DataProcessing2V2.ipynb

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        "<a href=\"https://colab.research.google.com/github/ogut77/DataScience/blob/main/DataProcessing2V2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "## 1. Demonstration of main Pandas methods\n",
        "Well... There are dozens of cool tutorials on Pandas and visual data analysis. If you are already familiar with these topics, you can wait for the 3rd article in the series, where we get into machine learning.  \n",
        "\n",
        "**[Pandas](http://pandas.pydata.org)** is a Python library that provides extensive means for data analysis. Data scientists often work with data stored in table formats like `.csv`, `.tsv`, or `.xlsx`. Pandas makes it very convenient to load, process, and analyze such tabular data using SQL-like queries. In conjunction with `Matplotlib` and `Seaborn`, `Pandas` provides a wide range of opportunities for visual analysis of tabular data.\n",
        "\n",
        "The main data structures in `Pandas` are implemented with **Series** and **DataFrame** classes. The former is a one-dimensional indexed array of some fixed data type. The latter is a two-dimensional data structure - a table - where each column contains data of the same type. You can see it as a dictionary of `Series` instances. `DataFrames` are great for representing real data: rows correspond to instances (examples, observations, etc.), and columns correspond to features of these instances."
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "pd.set_option(\"display.precision\", 2)"
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        "We'll demonstrate the main methods in action by analyzing a [dataset](https://bigml.com/user/francisco/gallery/dataset/5163ad540c0b5e5b22000383) on the churn rate of telecom operator clients. Let's read the data (using `read_csv`), and take a look at the first 5 lines using the `head` method:"
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      "source": [
        "df = pd.read_csv(\"https://raw.githubusercontent.com/ogut77/DataScience/main/data/telecom_churn.csv\")\n",
        "df.head()"
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              "  State  Account length  Area code International plan Voice mail plan  \\\n",
              "0    KS             128        415                 No             Yes   \n",
              "1    OH             107        415                 No             Yes   \n",
              "2    NJ             137        415                 No              No   \n",
              "3    OH              84        408                Yes              No   \n",
              "4    OK              75        415                Yes              No   \n",
              "\n",
              "   Number vmail messages  Total day minutes  Total day calls  \\\n",
              "0                     25              265.1              110   \n",
              "1                     26              161.6              123   \n",
              "2                      0              243.4              114   \n",
              "3                      0              299.4               71   \n",
              "4                      0              166.7              113   \n",
              "\n",
              "   Total day charge  Total eve minutes  Total eve calls  Total eve charge  \\\n",
              "0             45.07              197.4               99             16.78   \n",
              "1             27.47              195.5              103             16.62   \n",
              "2             41.38              121.2              110             10.30   \n",
              "3             50.90               61.9               88              5.26   \n",
              "4             28.34              148.3              122             12.61   \n",
              "\n",
              "   Total night minutes  Total night calls  Total night charge  \\\n",
              "0                244.7                 91               11.01   \n",
              "1                254.4                103               11.45   \n",
              "2                162.6                104                7.32   \n",
              "3                196.9                 89                8.86   \n",
              "4                186.9                121                8.41   \n",
              "\n",
              "   Total intl minutes  Total intl calls  Total intl charge  \\\n",
              "0                10.0                 3               2.70   \n",
              "1                13.7                 3               3.70   \n",
              "2                12.2                 5               3.29   \n",
              "3                 6.6                 7               1.78   \n",
              "4                10.1                 3               2.73   \n",
              "\n",
              "   Customer service calls  Churn  \n",
              "0                       1  False  \n",
              "1                       1  False  \n",
              "2                       0  False  \n",
              "3                       2  False  \n",
              "4                       3  False  "
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    {
      "cell_type": "markdown",
      "source": [
        "<details>\n",
        "<summary>About printing DataFrames in Jupyter notebooks</summary>\n",
        "<p>\n",
        "In Jupyter notebooks, Pandas DataFrames are printed as these pretty tables seen above while `print(df.head())` is less nicely formatted.\n",
        "By default, Pandas displays 20 columns and 60 rows, so, if your DataFrame is bigger, use the `set_option` function as shown in the example below:\n",
        "\n",
        "```python\n",
        "pd.set_option('display.max_columns', 100)\n",
        "pd.set_option('display.max_rows', 100)\n",
        "```\n",
        "</p>\n",
        "</details>\n",
        "\n",
        "Recall that each row corresponds to one client, an **instance**, and columns are **features** of this instance."
      ],
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        "id": "pOelVCGKUhyn"
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    {
      "cell_type": "markdown",
      "source": [
        "Let’s have a look at data dimensionality, feature names, and feature types."
      ],
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        "id": "jBhMKidQUhyp"
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        "df.shape"
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        "id": "KPsiDv_HUhyr",
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        },
        "outputId": "7931d1e4-f425-4995-8fec-0ecb4c0252df"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3333, 20)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
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    {
      "cell_type": "markdown",
      "source": [
        "From the output, we can see that the table contains 3333 rows and 20 columns.\n",
        "\n",
        "Now let's try printing out column names using `columns`:"
      ],
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        "df.columns"
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        "outputId": "faa4c172-ebb4-49ba-c2fc-099bea237804"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['State', 'Account length', 'Area code', 'International plan',\n",
              "       'Voice mail plan', 'Number vmail messages', 'Total day minutes',\n",
              "       'Total day calls', 'Total day charge', 'Total eve minutes',\n",
              "       'Total eve calls', 'Total eve charge', 'Total night minutes',\n",
              "       'Total night calls', 'Total night charge', 'Total intl minutes',\n",
              "       'Total intl calls', 'Total intl charge', 'Customer service calls',\n",
              "       'Churn'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can use the `info()` method to output some general information about the dataframe: "
      ],
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      "source": [
        "df.info()"
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      "execution_count": 10,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3333 entries, 0 to 3332\n",
            "Data columns (total 20 columns):\n",
            " #   Column                  Non-Null Count  Dtype  \n",
            "---  ------                  --------------  -----  \n",
            " 0   State                   3333 non-null   object \n",
            " 1   Account length          3333 non-null   int64  \n",
            " 2   Area code               3333 non-null   int64  \n",
            " 3   International plan      3333 non-null   object \n",
            " 4   Voice mail plan         3333 non-null   object \n",
            " 5   Number vmail messages   3333 non-null   int64  \n",
            " 6   Total day minutes       3333 non-null   float64\n",
            " 7   Total day calls         3333 non-null   int64  \n",
            " 8   Total day charge        3333 non-null   float64\n",
            " 9   Total eve minutes       3333 non-null   float64\n",
            " 10  Total eve calls         3333 non-null   int64  \n",
            " 11  Total eve charge        3333 non-null   float64\n",
            " 12  Total night minutes     3333 non-null   float64\n",
            " 13  Total night calls       3333 non-null   int64  \n",
            " 14  Total night charge      3333 non-null   float64\n",
            " 15  Total intl minutes      3333 non-null   float64\n",
            " 16  Total intl calls        3333 non-null   int64  \n",
            " 17  Total intl charge       3333 non-null   float64\n",
            " 18  Customer service calls  3333 non-null   int64  \n",
            " 19  Churn                   3333 non-null   int64  \n",
            "dtypes: float64(8), int64(9), object(3)\n",
            "memory usage: 520.9+ KB\n"
          ]
        }
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    {
      "cell_type": "code",
      "source": [
        "df.Churn.value_counts()"
      ],
      "metadata": {
        "id": "44kg_anl3Dd3",
        "outputId": "c108e775-6b37-4dc7-cf99-c663f211451d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False    2850\n",
              "True      483\n",
              "Name: Churn, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "`bool`, `int64`, `float64` and `object` are the data types of our features. We see that one feature is logical (`bool`), 3 features are of type `object`, and 16 features are numeric. With this same method, we can easily see if there are any missing values. Here, there are none because each column contains 3333 observations, the same number of rows we saw before with `shape`.\n",
        "\n",
        "We can **change the column type** with the `astype` method. Let's apply this method to the `Churn` feature to convert it into `int64`:"
      ],
      "metadata": {
        "_uuid": "6fac47f7e7bcf060164b1881d8e199973834cd38",
        "id": "xmu7gkDkUhyx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Churn\"] = df[\"Churn\"].astype(\"int64\")"
      ],
      "metadata": {
        "_uuid": "16a0073ab34daea8192eb549c4c8a94e33b34a13",
        "trusted": true,
        "id": "YoJaFZ1tUhyy"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.Churn.value_counts()"
      ],
      "metadata": {
        "id": "fotCZDJM3J2p",
        "outputId": "aa8a51ee-10f8-4e64-a4b2-c4ec4c3d5d08",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    2850\n",
              "1     483\n",
              "Name: Churn, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The `describe` method shows basic statistical characteristics of each numerical feature (`int64` and `float64` types): number of non-missing values, mean, standard deviation, range, median, 0.25 and 0.75 quartiles."
      ],
      "metadata": {
        "_uuid": "11030a8bd020c6a3a04beb73531e9676160bcab8",
        "id": "IdmXIxzYUhyz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "_uuid": "24e1d394f938980403a736421b4cf855ddb8ef8c",
        "trusted": true,
        "id": "f0vkW2rCUhy0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "outputId": "096c0a0c-ec82-41b2-bdb9-ee1c96b3641d"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Account length  Area code  Number vmail messages  Total day minutes  \\\n",
              "count         3333.00    3333.00                3333.00            3333.00   \n",
              "mean           101.06     437.18                   8.10             179.78   \n",
              "std             39.82      42.37                  13.69              54.47   \n",
              "min              1.00     408.00                   0.00               0.00   \n",
              "25%             74.00     408.00                   0.00             143.70   \n",
              "50%            101.00     415.00                   0.00             179.40   \n",
              "75%            127.00     510.00                  20.00             216.40   \n",
              "max            243.00     510.00                  51.00             350.80   \n",
              "\n",
              "       Total day calls  Total day charge  Total eve minutes  Total eve calls  \\\n",
              "count          3333.00           3333.00            3333.00          3333.00   \n",
              "mean            100.44             30.56             200.98           100.11   \n",
              "std              20.07              9.26              50.71            19.92   \n",
              "min               0.00              0.00               0.00             0.00   \n",
              "25%              87.00             24.43             166.60            87.00   \n",
              "50%             101.00             30.50             201.40           100.00   \n",
              "75%             114.00             36.79             235.30           114.00   \n",
              "max             165.00             59.64             363.70           170.00   \n",
              "\n",
              "       Total eve charge  Total night minutes  Total night calls  \\\n",
              "count           3333.00              3333.00            3333.00   \n",
              "mean              17.08               200.87             100.11   \n",
              "std                4.31                50.57              19.57   \n",
              "min                0.00                23.20              33.00   \n",
              "25%               14.16               167.00              87.00   \n",
              "50%               17.12               201.20             100.00   \n",
              "75%               20.00               235.30             113.00   \n",
              "max               30.91               395.00             175.00   \n",
              "\n",
              "       Total night charge  Total intl minutes  Total intl calls  \\\n",
              "count             3333.00             3333.00           3333.00   \n",
              "mean                 9.04               10.24              4.48   \n",
              "std                  2.28                2.79              2.46   \n",
              "min                  1.04                0.00              0.00   \n",
              "25%                  7.52                8.50              3.00   \n",
              "50%                  9.05               10.30              4.00   \n",
              "75%                 10.59               12.10              6.00   \n",
              "max                 17.77               20.00             20.00   \n",
              "\n",
              "       Total intl charge  Customer service calls    Churn  \n",
              "count            3333.00                 3333.00  3333.00  \n",
              "mean                2.76                    1.56     0.14  \n",
              "std                 0.75                    1.32     0.35  \n",
              "min                 0.00                    0.00     0.00  \n",
              "25%                 2.30                    1.00     0.00  \n",
              "50%                 2.78                    1.00     0.00  \n",
              "75%                 3.27                    2.00     0.00  \n",
              "max                 5.40                    9.00     1.00  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c7739f2b-8e39-4926-91f9-d2bd573e03e7\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Account length</th>\n",
              "      <th>Area code</th>\n",
              "      <th>Number vmail messages</th>\n",
              "      <th>Total day minutes</th>\n",
              "      <th>Total day calls</th>\n",
              "      <th>Total day charge</th>\n",
              "      <th>Total eve minutes</th>\n",
              "      <th>Total eve calls</th>\n",
              "      <th>Total eve charge</th>\n",
              "      <th>Total night minutes</th>\n",
              "      <th>Total night calls</th>\n",
              "      <th>Total night charge</th>\n",
              "      <th>Total intl minutes</th>\n",
              "      <th>Total intl calls</th>\n",
              "      <th>Total intl charge</th>\n",
              "      <th>Customer service calls</th>\n",
              "      <th>Churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3333.00</td>\n",
              "      <td>3333.00</td>\n",
              "      <td>3333.00</td>\n",
              "      <td>3333.00</td>\n",
              "      <td>3333.00</td>\n",
              "      <td>3333.00</td>\n",
              "      <td>3333.00</td>\n",
              "      <td>3333.00</td>\n",
              "      <td>3333.00</td>\n",
              "      <td>3333.00</td>\n",
              "      <td>3333.00</td>\n",
              "      <td>3333.00</td>\n",
              "      <td>3333.00</td>\n",
              "      <td>3333.00</td>\n",
              "      <td>3333.00</td>\n",
              "      <td>3333.00</td>\n",
              "      <td>3333.00</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>101.06</td>\n",
              "      <td>437.18</td>\n",
              "      <td>8.10</td>\n",
              "      <td>179.78</td>\n",
              "      <td>100.44</td>\n",
              "      <td>30.56</td>\n",
              "      <td>200.98</td>\n",
              "      <td>100.11</td>\n",
              "      <td>17.08</td>\n",
              "      <td>200.87</td>\n",
              "      <td>100.11</td>\n",
              "      <td>9.04</td>\n",
              "      <td>10.24</td>\n",
              "      <td>4.48</td>\n",
              "      <td>2.76</td>\n",
              "      <td>1.56</td>\n",
              "      <td>0.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>39.82</td>\n",
              "      <td>42.37</td>\n",
              "      <td>13.69</td>\n",
              "      <td>54.47</td>\n",
              "      <td>20.07</td>\n",
              "      <td>9.26</td>\n",
              "      <td>50.71</td>\n",
              "      <td>19.92</td>\n",
              "      <td>4.31</td>\n",
              "      <td>50.57</td>\n",
              "      <td>19.57</td>\n",
              "      <td>2.28</td>\n",
              "      <td>2.79</td>\n",
              "      <td>2.46</td>\n",
              "      <td>0.75</td>\n",
              "      <td>1.32</td>\n",
              "      <td>0.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.00</td>\n",
              "      <td>408.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>23.20</td>\n",
              "      <td>33.00</td>\n",
              "      <td>1.04</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>74.00</td>\n",
              "      <td>408.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>143.70</td>\n",
              "      <td>87.00</td>\n",
              "      <td>24.43</td>\n",
              "      <td>166.60</td>\n",
              "      <td>87.00</td>\n",
              "      <td>14.16</td>\n",
              "      <td>167.00</td>\n",
              "      <td>87.00</td>\n",
              "      <td>7.52</td>\n",
              "      <td>8.50</td>\n",
              "      <td>3.00</td>\n",
              "      <td>2.30</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>101.00</td>\n",
              "      <td>415.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>179.40</td>\n",
              "      <td>101.00</td>\n",
              "      <td>30.50</td>\n",
              "      <td>201.40</td>\n",
              "      <td>100.00</td>\n",
              "      <td>17.12</td>\n",
              "      <td>201.20</td>\n",
              "      <td>100.00</td>\n",
              "      <td>9.05</td>\n",
              "      <td>10.30</td>\n",
              "      <td>4.00</td>\n",
              "      <td>2.78</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>127.00</td>\n",
              "      <td>510.00</td>\n",
              "      <td>20.00</td>\n",
              "      <td>216.40</td>\n",
              "      <td>114.00</td>\n",
              "      <td>36.79</td>\n",
              "      <td>235.30</td>\n",
              "      <td>114.00</td>\n",
              "      <td>20.00</td>\n",
              "      <td>235.30</td>\n",
              "      <td>113.00</td>\n",
              "      <td>10.59</td>\n",
              "      <td>12.10</td>\n",
              "      <td>6.00</td>\n",
              "      <td>3.27</td>\n",
              "      <td>2.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>243.00</td>\n",
              "      <td>510.00</td>\n",
              "      <td>51.00</td>\n",
              "      <td>350.80</td>\n",
              "      <td>165.00</td>\n",
              "      <td>59.64</td>\n",
              "      <td>363.70</td>\n",
              "      <td>170.00</td>\n",
              "      <td>30.91</td>\n",
              "      <td>395.00</td>\n",
              "      <td>175.00</td>\n",
              "      <td>17.77</td>\n",
              "      <td>20.00</td>\n",
              "      <td>20.00</td>\n",
              "      <td>5.40</td>\n",
              "      <td>9.00</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c7739f2b-8e39-4926-91f9-d2bd573e03e7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c7739f2b-8e39-4926-91f9-d2bd573e03e7 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c7739f2b-8e39-4926-91f9-d2bd573e03e7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In order to see statistics on non-numerical features, one has to explicitly indicate data types of interest in the `include` parameter."
      ],
      "metadata": {
        "_uuid": "2314370435c6c44da57d4c97ad01ace128047fd5",
        "id": "DJrtNXdKUhy1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe(include=['object'])"
      ],
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        "_uuid": "c1bd67bc0b915dbac429152c649a7c118770d824",
        "trusted": true,
        "id": "AK5dN3iTUhy2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "fd7d2f5d-c1b5-4f53-fac8-92614b749bec"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       State International plan Voice mail plan\n",
              "count   3333               3333            3333\n",
              "unique    51                  2               2\n",
              "top       WV                 No              No\n",
              "freq     106               3010            2411"
            ],
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>State</th>\n",
              "      <th>International plan</th>\n",
              "      <th>Voice mail plan</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3333</td>\n",
              "      <td>3333</td>\n",
              "      <td>3333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>51</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>WV</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>106</td>\n",
              "      <td>3010</td>\n",
              "      <td>2411</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dd1513ae-9863-404c-be5a-9c8f08212a50')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For categorical (type `object`) and boolean (type `bool`) features we can use the `value_counts` method. Let's have a look at the distribution of `Churn`:"
      ],
      "metadata": {
        "_uuid": "7acac4f33f2229215aa7818b10f4dfb1fafd96af",
        "id": "-6hEu7YuUhy3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Churn'].value_counts()"
      ],
      "metadata": {
        "_uuid": "eef4f419ac223f320d1841635ffbb816c807fe54",
        "trusted": true,
        "id": "NbMvSINiUhy4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e2618d5a-5f3e-48a5-94a2-7b053e455866"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    2850\n",
              "1     483\n",
              "Name: Churn, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2850 users out of 3333 are *loyal*; their `Churn` value is `0`. To calculate fractions, pass `normalize=True` to the `value_counts` function."
      ],
      "metadata": {
        "_uuid": "78e28e7dbdd84116fe5c30a56f6c649befadd832",
        "id": "yy25ifdhUhy5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Churn'].value_counts(normalize=True)"
      ],
      "metadata": {
        "_uuid": "b516f5e583ac75e0379048738386ece5b208f0e7",
        "trusted": true,
        "id": "jfg12eoPUhy5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dd0230ac-cf38-4ec3-df93-a7ab10c8f705"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.86\n",
              "1    0.14\n",
              "Name: Churn, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.corr()"
      ],
      "metadata": {
        "id": "U2_kVxOT392W",
        "outputId": "55aca1f3-e44c-47e9-c9ca-9a50cd1d93f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        Account length  Area code  Number vmail messages  \\\n",
              "Account length                1.00e+00  -1.25e-02              -4.63e-03   \n",
              "Area code                    -1.25e-02   1.00e+00              -1.99e-03   \n",
              "Number vmail messages        -4.63e-03  -1.99e-03               1.00e+00   \n",
              "Total day minutes             6.22e-03  -8.26e-03               7.78e-04   \n",
              "Total day calls               3.85e-02  -9.65e-03              -9.55e-03   \n",
              "Total day charge              6.21e-03  -8.26e-03               7.76e-04   \n",
              "Total eve minutes            -6.76e-03   3.58e-03               1.76e-02   \n",
              "Total eve calls               1.93e-02  -1.19e-02              -5.86e-03   \n",
              "Total eve charge             -6.75e-03   3.61e-03               1.76e-02   \n",
              "Total night minutes          -8.96e-03  -5.82e-03               7.68e-03   \n",
              "Total night calls            -1.32e-02   1.65e-02               7.12e-03   \n",
              "Total night charge           -8.96e-03  -5.85e-03               7.66e-03   \n",
              "Total intl minutes            9.51e-03  -1.83e-02               2.86e-03   \n",
              "Total intl calls              2.07e-02  -2.42e-02               1.40e-02   \n",
              "Total intl charge             9.55e-03  -1.84e-02               2.88e-03   \n",
              "Customer service calls       -3.80e-03   2.76e-02              -1.33e-02   \n",
              "Churn                         1.65e-02   6.17e-03              -8.97e-02   \n",
              "\n",
              "                        Total day minutes  Total day calls  Total day charge  \\\n",
              "Account length                   6.22e-03         3.85e-02          6.21e-03   \n",
              "Area code                       -8.26e-03        -9.65e-03         -8.26e-03   \n",
              "Number vmail messages            7.78e-04        -9.55e-03          7.76e-04   \n",
              "Total day minutes                1.00e+00         6.75e-03          1.00e+00   \n",
              "Total day calls                  6.75e-03         1.00e+00          6.75e-03   \n",
              "Total day charge                 1.00e+00         6.75e-03          1.00e+00   \n",
              "Total eve minutes                7.04e-03        -2.15e-02          7.05e-03   \n",
              "Total eve calls                  1.58e-02         6.46e-03          1.58e-02   \n",
              "Total eve charge                 7.03e-03        -2.14e-02          7.04e-03   \n",
              "Total night minutes              4.32e-03         2.29e-02          4.32e-03   \n",
              "Total night calls                2.30e-02        -1.96e-02          2.30e-02   \n",
              "Total night charge               4.30e-03         2.29e-02          4.30e-03   \n",
              "Total intl minutes              -1.02e-02         2.16e-02         -1.02e-02   \n",
              "Total intl calls                 8.03e-03         4.57e-03          8.03e-03   \n",
              "Total intl charge               -1.01e-02         2.17e-02         -1.01e-02   \n",
              "Customer service calls          -1.34e-02        -1.89e-02         -1.34e-02   \n",
              "Churn                            2.05e-01         1.85e-02          2.05e-01   \n",
              "\n",
              "                        Total eve minutes  Total eve calls  Total eve charge  \\\n",
              "Account length                  -6.76e-03         1.93e-02         -6.75e-03   \n",
              "Area code                        3.58e-03        -1.19e-02          3.61e-03   \n",
              "Number vmail messages            1.76e-02        -5.86e-03          1.76e-02   \n",
              "Total day minutes                7.04e-03         1.58e-02          7.03e-03   \n",
              "Total day calls                 -2.15e-02         6.46e-03         -2.14e-02   \n",
              "Total day charge                 7.05e-03         1.58e-02          7.04e-03   \n",
              "Total eve minutes                1.00e+00        -1.14e-02          1.00e+00   \n",
              "Total eve calls                 -1.14e-02         1.00e+00         -1.14e-02   \n",
              "Total eve charge                 1.00e+00        -1.14e-02          1.00e+00   \n",
              "Total night minutes             -1.26e-02        -2.09e-03         -1.26e-02   \n",
              "Total night calls                7.59e-03         7.71e-03          7.60e-03   \n",
              "Total night charge              -1.26e-02        -2.06e-03         -1.26e-02   \n",
              "Total intl minutes              -1.10e-02         8.70e-03         -1.10e-02   \n",
              "Total intl calls                 2.54e-03         1.74e-02          2.54e-03   \n",
              "Total intl charge               -1.11e-02         8.67e-03         -1.11e-02   \n",
              "Customer service calls          -1.30e-02         2.42e-03         -1.30e-02   \n",
              "Churn                            9.28e-02         9.23e-03          9.28e-02   \n",
              "\n",
              "                        Total night minutes  Total night calls  \\\n",
              "Account length                    -8.96e-03          -1.32e-02   \n",
              "Area code                         -5.82e-03           1.65e-02   \n",
              "Number vmail messages              7.68e-03           7.12e-03   \n",
              "Total day minutes                  4.32e-03           2.30e-02   \n",
              "Total day calls                    2.29e-02          -1.96e-02   \n",
              "Total day charge                   4.32e-03           2.30e-02   \n",
              "Total eve minutes                 -1.26e-02           7.59e-03   \n",
              "Total eve calls                   -2.09e-03           7.71e-03   \n",
              "Total eve charge                  -1.26e-02           7.60e-03   \n",
              "Total night minutes                1.00e+00           1.12e-02   \n",
              "Total night calls                  1.12e-02           1.00e+00   \n",
              "Total night charge                 1.00e+00           1.12e-02   \n",
              "Total intl minutes                -1.52e-02          -1.36e-02   \n",
              "Total intl calls                  -1.24e-02           3.05e-04   \n",
              "Total intl charge                 -1.52e-02          -1.36e-02   \n",
              "Customer service calls            -9.29e-03          -1.28e-02   \n",
              "Churn                              3.55e-02           6.14e-03   \n",
              "\n",
              "                        Total night charge  Total intl minutes  \\\n",
              "Account length                   -8.96e-03            9.51e-03   \n",
              "Area code                        -5.85e-03           -1.83e-02   \n",
              "Number vmail messages             7.66e-03            2.86e-03   \n",
              "Total day minutes                 4.30e-03           -1.02e-02   \n",
              "Total day calls                   2.29e-02            2.16e-02   \n",
              "Total day charge                  4.30e-03           -1.02e-02   \n",
              "Total eve minutes                -1.26e-02           -1.10e-02   \n",
              "Total eve calls                  -2.06e-03            8.70e-03   \n",
              "Total eve charge                 -1.26e-02           -1.10e-02   \n",
              "Total night minutes               1.00e+00           -1.52e-02   \n",
              "Total night calls                 1.12e-02           -1.36e-02   \n",
              "Total night charge                1.00e+00           -1.52e-02   \n",
              "Total intl minutes               -1.52e-02            1.00e+00   \n",
              "Total intl calls                 -1.23e-02            3.23e-02   \n",
              "Total intl charge                -1.52e-02            1.00e+00   \n",
              "Customer service calls           -9.28e-03           -9.64e-03   \n",
              "Churn                             3.55e-02            6.82e-02   \n",
              "\n",
              "                        Total intl calls  Total intl charge  \\\n",
              "Account length                  2.07e-02           9.55e-03   \n",
              "Area code                      -2.42e-02          -1.84e-02   \n",
              "Number vmail messages           1.40e-02           2.88e-03   \n",
              "Total day minutes               8.03e-03          -1.01e-02   \n",
              "Total day calls                 4.57e-03           2.17e-02   \n",
              "Total day charge                8.03e-03          -1.01e-02   \n",
              "Total eve minutes               2.54e-03          -1.11e-02   \n",
              "Total eve calls                 1.74e-02           8.67e-03   \n",
              "Total eve charge                2.54e-03          -1.11e-02   \n",
              "Total night minutes            -1.24e-02          -1.52e-02   \n",
              "Total night calls               3.05e-04          -1.36e-02   \n",
              "Total night charge             -1.23e-02          -1.52e-02   \n",
              "Total intl minutes              3.23e-02           1.00e+00   \n",
              "Total intl calls                1.00e+00           3.24e-02   \n",
              "Total intl charge               3.24e-02           1.00e+00   \n",
              "Customer service calls         -1.76e-02          -9.67e-03   \n",
              "Churn                          -5.28e-02           6.83e-02   \n",
              "\n",
              "                        Customer service calls     Churn  \n",
              "Account length                       -3.80e-03  1.65e-02  \n",
              "Area code                             2.76e-02  6.17e-03  \n",
              "Number vmail messages                -1.33e-02 -8.97e-02  \n",
              "Total day minutes                    -1.34e-02  2.05e-01  \n",
              "Total day calls                      -1.89e-02  1.85e-02  \n",
              "Total day charge                     -1.34e-02  2.05e-01  \n",
              "Total eve minutes                    -1.30e-02  9.28e-02  \n",
              "Total eve calls                       2.42e-03  9.23e-03  \n",
              "Total eve charge                     -1.30e-02  9.28e-02  \n",
              "Total night minutes                  -9.29e-03  3.55e-02  \n",
              "Total night calls                    -1.28e-02  6.14e-03  \n",
              "Total night charge                   -9.28e-03  3.55e-02  \n",
              "Total intl minutes                   -9.64e-03  6.82e-02  \n",
              "Total intl calls                     -1.76e-02 -5.28e-02  \n",
              "Total intl charge                    -9.67e-03  6.83e-02  \n",
              "Customer service calls                1.00e+00  2.09e-01  \n",
              "Churn                                 2.09e-01  1.00e+00  "
            ],
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              "      <th></th>\n",
              "      <th>Account length</th>\n",
              "      <th>Area code</th>\n",
              "      <th>Number vmail messages</th>\n",
              "      <th>Total day minutes</th>\n",
              "      <th>Total day calls</th>\n",
              "      <th>Total day charge</th>\n",
              "      <th>Total eve minutes</th>\n",
              "      <th>Total eve calls</th>\n",
              "      <th>Total eve charge</th>\n",
              "      <th>Total night minutes</th>\n",
              "      <th>Total night calls</th>\n",
              "      <th>Total night charge</th>\n",
              "      <th>Total intl minutes</th>\n",
              "      <th>Total intl calls</th>\n",
              "      <th>Total intl charge</th>\n",
              "      <th>Customer service calls</th>\n",
              "      <th>Churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>Account length</th>\n",
              "      <td>1.00e+00</td>\n",
              "      <td>-1.25e-02</td>\n",
              "      <td>-4.63e-03</td>\n",
              "      <td>6.22e-03</td>\n",
              "      <td>3.85e-02</td>\n",
              "      <td>6.21e-03</td>\n",
              "      <td>-6.76e-03</td>\n",
              "      <td>1.93e-02</td>\n",
              "      <td>-6.75e-03</td>\n",
              "      <td>-8.96e-03</td>\n",
              "      <td>-1.32e-02</td>\n",
              "      <td>-8.96e-03</td>\n",
              "      <td>9.51e-03</td>\n",
              "      <td>2.07e-02</td>\n",
              "      <td>9.55e-03</td>\n",
              "      <td>-3.80e-03</td>\n",
              "      <td>1.65e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Area code</th>\n",
              "      <td>-1.25e-02</td>\n",
              "      <td>1.00e+00</td>\n",
              "      <td>-1.99e-03</td>\n",
              "      <td>-8.26e-03</td>\n",
              "      <td>-9.65e-03</td>\n",
              "      <td>-8.26e-03</td>\n",
              "      <td>3.58e-03</td>\n",
              "      <td>-1.19e-02</td>\n",
              "      <td>3.61e-03</td>\n",
              "      <td>-5.82e-03</td>\n",
              "      <td>1.65e-02</td>\n",
              "      <td>-5.85e-03</td>\n",
              "      <td>-1.83e-02</td>\n",
              "      <td>-2.42e-02</td>\n",
              "      <td>-1.84e-02</td>\n",
              "      <td>2.76e-02</td>\n",
              "      <td>6.17e-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Number vmail messages</th>\n",
              "      <td>-4.63e-03</td>\n",
              "      <td>-1.99e-03</td>\n",
              "      <td>1.00e+00</td>\n",
              "      <td>7.78e-04</td>\n",
              "      <td>-9.55e-03</td>\n",
              "      <td>7.76e-04</td>\n",
              "      <td>1.76e-02</td>\n",
              "      <td>-5.86e-03</td>\n",
              "      <td>1.76e-02</td>\n",
              "      <td>7.68e-03</td>\n",
              "      <td>7.12e-03</td>\n",
              "      <td>7.66e-03</td>\n",
              "      <td>2.86e-03</td>\n",
              "      <td>1.40e-02</td>\n",
              "      <td>2.88e-03</td>\n",
              "      <td>-1.33e-02</td>\n",
              "      <td>-8.97e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total day minutes</th>\n",
              "      <td>6.22e-03</td>\n",
              "      <td>-8.26e-03</td>\n",
              "      <td>7.78e-04</td>\n",
              "      <td>1.00e+00</td>\n",
              "      <td>6.75e-03</td>\n",
              "      <td>1.00e+00</td>\n",
              "      <td>7.04e-03</td>\n",
              "      <td>1.58e-02</td>\n",
              "      <td>7.03e-03</td>\n",
              "      <td>4.32e-03</td>\n",
              "      <td>2.30e-02</td>\n",
              "      <td>4.30e-03</td>\n",
              "      <td>-1.02e-02</td>\n",
              "      <td>8.03e-03</td>\n",
              "      <td>-1.01e-02</td>\n",
              "      <td>-1.34e-02</td>\n",
              "      <td>2.05e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total day calls</th>\n",
              "      <td>3.85e-02</td>\n",
              "      <td>-9.65e-03</td>\n",
              "      <td>-9.55e-03</td>\n",
              "      <td>6.75e-03</td>\n",
              "      <td>1.00e+00</td>\n",
              "      <td>6.75e-03</td>\n",
              "      <td>-2.15e-02</td>\n",
              "      <td>6.46e-03</td>\n",
              "      <td>-2.14e-02</td>\n",
              "      <td>2.29e-02</td>\n",
              "      <td>-1.96e-02</td>\n",
              "      <td>2.29e-02</td>\n",
              "      <td>2.16e-02</td>\n",
              "      <td>4.57e-03</td>\n",
              "      <td>2.17e-02</td>\n",
              "      <td>-1.89e-02</td>\n",
              "      <td>1.85e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total day charge</th>\n",
              "      <td>6.21e-03</td>\n",
              "      <td>-8.26e-03</td>\n",
              "      <td>7.76e-04</td>\n",
              "      <td>1.00e+00</td>\n",
              "      <td>6.75e-03</td>\n",
              "      <td>1.00e+00</td>\n",
              "      <td>7.05e-03</td>\n",
              "      <td>1.58e-02</td>\n",
              "      <td>7.04e-03</td>\n",
              "      <td>4.32e-03</td>\n",
              "      <td>2.30e-02</td>\n",
              "      <td>4.30e-03</td>\n",
              "      <td>-1.02e-02</td>\n",
              "      <td>8.03e-03</td>\n",
              "      <td>-1.01e-02</td>\n",
              "      <td>-1.34e-02</td>\n",
              "      <td>2.05e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total eve minutes</th>\n",
              "      <td>-6.76e-03</td>\n",
              "      <td>3.58e-03</td>\n",
              "      <td>1.76e-02</td>\n",
              "      <td>7.04e-03</td>\n",
              "      <td>-2.15e-02</td>\n",
              "      <td>7.05e-03</td>\n",
              "      <td>1.00e+00</td>\n",
              "      <td>-1.14e-02</td>\n",
              "      <td>1.00e+00</td>\n",
              "      <td>-1.26e-02</td>\n",
              "      <td>7.59e-03</td>\n",
              "      <td>-1.26e-02</td>\n",
              "      <td>-1.10e-02</td>\n",
              "      <td>2.54e-03</td>\n",
              "      <td>-1.11e-02</td>\n",
              "      <td>-1.30e-02</td>\n",
              "      <td>9.28e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total eve calls</th>\n",
              "      <td>1.93e-02</td>\n",
              "      <td>-1.19e-02</td>\n",
              "      <td>-5.86e-03</td>\n",
              "      <td>1.58e-02</td>\n",
              "      <td>6.46e-03</td>\n",
              "      <td>1.58e-02</td>\n",
              "      <td>-1.14e-02</td>\n",
              "      <td>1.00e+00</td>\n",
              "      <td>-1.14e-02</td>\n",
              "      <td>-2.09e-03</td>\n",
              "      <td>7.71e-03</td>\n",
              "      <td>-2.06e-03</td>\n",
              "      <td>8.70e-03</td>\n",
              "      <td>1.74e-02</td>\n",
              "      <td>8.67e-03</td>\n",
              "      <td>2.42e-03</td>\n",
              "      <td>9.23e-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total eve charge</th>\n",
              "      <td>-6.75e-03</td>\n",
              "      <td>3.61e-03</td>\n",
              "      <td>1.76e-02</td>\n",
              "      <td>7.03e-03</td>\n",
              "      <td>-2.14e-02</td>\n",
              "      <td>7.04e-03</td>\n",
              "      <td>1.00e+00</td>\n",
              "      <td>-1.14e-02</td>\n",
              "      <td>1.00e+00</td>\n",
              "      <td>-1.26e-02</td>\n",
              "      <td>7.60e-03</td>\n",
              "      <td>-1.26e-02</td>\n",
              "      <td>-1.10e-02</td>\n",
              "      <td>2.54e-03</td>\n",
              "      <td>-1.11e-02</td>\n",
              "      <td>-1.30e-02</td>\n",
              "      <td>9.28e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total night minutes</th>\n",
              "      <td>-8.96e-03</td>\n",
              "      <td>-5.82e-03</td>\n",
              "      <td>7.68e-03</td>\n",
              "      <td>4.32e-03</td>\n",
              "      <td>2.29e-02</td>\n",
              "      <td>4.32e-03</td>\n",
              "      <td>-1.26e-02</td>\n",
              "      <td>-2.09e-03</td>\n",
              "      <td>-1.26e-02</td>\n",
              "      <td>1.00e+00</td>\n",
              "      <td>1.12e-02</td>\n",
              "      <td>1.00e+00</td>\n",
              "      <td>-1.52e-02</td>\n",
              "      <td>-1.24e-02</td>\n",
              "      <td>-1.52e-02</td>\n",
              "      <td>-9.29e-03</td>\n",
              "      <td>3.55e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total night calls</th>\n",
              "      <td>-1.32e-02</td>\n",
              "      <td>1.65e-02</td>\n",
              "      <td>7.12e-03</td>\n",
              "      <td>2.30e-02</td>\n",
              "      <td>-1.96e-02</td>\n",
              "      <td>2.30e-02</td>\n",
              "      <td>7.59e-03</td>\n",
              "      <td>7.71e-03</td>\n",
              "      <td>7.60e-03</td>\n",
              "      <td>1.12e-02</td>\n",
              "      <td>1.00e+00</td>\n",
              "      <td>1.12e-02</td>\n",
              "      <td>-1.36e-02</td>\n",
              "      <td>3.05e-04</td>\n",
              "      <td>-1.36e-02</td>\n",
              "      <td>-1.28e-02</td>\n",
              "      <td>6.14e-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total night charge</th>\n",
              "      <td>-8.96e-03</td>\n",
              "      <td>-5.85e-03</td>\n",
              "      <td>7.66e-03</td>\n",
              "      <td>4.30e-03</td>\n",
              "      <td>2.29e-02</td>\n",
              "      <td>4.30e-03</td>\n",
              "      <td>-1.26e-02</td>\n",
              "      <td>-2.06e-03</td>\n",
              "      <td>-1.26e-02</td>\n",
              "      <td>1.00e+00</td>\n",
              "      <td>1.12e-02</td>\n",
              "      <td>1.00e+00</td>\n",
              "      <td>-1.52e-02</td>\n",
              "      <td>-1.23e-02</td>\n",
              "      <td>-1.52e-02</td>\n",
              "      <td>-9.28e-03</td>\n",
              "      <td>3.55e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total intl minutes</th>\n",
              "      <td>9.51e-03</td>\n",
              "      <td>-1.83e-02</td>\n",
              "      <td>2.86e-03</td>\n",
              "      <td>-1.02e-02</td>\n",
              "      <td>2.16e-02</td>\n",
              "      <td>-1.02e-02</td>\n",
              "      <td>-1.10e-02</td>\n",
              "      <td>8.70e-03</td>\n",
              "      <td>-1.10e-02</td>\n",
              "      <td>-1.52e-02</td>\n",
              "      <td>-1.36e-02</td>\n",
              "      <td>-1.52e-02</td>\n",
              "      <td>1.00e+00</td>\n",
              "      <td>3.23e-02</td>\n",
              "      <td>1.00e+00</td>\n",
              "      <td>-9.64e-03</td>\n",
              "      <td>6.82e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total intl calls</th>\n",
              "      <td>2.07e-02</td>\n",
              "      <td>-2.42e-02</td>\n",
              "      <td>1.40e-02</td>\n",
              "      <td>8.03e-03</td>\n",
              "      <td>4.57e-03</td>\n",
              "      <td>8.03e-03</td>\n",
              "      <td>2.54e-03</td>\n",
              "      <td>1.74e-02</td>\n",
              "      <td>2.54e-03</td>\n",
              "      <td>-1.24e-02</td>\n",
              "      <td>3.05e-04</td>\n",
              "      <td>-1.23e-02</td>\n",
              "      <td>3.23e-02</td>\n",
              "      <td>1.00e+00</td>\n",
              "      <td>3.24e-02</td>\n",
              "      <td>-1.76e-02</td>\n",
              "      <td>-5.28e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total intl charge</th>\n",
              "      <td>9.55e-03</td>\n",
              "      <td>-1.84e-02</td>\n",
              "      <td>2.88e-03</td>\n",
              "      <td>-1.01e-02</td>\n",
              "      <td>2.17e-02</td>\n",
              "      <td>-1.01e-02</td>\n",
              "      <td>-1.11e-02</td>\n",
              "      <td>8.67e-03</td>\n",
              "      <td>-1.11e-02</td>\n",
              "      <td>-1.52e-02</td>\n",
              "      <td>-1.36e-02</td>\n",
              "      <td>-1.52e-02</td>\n",
              "      <td>1.00e+00</td>\n",
              "      <td>3.24e-02</td>\n",
              "      <td>1.00e+00</td>\n",
              "      <td>-9.67e-03</td>\n",
              "      <td>6.83e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Customer service calls</th>\n",
              "      <td>-3.80e-03</td>\n",
              "      <td>2.76e-02</td>\n",
              "      <td>-1.33e-02</td>\n",
              "      <td>-1.34e-02</td>\n",
              "      <td>-1.89e-02</td>\n",
              "      <td>-1.34e-02</td>\n",
              "      <td>-1.30e-02</td>\n",
              "      <td>2.42e-03</td>\n",
              "      <td>-1.30e-02</td>\n",
              "      <td>-9.29e-03</td>\n",
              "      <td>-1.28e-02</td>\n",
              "      <td>-9.28e-03</td>\n",
              "      <td>-9.64e-03</td>\n",
              "      <td>-1.76e-02</td>\n",
              "      <td>-9.67e-03</td>\n",
              "      <td>1.00e+00</td>\n",
              "      <td>2.09e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Churn</th>\n",
              "      <td>1.65e-02</td>\n",
              "      <td>6.17e-03</td>\n",
              "      <td>-8.97e-02</td>\n",
              "      <td>2.05e-01</td>\n",
              "      <td>1.85e-02</td>\n",
              "      <td>2.05e-01</td>\n",
              "      <td>9.28e-02</td>\n",
              "      <td>9.23e-03</td>\n",
              "      <td>9.28e-02</td>\n",
              "      <td>3.55e-02</td>\n",
              "      <td>6.14e-03</td>\n",
              "      <td>3.55e-02</td>\n",
              "      <td>6.82e-02</td>\n",
              "      <td>-5.28e-02</td>\n",
              "      <td>6.83e-02</td>\n",
              "      <td>2.09e-01</td>\n",
              "      <td>1.00e+00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3b743336-d25f-426f-80df-159c4eb2fdf5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3b743336-d25f-426f-80df-159c4eb2fdf5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3b743336-d25f-426f-80df-159c4eb2fdf5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "fig, ax = plt.subplots(figsize=(15,15)) \n",
        "sns.heatmap(df.corr(),fmt='.2g',annot=True,ax=ax)"
      ],
      "metadata": {
        "id": "QifujkCI4Q-7",
        "outputId": "9c9c5290-aecd-4d31-f40e-1bae236029ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 746
        }
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f049a3f06d0>"
            ]
          },
          "metadata": {},
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x1080 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "### Sorting\n",
        "\n",
        "A DataFrame can be sorted by the value of one of the variables (i.e columns). For example, we can sort by *Total day charge* (use `ascending=False` to sort in descending order):\n"
      ],
      "metadata": {
        "_uuid": "7f54ceacde8fe8b61fca39694651c82ba43a5b2c",
        "id": "nFIW5DryUhy7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.sort_values(by='Total day charge',ascending=False).head()"
      ],
      "metadata": {
        "_uuid": "419200266ac1e7d0e96ba1a20d756c9d87eb2541",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "id": "ritYM2vPUhy7",
        "outputId": "02eab95d-15f7-4bd7-fc6f-f4434e8e6f8c"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     State  Account length  Area code International plan Voice mail plan  \\\n",
              "365     CO             154        415                 No              No   \n",
              "985     NY              64        415                Yes              No   \n",
              "2594    OH             115        510                Yes              No   \n",
              "156     OH              83        415                 No              No   \n",
              "605     MO             112        415                 No              No   \n",
              "\n",
              "      Number vmail messages  Total day minutes  Total day calls  \\\n",
              "365                       0              350.8               75   \n",
              "985                       0              346.8               55   \n",
              "2594                      0              345.3               81   \n",
              "156                       0              337.4              120   \n",
              "605                       0              335.5               77   \n",
              "\n",
              "      Total day charge  Total eve minutes  Total eve calls  Total eve charge  \\\n",
              "365              59.64              216.5               94             18.40   \n",
              "985              58.96              249.5               79             21.21   \n",
              "2594             58.70              203.4              106             17.29   \n",
              "156              57.36              227.4              116             19.33   \n",
              "605              57.04              212.5              109             18.06   \n",
              "\n",
              "      Total night minutes  Total night calls  Total night charge  \\\n",
              "365                 253.9                100               11.43   \n",
              "985                 275.4                102               12.39   \n",
              "2594                217.5                107                9.79   \n",
              "156                 153.9                114                6.93   \n",
              "605                 265.0                132               11.93   \n",
              "\n",
              "      Total intl minutes  Total intl calls  Total intl charge  \\\n",
              "365                 10.1                 9               2.73   \n",
              "985                 13.3                 9               3.59   \n",
              "2594                11.8                 8               3.19   \n",
              "156                 15.8                 7               4.27   \n",
              "605                 12.7                 8               3.43   \n",
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              "      Customer service calls  Churn  \n",
              "365                        1      1  \n",
              "985                        1      1  \n",
              "2594                       1      1  \n",
              "156                        0      1  \n",
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          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can also sort by multiple columns:"
      ],
      "metadata": {
        "_uuid": "ad38052b7cc86b6ff5b35907c5a69ed3dacfcdef",
        "id": "j5ug1wx7Uhy8"
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    },
    {
      "cell_type": "code",
      "source": [
        "df.sort_values(by=['State','Total day charge'],ascending=[False,True]).head()"
      ],
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        "_uuid": "db028bb799847b257a1107226ec599484596bc1e",
        "trusted": true,
        "id": "E9OD0LS-Uhy9",
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          "height": 305
        },
        "outputId": "40c2ce36-23ba-4bd5-9798-76d9f7bebaf0"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     State  Account length  Area code International plan Voice mail plan  \\\n",
              "1076    WY              53        415                 No             Yes   \n",
              "1999    WY             160        408                 No              No   \n",
              "238     WY             215        510                 No              No   \n",
              "1689    WY              90        408                 No             Yes   \n",
              "1423    WY             127        415                Yes             Yes   \n",
              "\n",
              "      Number vmail messages  Total day minutes  Total day calls  \\\n",
              "1076                     27               25.9              119   \n",
              "1999                      0               82.7              116   \n",
              "238                       0               83.6              148   \n",
              "1689                     39               94.8               89   \n",
              "1423                     28               95.9              117   \n",
              "\n",
              "      Total day charge  Total eve minutes  Total eve calls  Total eve charge  \\\n",
              "1076              4.40              206.5               96             17.55   \n",
              "1999             14.06              194.6               95             16.54   \n",
              "238              14.21              120.9               91             10.28   \n",
              "1689             16.12              219.1               91             18.62   \n",
              "1423             16.30              159.5              131             13.56   \n",
              "\n",
              "      Total night minutes  Total night calls  Total night charge  \\\n",
              "1076                228.1                 64               10.26   \n",
              "1999                159.0                 54                7.15   \n",
              "238                 226.6                110               10.20   \n",
              "1689                197.4                 65                8.88   \n",
              "1423                152.8                132                6.88   \n",
              "\n",
              "      Total intl minutes  Total intl calls  Total intl charge  \\\n",
              "1076                 6.5                 7               1.76   \n",
              "1999                10.9                 9               2.94   \n",
              "238                 10.7                 9               2.89   \n",
              "1689                11.4                 5               3.08   \n",
              "1423                10.4                 3               2.81   \n",
              "\n",
              "      Customer service calls  Churn  \n",
              "1076                       1      0  \n",
              "1999                       0      0  \n",
              "238                        0      0  \n",
              "1689                       1      0  \n",
              "1423                       1      0  "
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6651edb8-d627-49d8-93f9-e3581d366dbd button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6651edb8-d627-49d8-93f9-e3581d366dbd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Indexing and retrieving data\n",
        "\n",
        "A DataFrame can be indexed in a few different ways. \n",
        "\n",
        "To get a single column, you can use a `DataFrame['Name']` construction. Let's use this to answer a question about that column alone: **what is the proportion of churned users in our dataframe?**"
      ],
      "metadata": {
        "_uuid": "ff41ebcef7b883413530260388df7e23996862ba",
        "id": "zXeapCklUhy9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Churn'].mean()"
      ],
      "metadata": {
        "_uuid": "b020796a83bc9e10b1bc8e9c6439fc006302d999",
        "trusted": true,
        "id": "_ffeel8XUhy-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b0befa00-1cfa-4e26-a61d-5a2301fa0d6e"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.14491449144914492"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "14.5% is actually quite bad for a company; such a churn rate can make the company go bankrupt.\n",
        "\n",
        "**Boolean indexing** with one column is also very convenient. The syntax is `df[P(df['Name'])]`, where `P` is some logical condition that is checked for each element of the `Name` column. The result of such indexing is the DataFrame consisting only of rows that satisfy the `P` condition on the `Name` column. \n",
        "\n",
        "Let's use it to answer the question:\n",
        "\n",
        "**What are average values of numerical features for churned users?**"
      ],
      "metadata": {
        "_uuid": "8abe3b0982e4599e775f08d8f2feab2b8a702a7b",
        "id": "HkCackrQUhy_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[df[\"Churn\"] == 1].mean()"
      ],
      "metadata": {
        "_uuid": "3712d185e50d44c65128468c603b120b75786c84",
        "trusted": true,
        "id": "B4DqVN_YUhzA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7c92945b-66b3-4db1-9a78-c9fd0341da7a"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-31-c961b1669f63>:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
            "  df[df[\"Churn\"] == 1].mean()\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Account length            102.66\n",
              "Area code                 437.82\n",
              "Number vmail messages       5.12\n",
              "Total day minutes         206.91\n",
              "Total day calls           101.34\n",
              "Total day charge           35.18\n",
              "Total eve minutes         212.41\n",
              "Total eve calls           100.56\n",
              "Total eve charge           18.05\n",
              "Total night minutes       205.23\n",
              "Total night calls         100.40\n",
              "Total night charge          9.24\n",
              "Total intl minutes         10.70\n",
              "Total intl calls            4.16\n",
              "Total intl charge           2.89\n",
              "Customer service calls      2.23\n",
              "Churn                       1.00\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**How much time (on average) do churned users spend on the phone during daytime?**"
      ],
      "metadata": {
        "_uuid": "126e527e04c244c71edd9eac3abcc4a0a8bcd2cb",
        "id": "uMedVN3uUhzB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Total day minutes'][df[\"Churn\"] == 1].mean()"
      ],
      "metadata": {
        "_uuid": "314dfa8e0aeaa0a1d333e32234bd9b3e0a001348",
        "trusted": true,
        "id": "A8sutw-cUhzB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "88a080f9-b3d4-40fd-9c95-e129a85c7cdb"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "206.91407867494823"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "**What is the maximum length of international calls among loyal users (`Churn == 0`) who do not have an international plan?**\n",
        "\n"
      ],
      "metadata": {
        "_uuid": "ef0358493f1731814d1bb34759ec6a0fad822487",
        "id": "1VmOXlAQUhzC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Total intl minutes'][(df[\"Churn\"] == 0) & (df['International plan'] == 'No')].max()"
      ],
      "metadata": {
        "_uuid": "41874ec9bde4b2474a0bb9faf93795b2ae6cdf24",
        "trusted": true,
        "id": "f0geaUO-UhzD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "17db0723-a95b-4e1a-88a2-c527c32527f4"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "18.9"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "id": "_4PMa2Le7gwM",
        "outputId": "113e5ee2-a4cf-451c-e317-854763d0c4b8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['State', 'Account length', 'Area code', 'International plan',\n",
              "       'Voice mail plan', 'Number vmail messages', 'Total day minutes',\n",
              "       'Total day calls', 'Total day charge', 'Total eve minutes',\n",
              "       'Total eve calls', 'Total eve charge', 'Total night minutes',\n",
              "       'Total night calls', 'Total night charge', 'Total intl minutes',\n",
              "       'Total intl calls', 'Total intl charge', 'Customer service calls',\n",
              "       'Churn'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "DataFrames can be indexed by column name (label) or row name (index) or by the serial number of a row. The `loc` method is used for **indexing by name**, while `iloc()` is used for **indexing by number**.\n",
        "\n",
        "In the first case below, we say *\"give us the values of the rows with index from 0 to 5 (inclusive) and columns labeled from State to Area code (inclusive)\"*. In the second case, we say *\"give us the values of the first five rows in the first three columns\"* (as in a typical Python slice: the maximal value is not included)."
      ],
      "metadata": {
        "_uuid": "69353301d299fd09c3114afef0778693a163418e",
        "id": "bOcWO-TaUhzD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[0:5,'State':'Area code']"
      ],
      "metadata": {
        "_uuid": "e16024cbb42c7612a6805ed1442ca68cecd4526f",
        "trusted": true,
        "id": "vQCF-wuJUhzE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "55e5335f-7cdd-4efb-9092-477e0b36f818"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  State  Account length  Area code\n",
              "0    KS             128        415\n",
              "1    OH             107        415\n",
              "2    NJ             137        415\n",
              "3    OH              84        408\n",
              "4    OK              75        415\n",
              "5    AL             118        510"
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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      },
      "execution_count": 39,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  State  Account length  Area code\n",
              "0    KS             128        415\n",
              "1    OH             107        415\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "If we need the first or the last line of the data frame, we can use the `df[:1]` or `df[-1:]` construct:"
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      "execution_count": 41,
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              "  State  Account length  Area code International plan Voice mail plan  \\\n",
              "0    KS             128        415                 No             Yes   \n",
              "\n",
              "   Number vmail messages  Total day minutes  Total day calls  \\\n",
              "0                     25              265.1              110   \n",
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              "   Total day charge  Total eve minutes  Total eve calls  Total eve charge  \\\n",
              "0             45.07              197.4               99             16.78   \n",
              "\n",
              "   Total night minutes  Total night calls  Total night charge  \\\n",
              "0                244.7                 91               11.01   \n",
              "\n",
              "   Total intl minutes  Total intl calls  Total intl charge  \\\n",
              "0                10.0                 3                2.7   \n",
              "\n",
              "   Customer service calls  Churn  \n",
              "0                       1      0  "
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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    {
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        "df.iloc[-1:,:]"
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        "outputId": "8e8333a7-f329-48e5-d976-fdd257f4695d",
        "colab": {
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          "height": 179
        }
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     State  Account length  Area code International plan Voice mail plan  \\\n",
              "3332    TN              74        415                 No             Yes   \n",
              "\n",
              "      Number vmail messages  Total day minutes  Total day calls  \\\n",
              "3332                     25              234.4              113   \n",
              "\n",
              "      Total day charge  Total eve minutes  Total eve calls  Total eve charge  \\\n",
              "3332             39.85              265.9               82              22.6   \n",
              "\n",
              "      Total night minutes  Total night calls  Total night charge  \\\n",
              "3332                241.4                 77               10.86   \n",
              "\n",
              "      Total intl minutes  Total intl calls  Total intl charge  \\\n",
              "3332                13.7                 4                3.7   \n",
              "\n",
              "      Customer service calls  Churn  \n",
              "3332                       0      0  "
            ],
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              "      <th>3332</th>\n",
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              "      <td>25</td>\n",
              "      <td>234.4</td>\n",
              "      <td>113</td>\n",
              "      <td>39.85</td>\n",
              "      <td>265.9</td>\n",
              "      <td>82</td>\n",
              "      <td>22.6</td>\n",
              "      <td>241.4</td>\n",
              "      <td>77</td>\n",
              "      <td>10.86</td>\n",
              "      <td>13.7</td>\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    }\n",
              "\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-663a02b5-5ae6-45dc-857f-890e09f07af0 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-663a02b5-5ae6-45dc-857f-890e09f07af0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
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              "  </div>\n",
              "  "
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          "metadata": {},
          "execution_count": 42
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    {
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        "df.iloc[:,:1]"
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        "colab": {
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          "height": 424
        }
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     State\n",
              "0       KS\n",
              "1       OH\n",
              "2       NJ\n",
              "3       OH\n",
              "4       OK\n",
              "...    ...\n",
              "3328    AZ\n",
              "3329    WV\n",
              "3330    RI\n",
              "3331    CT\n",
              "3332    TN\n",
              "\n",
              "[3333 rows x 1 columns]"
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              "      <th></th>\n",
              "      <th>State</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>KS</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>OH</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NJ</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>OH</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>OK</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3328</th>\n",
              "      <td>AZ</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3329</th>\n",
              "      <td>WV</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3330</th>\n",
              "      <td>RI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3331</th>\n",
              "      <td>CT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3332</th>\n",
              "      <td>TN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3333 rows × 1 columns</p>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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        "df.iloc[:,-1:]"
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      "execution_count": 44,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "      Churn\n",
              "0         0\n",
              "1         0\n",
              "2         0\n",
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              "4         0\n",
              "...     ...\n",
              "3328      0\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          "metadata": {},
          "execution_count": 44
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    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "### Applying Functions to Cells, Columns and Rows\n",
        "\n",
        "**To apply functions to each column, use `apply()`:**\n"
      ],
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        "df.apply(np.max)"
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      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "State                        WY\n",
              "Account length              243\n",
              "Area code                   510\n",
              "International plan          Yes\n",
              "Voice mail plan             Yes\n",
              "Number vmail messages        51\n",
              "Total day minutes         350.8\n",
              "Total day calls             165\n",
              "Total day charge          59.64\n",
              "Total eve minutes         363.7\n",
              "Total eve calls             170\n",
              "Total eve charge          30.91\n",
              "Total night minutes       395.0\n",
              "Total night calls           175\n",
              "Total night charge        17.77\n",
              "Total intl minutes         20.0\n",
              "Total intl calls             20\n",
              "Total intl charge           5.4\n",
              "Customer service calls        9\n",
              "Churn                         1\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The `apply` method can also be used to apply a function to each row. To do this, specify `axis=1`. Lambda functions are very convenient in such scenarios. For example, if we need to select all states starting with W, we can do it like this:"
      ],
      "metadata": {
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        "id": "_BWgChANUhzJ"
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    {
      "cell_type": "code",
      "source": [
        "df[df[\"State\"].apply(lambda state: state[0] == \"W\")].head()"
      ],
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        "_uuid": "39c1d7262441f2fb8ab93b359b8cb1b773fcbaee",
        "trusted": true,
        "id": "hJtbCp1JUhzJ",
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          "height": 305
        },
        "outputId": "22bbb307-c92c-4bf3-a3cd-e79f0877c834"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   State  Account length  Area code International plan Voice mail plan  \\\n",
              "9     WV             141        415                Yes             Yes   \n",
              "26    WY              57        408                 No             Yes   \n",
              "44    WI              64        510                 No              No   \n",
              "49    WY              97        415                 No             Yes   \n",
              "54    WY              87        415                 No              No   \n",
              "\n",
              "    Number vmail messages  Total day minutes  Total day calls  \\\n",
              "9                      37              258.6               84   \n",
              "26                     39              213.0              115   \n",
              "44                      0              154.0               67   \n",
              "49                     24              133.2              135   \n",
              "54                      0              151.0               83   \n",
              "\n",
              "    Total day charge  Total eve minutes  Total eve calls  Total eve charge  \\\n",
              "9              43.96              222.0              111             18.87   \n",
              "26             36.21              191.1              112             16.24   \n",
              "44             26.18              225.8              118             19.19   \n",
              "49             22.64              217.2               58             18.46   \n",
              "54             25.67              219.7              116             18.67   \n",
              "\n",
              "    Total night minutes  Total night calls  Total night charge  \\\n",
              "9                 326.4                 97               14.69   \n",
              "26                182.7                115                8.22   \n",
              "44                265.3                 86               11.94   \n",
              "49                 70.6                 79                3.18   \n",
              "54                203.9                127                9.18   \n",
              "\n",
              "    Total intl minutes  Total intl calls  Total intl charge  \\\n",
              "9                 11.2                 5               3.02   \n",
              "26                 9.5                 3               2.57   \n",
              "44                 3.5                 3               0.95   \n",
              "49                11.0                 3               2.97   \n",
              "54                 9.7                 3               2.62   \n",
              "\n",
              "    Customer service calls  Churn  \n",
              "9                        0      0  \n",
              "26                       0      0  \n",
              "44                       1      0  \n",
              "49                       1      0  \n",
              "54                       5      1  "
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              "      <td>115</td>\n",
              "      <td>8.22</td>\n",
              "      <td>9.5</td>\n",
              "      <td>3</td>\n",
              "      <td>2.57</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>WI</td>\n",
              "      <td>64</td>\n",
              "      <td>510</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>67</td>\n",
              "      <td>26.18</td>\n",
              "      <td>225.8</td>\n",
              "      <td>118</td>\n",
              "      <td>19.19</td>\n",
              "      <td>265.3</td>\n",
              "      <td>86</td>\n",
              "      <td>11.94</td>\n",
              "      <td>3.5</td>\n",
              "      <td>3</td>\n",
              "      <td>0.95</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>WY</td>\n",
              "      <td>97</td>\n",
              "      <td>415</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>24</td>\n",
              "      <td>133.2</td>\n",
              "      <td>135</td>\n",
              "      <td>22.64</td>\n",
              "      <td>217.2</td>\n",
              "      <td>58</td>\n",
              "      <td>18.46</td>\n",
              "      <td>70.6</td>\n",
              "      <td>79</td>\n",
              "      <td>3.18</td>\n",
              "      <td>11.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2.97</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>WY</td>\n",
              "      <td>87</td>\n",
              "      <td>415</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>151.0</td>\n",
              "      <td>83</td>\n",
              "      <td>25.67</td>\n",
              "      <td>219.7</td>\n",
              "      <td>116</td>\n",
              "      <td>18.67</td>\n",
              "      <td>203.9</td>\n",
              "      <td>127</td>\n",
              "      <td>9.18</td>\n",
              "      <td>9.7</td>\n",
              "      <td>3</td>\n",
              "      <td>2.62</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
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              "\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The `map` method can be used to **replace values in a column** by passing a dictionary of the form `{old_value: new_value}` as its argument:"
      ],
      "metadata": {
        "_uuid": "8189ed0376dd712ccd2357081e224087f7d3d0aa",
        "id": "GOY6ZMp-UhzK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "d = {\"No\": False, \"Yes\": True}\n",
        "df[\"International plan\"] = df[\"International plan\"].map(d)\n",
        "df.head()"
      ],
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        "_uuid": "c01e23976470fd6efc3604e4d755e93ac11c3eac",
        "trusted": true,
        "id": "3Ht8Rnt9UhzL",
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          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "outputId": "f3392333-8f5f-4e28-a9d5-6d5d552783f9"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  State  Account length  Area code  International plan Voice mail plan  \\\n",
              "0    KS             128        415               False             Yes   \n",
              "1    OH             107        415               False             Yes   \n",
              "2    NJ             137        415               False              No   \n",
              "3    OH              84        408                True              No   \n",
              "4    OK              75        415                True              No   \n",
              "\n",
              "   Number vmail messages  Total day minutes  Total day calls  \\\n",
              "0                     25              265.1              110   \n",
              "1                     26              161.6              123   \n",
              "2                      0              243.4              114   \n",
              "3                      0              299.4               71   \n",
              "4                      0              166.7              113   \n",
              "\n",
              "   Total day charge  Total eve minutes  Total eve calls  Total eve charge  \\\n",
              "0             45.07              197.4               99             16.78   \n",
              "1             27.47              195.5              103             16.62   \n",
              "2             41.38              121.2              110             10.30   \n",
              "3             50.90               61.9               88              5.26   \n",
              "4             28.34              148.3              122             12.61   \n",
              "\n",
              "   Total night minutes  Total night calls  Total night charge  \\\n",
              "0                244.7                 91               11.01   \n",
              "1                254.4                103               11.45   \n",
              "2                162.6                104                7.32   \n",
              "3                196.9                 89                8.86   \n",
              "4                186.9                121                8.41   \n",
              "\n",
              "   Total intl minutes  Total intl calls  Total intl charge  \\\n",
              "0                10.0                 3               2.70   \n",
              "1                13.7                 3               3.70   \n",
              "2                12.2                 5               3.29   \n",
              "3                 6.6                 7               1.78   \n",
              "4                10.1                 3               2.73   \n",
              "\n",
              "   Customer service calls  Churn  \n",
              "0                       1      0  \n",
              "1                       1      0  \n",
              "2                       0      0  \n",
              "3                       2      0  \n",
              "4                       3      0  "
            ],
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              "      <th>State</th>\n",
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              "      <th>International plan</th>\n",
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              "      <th>Total eve charge</th>\n",
              "      <th>Total night minutes</th>\n",
              "      <th>Total night calls</th>\n",
              "      <th>Total night charge</th>\n",
              "      <th>Total intl minutes</th>\n",
              "      <th>Total intl calls</th>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NJ</td>\n",
              "      <td>137</td>\n",
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              "      <td>0</td>\n",
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              "      <th>3</th>\n",
              "      <td>OH</td>\n",
              "      <td>84</td>\n",
              "      <td>408</td>\n",
              "      <td>True</td>\n",
              "      <td>No</td>\n",
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              "      <td>299.4</td>\n",
              "      <td>71</td>\n",
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              "      <td>61.9</td>\n",
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              "      <td>5.26</td>\n",
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              "      <td>89</td>\n",
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              "      <td>415</td>\n",
              "      <td>True</td>\n",
              "      <td>No</td>\n",
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              "      <td>166.7</td>\n",
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              "      <td>28.34</td>\n",
              "      <td>148.3</td>\n",
              "      <td>122</td>\n",
              "      <td>12.61</td>\n",
              "      <td>186.9</td>\n",
              "      <td>121</td>\n",
              "      <td>8.41</td>\n",
              "      <td>10.1</td>\n",
              "      <td>3</td>\n",
              "      <td>2.73</td>\n",
              "      <td>3</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d2913a5c-68fb-4468-a909-9bd8ad97fab8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The same thing can be done with the `replace` method:"
      ],
      "metadata": {
        "_uuid": "c4a2e2296e26b94ef6e8f0f457b13c42a5559568",
        "id": "B2dB7AE-UhzL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.replace({\"Voice mail plan\": d})\n",
        "df.head()"
      ],
      "metadata": {
        "_uuid": "624736c649a9875bfee158df92238c6348f5a6b0",
        "trusted": true,
        "id": "9H66oeFlUhzM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "outputId": "9bdbbdc3-3875-4ec1-f99b-dce1f009e418"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  State  Account length  Area code  International plan  Voice mail plan  \\\n",
              "0    KS             128        415               False             True   \n",
              "1    OH             107        415               False             True   \n",
              "2    NJ             137        415               False            False   \n",
              "3    OH              84        408                True            False   \n",
              "4    OK              75        415                True            False   \n",
              "\n",
              "   Number vmail messages  Total day minutes  Total day calls  \\\n",
              "0                     25              265.1              110   \n",
              "1                     26              161.6              123   \n",
              "2                      0              243.4              114   \n",
              "3                      0              299.4               71   \n",
              "4                      0              166.7              113   \n",
              "\n",
              "   Total day charge  Total eve minutes  Total eve calls  Total eve charge  \\\n",
              "0             45.07              197.4               99             16.78   \n",
              "1             27.47              195.5              103             16.62   \n",
              "2             41.38              121.2              110             10.30   \n",
              "3             50.90               61.9               88              5.26   \n",
              "4             28.34              148.3              122             12.61   \n",
              "\n",
              "   Total night minutes  Total night calls  Total night charge  \\\n",
              "0                244.7                 91               11.01   \n",
              "1                254.4                103               11.45   \n",
              "2                162.6                104                7.32   \n",
              "3                196.9                 89                8.86   \n",
              "4                186.9                121                8.41   \n",
              "\n",
              "   Total intl minutes  Total intl calls  Total intl charge  \\\n",
              "0                10.0                 3               2.70   \n",
              "1                13.7                 3               3.70   \n",
              "2                12.2                 5               3.29   \n",
              "3                 6.6                 7               1.78   \n",
              "4                10.1                 3               2.73   \n",
              "\n",
              "   Customer service calls  Churn  \n",
              "0                       1      0  \n",
              "1                       1      0  \n",
              "2                       0      0  \n",
              "3                       2      0  \n",
              "4                       3      0  "
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "### Grouping\n",
        "\n",
        "In general, grouping data in Pandas works as follows:\n"
      ],
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      "cell_type": "markdown",
      "source": [
        "\n",
        "```python\n",
        "df.groupby(by=grouping_columns)[columns_to_show].function()\n",
        "```"
      ],
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        "_uuid": "850ace3defaca6b72ef976c86a5020c4ed8f65b3",
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      "source": [
        "\n",
        "1. First, the `groupby` method divides the `grouping_columns` by their values. They become a new index in the resulting dataframe.\n",
        "2. Then, columns of interest are selected (`columns_to_show`). If `columns_to_show` is not included, all non groupby clauses will be included.\n",
        "3. Finally, one or several functions are applied to the obtained groups per selected columns.\n",
        "\n",
        "Here is an example where we group the data according to the values of the `Churn` variable and display statistics of three columns in each group:"
      ],
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      "source": [
        "columns_to_show = [\"Total day minutes\", \"Total eve minutes\", \"Total night minutes\"]\n",
        "\n",
        "df.groupby([\"Churn\"])[columns_to_show].describe(percentiles=[])"
      ],
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        "id": "cz20FtTIUhzQ",
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        "outputId": "ba3fb95b-a1e6-4ff0-8ed9-5e4b6e342e67"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Total day minutes                                   Total eve minutes  \\\n",
              "                  count    mean    std  min    50%    max             count   \n",
              "Churn                                                                         \n",
              "0                2850.0  175.18  50.18  0.0  177.2  315.6            2850.0   \n",
              "1                 483.0  206.91  69.00  0.0  217.6  350.8             483.0   \n",
              "\n",
              "                                         Total night minutes                 \\\n",
              "         mean    std   min    50%    max               count    mean    std   \n",
              "Churn                                                                         \n",
              "0      199.04  50.29   0.0  199.6  361.8              2850.0  200.13  51.11   \n",
              "1      212.41  51.73  70.9  211.3  363.7               483.0  205.23  47.13   \n",
              "\n",
              "                            \n",
              "        min     50%    max  \n",
              "Churn                       \n",
              "0      23.2  200.25  395.0  \n",
              "1      47.4  204.80  354.9  "
            ],
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              "\n",
              "        async function convertToInteractive(key) {\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Let’s do the same thing, but slightly differently by passing a list of functions to `agg()`:"
      ],
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      "cell_type": "code",
      "source": [
        "columns_to_show = [\"Total day minutes\", \"Total eve minutes\", \"Total night minutes\"]\n",
        "\n",
        "df.groupby([\"Churn\"])[columns_to_show].agg([np.mean, np.std, np.min, np.max])"
      ],
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        "trusted": true,
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          "height": 219
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        "outputId": "1060bc73-aec4-4365-c80e-875c240a15fc"
      },
      "execution_count": 51,
      "outputs": [
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            "text/plain": [
              "      Total day minutes                    Total eve minutes               \\\n",
              "                   mean    std amin   amax              mean    std  amin   \n",
              "Churn                                                                       \n",
              "0                175.18  50.18  0.0  315.6            199.04  50.29   0.0   \n",
              "1                206.91  69.00  0.0  350.8            212.41  51.73  70.9   \n",
              "\n",
              "             Total night minutes                      \n",
              "        amax                mean    std  amin   amax  \n",
              "Churn                                                 \n",
              "0      361.8              200.13  51.11  23.2  395.0  \n",
              "1      363.7              205.23  47.13  47.4  354.9  "
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    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "### Summary tables\n",
        "\n",
        "Suppose we want to see how the observations in our sample are distributed in the context of two variables - `Churn` and `International plan`. To do so, we can build a **contingency table** using the `crosstab` method:\n",
        "\n"
      ],
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      "source": [
        "pd.crosstab(df['International plan'],df['Churn'])"
      ],
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      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Churn                  0    1\n",
              "International plan           \n",
              "False               2664  346\n",
              "True                 186  137"
            ],
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              "      <th>Churn</th>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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        "pd.crosstab(df['International plan'],df['Churn'], normalize=True)"
      ],
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      "execution_count": 53,
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            "text/plain": [
              "Churn                  0     1\n",
              "International plan            \n",
              "False               0.80  0.10\n",
              "True                0.06  0.04"
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              "  "
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          },
          "metadata": {},
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      ]
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    {
      "cell_type": "code",
      "source": [
        "pd.crosstab(df['International plan'],df['Churn'],margins=True)"
      ],
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      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Churn                  0    1   All\n",
              "International plan                 \n",
              "False               2664  346  3010\n",
              "True                 186  137   323\n",
              "All                 2850  483  3333"
            ],
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      "cell_type": "markdown",
      "source": [
        "We can see that most of the users are loyal and do not use additional services (International Plan/Voice mail).\n",
        "\n",
        "This will resemble **pivot tables** to those familiar with Excel. And, of course, pivot tables are implemented in Pandas: the `pivot_table` method takes the following parameters:\n",
        "\n",
        "* `values` – a list of variables to calculate statistics for,\n",
        "* `index` – a list of variables to group data by,\n",
        "* `aggfunc` – what statistics we need to calculate for groups, ex. sum, mean, maximum, minimum or something else.\n",
        "\n",
        "Let's take a look at the average number of day, evening, and night calls by area code:"
      ],
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      "source": [
        "df.pivot_table(\n",
        "    [\"Total day calls\", \"Total eve calls\", \"Total night calls\"],\n",
        "    [\"Area code\"],\n",
        "    aggfunc=\"mean\",\n",
        ")"
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          "height": 175
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      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Total day calls  Total eve calls  Total night calls\n",
              "Area code                                                     \n",
              "408                 100.50            99.79              99.04\n",
              "415                 100.58           100.50             100.40\n",
              "510                 100.10            99.67             100.60"
            ],
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              "                                                     [key], {});\n",
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              "\n",
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          "metadata": {},
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    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "### DataFrame transformations\n",
        "\n",
        "Like many other things in Pandas, adding columns to a DataFrame is doable in many ways.\n",
        "\n",
        "For example, if we want to calculate the total number of calls for all users\n",
        "\n"
      ],
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      "cell_type": "code",
      "source": [
        "df['Total charge']\n",
        "df.head()"
      ],
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        "trusted": true,
        "id": "pSw5W8GoUhza",
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          "height": 334
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        "outputId": "a9535d3d-e34c-4c4f-940b-7f5382329110"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  State  Account length  Area code  International plan  Voice mail plan  \\\n",
              "0    KS             128        415               False             True   \n",
              "1    OH             107        415               False             True   \n",
              "2    NJ             137        415               False            False   \n",
              "3    OH              84        408                True            False   \n",
              "4    OK              75        415                True            False   \n",
              "\n",
              "   Number vmail messages  Total day minutes  Total day calls  \\\n",
              "0                     25              265.1              110   \n",
              "1                     26              161.6              123   \n",
              "2                      0              243.4              114   \n",
              "3                      0              299.4               71   \n",
              "4                      0              166.7              113   \n",
              "\n",
              "   Total day charge  Total eve minutes  ...  Total eve charge  \\\n",
              "0             45.07              197.4  ...             16.78   \n",
              "1             27.47              195.5  ...             16.62   \n",
              "2             41.38              121.2  ...             10.30   \n",
              "3             50.90               61.9  ...              5.26   \n",
              "4             28.34              148.3  ...             12.61   \n",
              "\n",
              "   Total night minutes  Total night calls  Total night charge  \\\n",
              "0                244.7                 91               11.01   \n",
              "1                254.4                103               11.45   \n",
              "2                162.6                104                7.32   \n",
              "3                196.9                 89                8.86   \n",
              "4                186.9                121                8.41   \n",
              "\n",
              "   Total intl minutes  Total intl calls  Total intl charge  \\\n",
              "0                10.0                 3               2.70   \n",
              "1                13.7                 3               3.70   \n",
              "2                12.2                 5               3.29   \n",
              "3                 6.6                 7               1.78   \n",
              "4                10.1                 3               2.73   \n",
              "\n",
              "   Customer service calls  Churn  Total charge  \n",
              "0                       1      0         75.56  \n",
              "1                       1      0         59.24  \n",
              "2                       0      0         62.29  \n",
              "3                       2      0         66.80  \n",
              "4                       3      0         52.09  \n",
              "\n",
              "[5 rows x 21 columns]"
            ],
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              "      <th>State</th>\n",
              "      <th>Account length</th>\n",
              "      <th>Area code</th>\n",
              "      <th>International plan</th>\n",
              "      <th>Voice mail plan</th>\n",
              "      <th>Number vmail messages</th>\n",
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              "      <th>Total day calls</th>\n",
              "      <th>Total day charge</th>\n",
              "      <th>Total eve minutes</th>\n",
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              "      <th>Total eve charge</th>\n",
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              "      <th>Total night calls</th>\n",
              "      <th>Total night charge</th>\n",
              "      <th>Total intl minutes</th>\n",
              "      <th>Total intl calls</th>\n",
              "      <th>Total intl charge</th>\n",
              "      <th>Customer service calls</th>\n",
              "      <th>Churn</th>\n",
              "      <th>Total charge</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>KS</td>\n",
              "      <td>128</td>\n",
              "      <td>415</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>25</td>\n",
              "      <td>265.1</td>\n",
              "      <td>110</td>\n",
              "      <td>45.07</td>\n",
              "      <td>197.4</td>\n",
              "      <td>...</td>\n",
              "      <td>16.78</td>\n",
              "      <td>244.7</td>\n",
              "      <td>91</td>\n",
              "      <td>11.01</td>\n",
              "      <td>10.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2.70</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>75.56</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>OH</td>\n",
              "      <td>107</td>\n",
              "      <td>415</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>26</td>\n",
              "      <td>161.6</td>\n",
              "      <td>123</td>\n",
              "      <td>27.47</td>\n",
              "      <td>195.5</td>\n",
              "      <td>...</td>\n",
              "      <td>16.62</td>\n",
              "      <td>254.4</td>\n",
              "      <td>103</td>\n",
              "      <td>11.45</td>\n",
              "      <td>13.7</td>\n",
              "      <td>3</td>\n",
              "      <td>3.70</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>59.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NJ</td>\n",
              "      <td>137</td>\n",
              "      <td>415</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>243.4</td>\n",
              "      <td>114</td>\n",
              "      <td>41.38</td>\n",
              "      <td>121.2</td>\n",
              "      <td>...</td>\n",
              "      <td>10.30</td>\n",
              "      <td>162.6</td>\n",
              "      <td>104</td>\n",
              "      <td>7.32</td>\n",
              "      <td>12.2</td>\n",
              "      <td>5</td>\n",
              "      <td>3.29</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>62.29</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>OH</td>\n",
              "      <td>84</td>\n",
              "      <td>408</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>299.4</td>\n",
              "      <td>71</td>\n",
              "      <td>50.90</td>\n",
              "      <td>61.9</td>\n",
              "      <td>...</td>\n",
              "      <td>5.26</td>\n",
              "      <td>196.9</td>\n",
              "      <td>89</td>\n",
              "      <td>8.86</td>\n",
              "      <td>6.6</td>\n",
              "      <td>7</td>\n",
              "      <td>1.78</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>66.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>OK</td>\n",
              "      <td>75</td>\n",
              "      <td>415</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>166.7</td>\n",
              "      <td>113</td>\n",
              "      <td>28.34</td>\n",
              "      <td>148.3</td>\n",
              "      <td>...</td>\n",
              "      <td>12.61</td>\n",
              "      <td>186.9</td>\n",
              "      <td>121</td>\n",
              "      <td>8.41</td>\n",
              "      <td>10.1</td>\n",
              "      <td>3</td>\n",
              "      <td>2.73</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>52.09</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 21 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-704fec9b-c118-40e8-8d0c-080ade37c18b')\"\n",
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          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To delete columns or rows, use the `drop` method, passing the required indexes and the `axis` parameter (`1` if you delete columns, and nothing or `0` if you delete rows). The `inplace` argument tells whether to change the original DataFrame. With `inplace=False`, the `drop` method doesn't change the existing DataFrame and returns a new one with dropped rows or columns. With `inplace=True`, it alters the DataFrame."
      ],
      "metadata": {
        "_uuid": "83e0b3c996775535de55a8773bdd7dee215e2a3b",
        "id": "XFQCd8V2Uhzb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# get rid of just created columns\n",
        "\n",
        "# and here’s how you can delete rows\n",
        "\n",
        "df"
      ],
      "metadata": {
        "_uuid": "48fbc2e42a623ee78f9e2d9222186387561bbb0a",
        "trusted": true,
        "id": "KJTir47eUhzc",
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          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "outputId": "5ea803ca-6371-41e8-b1d9-8a0077875afa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     State  Account length  Area code  International plan  Voice mail plan  \\\n",
              "0       KS             128        415               False             True   \n",
              "1       OH             107        415               False             True   \n",
              "2       NJ             137        415               False            False   \n",
              "3       OH              84        408                True            False   \n",
              "4       OK              75        415                True            False   \n",
              "...    ...             ...        ...                 ...              ...   \n",
              "3328    AZ             192        415               False             True   \n",
              "3329    WV              68        415               False            False   \n",
              "3330    RI              28        510               False            False   \n",
              "3331    CT             184        510                True            False   \n",
              "3332    TN              74        415               False             True   \n",
              "\n",
              "      Number vmail messages  Total day minutes  Total day calls  \\\n",
              "0                        25              265.1              110   \n",
              "1                        26              161.6              123   \n",
              "2                         0              243.4              114   \n",
              "3                         0              299.4               71   \n",
              "4                         0              166.7              113   \n",
              "...                     ...                ...              ...   \n",
              "3328                     36              156.2               77   \n",
              "3329                      0              231.1               57   \n",
              "3330                      0              180.8              109   \n",
              "3331                      0              213.8              105   \n",
              "3332                     25              234.4              113   \n",
              "\n",
              "      Total day charge  Total eve minutes  Total eve calls  Total eve charge  \\\n",
              "0                45.07              197.4               99             16.78   \n",
              "1                27.47              195.5              103             16.62   \n",
              "2                41.38              121.2              110             10.30   \n",
              "3                50.90               61.9               88              5.26   \n",
              "4                28.34              148.3              122             12.61   \n",
              "...                ...                ...              ...               ...   \n",
              "3328             26.55              215.5              126             18.32   \n",
              "3329             39.29              153.4               55             13.04   \n",
              "3330             30.74              288.8               58             24.55   \n",
              "3331             36.35              159.6               84             13.57   \n",
              "3332             39.85              265.9               82             22.60   \n",
              "\n",
              "      Total night minutes  Total night calls  Total night charge  \\\n",
              "0                   244.7                 91               11.01   \n",
              "1                   254.4                103               11.45   \n",
              "2                   162.6                104                7.32   \n",
              "3                   196.9                 89                8.86   \n",
              "4                   186.9                121                8.41   \n",
              "...                   ...                ...                 ...   \n",
              "3328                279.1                 83               12.56   \n",
              "3329                191.3                123                8.61   \n",
              "3330                191.9                 91                8.64   \n",
              "3331                139.2                137                6.26   \n",
              "3332                241.4                 77               10.86   \n",
              "\n",
              "      Total intl minutes  Total intl calls  Total intl charge  \\\n",
              "0                   10.0                 3               2.70   \n",
              "1                   13.7                 3               3.70   \n",
              "2                   12.2                 5               3.29   \n",
              "3                    6.6                 7               1.78   \n",
              "4                   10.1                 3               2.73   \n",
              "...                  ...               ...                ...   \n",
              "3328                 9.9                 6               2.67   \n",
              "3329                 9.6                 4               2.59   \n",
              "3330                14.1                 6               3.81   \n",
              "3331                 5.0                10               1.35   \n",
              "3332                13.7                 4               3.70   \n",
              "\n",
              "      Customer service calls  Churn  \n",
              "0                          1      0  \n",
              "1                          1      0  \n",
              "2                          0      0  \n",
              "3                          2      0  \n",
              "4                          3      0  \n",
              "...                      ...    ...  \n",
              "3328                       2      0  \n",
              "3329                       3      0  \n",
              "3330                       2      0  \n",
              "3331                       2      0  \n",
              "3332                       0      0  \n",
              "\n",
              "[3333 rows x 20 columns]"
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              "      <td>27.47</td>\n",
              "      <td>195.5</td>\n",
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              "      <td>NJ</td>\n",
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              "      <td>415</td>\n",
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              "      <td>False</td>\n",
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              "      <td>121.2</td>\n",
              "      <td>110</td>\n",
              "      <td>10.30</td>\n",
              "      <td>162.6</td>\n",
              "      <td>104</td>\n",
              "      <td>7.32</td>\n",
              "      <td>12.2</td>\n",
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              "      <td>408</td>\n",
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              "      <td>89</td>\n",
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              "      <td>186.9</td>\n",
              "      <td>121</td>\n",
              "      <td>8.41</td>\n",
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              "      <td>3</td>\n",
              "      <td>2.73</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
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              "    <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",
              "      <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>3328</th>\n",
              "      <td>AZ</td>\n",
              "      <td>192</td>\n",
              "      <td>415</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>36</td>\n",
              "      <td>156.2</td>\n",
              "      <td>77</td>\n",
              "      <td>26.55</td>\n",
              "      <td>215.5</td>\n",
              "      <td>126</td>\n",
              "      <td>18.32</td>\n",
              "      <td>279.1</td>\n",
              "      <td>83</td>\n",
              "      <td>12.56</td>\n",
              "      <td>9.9</td>\n",
              "      <td>6</td>\n",
              "      <td>2.67</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3329</th>\n",
              "      <td>WV</td>\n",
              "      <td>68</td>\n",
              "      <td>415</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>231.1</td>\n",
              "      <td>57</td>\n",
              "      <td>39.29</td>\n",
              "      <td>153.4</td>\n",
              "      <td>55</td>\n",
              "      <td>13.04</td>\n",
              "      <td>191.3</td>\n",
              "      <td>123</td>\n",
              "      <td>8.61</td>\n",
              "      <td>9.6</td>\n",
              "      <td>4</td>\n",
              "      <td>2.59</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3330</th>\n",
              "      <td>RI</td>\n",
              "      <td>28</td>\n",
              "      <td>510</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>180.8</td>\n",
              "      <td>109</td>\n",
              "      <td>30.74</td>\n",
              "      <td>288.8</td>\n",
              "      <td>58</td>\n",
              "      <td>24.55</td>\n",
              "      <td>191.9</td>\n",
              "      <td>91</td>\n",
              "      <td>8.64</td>\n",
              "      <td>14.1</td>\n",
              "      <td>6</td>\n",
              "      <td>3.81</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3331</th>\n",
              "      <td>CT</td>\n",
              "      <td>184</td>\n",
              "      <td>510</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>213.8</td>\n",
              "      <td>105</td>\n",
              "      <td>36.35</td>\n",
              "      <td>159.6</td>\n",
              "      <td>84</td>\n",
              "      <td>13.57</td>\n",
              "      <td>139.2</td>\n",
              "      <td>137</td>\n",
              "      <td>6.26</td>\n",
              "      <td>5.0</td>\n",
              "      <td>10</td>\n",
              "      <td>1.35</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3332</th>\n",
              "      <td>TN</td>\n",
              "      <td>74</td>\n",
              "      <td>415</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>25</td>\n",
              "      <td>234.4</td>\n",
              "      <td>113</td>\n",
              "      <td>39.85</td>\n",
              "      <td>265.9</td>\n",
              "      <td>82</td>\n",
              "      <td>22.60</td>\n",
              "      <td>241.4</td>\n",
              "      <td>77</td>\n",
              "      <td>10.86</td>\n",
              "      <td>13.7</td>\n",
              "      <td>4</td>\n",
              "      <td>3.70</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3333 rows × 20 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fcd34730-6ae7-4534-9278-06e175db809c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fcd34730-6ae7-4534-9278-06e175db809c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fcd34730-6ae7-4534-9278-06e175db809c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Basic Visualization"
      ],
      "metadata": {
        "id": "4KxdVMermtWn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#kind : str\n",
        "\n",
        "\n",
        "\n",
        "‘line’ : line plot (default)\n",
        "\n",
        "‘bar’ : vertical bar plot\n",
        "\n",
        "‘barh’ : horizontal bar plot\n",
        "\n",
        "‘hist’ : histogram\n",
        "\n",
        "‘box’ : boxplot\n",
        "\n",
        "‘kde’ : Kernel Density Estimation plot\n",
        "\n",
        "‘density’ : same as ‘kde’\n",
        "\n",
        "‘area’ : area plot\n",
        "\n",
        "‘pie’ : pie plot\n",
        "\n",
        "‘scatter’ : scatter plot\n",
        "\n",
        "‘hexbin’ : hexbin plot"
      ],
      "metadata": {
        "id": "Ep-0Djlxg7JU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Total day minutes'].plot(kind='box')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "Za_X4saDfJYo",
        "outputId": "17f24d1f-9fe9-45b3-d28a-872d4d6efaf8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc2837506a0>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Total day minutes'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vuSLbLkRfaQG",
        "outputId": "c9cadca4-2ebe-4844-9d27-992fa6ce9b2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    3333.00\n",
              "mean      179.78\n",
              "std        54.47\n",
              "min         0.00\n",
              "25%       143.70\n",
              "50%       179.40\n",
              "75%       216.40\n",
              "max       350.80\n",
              "Name: Total day minutes, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Total day minutes'].plot(kind='density')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "B0Qwg5tagy5g",
        "outputId": "2cd692f1-c266-4c1f-b354-b7a252c48b5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc283622a60>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Total day minutes'].plot(kind='hist')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "yqFCoctFhb-s",
        "outputId": "0f6d5c74-a792-421e-c20e-84646ec381c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc27b2e0640>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Churn'].value_counts().plot(kind='pie')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "KBL2qp6Nho3S",
        "outputId": "695dce08-3569-4032-aa10-a78a0fae86f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc2776a40a0>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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lYQrQJBdKB6jnRKlTPyD5nFAV3CFdX5/6uh35vvXkS+pp4JfSIeo5U+o49F8kuRKuCi6ZmBKOq7kxMeUiKtVCfQ7vTKlT5wNvSYdQjb3IuAnHdp/0fMknpiylwds+Y8xYY8zi9LHEGPNC3a+HtSKUU6WOQ38J8GPpHCqbP9Z22eE3tT3LPDHlfCrVfgcRa+0r1trtrbXbk/zdvPidX1tru4wxWfeIz8ypUqcuAN6UDqGymdt9zPTn7bh7pHMMwvMMcgAxxlxujPmxMeYe4EJjTMUYM7fu9x82xnjpf3/eGHNvOrL/xBjT2ej1nSt1HPpLgR9K51BZGTNzxfkf7LKdZdtO6Vwq1RWNn7ZKGwFTrbWnrOoJxpitgIOBaelI3wsc2uiFnSt16kJgmXQIlc0y1hx9UFely9rSnGE9Bfz3ar7Gr6y1ja4n7APsCCw0xixOf/2BRi/sZKnj0H8JqEjnUNk9aDeb/K2eg/8inSOj46lUV3f31fqZdT28t4sj0q8G+Fnde/AtrbWVRi/sZKlTl6CLE5bKD3sPmHZfbfLt0jkauIpK9cYmv2YMTAEwxkwBJqXfvxk4yBgzPv29McaYTRu9mLOljkO/BzhBOocamM92nbnrMrtGUf8xfplkJ9hm+w0wxhjzCMnf2ccBrLWPAmcANxljHgT+BExo9GLGWrfvWvSC6HLgMOkcKruNzEsv3j7s5OEdxo6VztLHF6hUr5QO0YizI3WdUwEXZi61jeftehue0H3iM9ZSk85S549lKDS0Qanj0H8FOEk6hxqY39c+MuW62rSivL9+A/iSdIisnC81QBz6VwO/kM6hBuY/u4+b/oIde690DuBMKtVYOkRWbVHq1JfQLXBLxpiZK87fslt2YspC4LuCxx+wtil1HPrLSGbnFGLJGZXN64waPbvrrLetFblRpwp8nkq1SO/tG2qbUgPEoX8/cJp0DjUwi+3mW17UM/u+nA9bAz5Hpfp4zsddbW1VaoA49C9BNwEone/3ztp9cW2zPO/o+iqV6h9yPF7TtF2pU0eQzOJRJfKZrm/sstyOeDSHQ/2CSrVQSxQNRFuWOg79V4FPAK9JZ1HZdTF0+MyucHTNmn+18DD3AUe18PVbri1LDRCH/mPAgeiFs1J5zo6feFL38U+1aGLKUuDARgsfFF3blhogDv35wOHoDh+lckNt6k6/q+3a7IkpXcCnqVSfb/Lr5q6tSw3/npjyNekcamC+3P3l6Uvsugub+JLHUakuaOLriXH+ho6svCD6ESWaCqhgNMtfWzT82GVDTe/Gq/EyFjiRSvX7zcolre1H6jonAD+XDqGyqzJqnYO7znzD2kGv926BY10qNGip/y0O/V6SWzR/Kp1FZXe/3eKDl/TOWjSIH60BR1GpOrcPm5a6Thz6NeBo4HvSWVR2F/fM3v2hmjeQiSm9wOFUqk5u1aTvqVfBC6ILgNOlc6hshtP19v3Dj3l6TbNiqwZP7SFZ7ODqPHJJ0JF6FeLQ/wpwtnQOlc0Kho34RNf5a9Usr/bztG6S+dzOFhq01P2KQ78CnAil3hqmbTxjN9jolO7jnrR2pfMO3gJmU6n+Ou9cedNSNxCH/vcAn+Q2PFVw19V23+kPtV36bpX7HLA7lWpb3Mij76kz8oJoK+B6YHPpLKp/hlrtnuEn3D/evLYTcAdwEJXqP6Vz5UVH6ozSueI7AZF0FtU/S0fHjBUXbFa1I78N7NNOhQYdqQfMCyIDnAWcif6jWFTLgWPi0G/Ldem01IPkBdEewOVk2NtI5eohYHYc+n+TDiJFR5pBikP/DmBbdD/sougBzgN2budCg47UTeEF0QxgHjBROkub+gtwZBz6Zdlgr6V0pG6COPRvArZBbwjJ29skt83uooV+l47UTeYF0d7Ad4DtpbM4bgHJ6NzWp9oroyN1k8WhfyvJRuFHAkuE47jo78BngT200CunI3ULeUE0CghINukb0eDpqn9Lgf8CLotDf3U3fHealjoHXhBtQnJzyKHAUOE4ZfM68C3g4jj035AOUwZa6hx5QbQRyablRwNrCccpuleAy4CL4tB/WTpMmWipBXhBNBo4luQOsAnCcYrmYZIN6a6KQ7/US/VK0VIL8oJoGDCHpOA7CseRVANuAC6JQ/8W6TBlp6UuCC+IPkyyBvmhwPqyaXLzd+Bq4PI49J+SDuMKLXXBeEHUCXwU+BzwKWC0bKKmexa4Brg6Dv0ibCjvHC11gaWn59OAj6WPKZRvbkEvyTTOPwHXxKE/mJU/1QBoqUvEC6KxJKP4OyX3RAOtXDewCJifPhbEob9MNlJ70VKXmBdE65HcKbZd+nVbYGtgeE4RXgP+CjyWfr0fuDMO/TdzOr5aCS21Y7wgGgJsCWxG8nHZBiv5Oo6k+J2reJkVwL9IPivu+zUmLXEc+joNtoC01G0svSg3jKTcNn3U4tAf7DY2qgC01Eo5pmxXUpVSDWiplXKMllopx2iplXKMllopx2iplXKMllopx2iplXKMllopx2iplXKMllopx2iplXKMllopx2iplXKMllopx2iplXKMllopx2iplXKMllopx2iplXKMllopx2iplXKMllopx2iplXKMllopx2iplXKMllopx/w/uo5o636C47kAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "Wu2A5Pe-i4Av",
        "outputId": "f08009bc-67d1-45a9-a49a-e2a9da0baaeb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     State  Account length  Area code International plan Voice mail plan  \\\n",
              "0       KS             128        415                 No             Yes   \n",
              "1       OH             107        415                 No             Yes   \n",
              "2       NJ             137        415                 No              No   \n",
              "3       OH              84        408                Yes              No   \n",
              "4       OK              75        415                Yes              No   \n",
              "...    ...             ...        ...                ...             ...   \n",
              "3328    AZ             192        415                 No             Yes   \n",
              "3329    WV              68        415                 No              No   \n",
              "3330    RI              28        510                 No              No   \n",
              "3331    CT             184        510                Yes              No   \n",
              "3332    TN              74        415                 No             Yes   \n",
              "\n",
              "      Number vmail messages  Total day minutes  Total day calls  \\\n",
              "0                        25              265.1              110   \n",
              "1                        26              161.6              123   \n",
              "2                         0              243.4              114   \n",
              "3                         0              299.4               71   \n",
              "4                         0              166.7              113   \n",
              "...                     ...                ...              ...   \n",
              "3328                     36              156.2               77   \n",
              "3329                      0              231.1               57   \n",
              "3330                      0              180.8              109   \n",
              "3331                      0              213.8              105   \n",
              "3332                     25              234.4              113   \n",
              "\n",
              "      Total day charge  Total eve minutes  Total eve calls  Total eve charge  \\\n",
              "0                45.07              197.4               99             16.78   \n",
              "1                27.47              195.5              103             16.62   \n",
              "2                41.38              121.2              110             10.30   \n",
              "3                50.90               61.9               88              5.26   \n",
              "4                28.34              148.3              122             12.61   \n",
              "...                ...                ...              ...               ...   \n",
              "3328             26.55              215.5              126             18.32   \n",
              "3329             39.29              153.4               55             13.04   \n",
              "3330             30.74              288.8               58             24.55   \n",
              "3331             36.35              159.6               84             13.57   \n",
              "3332             39.85              265.9               82             22.60   \n",
              "\n",
              "      Total night minutes  Total night calls  Total night charge  \\\n",
              "0                   244.7                 91               11.01   \n",
              "1                   254.4                103               11.45   \n",
              "2                   162.6                104                7.32   \n",
              "3                   196.9                 89                8.86   \n",
              "4                   186.9                121                8.41   \n",
              "...                   ...                ...                 ...   \n",
              "3328                279.1                 83               12.56   \n",
              "3329                191.3                123                8.61   \n",
              "3330                191.9                 91                8.64   \n",
              "3331                139.2                137                6.26   \n",
              "3332                241.4                 77               10.86   \n",
              "\n",
              "      Total intl minutes  Total intl calls  Total intl charge  \\\n",
              "0                   10.0                 3               2.70   \n",
              "1                   13.7                 3               3.70   \n",
              "2                   12.2                 5               3.29   \n",
              "3                    6.6                 7               1.78   \n",
              "4                   10.1                 3               2.73   \n",
              "...                  ...               ...                ...   \n",
              "3328                 9.9                 6               2.67   \n",
              "3329                 9.6                 4               2.59   \n",
              "3330                14.1                 6               3.81   \n",
              "3331                 5.0                10               1.35   \n",
              "3332                13.7                 4               3.70   \n",
              "\n",
              "      Customer service calls  Churn  \n",
              "0                          1  False  \n",
              "1                          1  False  \n",
              "2                          0  False  \n",
              "3                          2  False  \n",
              "4                          3  False  \n",
              "...                      ...    ...  \n",
              "3328                       2  False  \n",
              "3329                       3  False  \n",
              "3330                       2  False  \n",
              "3331                       2  False  \n",
              "3332                       0  False  \n",
              "\n",
              "[3333 rows x 20 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d6badfa9-9c20-4275-8e39-51bb20a9ecb9\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>State</th>\n",
              "      <th>Account length</th>\n",
              "      <th>Area code</th>\n",
              "      <th>International plan</th>\n",
              "      <th>Voice mail plan</th>\n",
              "      <th>Number vmail messages</th>\n",
              "      <th>Total day minutes</th>\n",
              "      <th>Total day calls</th>\n",
              "      <th>Total day charge</th>\n",
              "      <th>Total eve minutes</th>\n",
              "      <th>Total eve calls</th>\n",
              "      <th>Total eve charge</th>\n",
              "      <th>Total night minutes</th>\n",
              "      <th>Total night calls</th>\n",
              "      <th>Total night charge</th>\n",
              "      <th>Total intl minutes</th>\n",
              "      <th>Total intl calls</th>\n",
              "      <th>Total intl charge</th>\n",
              "      <th>Customer service calls</th>\n",
              "      <th>Churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>KS</td>\n",
              "      <td>128</td>\n",
              "      <td>415</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>25</td>\n",
              "      <td>265.1</td>\n",
              "      <td>110</td>\n",
              "      <td>45.07</td>\n",
              "      <td>197.4</td>\n",
              "      <td>99</td>\n",
              "      <td>16.78</td>\n",
              "      <td>244.7</td>\n",
              "      <td>91</td>\n",
              "      <td>11.01</td>\n",
              "      <td>10.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2.70</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>OH</td>\n",
              "      <td>107</td>\n",
              "      <td>415</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>26</td>\n",
              "      <td>161.6</td>\n",
              "      <td>123</td>\n",
              "      <td>27.47</td>\n",
              "      <td>195.5</td>\n",
              "      <td>103</td>\n",
              "      <td>16.62</td>\n",
              "      <td>254.4</td>\n",
              "      <td>103</td>\n",
              "      <td>11.45</td>\n",
              "      <td>13.7</td>\n",
              "      <td>3</td>\n",
              "      <td>3.70</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NJ</td>\n",
              "      <td>137</td>\n",
              "      <td>415</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>243.4</td>\n",
              "      <td>114</td>\n",
              "      <td>41.38</td>\n",
              "      <td>121.2</td>\n",
              "      <td>110</td>\n",
              "      <td>10.30</td>\n",
              "      <td>162.6</td>\n",
              "      <td>104</td>\n",
              "      <td>7.32</td>\n",
              "      <td>12.2</td>\n",
              "      <td>5</td>\n",
              "      <td>3.29</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>OH</td>\n",
              "      <td>84</td>\n",
              "      <td>408</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>299.4</td>\n",
              "      <td>71</td>\n",
              "      <td>50.90</td>\n",
              "      <td>61.9</td>\n",
              "      <td>88</td>\n",
              "      <td>5.26</td>\n",
              "      <td>196.9</td>\n",
              "      <td>89</td>\n",
              "      <td>8.86</td>\n",
              "      <td>6.6</td>\n",
              "      <td>7</td>\n",
              "      <td>1.78</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>OK</td>\n",
              "      <td>75</td>\n",
              "      <td>415</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>166.7</td>\n",
              "      <td>113</td>\n",
              "      <td>28.34</td>\n",
              "      <td>148.3</td>\n",
              "      <td>122</td>\n",
              "      <td>12.61</td>\n",
              "      <td>186.9</td>\n",
              "      <td>121</td>\n",
              "      <td>8.41</td>\n",
              "      <td>10.1</td>\n",
              "      <td>3</td>\n",
              "      <td>2.73</td>\n",
              "      <td>3</td>\n",
              "      <td>False</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",
              "      <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>3328</th>\n",
              "      <td>AZ</td>\n",
              "      <td>192</td>\n",
              "      <td>415</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>36</td>\n",
              "      <td>156.2</td>\n",
              "      <td>77</td>\n",
              "      <td>26.55</td>\n",
              "      <td>215.5</td>\n",
              "      <td>126</td>\n",
              "      <td>18.32</td>\n",
              "      <td>279.1</td>\n",
              "      <td>83</td>\n",
              "      <td>12.56</td>\n",
              "      <td>9.9</td>\n",
              "      <td>6</td>\n",
              "      <td>2.67</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3329</th>\n",
              "      <td>WV</td>\n",
              "      <td>68</td>\n",
              "      <td>415</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>231.1</td>\n",
              "      <td>57</td>\n",
              "      <td>39.29</td>\n",
              "      <td>153.4</td>\n",
              "      <td>55</td>\n",
              "      <td>13.04</td>\n",
              "      <td>191.3</td>\n",
              "      <td>123</td>\n",
              "      <td>8.61</td>\n",
              "      <td>9.6</td>\n",
              "      <td>4</td>\n",
              "      <td>2.59</td>\n",
              "      <td>3</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3330</th>\n",
              "      <td>RI</td>\n",
              "      <td>28</td>\n",
              "      <td>510</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>180.8</td>\n",
              "      <td>109</td>\n",
              "      <td>30.74</td>\n",
              "      <td>288.8</td>\n",
              "      <td>58</td>\n",
              "      <td>24.55</td>\n",
              "      <td>191.9</td>\n",
              "      <td>91</td>\n",
              "      <td>8.64</td>\n",
              "      <td>14.1</td>\n",
              "      <td>6</td>\n",
              "      <td>3.81</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3331</th>\n",
              "      <td>CT</td>\n",
              "      <td>184</td>\n",
              "      <td>510</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>213.8</td>\n",
              "      <td>105</td>\n",
              "      <td>36.35</td>\n",
              "      <td>159.6</td>\n",
              "      <td>84</td>\n",
              "      <td>13.57</td>\n",
              "      <td>139.2</td>\n",
              "      <td>137</td>\n",
              "      <td>6.26</td>\n",
              "      <td>5.0</td>\n",
              "      <td>10</td>\n",
              "      <td>1.35</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3332</th>\n",
              "      <td>TN</td>\n",
              "      <td>74</td>\n",
              "      <td>415</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>25</td>\n",
              "      <td>234.4</td>\n",
              "      <td>113</td>\n",
              "      <td>39.85</td>\n",
              "      <td>265.9</td>\n",
              "      <td>82</td>\n",
              "      <td>22.60</td>\n",
              "      <td>241.4</td>\n",
              "      <td>77</td>\n",
              "      <td>10.86</td>\n",
              "      <td>13.7</td>\n",
              "      <td>4</td>\n",
              "      <td>3.70</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3333 rows × 20 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d6badfa9-9c20-4275-8e39-51bb20a9ecb9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d6badfa9-9c20-4275-8e39-51bb20a9ecb9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d6badfa9-9c20-4275-8e39-51bb20a9ecb9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "ax.scatter(df['Total eve charge'], df['Customer service calls'], c=df['Churn'])\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "7nuYE7VRmM8N",
        "outputId": "8d0be76c-21d7-45c9-e960-e75ab52362f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Classification Prediction"
      ],
      "metadata": {
        "id": "U6BFWPtPm1-D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# import the necessary libraries\n",
        "df2=pd.get_dummies(df)\n",
        "import pandas as pd\n",
        "from xgboost import XGBClassifier\n",
        "\n",
        "# load the dataset\n",
        "data = df2\n",
        "\n",
        "# separate the features and target variable\n",
        "X = data.drop([\"Churn\"], axis=1)\n",
        "y = data[\"Churn\"]\n",
        "\n",
        "# split the dataset into training and test sets\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
        "\n",
        "# build the XGBoost model\n",
        "model = XGBClassifier()\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# evaluate the model on the test set\n",
        "from sklearn.metrics import accuracy_score,confusion_matrix\n",
        "y_pred = model.predict(X_test)\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))\n",
        "print(cm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "klPGyvG51Izc",
        "outputId": "99a434e3-fa84-4ea4-ac3a-ed08b2ec3690"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 95.00%\n",
            "[[846  16]\n",
            " [ 34 104]]\n"
          ]
        }
      ]
    }
  ]
}