17 - Pandas (les Bases).ipynb

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        "<a href=\"https://colab.research.google.com/github/MachineLearnia/Python-Machine-Learning/blob/master/17%20-%20Pandas%20(les%20Bases).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# 17/30 Pandas (les Bases) Et Analyse du Titanic"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rxagEuvWL6t0",
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      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
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      "source": [
        "## 1. Charger vos données dans un DataFrame Pandas\n",
        "\n",
        "https://www.youtube.com/redirect?q=http%3A%2F%2Fbiostat.mc.vanderbilt.edu%2Fwiki%2Fpub%2FMain%2FDataSets%2Ftitanic3.xls&redir_token=IS7fnKxJQSAQBgyL_W_n-Yg2XZJ8MTU4NzkxOTk0MkAxNTg3ODMzNTQy&v=zZkNOdBWgFQ&event=video_description\n",
        "\n",
        "\n",
        "Les options les plus courantes :\n",
        "- read_csv\n",
        "- read_excel\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PWA6lBBPMIu2",
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      "source": [
        "data = pd.read_excel('titanic.xls')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
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        "outputId": "c3cd9590-b1c1-4c08-86f2-f762d26c55ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "data.shape\n",
        "data.head()"
      ],
      "execution_count": 14,
      "outputs": [
        {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>pclass</th>\n",
              "      <th>survived</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",
              "      <th>boat</th>\n",
              "      <th>body</th>\n",
              "      <th>home.dest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Allen, Miss. Elisabeth Walton</td>\n",
              "      <td>female</td>\n",
              "      <td>29.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>24160</td>\n",
              "      <td>211.3375</td>\n",
              "      <td>B5</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>St Louis, MO</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Allison, Master. Hudson Trevor</td>\n",
              "      <td>male</td>\n",
              "      <td>0.9167</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>113781</td>\n",
              "      <td>151.5500</td>\n",
              "      <td>C22 C26</td>\n",
              "      <td>S</td>\n",
              "      <td>11</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Allison, Miss. Helen Loraine</td>\n",
              "      <td>female</td>\n",
              "      <td>2.0000</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>113781</td>\n",
              "      <td>151.5500</td>\n",
              "      <td>C22 C26</td>\n",
              "      <td>S</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
              "      <td>male</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>113781</td>\n",
              "      <td>151.5500</td>\n",
              "      <td>C22 C26</td>\n",
              "      <td>S</td>\n",
              "      <td>NaN</td>\n",
              "      <td>135.0</td>\n",
              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
              "      <td>female</td>\n",
              "      <td>25.0000</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>113781</td>\n",
              "      <td>151.5500</td>\n",
              "      <td>C22 C26</td>\n",
              "      <td>S</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "text/plain": [
              "   pclass  survived  ...   body                        home.dest\n",
              "0       1         1  ...    NaN                     St Louis, MO\n",
              "1       1         1  ...    NaN  Montreal, PQ / Chesterville, ON\n",
              "2       1         0  ...    NaN  Montreal, PQ / Chesterville, ON\n",
              "3       1         0  ...  135.0  Montreal, PQ / Chesterville, ON\n",
              "4       1         0  ...    NaN  Montreal, PQ / Chesterville, ON\n",
              "\n",
              "[5 rows x 14 columns]"
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          "metadata": {
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          "execution_count": 14
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      },
      "source": [
        "data.describe()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pclass</th>\n",
              "      <th>survived</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>fare</th>\n",
              "      <th>body</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1309.000000</td>\n",
              "      <td>1309.000000</td>\n",
              "      <td>1046.000000</td>\n",
              "      <td>1309.000000</td>\n",
              "      <td>1309.000000</td>\n",
              "      <td>1308.000000</td>\n",
              "      <td>121.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.294882</td>\n",
              "      <td>0.381971</td>\n",
              "      <td>29.881135</td>\n",
              "      <td>0.498854</td>\n",
              "      <td>0.385027</td>\n",
              "      <td>33.295479</td>\n",
              "      <td>160.809917</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.837836</td>\n",
              "      <td>0.486055</td>\n",
              "      <td>14.413500</td>\n",
              "      <td>1.041658</td>\n",
              "      <td>0.865560</td>\n",
              "      <td>51.758668</td>\n",
              "      <td>97.696922</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.166700</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.895800</td>\n",
              "      <td>72.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.454200</td>\n",
              "      <td>155.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.275000</td>\n",
              "      <td>256.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>512.329200</td>\n",
              "      <td>328.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            pclass     survived  ...         fare        body\n",
              "count  1309.000000  1309.000000  ...  1308.000000  121.000000\n",
              "mean      2.294882     0.381971  ...    33.295479  160.809917\n",
              "std       0.837836     0.486055  ...    51.758668   97.696922\n",
              "min       1.000000     0.000000  ...     0.000000    1.000000\n",
              "25%       2.000000     0.000000  ...     7.895800   72.000000\n",
              "50%       3.000000     0.000000  ...    14.454200  155.000000\n",
              "75%       3.000000     1.000000  ...    31.275000  256.000000\n",
              "max       3.000000     1.000000  ...   512.329200  328.000000\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
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          "metadata": {
            "tags": []
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          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-XGnMGyMO619",
        "colab_type": "text"
      },
      "source": [
        "## 2. Nettoyer votre Dataset avec drop(), dropna() et fillna()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0NrOS6LGOVpc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = data.drop(['name', 'sibsp', 'parch', 'ticket', 'fare', 'cabin', 'embarked', 'boat', 'body', 'home.dest'], axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HZxs46tQOciZ",
        "colab_type": "code",
        "outputId": "cc83d17a-ee59-41b6-e8ce-e9583189f048",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "data = data.dropna(axis=0)\n",
        "data.shape"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1046, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qUjENeV1Od7M",
        "colab_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "data['age'].hist()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc227570d68>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "50a38XraOx_H",
        "colab_type": "text"
      },
      "source": [
        "## 3. Groupby() et value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YzT4WdqIP3kC",
        "colab_type": "code",
        "outputId": "58666a52-07b7-49fd-f486-25ce969a2423",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        }
      },
      "source": [
        "data.groupby(['sex']).mean()"
      ],
      "execution_count": 19,
      "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>pclass</th>\n",
              "      <th>survived</th>\n",
              "      <th>age</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>female</th>\n",
              "      <td>2.048969</td>\n",
              "      <td>0.752577</td>\n",
              "      <td>28.687071</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>2.300912</td>\n",
              "      <td>0.205167</td>\n",
              "      <td>30.585233</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          pclass  survived        age\n",
              "sex                                  \n",
              "female  2.048969  0.752577  28.687071\n",
              "male    2.300912  0.205167  30.585233"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DVtVErEbOiM-",
        "colab_type": "code",
        "outputId": "a558bdb2-9399-43f7-ad48-41cc87e4d162",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        }
      },
      "source": [
        "data.groupby(['sex', 'pclass']).mean()"
      ],
      "execution_count": 20,
      "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></th>\n",
              "      <th>survived</th>\n",
              "      <th>age</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <th>pclass</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">female</th>\n",
              "      <th>1</th>\n",
              "      <td>0.962406</td>\n",
              "      <td>37.037594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.893204</td>\n",
              "      <td>27.499191</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.473684</td>\n",
              "      <td>22.185307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">male</th>\n",
              "      <th>1</th>\n",
              "      <td>0.350993</td>\n",
              "      <td>41.029250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.145570</td>\n",
              "      <td>30.815401</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.169054</td>\n",
              "      <td>25.962273</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               survived        age\n",
              "sex    pclass                     \n",
              "female 1       0.962406  37.037594\n",
              "       2       0.893204  27.499191\n",
              "       3       0.473684  22.185307\n",
              "male   1       0.350993  41.029250\n",
              "       2       0.145570  30.815401\n",
              "       3       0.169054  25.962273"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yfQpGJRfQGh6",
        "colab_type": "code",
        "outputId": "1c9ef555-5395-4fb1-aa69-8c1a21ab66e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "data['pclass'].value_counts()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3    501\n",
              "1    284\n",
              "2    261\n",
              "Name: pclass, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ebXdlFfCOmr3",
        "colab_type": "code",
        "outputId": "e043f673-665d-472f-c508-984a33813543",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "data[data['age'] < 18]['pclass'].value_counts()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3    106\n",
              "2     33\n",
              "1     15\n",
              "Name: pclass, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9sXTWzZqYv2T",
        "colab_type": "text"
      },
      "source": [
        "## 4. Exercice\n",
        "- Créer des catégories d'ages avec la fonction map() de pandas\n",
        "- Créer des catégories de genres avec cat.codes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xB7KJby3YgeY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def category_ages(age):\n",
        "    if age <= 20:\n",
        "        return '<20 ans'\n",
        "    elif (age > 20) & (age <= 30):\n",
        "        return '20-30 ans'\n",
        "    elif (age > 30) & (age <= 40):\n",
        "        return '30-40 ans'\n",
        "    else:\n",
        "        return '+40 ans'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HRyqvMbPZAc5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data['age'] = data['age'].map(category_ages)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cOfqUmDeZCBH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "02a1b0ab-df8f-492a-e4a8-1b5a13d75a07"
      },
      "source": [
        "data['sex'].astype('category').cat.codes"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       0\n",
              "1       1\n",
              "2       0\n",
              "3       1\n",
              "4       0\n",
              "       ..\n",
              "1301    1\n",
              "1304    0\n",
              "1306    1\n",
              "1307    1\n",
              "1308    1\n",
              "Length: 1046, dtype: int8"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iBdpYUB0ZIkT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}