863-440a-8bb6-cac593024a0a",
        "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": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XXV_KeVB_lZs",
        "colab_type": "text"
      },
      "source": [
        "Fancy indexing, like indexing with a list or boolean indexing:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3hsOt5zh_lZt",
        "colab_type": "code",
        "outputId": "f96a9598-d06c-4583-caae-e0fc842e5db7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        }
      },
      "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": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gabmNaov_lZv",
        "colab_type": "text"
      },
      "source": [
        "But also a lot of pandas specific methods, e.g."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Px4pvlO0_lZw",
        "colab_type": "code",
        "outputId": "04820001-1aa3-4477-8e75-0e0b2681f843",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "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": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_kUpG_yo_lZy",
        "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": "Ngy0jm-u_lZz",
        "colab_type": "code",
        "outputId": "a55eae69-cd44-4af7-aae9-14a2295d4638",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# code here (for maximum)\n",
        "df['Fare'].max()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "512.3292"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "clear_cell": true,
        "id": "-uVmKAcR_lZ1",
        "colab_type": "code",
        "outputId": "efcea4b7-11d6-4271-96f9-2281b1ffa71a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# code here (for median)\n",
        "df['Fare'].median()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14.4542"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w5WxLvEB_lZ2",
        "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": "oJdCTZD0_lZ4",
        "colab_type": "code",
        "outputId": "77cc9074-5fe2-4e98-947b-8449889f37c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "# code here \n",
        "x = df.groupby('Survived')['PassengerId'].count()\n",
        "x.index = ['Deceased','Survived']\n",
        "print(x)\n",
        "ratio = x.Survived/x.sum()\n",
        "print('Survival ratio = {}'.format(ratio))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Deceased    549\n",
            "Survived    342\n",
            "Name: PassengerId, dtype: int64\n",
            "Survival ratio = 0.3838383838383838\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-eLr2npd_lZ-",
        "colab_type": "text"
      },
      "source": [
        "# 3. Data import and export"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "litxSled_lZ_",
        "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": "-LjAnuo7_lZ_",
        "colab_type": "text"
      },
      "source": [
        "Very powerful csv reader:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GNQVuWuA_laA",
        "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": "zFSC7OJy_laA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv(\"titanic.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5sewGMoj_laD",
        "colab_type": "code",
        "outputId": "7e170644-dadf-43f6-f3f8-e69aea4d3693",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 229
        }
      },
      "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",
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              "\n",
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              "    }\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": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "itVBfE6z_laF",
        "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": "6oWt8t_X_laF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# code here \n",
        "no2 = pd.read_csv('20000101_20161231-NO2.csv', sep=';', skiprows=[1], na_values=['n/d'], index_col=0, parse_dates=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "clear_cell": false,
        "id": "_t0TIgbv_laI",
        "colab_type": "code",
        "outputId": "d6a65755-8d50-4a64-94eb-28c1e957f8b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        }
      },
      "source": [
        "no2"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>...</th>\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",
              "    </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",
              "    </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",
              "    </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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>149039 rows × 4 columns</p>\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",
              "...                    ...    ...   ...   ...\n",
              "2016-12-31 20:00:00   73.0   51.0  49.0  20.0\n",
              "2016-12-31 21:00:00   61.0   51.0  48.0  16.0\n",
              "2016-12-31 22:00:00   57.0   49.0  45.0  14.0\n",
              "2016-12-31 23:00:00   51.0   47.0  45.0  12.0\n",
              "\n",
              "[149039 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1vL2vCUi_laL",
        "colab_type": "text"
      },
      "source": [
        "# 4. Exploration"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fYVBxsR4_laL",
        "colab_type": "text"
      },
      "source": [
        "Some useful methods:\n",
        "\n",
        "`head` and `tail`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Aa5R5Ioa_laM",
        "colab_type": "code",
        "outputId": "6b7fcd5a-6814-4ce8-b391-631b68669fe9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "no2.head(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",
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              "    .dataframe tbody tr th {\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>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",
              "  </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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RKHxEP05_laP",
        "colab_type": "code",
        "outputId": "101e5032-8a03-4000-e237-a94339eddb29",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        }
      },
      "source": [
        "no2.tail()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "</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>2016-12-31 19:00:00</th>\n",
              "      <td>77.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>23.0</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",
              "    </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",
              "    </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",
              "    </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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     BASCH  BONAP  PA18  VERS\n",
              "timestamp                                    \n",
              "2016-12-31 19:00:00   77.0   49.0  52.0  23.0\n",
              "2016-12-31 20:00:00   73.0   51.0  49.0  20.0\n",
              "2016-12-31 21:00:00   61.0   51.0  48.0  16.0\n",
              "2016-12-31 22:00:00   57.0   49.0  45.0  14.0\n",
              "2016-12-31 23:00:00   51.0   47.0  45.0  12.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TbEMJBpv_laR",
        "colab_type": "text"
      },
      "source": [
        "`info()`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MZNLsdgs_laS",
        "colab_type": "code",
        "outputId": "5e5b934a-f4c2-42d0-dbf4-5ed54638f180",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "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 4 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",
            "dtypes: float64(4)\n",
            "memory usage: 5.7 MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "id": "w3oAqVV5_laT",
        "colab_type": "text"
      },
      "source": [
        "Getting some basic summary statistics about the data with `describe`:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TJd1ICs6_laU",
        "colab_type": "code",
        "outputId": "0234cf48-f37b-4b52-c33c-b208884c8ac7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        }
      },
      "source": [
        "no2.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<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",
              "  </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",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               BASCH          BONAP           PA18           VERS\n",
              "count  139949.000000  136493.000000  142259.000000  143813.000000\n",
              "mean       89.270098      64.001714      45.104211      27.613227\n",
              "std        36.772855      27.866767      23.212719      19.604953\n",
              "min         4.000000       0.000000       2.000000       0.000000\n",
              "25%        63.000000      44.000000      28.000000      13.000000\n",
              "50%        87.000000      62.000000      42.000000      22.000000\n",
              "75%       112.000000      81.000000      59.000000      38.000000\n",
              "max       358.000000     345.000000     306.000000     197.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hAeYQtXf_laX",
        "colab_type": "text"
      },
      "source": [
        "Quickly visualizing the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3npb5dBl_laX",
        "colab_type": "code",
        "outputId": "2054e096-cebf-4dae-a50a-b2d0b8c01ca1",
        "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 0x7f60caf917f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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sLHZJBTHaoN8CXAxcAbwC/NVInyAiHomIFRGxYv78+aMsw8yscDKZDNdeey3Tp0/n2muv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\nOQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xHabyBYW_laZ",
        "colab_type": "code",
        "outputId": "b913544c-d502-41aa-e0dd-eba927b8425b",
        "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 0x7f60caf2abe0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        },
        {
          "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": "quYn58TD_lac",
        "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": "-5U2ZYVW_lad",
        "colab_type": "code",
        "outputId": "663d9309-53c5-4c89-8d51-1694bb26a89d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "# code here\n",
        "df['Age'].plot(kind='hist', bins = 8)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f60cae71be0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Lqmrp2CGRQ7+WAP8EfLGqngMczuh3N2iuqrqt+z0tBZ4P3A9cNnSuJAcAbwSWVdXvMzqA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\npCYLQpLUZEFIkposCElS0/8BMqNBqW68EKcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FKfVKx8e_lae",
        "colab_type": "text"
      },
      "source": [
        "The default plot (when not specifying `kind`) is a line plot of all columns:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-AlPn6Jm_laf",
        "colab_type": "code",
        "outputId": "f5e731e7-b9c8-4836-8e37-fd4e2c505df1",
        "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 0x7f60caf360b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tt99GQUFBSLpqH8nJyTjxxBPx6quvYtKkSZg1axa2bt2KX375Bbt27UJcXBwqKirQ1tYW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1Fc9FG6/OPjF9rTsiR1hDIeo+P/Fi6biFQzAMAJjms3AhhGKe0KYJjxGwD+AKCr8d9FAJ4RLokE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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tOH2ueD8_lah",
        "colab_type": "text"
      },
      "source": [
        "This does not say too much .."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5SWtAcGa_lah",
        "colab_type": "text"
      },
      "source": [
        "We can select part of the data (eg the latest 500 data points):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CKqadjRS_laj",
        "colab_type": "code",
        "outputId": "a94ed3e6-622f-4108-c52e-189cda21dbc0",
        "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 0x7f60c839a630>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        },
        {
          "output_type": "display_data",
          "data": {
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4lVFsQZyMha1McqFwD/DmJBsGh6pzu3Avk9egGxxxWUI8IiGraugbs0RyW0Ek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731E/jOPhvviuVzNrZEIpm/ONUuQhs2oMzRgzrYtq/0jh2TJq64ScSneH6EZ1EL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\ncZFsZmZmZlbyHzZMD/1cAw0QAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "22XQ_UgtEoyw",
        "colab_type": "code",
        "outputId": "9ec21939-1e59-4428-cc45-52cbadbbdb29",
        "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-ebd80dc5-7397-4e3e-9d65-26997e98f7bb\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-ebd80dc5-7397-4e3e-9d65-26997e98f7bb\">\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.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EGQRa-0k_lal",
        "colab_type": "text"
      },
      "source": [
        "Or we can use some more advanced time series features -> see further in this notebook!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zkJvkwrL_lal",
        "colab_type": "text"
      },
      "source": [
        "# 5. Selecting and filtering data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NvDcrpI-_lao",
        "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": "code",
      "metadata": {
        "id": "I6t60-Bf_lap",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv(\"titanic.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tBwcsMqF_lar",
        "colab_type": "text"
      },
      "source": [
        "### `df[]` provides some convenience shortcuts "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zxpXdDe8_las",
        "colab_type": "text"
      },
      "source": [
        "For a DataFrame, basic indexing selects the columns.\n",
        "\n",
        "Selecting a single column:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NmKoy2Fd_las",
        "colab_type": "code",
        "outputId": "604cefa4-2386-4770-a922-3c159f75628e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        }
      },
      "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": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JKeeepf9_lav",
        "colab_type": "text"
      },
      "source": [
        "or multiple columns:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tbU7dXOb_lav",
        "colab_type": "code",
        "outputId": "e1b159a2-e0f9-4fb4-f2aa-bf5b36bcff6d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        }
      },
      "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": 89
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S_-SKBe3_lax",
        "colab_type": "text"
      },
      "source": [
        "But, slicing accesses the rows:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HNUgXlg__lay",
        "colab_type": "code",
        "outputId": "1376684f-48a7-4e64-b73a-239d08f132f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "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": 90
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TpNJTLnF_la0",
        "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": "Wb0DK7CB_la0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = df.set_index('Name')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HxeXJOp4_la2",
        "colab_type": "code",
        "outputId": "45797885-cd55-4f03-a2d0-f5c28d15996d",
        "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": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "XeoqKQJQ_la4",
        "colab_type": "code",
        "outputId": "bbc24ca1-65a2-42e8-d0ac-40da0fabd7ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "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": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hTh3eY-s_la5",
        "colab_type": "text"
      },
      "source": [
        "Selecting by position with `iloc` works similar as indexing numpy arrays:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aiQ9asMg_la5",
        "colab_type": "code",
        "outputId": "1cf0a4f8-47a4-4ea3-f23c-5ad78a8ab47f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "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": 93
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wZlr5h4h_la6",
        "colab_type": "text"
      },
      "source": [
        "The different indexing methods can also be used to assign data:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q7e5DRr3_la7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.loc['Braund, Mr. Owen Harris', 'Survived'] = 100"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BNQsIaN0_la8",
        "colab_type": "code",
        "outputId": "e004d12e-fba5-43ff-c9dc-d535e25b4295",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "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",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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": 95
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6ZPMHBXq_la_",
        "colab_type": "text"
      },
      "source": [
        "### Boolean indexing (filtering)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t3MLGl1C_la_",
        "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": "7fcQF6Cg_lbA",
        "colab_type": "code",
        "outputId": "90996d16-e233-4629-e082-d647fa6d9b57",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "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": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "run_control": {
          "frozen": false,
          "read_only": false
        },
        "scrolled": false,
        "id": "PCKJZSED_lbC",
        "colab_type": "code",
        "outputId": "fa698cb3-c0ff-4c19-a1ea-bbd112489686",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        }
      },
      "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": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SfgGoPrr_lbD",
        "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": "SToCV8x5_lbD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv(\"titanic.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "clear_cell": true,
        "id": "4xk4hxMY_lbH",
        "colab_type": "code",
        "outputId": "07691928-d33b-4363-c0a6-0fed285df996",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Code here\n",
        "df[df['Sex'] == 'male']['Age'].mean()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30.72664459161148"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "clear_cell": true,
        "id": "K12Qg-Vl_lbI",
        "colab_type": "code",
        "outputId": "8e944193-21be-472d-c2ad-1b106be40ede",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Code here\n",
        "df[df['Sex'] == 'female']['Age'].mean()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "27.915708812260537"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "clear_cell": true,
        "id": "dL5W-gNX_lbJ",
        "colab_type": "code",
        "outputId": "ee65cf9f-7dbb-4812-b62e-ea0a41633a12",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Code here\n",
        "df['Age'].mean()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "29.69911764705882"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b9bfY7Oi_lbK",
        "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": {
        "clear_cell": true,
        "id": "3NAZnA2M_lbK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Code here \n",
        "x=df[df['Age'] > 70]['PassengerId'].count()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "clear_cell": true,
        "id": "DMk91Vyq_lbM",
        "colab_type": "code",
        "outputId": "a51e8360-fec0-407b-f89d-6beaf0cbf49e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Code here\n",
        "print('passengers older than 70=',x)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "passengers older than 70= 5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qTKc60FT_lbN",
        "colab_type": "text"
      },
      "source": [
        "# 6. The group-by operation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uGOs75Ag_lbO",
        "colab_type": "text"
      },
      "source": [
        "### Some 'theory': the groupby operation (split-apply-combine)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "run_control": {
          "frozen": false,
          "read_only": false
        },
        "id": "UlbdhRap_lbO",
        "colab_type": "code",
        "outputId": "16238888-551d-4eca-d691-ffd977d5445a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        }
      },
      "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": {
            "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>key</th>\n",
              "      <th>data</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>B</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>C</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>C</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>A</td>\n",
              "      <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": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gUDcrvea_lbP",
        "colab_type": "text"
      },
      "source": [
        "### Recap: aggregating functions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vAh9plup_lbQ",
        "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": "h5-4DmnX_lbQ",
        "colab_type": "code",
        "outputId": "71f38d0c-446d-4b4f-e747-f0ef70731730",
        "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": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wRMfLQKP_lbS",
        "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": "0BYTDc6o_lbS",
        "colab_type": "code",
        "outputId": "3b41936b-59b2-4324-82ff-6ff9299769ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "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": "rdUPieNM_lbV",
        "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": "oqCAOSm9_lbW",
        "colab_type": "text"
      },
      "source": [
        "### Groupby: applying functions per group"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fvweQzZU_lbW",
        "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": "zVuDS1Ny_lbW",
        "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": "njhYu23z_lbX",
        "colab_type": "code",
        "outputId": "495a2bd4-2c2c-439e-c96b-adff70ab2f78",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "df.groupby('key').sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>C</th>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            "text/plain": [
              "     data\n",
              "key      \n",
              "A      15\n",
              "B      30\n",
              "C      45"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "run_control": {
          "frozen": false,
          "read_only": false
        },
        "id": "2d1Z0qeu_lbZ",
        "colab_type": "code",
        "outputId": "c01197b3-5e16-425d-8fd7-36bdfe721229",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "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",
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              "      <td>30</td>\n",
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              "      <td>45</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>"
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            "text/plain": [
              "     data\n",
              "key      \n",
              "A      15\n",
              "B      30\n",
              "C      45"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "399k6KUm_lbc",
        "colab_type": "text"
      },
      "source": [
        "And many more methods are available. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "run_control": {
          "frozen": false,
          "read_only": false
        },
        "id": "_oVtQcXb_lbd",
        "colab_type": "code",
        "outputId": "c34c9ce7-33a7-42dc-f9e2-acb70033e90e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "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": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3dZ3L7nE_lbf",
        "colab_type": "text"
      },
      "source": [
        "### Application of the groupby concept on the titanic data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ryHU3NeD_lbg",
        "colab_type": "text"
      },
      "source": [
        "We go back to the titanic passengers survival data:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "run_control": {
          "frozen": false,
          "read_only": false
        },
        "id": "7NZsfonc_lbh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv(\"titanic.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "run_control": {
          "frozen": false,
          "read_only": false
        },
        "id": "HRh0-6ph_lbj",
        "colab_type": "code",
        "outputId": "c312e9d7-b8f7-4169-f848-bdfd6f4e324f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 229
        }
      },
      "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": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fxmQzo7U_lbk",
        "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": "TumTZfIq_lbk",
        "colab_type": "code",
        "outputId": "63b12888-7aba-4689-8ee2-b6eaae4c3a87",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "# Code here\n",
        "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": 104
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qrK_lBHN_lbl",
        "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": "dA0hHmS9_lbl",
        "colab_type": "code",
        "outputId": "87c2c9d2-090b-499b-b983-76672a41ba58",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Code here\n",
        "x = df.groupby('Survived')['PassengerId'].count()\n",
        "ratio = x[1]/x.sum()\n",
        "print('The average survival ratio = ',ratio)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The average survival ratio =  0.3838383838383838\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "py5KustN_lbm",
        "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": "GxP2iLFW_lbm",
        "colab_type": "code",
        "outputId": "47d1601a-99b0-46e1-e377-5c0a8d233159",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Code here\n",
        "df[df['Age']<25]['Survived'].mean()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4244604316546763"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rTD9qEhILViD",
        "colab_type": "code",
        "outputId": "42f64f2b-9960-4abb-cebc-55b4fcf85c8c",
        "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-e5a4ae19-1a17-46fb-87c8-403e4d968aa3\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-e5a4ae19-1a17-46fb-87c8-403e4d968aa3\">\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": "_p8-sO55LreI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import io\n",
        "df=pd.read_csv('titanic.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nHe-wFmA_lbq",
        "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": "IRiTfTWY_lbq",
        "colab_type": "code",
        "outputId": "33ea6f29-3ca4-4c81-db9e-ac926a2f4f7a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Code here\n",
        "\n",
        "\n",
        "ratio=df.groupby('Sex')['Survived'].mean()\n",
        "diff = abs(ratio[0]-ratio[1])\n",
        "diff"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5531300709799203"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Suy3hterPQza",
        "colab_type": "code",
        "outputId": "a84aa430-7af3-463f-fdb2-9f74c2c5bc31",
        "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": 41
        }
      },
      "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-82ea9e2c-7642-4613-9470-f5c3bff9ee11\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-82ea9e2c-7642-4613-9470-f5c3bff9ee11\">\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": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WkliyPhf_lbr",
        "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": "QAuEyjAz_lbr",
        "colab_type": "code",
        "outputId": "19a58796-f254-4405-91e0-2655d6e2416b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "# Code here \n",
        "survival_ratio = df.groupby('Pclass')['Survived'].mean()\n",
        "survival_ratio.plot(kind='bar')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6f66056940>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        },
        {
          "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": "JhVRD_mh_lbs",
        "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": "FR0x8K4W_lbt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['AgeClass'] = pd.cut(df['Age'], bins=np.arange(0,90,10))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "clear_cell": true,
        "run_control": {
          "frozen": false,
          "read_only": false
        },
        "scrolled": true,
        "id": "EjZ5wF-o_lbt",
        "colab_type": "code",
        "outputId": "38bbadb5-7a71-4cfd-b0a2-53ccd80744f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "# Code here \n",
        "x_class = ['(0, 10]','(10, 20]','(20, 30]', '(30, 40]', '(40, 50]', '(50, 60]', '(60, 70]', '(70, 80]']\n",
        "fare.range = df.groupby('AgeClass')['PassengerId'].count()\n",
        "plt.bar(x_class,fare.range)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BarContainer object of 8 artists>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JEbEmIq6JiKlQ6rJNnwNkRFwZETdExF+3auu5nDPU9hzgpxHxxYj4bkR8sFzpdtj3w+uB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\noQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "id": "AQPAyna5_lbu",
        "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": "gPjPy12c_lbv",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "```\n",
        "# This is formatted as code\n",
        "```\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "erNR0vRa_lbv",
        "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": "2zTZo7yK_lbv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "##Code here, Refer to google for any doubts firsthand there is precedence for this task\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KsmWE_dn_G77",
        "colab_type": "code",
        "outputId": "c1baa4b0-f393-4307-9589-113d83f9b115",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 229
        }
      },
      "source": [
        "df = pd.read_csv(\"titanic.csv\") \n",
        "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": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S8CKKuqm_N-2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nwb7eF_y_lbw",
        "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": {
        "id": "c95PLN8H_lbw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "##Code here, Refer to google for any doubts firsthand there is precedence for this task\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7YU9MUz--z_j",
        "colab_type": "code",
        "outputId": "dbc1b221-e3cb-4cb9-fac0-d43510154d38",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 229
        }
      },
      "source": [
        "df = pd.read_csv(\"titanic.csv\") \n",
        "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": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4GcT_pcEALgu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1LU2Bk2M_lbx",
        "colab_type": "text"
      },
      "source": [
        "# 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)"
      ]
    }
  ]
}