DataProcessing2.ipynb

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        "<a href=\"https://colab.research.google.com/github/ogut77/DataScience/blob/main/DataProcessing2.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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          "text": [
            "(3333, 20)\n"
          ]
        }
      ]
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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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      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "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')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can use the `info()` method to output some general information about the dataframe:"
      ],
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        {
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          "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   bool   \n",
            "dtypes: bool(1), float64(8), int64(8), object(3)\n",
            "memory usage: 498.1+ KB\n",
            "None\n"
          ]
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      "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": null,
      "outputs": []
    },
    {
      "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"
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        "trusted": true,
        "id": "f0vkW2rCUhy0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "outputId": "fe30a839-d483-42a3-d7ca-857536410ae3"
      },
      "execution_count": null,
      "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  "
            ],
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              "\n",
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              "    <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",
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              "      <td>3333.00</td>\n",
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              "      <th>mean</th>\n",
              "      <td>101.06</td>\n",
              "      <td>437.18</td>\n",
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              "      <td>179.78</td>\n",
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              "      <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",
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              "  </tbody>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\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",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "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."
      ],
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      "execution_count": null,
      "outputs": [
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          "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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              "      <th>top</th>\n",
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              "      <td>No</td>\n",
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              "      <th>freq</th>\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a59441e2-55af-4561-88b6-8dfed9687992');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "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": [],
      "metadata": {
        "_uuid": "eef4f419ac223f320d1841635ffbb816c807fe54",
        "trusted": true,
        "id": "NbMvSINiUhy4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3edc71c3-b4ab-4580-d0d5-b0324501b97e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    2850\n",
              "1     483\n",
              "Name: Churn, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "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": [],
      "metadata": {
        "_uuid": "b516f5e583ac75e0379048738386ece5b208f0e7",
        "trusted": true,
        "id": "jfg12eoPUhy5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ad0629cf-7e0c-45f9-862d-25a9fd8a24b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.86\n",
              "1    0.14\n",
              "Name: Churn, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Correlation"
      ],
      "metadata": {
        "id": "Wkc2KUxoNgTX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Correlation Matrix\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "A9Q60xYtNfu4",
        "outputId": "e6540513-94ba-4699-8cbd-2f59bf266a62"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        Account length  Area code  Number vmail messages  \\\n",
              "Account length                    1.00      -0.01                  -0.00   \n",
              "Area code                        -0.01       1.00                  -0.00   \n",
              "Number vmail messages            -0.00      -0.00                   1.00   \n",
              "Total day minutes                 0.01      -0.01                   0.00   \n",
              "Total day calls                   0.04      -0.01                  -0.01   \n",
              "Total day charge                  0.01      -0.01                   0.00   \n",
              "Total eve minutes                -0.01       0.00                   0.02   \n",
              "Total eve calls                   0.02      -0.01                  -0.01   \n",
              "Total eve charge                 -0.01       0.00                   0.02   \n",
              "Total night minutes              -0.01      -0.01                   0.01   \n",
              "Total night calls                -0.01       0.02                   0.01   \n",
              "Total night charge               -0.01      -0.01                   0.01   \n",
              "Total intl minutes                0.01      -0.02                   0.00   \n",
              "Total intl calls                  0.02      -0.02                   0.01   \n",
              "Total intl charge                 0.01      -0.02                   0.00   \n",
              "Customer service calls           -0.00       0.03                  -0.01   \n",
              "Churn                             0.02       0.01                  -0.09   \n",
              "\n",
              "                        Total day minutes  Total day calls  Total day charge  \\\n",
              "Account length                       0.01             0.04              0.01   \n",
              "Area code                           -0.01            -0.01             -0.01   \n",
              "Number vmail messages                0.00            -0.01              0.00   \n",
              "Total day minutes                    1.00             0.01              1.00   \n",
              "Total day calls                      0.01             1.00              0.01   \n",
              "Total day charge                     1.00             0.01              1.00   \n",
              "Total eve minutes                    0.01            -0.02              0.01   \n",
              "Total eve calls                      0.02             0.01              0.02   \n",
              "Total eve charge                     0.01            -0.02              0.01   \n",
              "Total night minutes                  0.00             0.02              0.00   \n",
              "Total night calls                    0.02            -0.02              0.02   \n",
              "Total night charge                   0.00             0.02              0.00   \n",
              "Total intl minutes                  -0.01             0.02             -0.01   \n",
              "Total intl calls                     0.01             0.00              0.01   \n",
              "Total intl charge                   -0.01             0.02             -0.01   \n",
              "Customer service calls              -0.01            -0.02             -0.01   \n",
              "Churn                                0.21             0.02              0.21   \n",
              "\n",
              "                        Total eve minutes  Total eve calls  Total eve charge  \\\n",
              "Account length                      -0.01             0.02             -0.01   \n",
              "Area code                            0.00            -0.01              0.00   \n",
              "Number vmail messages                0.02            -0.01              0.02   \n",
              "Total day minutes                    0.01             0.02              0.01   \n",
              "Total day calls                     -0.02             0.01             -0.02   \n",
              "Total day charge                     0.01             0.02              0.01   \n",
              "Total eve minutes                    1.00            -0.01              1.00   \n",
              "Total eve calls                     -0.01             1.00             -0.01   \n",
              "Total eve charge                     1.00            -0.01              1.00   \n",
              "Total night minutes                 -0.01            -0.00             -0.01   \n",
              "Total night calls                    0.01             0.01              0.01   \n",
              "Total night charge                  -0.01            -0.00             -0.01   \n",
              "Total intl minutes                  -0.01             0.01             -0.01   \n",
              "Total intl calls                     0.00             0.02              0.00   \n",
              "Total intl charge                   -0.01             0.01             -0.01   \n",
              "Customer service calls              -0.01             0.00             -0.01   \n",
              "Churn                                0.09             0.01              0.09   \n",
              "\n",
              "                        Total night minutes  Total night calls  \\\n",
              "Account length                        -0.01              -0.01   \n",
              "Area code                             -0.01               0.02   \n",
              "Number vmail messages                  0.01               0.01   \n",
              "Total day minutes                      0.00               0.02   \n",
              "Total day calls                        0.02              -0.02   \n",
              "Total day charge                       0.00               0.02   \n",
              "Total eve minutes                     -0.01               0.01   \n",
              "Total eve calls                       -0.00               0.01   \n",
              "Total eve charge                      -0.01               0.01   \n",
              "Total night minutes                    1.00               0.01   \n",
              "Total night calls                      0.01               1.00   \n",
              "Total night charge                     1.00               0.01   \n",
              "Total intl minutes                    -0.02              -0.01   \n",
              "Total intl calls                      -0.01               0.00   \n",
              "Total intl charge                     -0.02              -0.01   \n",
              "Customer service calls                -0.01              -0.01   \n",
              "Churn                                  0.04               0.01   \n",
              "\n",
              "                        Total night charge  Total intl minutes  \\\n",
              "Account length                       -0.01                0.01   \n",
              "Area code                            -0.01               -0.02   \n",
              "Number vmail messages                 0.01                0.00   \n",
              "Total day minutes                     0.00               -0.01   \n",
              "Total day calls                       0.02                0.02   \n",
              "Total day charge                      0.00               -0.01   \n",
              "Total eve minutes                    -0.01               -0.01   \n",
              "Total eve calls                      -0.00                0.01   \n",
              "Total eve charge                     -0.01               -0.01   \n",
              "Total night minutes                   1.00               -0.02   \n",
              "Total night calls                     0.01               -0.01   \n",
              "Total night charge                    1.00               -0.02   \n",
              "Total intl minutes                   -0.02                1.00   \n",
              "Total intl calls                     -0.01                0.03   \n",
              "Total intl charge                    -0.02                1.00   \n",
              "Customer service calls               -0.01               -0.01   \n",
              "Churn                                 0.04                0.07   \n",
              "\n",
              "                        Total intl calls  Total intl charge  \\\n",
              "Account length                      0.02               0.01   \n",
              "Area code                          -0.02              -0.02   \n",
              "Number vmail messages               0.01               0.00   \n",
              "Total day minutes                   0.01              -0.01   \n",
              "Total day calls                     0.00               0.02   \n",
              "Total day charge                    0.01              -0.01   \n",
              "Total eve minutes                   0.00              -0.01   \n",
              "Total eve calls                     0.02               0.01   \n",
              "Total eve charge                    0.00              -0.01   \n",
              "Total night minutes                -0.01              -0.02   \n",
              "Total night calls                   0.00              -0.01   \n",
              "Total night charge                 -0.01              -0.02   \n",
              "Total intl minutes                  0.03               1.00   \n",
              "Total intl calls                    1.00               0.03   \n",
              "Total intl charge                   0.03               1.00   \n",
              "Customer service calls             -0.02              -0.01   \n",
              "Churn                              -0.05               0.07   \n",
              "\n",
              "                        Customer service calls  Churn  \n",
              "Account length                           -0.00   0.02  \n",
              "Area code                                 0.03   0.01  \n",
              "Number vmail messages                    -0.01  -0.09  \n",
              "Total day minutes                        -0.01   0.21  \n",
              "Total day calls                          -0.02   0.02  \n",
              "Total day charge                         -0.01   0.21  \n",
              "Total eve minutes                        -0.01   0.09  \n",
              "Total eve calls                           0.00   0.01  \n",
              "Total eve charge                         -0.01   0.09  \n",
              "Total night minutes                      -0.01   0.04  \n",
              "Total night calls                        -0.01   0.01  \n",
              "Total night charge                       -0.01   0.04  \n",
              "Total intl minutes                       -0.01   0.07  \n",
              "Total intl calls                         -0.02  -0.05  \n",
              "Total intl charge                        -0.01   0.07  \n",
              "Customer service calls                    1.00   0.21  \n",
              "Churn                                     0.21   1.00  "
            ],
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              "      <th>Account length</th>\n",
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              "      <th>Number vmail messages</th>\n",
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              "      <th>Total eve calls</th>\n",
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              "      <th>Account length</th>\n",
              "      <td>1.00</td>\n",
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              "      <th>Area code</th>\n",
              "      <td>-0.01</td>\n",
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              "      <td>-0.01</td>\n",
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              "      <td>-0.02</td>\n",
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              "      <td>0.03</td>\n",
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              "      <th>Number vmail messages</th>\n",
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              "      <td>0.01</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.01</td>\n",
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              "      <td>0.01</td>\n",
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              "      <td>-0.01</td>\n",
              "      <td>0.21</td>\n",
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              "      <th>Total day calls</th>\n",
              "      <td>0.04</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.01</td>\n",
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              "      <td>0.02</td>\n",
              "      <td>0.02</td>\n",
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              "      <td>0.02</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>0.02</td>\n",
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              "    <tr>\n",
              "      <th>Total day charge</th>\n",
              "      <td>0.01</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.01</td>\n",
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              "      <td>0.01</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total eve minutes</th>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.01</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>0.01</td>\n",
              "      <td>1.00</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>1.00</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.09</td>\n",
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              "    <tr>\n",
              "      <th>Total eve calls</th>\n",
              "      <td>0.02</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.01</td>\n",
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              "      <td>0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
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              "    <tr>\n",
              "      <th>Total eve charge</th>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.01</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>0.01</td>\n",
              "      <td>1.00</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>1.00</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total night minutes</th>\n",
              "      <td>-0.01</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>-0.00</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>1.00</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total night calls</th>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.02</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total night charge</th>\n",
              "      <td>-0.01</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>-0.00</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>1.00</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total intl minutes</th>\n",
              "      <td>0.01</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.02</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.03</td>\n",
              "      <td>1.00</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total intl calls</th>\n",
              "      <td>0.02</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.03</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.03</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>-0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total intl charge</th>\n",
              "      <td>0.01</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.02</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.03</td>\n",
              "      <td>1.00</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Customer service calls</th>\n",
              "      <td>-0.00</td>\n",
              "      <td>0.03</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Churn</th>\n",
              "      <td>0.02</td>\n",
              "      <td>0.01</td>\n",
              "      <td>-0.09</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.07</td>\n",
              "      <td>-0.05</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.21</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f89847d5-1f65-4b9a-95b0-9e8971871ba7')\"\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-f89847d5-1f65-4b9a-95b0-9e8971871ba7 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-f89847d5-1f65-4b9a-95b0-9e8971871ba7');\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": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# annot = True to print the values inside the square\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "bJmNebiHN5oB",
        "outputId": "f144058e-caca-4d1b-fd99-cc92fc2fae18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa909288070>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 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=).head()"
      ],
      "metadata": {
        "_uuid": "419200266ac1e7d0e96ba1a20d756c9d87eb2541",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "id": "ritYM2vPUhy7",
        "outputId": "5b51caee-5d2b-409c-9866-f453de9dd5fd"
      },
      "execution_count": null,
      "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",
              "\n",
              "      Customer service calls  Churn  \n",
              "365                        1      1  \n",
              "985                        1      1  \n",
              "2594                       1      1  \n",
              "156                        0      1  \n",
              "605                        2      1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5e556a87-0e98-4dcd-9d70-a55ec9495129\">\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>365</th>\n",
              "      <td>CO</td>\n",
              "      <td>154</td>\n",
              "      <td>415</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>350.8</td>\n",
              "      <td>75</td>\n",
              "      <td>59.64</td>\n",
              "      <td>216.5</td>\n",
              "      <td>94</td>\n",
              "      <td>18.40</td>\n",
              "      <td>253.9</td>\n",
              "      <td>100</td>\n",
              "      <td>11.43</td>\n",
              "      <td>10.1</td>\n",
              "      <td>9</td>\n",
              "      <td>2.73</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>985</th>\n",
              "      <td>NY</td>\n",
              "      <td>64</td>\n",
              "      <td>415</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>346.8</td>\n",
              "      <td>55</td>\n",
              "      <td>58.96</td>\n",
              "      <td>249.5</td>\n",
              "      <td>79</td>\n",
              "      <td>21.21</td>\n",
              "      <td>275.4</td>\n",
              "      <td>102</td>\n",
              "      <td>12.39</td>\n",
              "      <td>13.3</td>\n",
              "      <td>9</td>\n",
              "      <td>3.59</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2594</th>\n",
              "      <td>OH</td>\n",
              "      <td>115</td>\n",
              "      <td>510</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>345.3</td>\n",
              "      <td>81</td>\n",
              "      <td>58.70</td>\n",
              "      <td>203.4</td>\n",
              "      <td>106</td>\n",
              "      <td>17.29</td>\n",
              "      <td>217.5</td>\n",
              "      <td>107</td>\n",
              "      <td>9.79</td>\n",
              "      <td>11.8</td>\n",
              "      <td>8</td>\n",
              "      <td>3.19</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156</th>\n",
              "      <td>OH</td>\n",
              "      <td>83</td>\n",
              "      <td>415</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>337.4</td>\n",
              "      <td>120</td>\n",
              "      <td>57.36</td>\n",
              "      <td>227.4</td>\n",
              "      <td>116</td>\n",
              "      <td>19.33</td>\n",
              "      <td>153.9</td>\n",
              "      <td>114</td>\n",
              "      <td>6.93</td>\n",
              "      <td>15.8</td>\n",
              "      <td>7</td>\n",
              "      <td>4.27</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>605</th>\n",
              "      <td>MO</td>\n",
              "      <td>112</td>\n",
              "      <td>415</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>335.5</td>\n",
              "      <td>77</td>\n",
              "      <td>57.04</td>\n",
              "      <td>212.5</td>\n",
              "      <td>109</td>\n",
              "      <td>18.06</td>\n",
              "      <td>265.0</td>\n",
              "      <td>132</td>\n",
              "      <td>11.93</td>\n",
              "      <td>12.7</td>\n",
              "      <td>8</td>\n",
              "      <td>3.43</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\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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        "trusted": true,
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          "height": 305
        },
        "outputId": "17b4b7b2-86dc-4ad5-f623-c3bfb9308242"
      },
      "execution_count": null,
      "outputs": [
        {
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              "     State  Account length  Area code International plan Voice mail plan  \\\n",
              "688     MN              13        510                 No             Yes   \n",
              "2259    NC             210        415                 No             Yes   \n",
              "534     LA              67        510                 No              No   \n",
              "575     SD             114        415                 No             Yes   \n",
              "2858    AL             141        510                 No             Yes   \n",
              "\n",
              "      Number vmail messages  Total day minutes  Total day calls  \\\n",
              "688                      21              315.6              105   \n",
              "2259                     31              313.8               87   \n",
              "534                       0              310.4               97   \n",
              "575                      36              309.9               90   \n",
              "2858                     28              308.0              123   \n",
              "\n",
              "      Total day charge  Total eve minutes  Total eve calls  Total eve charge  \\\n",
              "688              53.65              208.9               71             17.76   \n",
              "2259             53.35              147.7              103             12.55   \n",
              "534              52.77               66.5              123              5.65   \n",
              "575              52.68              200.3               89             17.03   \n",
              "2858             52.36              247.8              128             21.06   \n",
              "\n",
              "      Total night minutes  Total night calls  Total night charge  \\\n",
              "688                 260.1                123               11.70   \n",
              "2259                192.7                 97                8.67   \n",
              "534                 246.5                 99               11.09   \n",
              "575                 183.5                105                8.26   \n",
              "2858                152.9                103                6.88   \n",
              "\n",
              "      Total intl minutes  Total intl calls  Total intl charge  \\\n",
              "688                 12.1                 3               3.27   \n",
              "2259                10.1                 7               2.73   \n",
              "534                  9.2                10               2.48   \n",
              "575                 14.2                 2               3.83   \n",
              "2858                 7.4                 3               2.00   \n",
              "\n",
              "      Customer service calls  Churn  \n",
              "688                        3      0  \n",
              "2259                       3      0  \n",
              "534                        4      0  \n",
              "575                        1      0  \n",
              "2858                       1      0  "
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              "      <td>SD</td>\n",
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              "      <td>415</td>\n",
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              "      <td>Yes</td>\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": 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"
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        "_uuid": "b020796a83bc9e10b1bc8e9c6439fc006302d999",
        "trusted": true,
        "id": "_ffeel8XUhy-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aecf3fdc-616b-4c40-a28d-0982d203dad9"
      },
      "execution_count": null,
      "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_"
      }
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    {
      "cell_type": "code",
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        "_uuid": "3712d185e50d44c65128468c603b120b75786c84",
        "trusted": true,
        "id": "B4DqVN_YUhzA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e34afb06-9431-43b5-f8ca-ef1caa3cd856"
      },
      "execution_count": null,
      "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?**"
      ],
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      },
      "execution_count": null,
      "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"
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        "trusted": true,
        "id": "f0geaUO-UhzD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "efe1bb3b-e795-4148-f0d8-a3cc82f2aaa2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "18.9"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
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        "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)."
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        "df.loc"
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        "trusted": true,
        "id": "vQCF-wuJUhzE",
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          "height": 238
        },
        "outputId": "4574361f-e81d-40b3-a876-db02502caaaf"
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      "execution_count": null,
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        {
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              "  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",
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              "  State  Account length  Area code\n",
              "0    KS             128        415\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",
              "            + ' to learn more about interactive tables.';\n",
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          "metadata": {},
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    {
      "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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        "_uuid": "35d8ce07aeb613e44dbd577ff1b17d4d109a7e5d",
        "id": "MxTLgGxUUhzG"
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        "#Get first row\n",
        "df.iloc[:1,:]"
      ],
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "  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",
              "\n",
              "   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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              "      <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>2.7</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0a523b33-88e9-44cc-8ccb-a346386a101f')\"\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",
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              "    <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",
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              "      display: 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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0a523b33-88e9-44cc-8ccb-a346386a101f 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-0a523b33-88e9-44cc-8ccb-a346386a101f');\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": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Get last row\n",
        "df.iloc[-1:,:]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 179
        },
        "id": "8g6GwsfbPVOv",
        "outputId": "1f2ee4c1-da4c-492b-daab-39b499e5b4a7"
      },
      "execution_count": null,
      "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  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1626e3fe-e18a-4d37-ba32-e68eb880fa21\">\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <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>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>22.6</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>3.7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1626e3fe-e18a-4d37-ba32-e68eb880fa21')\"\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",
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              "    <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",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      width: 32px;\n",
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              "\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-1626e3fe-e18a-4d37-ba32-e68eb880fa21 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-1626e3fe-e18a-4d37-ba32-e68eb880fa21');\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": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Get first column\n",
        "df.iloc[:,:1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "8fP9JU64PcuO",
        "outputId": "469d7f6e-6c51-4979-f2c3-503718b29f80"
      },
      "execution_count": null,
      "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]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-eb9bc724-820e-4d94-bb46-94f8e1f803b1\">\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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>KS</td>\n",
              "    </tr>\n",
              "    <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",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eb9bc724-820e-4d94-bb46-94f8e1f803b1')\"\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",
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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",
              "      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-eb9bc724-820e-4d94-bb46-94f8e1f803b1 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-eb9bc724-820e-4d94-bb46-94f8e1f803b1');\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": [
        "#Get last column\n",
        "df.iloc[:,-1:]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "DOyjHal5ReHO",
        "outputId": "fef6fa4d-cfe0-42ac-b15d-b8a4a256eada"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Churn\n",
              "0         0\n",
              "1         0\n",
              "2         0\n",
              "3         0\n",
              "4         0\n",
              "...     ...\n",
              "3328      0\n",
              "3329      0\n",
              "3330      0\n",
              "3331      0\n",
              "3332      0\n",
              "\n",
              "[3333 rows x 1 columns]"
            ],
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              "\n",
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          "metadata": {},
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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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      "source": [
        "df.apply(np.max)"
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        "id": "uA6iXklAUhzI",
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        },
        "outputId": "f6ddbee3-c13e-41e7-c93b-82fd473d4562"
      },
      "execution_count": null,
      "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": 43
        }
      ]
    },
    {
      "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": {
        "_uuid": "613f8dbb88fbaf512638ad5525f5fc14a94732e7",
        "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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "outputId": "88369fdf-cdeb-4699-eebc-72e7cfb85a57"
      },
      "execution_count": null,
      "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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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-00e47fe1-7070-4cbe-b29f-66260ae5191f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-00e47fe1-7070-4cbe-b29f-66260ae5191f 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-00e47fe1-7070-4cbe-b29f-66260ae5191f');\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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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
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      "cell_type": "markdown",
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        "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:"
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      "source": [
        "d = {\"No\": False, \"Yes\": True}\n",
        "df[\"International plan\"] = df[\"International plan\"].map(d)\n",
        "df.head()"
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          "height": 305
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        "outputId": "51a56285-4815-4fd3-e862-81807ed22993"
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      "execution_count": null,
      "outputs": [
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              "  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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          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The same thing can be done with the `replace` method:"
      ],
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        "id": "B2dB7AE-UhzL"
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    {
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      "source": [
        "df = df.replace({\"Voice mail plan\": d})\n",
        "df.head()"
      ],
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      "execution_count": null,
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          "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",
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              "   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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              "      <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>0</td>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2a4c5f22-7732-4ca3-a908-77860853d506');\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",
              "            + ' 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": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "### Grouping\n",
        "\n",
        "In general, grouping data in Pandas works as follows:\n"
      ],
      "metadata": {
        "_uuid": "22f7916fb05500116fdd6e52b86bf7949912bd9c",
        "id": "RNrnibuJUhzN"
      }
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    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "```python\n",
        "df.groupby(by=grouping_columns)[columns_to_show].function()\n",
        "```"
      ],
      "metadata": {
        "_uuid": "850ace3defaca6b72ef976c86a5020c4ed8f65b3",
        "id": "wkO5Crn_UhzO"
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    {
      "cell_type": "markdown",
      "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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        "_uuid": "1ac45d322f049449259ff21197e9977e220e82d1",
        "id": "leZrM2KuUhzP"
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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].describe(percentiles=[])"
      ],
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        "_uuid": "180e6b3d26c8adc52e46d8ab82dad34f9a49e234",
        "trusted": true,
        "id": "cz20FtTIUhzQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 239
        },
        "outputId": "c960b47a-b60b-4138-bf3b-dda9737e4eb1"
      },
      "execution_count": null,
      "outputs": [
        {
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          "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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              "      <td>483.0</td>\n",
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              "      <td>47.13</td>\n",
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              "      <td>204.80</td>\n",
              "      <td>354.9</td>\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-73edd3c2-15e2-485e-8780-2bcdd4d9cf21');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "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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        "_uuid": "7fc26768e62869d3ac4f1c0c379356a642240c7f",
        "id": "oOfK4eVCUhzR"
      }
    },
    {
      "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])"
      ],
      "metadata": {
        "_uuid": "6e87a8f4b10035128312771fc7e802f1bb660b4c",
        "trusted": true,
        "id": "sKL7wHe8UhzS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 219
        },
        "outputId": "8542fb20-3f57-41d6-da0e-bcb35c522b2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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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              "      <th>Churn</th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
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        "\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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            "text/plain": [
              "International plan  False  True\n",
              "Churn                          \n",
              "0                    2664   186\n",
              "1                     346   137"
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            "text/plain": [
              "Voice mail plan  False  True\n",
              "Churn                       \n",
              "0                 0.60  0.25\n",
              "1                 0.12  0.02"
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        "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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        "df.pivot_table(\n",
        "    [\"Total day calls\", \"Total eve calls\", \"Total night calls\"],\n",
        "    [\"Area code\"],\n",
        "    aggfunc=\"mean\",\n",
        ")"
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            "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",
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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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      "source": [
        "df['Total charge']\n",
        "df.head()"
      ],
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      "execution_count": null,
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          "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>Total eve charge</th>\n",
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              "      <th>Total night charge</th>\n",
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              "      <th>Total intl calls</th>\n",
              "      <th>Total intl charge</th>\n",
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              "      <td>KS</td>\n",
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              "      <td>148.3</td>\n",
              "      <td>...</td>\n",
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              "<p>5 rows × 21 columns</p>\n",
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              "\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."
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        "id": "XFQCd8V2Uhzb"
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    {
      "cell_type": "code",
      "source": [
        "# get rid of just created columns\n",
        "\n",
        "# and here’s how you can delete rows\n",
        "\n",
        "df"
      ],
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      },
      "execution_count": null,
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          "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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              "      <th></th>\n",
              "      <th>State</th>\n",
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              "      <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",
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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",
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              "  <tbody>\n",
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              "      <td>254.4</td>\n",
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              "      <th>2</th>\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",
              "      <td>5</td>\n",
              "      <td>3.29</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>OH</td>\n",
              "      <td>84</td>\n",
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              "      <td>True</td>\n",
              "      <td>False</td>\n",
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              "      <td>61.9</td>\n",
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              "      <td>89</td>\n",
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              "      <td>7</td>\n",
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              "      <td>28.34</td>\n",
              "      <td>148.3</td>\n",
              "      <td>122</td>\n",
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              "      <td>121</td>\n",
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              "      <td>10.1</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>83</td>\n",
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              "      <td>55</td>\n",
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              "    <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",
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              "      <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",
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              "      <td>14.1</td>\n",
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              "    <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",
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              "      <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",
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              "    <tr>\n",
              "      <th>3332</th>\n",
              "      <td>TN</td>\n",
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              "      <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",
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              "  </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",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fcd34730-6ae7-4534-9278-06e175db809c');\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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            ]
          },
          "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": [],
      "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": [],
      "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": [],
      "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": [],
      "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": [],
      "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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\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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7t5IGZNsi3TyCtgdvy6ho++OS2OC2JFsnzNZFKblIN9FApkjNlnHa202DYa9Pji2I1TdO07R+B9HneDR1+5quEWtIvb3iDSauk3w269gCvYMLioGQ03zW2mmyoZ0RWtI4KSV2wk45tWV2/GUbCKXul6YLzOiBE9GVI6czhdoHHAs8IKUcDTQAP2s7kZTy31LKsVLKsYWF7V/XU46ywCRSfuy+Ic3XdpPnmdj59kUYETyt+ddjzxiJ0FoKxMdvZacuGFo26Cly+rRi74ZjGwkLPnnLu6ZuBFyOP6MW0MCYcIA+Jy/75Cl1+APJw4ORID0HdUvZytgzj0G6yYUtGHZxUtdJXEeQcIcSDAfa6Vs7gufSEry7Pwn+Y1oN0TSNYyYOTT6j1ySjJ9YDIRA5SS0lTMEnb6e+R5GZm0GXkuTPQPn8dKZQ7wR2SinnN/7+Il7hVr6AROZPQMul5cA3QEQQ2b9tfx5fL4hcDYRoSdwOgz6QpBRuY1LzUw8A37v3m2TmRppTtl/6d3cq9hq4smn5fiCEyP5jyicivNTwP3pt4we8M+CqfX6e+ksxwbDLKRdVM2iUBJGJyLq14z4bExr73BRAG+KS70Fx7yKCEa+A+vw6gXCAnz7+g3afeCjqVcjlP7uYQNhACO/MOhh2OO7Uenr2d5ESmi4fS+ldnvnnr0r4wf3fb3c7t0eEp4Gvf+O6QOtU9OSi/4P7ryGSHcEIen03gi4ZWQ7f+20lGKMg595W21PKINUVBi/+s0erdnSfTrBxO3yWp1WUz65T4bZCiNnANVLKdUKIXwMRKeXN7U2vwm3Tm3RrkdEXIbEMfAMQ4csQKc5ak+azFiFjL4NMIELngTEREkuRsZdAmojguRCYlPSoXW1lHdMf/oB1izbRb2Rvzvn2iWRnzILEXNB7IkKXIXwdp15LZzcy+iw429i1tQcv/9tPPOoyeWqMY0/agzCGI8LTEFpumz4v9K6Z799ntxQZfQ6creA/DhG6CMv0M+Opj1ny/gqKehUw5btn0K1f6qdQ9rdm7jqm/2cGVv0WJl5Yy7izeqFnXOZdgqj8HWb9avbuiLB29YVMmHoV+V1zD9hmyvWXFsTfRpozQSv0PrMUT3w0qS6r4a2HZrBxyQoGjijj7K/7yO52LgROQwhfq+2Jfxwx+yzefWw+Kz5cgxlPoOsavYf1YMp3z6RLH3U2/Xn4zCnkQohReI/nGcBm4GopZbt3F1ShVhRFOTgdFepOPUctpVwGpGxAURRFObLU/3KkKIqS5lShVhRFSXOqUCuKoqQ5VagVRVHSnCrUiqIoaU4VakVRlDSnCrWiKEqaU4VaURQlzalCrSiKkuZUoVYURUlzqlAriqKkOVWoFUVR0pxKIf8Ck249mB/iJYCfjNALDq0daXntuLVgHI/w9UBK23sVaGIF+Mcgwpc0v77Um/4jcKuRvsEIZxOgUVtTyI5VM3BkDv3GnE0ktBwIQuAUhBYGvFeeLnx7GQBjzhrJhkWbKN9VyeDjB1AyvBeutYKVsz5m9xbBsPFhevTTIbER7AVeCG74KpDV4JaBa4KsBXzgH4IIngr4cGMfsGXleqr2muzabLFplUHv4SM451uTiWR74US7N+1hxYdryMrPpGd/QdWu6fiMXPbtHcTGRR+i6w1E8kYwcvLJDBgVAPN9cHeCfxQicFrz+nhtbWfJ9Jco27GZ3IJ99BlUR9feAbKLR9Bgf5VF75USDFUw7rQEgUg2yBhChJD+YxGJRVTuKWfZJ7mEs7sxdnIMn7aHXdu6sWquRXZhFmPPGommayx8eykL3lpKXtcczvzmZIp6FrBt7U4+nbeSkkFb6TesDqFFQOgkrDwWfJBDQ52f0acOp7h3YWPy+FKwN4KvH/iPxU7YLHx7GbUVdfQb1Ydta3YihGDcuaOTQnillKxftIllM1dRV1XPwDH9OP68YwmEDjII4RDJxEpIrAW9Nxjj0uod2V7a/UfgNkBgQrtBzYeqU685PVjqNadHnpew/X1aErYdyLwZLfKNg2snsRpZ+U3AbmknMAXM1/HCVJuEoeAthKxGVl7lTS8tIAH4cF0XIVwsE5yEhmkKdmzM5JgTEoBE5DzAB/+1+fM1/0T3abiuixm1MIJ+hKaBlIyelGDPthihsM3tj23Fb0h0n8QIyKTEktR8uK6GGbcxDJe3n8rj/l/2wHUABJqucevzN7Fs5irefmgGmq7hOibBkM3379zJA7/qjhnzwmBdFzRdouuCwcc28NsnNxMMNx0rQUTug2CM4x8/+CNvPLgATQPdL3FdkI7gjMsquf53uxACnvpzMS/+q5Dzryrn6p/vQdME4EdKC8v04dguuk/y4B3dmP1GDsdMiDLv3Qw0TUPTg/gMP0IIasprW1ZVQJ9hPcnK2sSv/7OOUNil6TXg0oW1S8Lc8e0+xKMBXFdn6g1ncvWPXwdnk5dkIASb1vblZ5dmk7AcEqaFbTnofh0j4MdxXG7+z/c4ZdqJAMSjJreceydr567HTngRZ0IThCJBfj/9Fww9YdBB7XcHQ8o4svJasJd7iXFCgNYVkf8UQss7YsvtdP+sJciqa/A653rHUMZ1aBnXH1Q7n/l91AdLFeojS7r1yLITvQTxVoKI/BcQ/s4dNFI6yLKTwS3v3IK13kAc3L0HnNR1Yc92g6zcBBnZEleGmTZ8MHVV7ecYarpEIHl03qcUdE3QTrhKp+zeavDdUwdhxVs3ovs0fIavTQagRNO9AidTBOL6DJeLvlXOd35V2jJQRJgz9z7uuuI+LFMgXdg/5isYdvh/9+3g5PNqkBJuuaKE2x7eSjDU/vFmxgRXjR9Mdbk/ZT+S+uV3eXbZGjJzk7epGRe89kg+D/22OwCBsODXj2zl2IleLrXrwpXHDaW81N9u+0bI4PGNfye/ay7/uvlxXvnbW9hW8rIy8zJ4vvRBfP4j8we6W3cPNDxK61BmHwQmouX+84gss7OktJD7JjT+Zbe/ECLvIYRxXKfb6qhQq2vUX0TmLFJ/dFarBPADSixPUew74G4Dt65Tk2oa5BYmWDEvEwDHdhh9ck3HzTuCvsPiZGQ7h1Sk9z/nmPlKTsrsQsd2UwS1ClwndZEGsC2Nd59rc+YmXd78x5OYMQ1Nk7TNYoxHdd58PL/592/fUorf3/FJkaZL/AHZqSINcMwJDWh66kDaQFBy5uUt2R5m1OXtp7Kaf9+4MkRD7YE38uz/zgPg3UdnpSzSALZls3zW6k71+ZDEXqR1kQawwfzIuwx3NFnzaZtq74kjYy8etsWoQv1FJE28P7Pacg+u8EqTVGGvh4uUAuk0/4LfaCf1dT9G0G08O/1srLiG6xy+dUtYbduSxKNewnp7l2XMWMvhFQi2XJpoj657l006yx/oeEP5jf33EdEqbd2Kawfsj2s7WDGvECasRPsTCjBjR7BgyvaWLUEeeJ86omS8vRHgHsSxeACqUH8RBU5OvYOKECJ4VufbMUaT+mygHSK7/aqUgp0QDBgZBUD3w4q5HecFCiFZv6ydJPSDdMKZtRjB5HUTmsAIGinmaJ+mycaU8/1JTr3ibIygi5OiuAZCLpMvbjmjffWRglaFOxXLFKBJUn8JJ1s5NwNfO1cu7ATMe7flDDoYhskXt6zDwFHRA7av+XSOnzIGgPHnjWmVJr8/13YZecqwTvX5kAS8m8RJfENb3dQ9Kozxqb9IRNjL6DxMVKH+AhJ6EWTehJem3fgRihAEzmiVAH7AdkQQsn7f2E7jEd+cLt72oBSQ83fIuqtx+tYHzv6p25YJ8Zhg6ewIRd1dIIiW9VMmTjuXQDjg3a1vbF7Tvf4HIwZFPR2MINx7c3fiUUGi8STN7eR3iRAarqPjODBodJRTLqrGb7h4hU8iNMFlP7mQAceWEMzwUtB1XWIEXb5+014CIRefv2lh3sr4Ay45BTbfuXX3fkvyQ+bPOPvbUxkwugif323+8dbFoWRIjLMur0RKmP9+Jm8+kc/CGZnY+x3TUoLjeOsXa9D44OVcair89OhnEox4bfkMHX/Aj+7Tk9bXlWEeuK0EM976so9tQ02lj//8vqvXn4wAQ8YPYNKFkqYkcyMQ5Ob7KgmE/Pj8rdsWmiAQDnDxD86h9xAvmfzau79BbnF28+fVvCUCfn7wj2uIZB25gikybwYtn5YU9qCXOJ/9+yO2zM4SWiZk/RrvmGjcjiIMxjgInH74lqNuJn5xycSaxoTtOCJ4NhgnHNIjS9Le6iWJuxWIwGQITEY6O6HuD2BvAN8QyPwZmq9b4/TbvetvTjn4eoKzFxCUboPKnSuIxbIo6H0ifQZs987yQxcj/IMBWPXJp8x89mOEEIw5YySbV2yldPM+Rp4yjIlfOYZYxeu898QcqvbZTLxQp2Soi19s8B7HIwCBE4Ggd0NT1jde6gmAbxAi8jUQARr2PU3pxk/Zt8PkvedDbF8Xpku/Eq64dRrDThiEYzvMeXUhC6YvJTs/g75D9pKXO4fqqiwWf9ST0k0b0TULX7AL4847m9Mu1Qj7XvJuuvqGISJXNd+wdWyHOS+/zsxn3sRs2EdGZoxjT6nlhDMa0ANd2b3nAt74j0YkXMrpl9XTZ3AQcEDLBf8wpLmc0k27+PitPOKJ/px1eS35RaXMnd6FRbP85Hcr4KyrJxMIGTz7h1dYOmMlmXkZTL3hPMaePYo5Ly9g6/IPGX/GevoNrcNv+IAwO7fl8e6zxdTW5HDC+WMZd+5oNBH1UuTtNeAbgghdTOmWKNP/M5OqvdV0KSmmYlcFmq5x6tdOZuj4ga32k1h9jPee+IiPX5pPrD7GoOP6c/7/ndVczI8k6TYgY697T37o/RDhqWnxxEcTaW9sPIZqEcHTwZjY/DhrZ6mnPhRFUdKceupDURTlC0wVakVRlDSnCrWiKEqaU4VaURQlzalCrSiKkuZUoVYURUlzqlAriqKkOVWoFUVR0pwq1IqiKGlOFWpFUZQ0pwq1oihKmlOFWlEUJc2pQq0oipLmVAr558BxHBa9s5zSTXvpN6oPwyb0QFgfeK/pNE5E+PogpQnmTHDKwBiD8A9NakdKCdYCsNeBr483r0h+T3GreeztYH2MlC4kPgVZB6EL0YKnNo7fCtYnIDIgcBoSAxoeZO/mJWxYVYweuZD87nl8On8j+V1z6TeqD0veX0lOXinHTjIJZfVEGhMh/hbE38ZO2GxcM4DN64czctIwuvdaQun6D1m7oJ6tGwdz+lVXYPhWs2j6Qrr0chhxYi4B/yZAh8DpOG4+89+cTdnOKIXdY2RG1vLp4gQfvRZBSoNhJ5Zw5W9uIiu3gJq9c5n74iPE6qso6OZD0xyKe2XQa8S5bN04mJ1rZhAJLaZ3/wqkryerFo3GNms44SybzJwayndXU7VXw3FcEg1bkELSEB3EjvUWq+bEGHkSnDotjOZuYfcWm/df7MrKBfkEQiGkU06sroHCHjoXf6eWzOwa4lE/ZizIvHczmP+eQTBik98lQlVZEDMao9fAPUy9poxwdiZlFRdSWd6XTcu3EAjC1P8Lk1sYY/vaXcTjFkve283OjXVk5+tM/koEXa8l2hAir1tf5r21lzWLoGRYAZf8cBD5PcdQHx3A3JeeYcuKzQhhk5mToOSYAQw/+ST2bf6EZbM2YyfqGTZOUtBrPFs2DGTdwt04iXL6DlhMZo5JUd+T6T7im6z6eDObl2+jW/8ujDnzGKyYxUv3vcXmFdsYcdJgzr32DIyA9/5y13WZ9dwc5r62kIIe+Uy7+UJyi7Kb979taz6lbOMLZOZAXu8pLHyvEqRk/PljyOvSOkhiz9Z9LJy+jEDIYMKFx5GRE9lvP94I5jwv7RsdoRdD8FSESA6acGyHhdOXsWfLPvofW8KwCYMQQrBp+VZWzl5LbnEOJ5w/5qADJI6mTr/mVHgVYRGwS0o5paNp1WtOW1SUVvGjk35JTXktTsJhxAkN/OqhDQRCBoLGl9oHz4X4ByDsxuQWAYFTEDl/aS7E0q1HVl4JzhYv5Vj4QCtE5D2D0PNTLtutvQeij+KluLRJodBKIHAKxJ72locGuEiZwIy7CAGuLdixOcBPLumHnQgAkDBtrrtjN+dcUYFA4jMMdN3Ls9t/V0pYAiklAoFtCy+4VquL1t0AACAASURBVMDLDxbwxD3F3PvaJnoPjOH3mkUIb/7qMp0fTunP1GsqOPcbFUjp9QMBt369hFULIiDgmz93eObPGkKTzaG0V9y4h0v+r4w/XN+Tqd8tp++QGIaXD4CUXuLMY38s5vX/FHLnM5sYPq51yonrwrplYZ66p5hbH95KINj62JAS/n5LN958vACA4eMa+M2TWwDw+by8Q8cB24Jt60L85NK+xKPe51cyJMYfnt9MRraD4wh+fXUfVszJaI74CkZc7ntjA0XdLX77nT6sXhjBdQS6XxKKuNzz8ka69rZwXXjjsXz+8ctugEDTYOp1FbzxaB5WXDSHLOh+SXaezeBjoyyemYVEovu8oITfP7uZHn3jPPPXIr5+0z5cVyCERGjwxN1deO2x7kgHdL9OODNE5Z5qpNuyLYyQwT8W/YHcomyuGXEjVXtasjCFENzwz+9w7jWn8/ydtzDlqy8jJc2f/xuPFfHYn3ojXZfv3Xc1533nDACe+u2LPP27lxBCIHQN6Upuff4mxp0zGll7G8RebtyHm4IdgiAMRN5jCH9LukzZzgp+dPIvqatswEnYaLpGv1F9yOuSw4K3lyJdic+vo/t93DPz15SM6J184Bwlh+V91EKIm4CxQJYq1J13yzl3smTGChzbxW+4PLt8NRnZbSNLBMnxSyFE1i8R4UsBcGtuh9gLwP7ZdD4ITEbLvT9pudJagKz8DtBRbpsPaB3p5Thedl8TyxRMfzqP+3/R9HJ4SY9+Jg/PXteyLFInL0qZnNwVjwpuvbKEK28u5ZjxyX2TEtYtDdF7kEko0no71VXrXD5yKHZCS7nUQNDlomvKyClMcN6VFQSCyX0yY4Ifnd+fe17ZRDijdftSwl3f78kNf9yVNK6JbcPVJwyhcp+PZ5evITMndeBrwhS88Xg+/7ytOyB58MN19OhnomnwwgMFPPGnrpj7JaQLTdJvWIxJF1Tz5D1dksYNGBHlb29vBLzP5PZv9WHRTC9qy2+4JKxUVzFdhCaQbuvtVNDV4vEFa3ESAqPNl5EZE1x32iB2bw2kXK8mRb0K6DOsJwveXpo0TtM1bnjgm5w88SYiWa23T6xBcNs3S1j+SSZGyODh1X+hpqyWH0++LSl0OBgO8MKOr2JYt7SfBaoVIwo/bH5J/82n3c6Kj9bgOi2fn+7TQYCTaN2Xrn2LeWzD3w4pbONI+MzvoxZC9ADOAx46nB37X2fFLZbMWIljezvNiBMa2okcTPVlGUNGn235Nf4arYs0eEnMM5Ep8hNl7L9Ae8Gb+83fht7mSooRkJw6tWq/IYKyXQal24z9hnSeEZSceVkVq+dnpBwvBAwcFUsq0uBlF44Y39Bu2wlLMP2ZPCZfXJ2ySAPoPsm06/fR3vnJuVdUtjsOvO1z8pRqRoxvaEwfT80fkJx2ibfduva2KOphNSerT386v1UhBpCuYNv6IG88nnrclrUhqsq8K5VGQHL21yqbxnYQVaYlFWmAhlqd9ctDKefTdMnE86vbXa8m5TsrWDg9uUgDuI7LR88+R6r9OhCSnHlZZct0L8zlvSc+xIon5w4KXVC/+z8dBzbLOrDXAhCti7Hq47WtijR4l0LaFmmAqr3VbF+7s/2200hnbybeC/yEDpJQhRDXCiEWCSEWlZWVHZbOfdG5buugUp/vYNN09t95U5+50XT5pC2ZSD38EOht7mQITaZI5e4cTQOfIVMGwja330HTegfb0MsgFElfNq3a1tqmc7dert+QB/zi0f2yw340T6d70/j8slWyup1IvQQhwGknVFuI1vN5WZBwKCnyQoDrkDKFXNP2b7ujRujwC81tZ0W0/ba/dFzshINt2a0urTSTgOgg/by5I4nGZboHtccLIbBTFPB0dMBCLYSYAuyTUi7uaDop5b+llGOllGMLCwsPWwe/yILhAAPH9m/+02rF3EinDvDGuSF4YcuvgdNJvvergXEcQiRHUYvgFC9ks0MpAlPbHKN2AuZMz2o1LCPboWd/8wBtpxZr0Jj5cg5DxqQ+M5YSdmw0iDUk75qaDivnpT4TBy/5++Rzq5kzPas5GLethCl45eHClF+aUsLbT+d1+Bm5Dsydns3KeRloHXwheNvNu6m2Y2OA+pqWz27yRdX4A8nFsLCrxalTU48r6mFR2M0rSAlL8MF/O050b1wjhEheF90v6T88lrLEJyzBJ9NzDthyRm4GQ8YPSDlO0wRjz5mCniIhPVavMfNlr+8+w8eEC49j4qUTCEaSL7XYtkOo4DJaQm1T8YF/uNennAh9R/RO+qIXmkgK5QUIRgKUjOjVQdvpozNn1CcCFwghtgLPAqcKIZ48or36H/L/HvkeGbkRguEA8ajOfT/rhxnXkE1FV4TBfxzezhjYb9ggROSK5nZE5k9BK9yv+IZAZCOyfpt6wYFTwDjFSydPKRuMSfu15wcMBF6COHgHVeU+Pw/e4YXaNu3sX71hb3MhdZyWatWURC6ldy03YXnXUxNWU9K2YMH7mSydHaF7X6u5mDadmUkJZgzuuKY3Sz7KINbg3RxrSjW/+0c9MGPe9fxzrmjACLroPheEJBByOOWiaq69fTczX8mlYo8fy2zdLzPmXW9ftzTMynmRpD5LCSedV83fft4dKy5aJatL6a3DSw8WsGNjADMmuPuGnsRjAsv0xjV9ycWjULHHz4O/6dr06fGH63sSq9dwHZh2/T669zEJBFsSz42Ay4337ODyH+6jWx+TUMQ70zOCLuFMh5/dv93rgwNLZ0eY/UY2INF0lxPPrcMItqStA2iaSzDi0m94jGBjW37DJRBy+Pn925DAO8/mEo8JHNu7NxGPCd58Ip9dWzIBr5CFMpKvIWm6xq//+/+45ekfEQglPzlx6c0XMPXGS3j5kfGYMW2/z19j0axMFryfTSBsMPVH59FnWE9GnzqcSZee4BVr4V1TNkIG1//1W4QLp4ExJsVJhw8INd5wb/kS/Mlj1xPJiRAIe/0KZgTp0qeI/seWNK+LEfQTjAT4xTM3omlfjCeUDyrcVghxCvD/1M3Eg9NQ08CMpz5mx7pdDBzbj0lTe+Fz3wRZ66V+G+PBrfASot19CON4CExOevROyjjE30Jaa8DXDxE6H6G1f4YppYTEQmR8BrgNYG8E4hA8C8Lf8dq35iHNmSCyEKGLkPhxqu6idNMa1i8voDZ+IZm5RaxfsoWinvn0GdaT5bNW0GfgZo6bbJJZ2M872294BqyZWJbN5k97s+STURwzcShDRs5n+6p57NxosmJ+X0ae8VWys1azYPpKBo+OMfoUg3CgFIQOgZOpqy3m/ScXsXtLAyWDq+nZdxdb1ui89VQulXsD9B2RybV3X0+/0aPYvuwJZj71KvV1MfqPMOnexyIjJ0Rer8msXDic6p3v07NkFd1L6olG81n04RBqKlwmX1RL977V1JQ3UFOu4boJEvFaLBMSiVwWfhBkyxrByVOiTDjHwe+vonSb4PVHi5n7Th4IH0YghhV36D3Q4rLry8nMMTHjOglLY847Wcx8KRvXFUQyBQ11fqSbYMI5FVx6XRmBsMaqZeNYOXcoO9bvJRiymfp/BiVDayjfWY4ZjTP/nSi7t2jkFjqc+pU4uYUm0XqDhmghH7zgsmmlny59glxxcw8Gn3Aiu3cO4r1HnmDD0l2ARdc+FoPG9uCYicexedkiFr63h0AgyvFnxskuHsGny0ew4N19ZGTs4LhT1hIM24SyR9F/wk18/Mom1i/aRK/B3Tn1ipOpKavl0V89y/a1uxh0XH+uuuMy8oq9s+5YfYxn73qFBdOXktcllyt/dSmDx/UHvEf3Vsx8F7PyBbJyJSJ0Gp+86V0GmjRtAgOO7dtqX139yad88upCgmGD066YSI+Bjan30gXrE6T5Ebg1gA98vRChixB6l6T9vr66gRlPfcTO9aUMOX4AJ31lPLpPY/6bS1g2cxX53fI448qJSY8HHm2HLYVcFWpFUZQjo6NCfVD/w4uUchYw6zD0SVEURemkL8YFGkVRlC8xVagVRVHSnCrUiqIoaU4VakVRlDSnCrWiKEqaU4VaURQlzalCrSiKkuZUoVYURUlzqlAriqKkOVWoFUVR0pwq1IqiKGlOFWpFUZQ096VLId/+6S4WvLWEQMjgpK+Mb5WYfCTt3FDK/DcW4w/4OWnquFavWJSJtWDNAZEJwbOI1vv56MV5rFuwAZ/hp+fAriSsBD6/nwkXHUe8Ic4Td7zI2rnriWQHOPuqAkaMD7F0djbhnF6c/JXxZOV77xQ2YyafvLyA8l2VDBrXn56DujH7xfks+WAl9VX19BrSg6HjBzLjmdlsWroFI2Bw7rUncNE1gu1rVrN0djYLZ0SJ1sQoGd6T/J4FrJ27nvKdFQhdEKuPYccdcrtkk1OUzfZ1u2iobkDXdbr0LaLXkB7sXF+KWR+nuE8huV2yWfTOcmor6kGCz9DRDR0zaiGdljc5hrPhyp9EGD6hG0/8YRsLprdOAtB0G+lqrVJGhKYhNHCbwkUE6JqL64KUGj4DQpEE2XkONZU+6qp8gKRrH5O8Qpt9u/1U7jUIRRwycl3qq4M4toHu14jWx5GuJDPbBuniOALHkRR1t6ku8xNtENiNqTeRLJczplXRa6DNopkZzHk7A4RA9+l07WXRd2gDNVWCrBybQaPjbFkTYd3yICecVY3P57JwZiZb1oRw7DbnUYLGEArJsZNi5BWZbFtnsGe7QTzqLbuga4JQRCMj10BKP7mFYbKLCikvjTFwxB5cp45IJE4wAnV1uezYPIRAOJ8eJatZt7Cc9cuDOE4Bg47vx5BxA5n939mceOZqJpxdCVqI0u3dqa4Is355NgW9j6fvyL4sn7maJTNWkrBsinvlE6s3qdpXQyDoJ69rLpmNIQNSQrw+zsjJwxg4pt8BjxnXjbJ79ROUb/8UyxnGiMlfI5SZHIYhpWTl7LWsnb+egqK9nHiuxAgbIG2ElgPBsxFaVoolpGYnbOa9sZhdG/ZQMqIXY848Br2j2KDPwUG95rSz0vU1pw/9/Cle/utbSNdF072D/OdP/pCTLj7+iC738duf57k/vIKUEk3TkHiBAqdMm4Cs/SXEXge8ZPG1i4P8/PJ+xKPJ8UT+gB/Hdtpkwu0X9eWX6H4d8HPbi/+P4j5F3DjxVhJmAiuWQGgC22on66mR0CRdelr8/rlNzH83k4fv7I7jgpMQHErs06Hx1mn0xFp+/chWQDD3nWzu+v7RSuNIFT7cMSMoGX9GDTf9ZQevPFTAo3d19ZLWAZ8ukXjZh9kFCU65sJqv/WhfyyIEPHhHV15/tIC229wIuvzuqU08/ddils/JaPxcDqxbicmfXtxEVp7THIXlul70V+k2g59e2p94VMMyvWTzhCXILrB5bO4agm1q475dfm68oD+1lb7GSLbO7xe6T8Nn+DjxonH89PEftPvifie+ltjOaUACI+CF965fnklmyVP0Hz2weTrLTHDLOXeybuFGEmYcI+DiNyT3vLKRXgNMvIANgcj9N8IYd8D+le+u5IYTf0FdZT1WLIER9FPcp5C/fPQbMnIinV7PQ/GZw23/F6yes45X/vY2VswiYdqYUQsrZnHXlX+loTZ6xJa7fvEmnv/Tq1jxhLfcmLfcu6/+B7W734b4G3ghtAkcO8btV3clVp9ImSGXMBNJwZ3eQeL92AkNMyoxoxa/mfZn7rj0buoq6ojVxXFs54BFGrwg1X27/Dxwa3cevrM7lqnhJDQ+vyINTeuz7ONMZr6cRzAsGX9mLbmFNvuv7+f3w0HPY8U1FszIYt472Vx8TTmDRkeRUoAU2LaGY2vEGnSQgq/9aB+BoCQQavwJSr5zaynFPS3afkFcdv1eNq4KsWp+ZL/P5cA/pVsDvP5YAUZAIoSXm6jr4Dfg3pt7UlOhE2vQcWyNhOX9ZTJoZAPBMM3TN/0UdU/wrVtKG5PPD26/cGwXM2ox59WFfPTC3Hana9hxLcGwSTjDxeeHUMRl0Kha5j5/I/ufXL5835usnb+BeIOJYwtiDTp11Tp3Xtu7cYoYyCiy6vtIeaD8Rbj3u/+ifGdl8zETq4+za30pD/3s6IZafWkK9ftPfogVSw7S03WdRdOXHbHlznzm45QJy7pPY96rL7ZKWN64MkQ8evg+kp3rSzsMIG2PY2ss/CCTVHmjnyfparz9dB7ghdrWVHyxrtTFozrTn8nDH5BMvrgq5TQnnlOTMsxXaHDyeTW0LYRnTqvivRfysOIH96e4lIL3nk9ONKmt1Fi/LIzrJiefr1sWSd03AROn1BzU8tuKN5i8/cgHqftq78Qwyml7sh0MS04+Zyfb1rQkh09/ZGbScS2lYPfWAPt27R/a6EAidWp68xS2w6J3liWdDCUsm1nPzTnwSh1BX5pCLdskgjcPRzamhR8Z7bUtZfJypSsO64nrZ2lKpt5cnzvpNq5FGvTlUEjXK2ztRfN5SeDJKyeETJkSLrT9tslB9yV5vrZhxq2m72Cbd5QU31nJfx02j2n38xa6RO7XaSnbaSNVSnp70za3Jdtd55Qp6Z+jL02hnnz5SQTCyUnHju0y9qyRR2y5k6ZNwAgmB4C6jsvxF1zI/gnLA46J4vcfnh3CdSVdSooOaV5Nl4w+qb7DlO3Pg9BczphWCYDjCLLyDnzpJp0Eww5nXFaFGdP46LXUyd5z3slKXUAdwSdvewG2+/vg5RxOubgSI0VSeUeEkJz6lcqk4TkFLv2GxZLSyoUmGTgymrJwSQmfTO/8zblUgpEAZ151SuqRek8sO3l7xWOCj9/qRu9hPZuHnf71iRjBtnHnkqLuFkXd2/wlaxzbYZ98fh+jJg9H01p/Hrpf56SpR/Y+1oF8aQr1MZOGcsY3JhEIB9A0gc/wYYQMbvz3d8nMbT8g9rMaOn4gU647g0DYQNO15uVe/7dvk9vzAi8tnBAg0H0Gv3xwN4GQnvJ0WNe1lLH3LenTEn8AjJDBTx//Ab987iYi2WEv3RnwGwe+dKBpktzCBD+4aydfv3EPRtBF0z/v02tveYNHRznrq5XE6jVWzIlQU+lL0Y9U/ZLtjJMpftob3nad207f8bBAyGHECfVMOKuad5/LZdWCcPM43ecV2VDEwYxqvPBAIWZMYCfATnhp6U/+uZjdWwza7gjP3FvMqAkN9B8RxWfsnzze3rp4wwq7WUy9ppyE1ZK47rpeUvyP7t5JRrZDMNyYfB5wkS5sWRtsnr55K0ioLvfx4B1dD3m/CEYCjJw8nFO/dlLK8UIIQt0eIBb1E496N/1j9RpbP41w7Pl/bHUD8pIfX0DJiF6EMrx9PBByiWS53PLAtsazfgMvrfxehEg+YWrrpgevI7swuzmxPJQZpKhnAdf+6cqDXs/D6Uv11Ad4N/fmvbGYYDjApGkTKO5d+Lksd+OyLcx9bRH+gJ9J006ga0kx0JQUvgRpfuw9QhQ8j5rKADOenM3a+evR/T56DOiKYzsYQT8nf2U89TVRHv3Vs2xeto1gxMfpl+UwalKYpbOzieSWMPnyEynong94CeiznpvDvh0VDB0/gB6Du/Hhc3NZ/N5yGmqi9BzcnUFj+zHzuU/Y8eku/AE/Z35jFJf9UGf3hrXMfTfC/OkJYg0m3ft3pahXPusWbKJqbzUAZszCsV0y8yJk5ETYu62MeEMcXdfJ65JD137F7N1WjmUmyO+SS0ZehLVz1xNvMAEQmkDTBI7d+gwxnOVwxU06g47L44W/lTF/epsbV8IBuX8RE23+2/Rr05/R3mWlUNghku3QUKsTq9dACLr2NsnOS1BWalC1zyAQdIhku0Tr/FimH6EJEo33GSJZtlfoXEDI5kf9zJiG63hPh2Tm2px2SQ39hsZZ9kmYGS/mAQIhoHs/k5IhMWoqNHKLbIYeG2XjqjAbVwU56dxqdB3mvZ/FxpUhbCv1nzSaLjn+jDoycxJsWh1iz3YDKybQNElOoU0gBKFMH07CR3ZBgLziHCr3mQwcXU7AV09uUYxgSFJRls2GlQPwh/LoM2At65fUs2FFkGh9Dv1Gl9B3ZG+Wvb+Qc766mnGnVoMWoHRbEXt2ZrBmYS65PcbRZ3hfls9axcqP1pIwE+R1zcGMJagtr8Mf8JFdlEV2fhaDjx+AEIJ4Q5zRp43gmIlDGx81bJ9rV7N9+YNU7V5H3BzKsMnfJis/+XFax3FYNH0Za+Z9SkHxXk650CaS4wfpIPQCCJ6H0Dt/nJsxk49emMfO9bvpe0xvJlx0HH6j7Vn74XfYUsg7K50LtaIoSjpSj+cpiqJ8galCrSiKkuZUoVYURUlzqlAriqKkOVWoFUVR0pwq1IqiKGlOFWpFUZQ0pwq1oihKmlOFWlEUJc2pQq0oipLmVKFWFEVJc6pQK4qipDlVqBVFUdLcFyvb6AtASsni95bzzqOziNfHGXfOsZxx1SSCKUILmtRXN/DE7S+wfvEmEpYXRKv7dU66eBxjzhzFsg9WAXDS1OPRNMFL973J9jU7Keieh8/wUb2vhr4jSzjzqkl07VvM0g9WsfrjT8kpzqJsewXvPDYLO2FT0D2P+qoGdJ9G76E9KN9ZRSQnzODj+mOZFqvmrGP7mp2YMav5NcOaJsjMDxGti2KbEMkOkFWYS1VpNa6boP9wk5En1rFpjWDBu1lI2fTd7722tNeAOH4D/EGX0m1+aioCGAE/juPgJJpebdr0XuO25w0uoQyXhCWwW+XzSXr0s8grMvEHoKZcY+fmEPGo3mrepukHjqqna2+LxbOyseIChIuUgpPOraVLb5MlszJZtzwMEgIhydhT6ug10GTRzAw2rAjRFG7btY+JdAUNdTrxqIZj0/hqU0+XXia9B5uYUY2tnwZJWBqu461ZwoIhY2L0GxZl0+owwZDLmEl1ZOUmeP/FfNYuDjPgmBgjT2ygbJcfV0qKeyTYsibIgg8yCYRcpKPhD7r0GRRj46oI9dXe4RsIO7i2txxd95JcHMdLL9J93qZwHIEmJNmFFuEMbxsGw5LaKp3aKgM7oWFbkJEbASlwnRgDRzVwzPgqQmGXumod29aJZEmMQAgtNJJX/1VK1b44mqaDZtBQE0O6EiPgUjLEosegIhL2UPqO7I3rSAIhg5O+Mp5d63fzwZNP4/dVMOH8Yo471ULIKior+vPBSxEiocUUdg9Q1O9CCksm88I9r7P6k3V069+FAWNKWDZjNVtWb8fn0xgwpi8TLhzH8ecei5SS2f+dx+yX5iNdyYQLjuO4c0cz77VFlO2oYNC4/ow9a2Tz+6ytuMXs/85n14ZSSo7pzQnnj8HnT8+SeMDXnAohegKPA8V4+8K/pZT3dTTPl/U1pwkrwU9Ov4PVc9a1iu7JyIlw/8K76NavS9I8q+eu46aJv+oglsgLDBCawHW9F7q395npfo3iPkVUllYTr49/9hUCUr8YviWV2+eXILyU8rbdEpo8xNiog0/9PhiF3S3ufW0jliW45fJ+VOz1ewW8USDoMmBkjPoanZ2bA9jWZ8+dGjKmgb7DYrzzbD6uI9B1L2rr+t/tZMvaAK8+XITPkEz9zj6u+uleLFMQCEp2b/Nz3eTBWGbrPhT3NCnommD1giMTetGll8m9r28iEHIJRVxiDRplu/0s+iCDqd+t4HtnDmDz6lCHbQgNrv3Vbh68oxuuK9D9Oq7toPtcdJ/EjGsEQy5Dxkb5zeOb8TW+8jlhCXSfJGEKPn4zhz/d0JOOguV0n063/sXEoyblOypa7YdCCIyQHzNqEcoI0mtId+6eeTs1ZbX8cMIviNXFiNXHCWUEye+Wy31z7iQrL/MwbMGD91lfc2oDP5ZSDgXGA98XQgw9nB38X/H6A++ydt76pHy1+uoG/njV35Oml1L+//bOPDyO4s77n+pjumdGtyzLtiQf8oVt8I3BNjY3GHMmWdgQQpLdTXbJzSZkk+XNtWz23bxJyOZaSEg2T5I3AQLhzgs2BGyMwfjCxveBb+u+r7m76/2jx7KOGUm2ZGtE6vM888you7rqV13d36mpLtWXb9z83T5FGsBxXBJxB9eRaUUawIm7VB6sHkKRhv5cub2emJa8Obqnke7AXbIH6/p9Jq97v3eSvKIEv/veWGorTGKR7nFGIzp7twY4ftDq0pMf3OvQrgCrHy0kEfMMBuIxjVhE42f3l3L7Z+oZNTZOLKIxd1k7SLD9nlv4D74wPinS3V81Jyymzg4nXUyG/hzd96OT5BQkCGS5CAGBLJdxE2L4/JI1z+ZyZK+/3zykC08/UsTyWzyTCSfuICUk4hrRsOfAHgnp7NkcYPVjhZ0u5z5Louueme3SlS1ccm1rz4uyG07C4eT+KuqON/TqLEgpiYY889twe4QjO4/zxPef44ef+jnNtS2Ek/dKuD1C9ZFafvW1P/RZ1nDRr1BLKauklO8kP7cBe4GScx3YSGT1b9b0cio5xd5NBwm1hbttO3mgkvamjvMR2nmgZ49nCNxPzwG6IZm3rB3DgLdeysVJpL4FnISG6wzdI5xYVCOR6H1OdF2ydW02S29owTBdZl/a0c2rct+2IKnPpWDtM3kUFPd2uB8slt9hxoIO9B4GM6YlWX5TC8//etQA3e0FdZUm19ze26uxK9GwzurHC1Lu8wddrrk9tYN7VwZqgBKLxHn5t2vZvnZXrw5SIu6w7skNA8rnfHNGV6IQYiIwD9iYYt8/CiG2CCG21NXVDU10I4z+rIV67u4vvWLokbLLoEqmnH7Rt+N32sOGIX6ZLPdMih5Q3UT6RP2Yh58xI/G+G7BQCyGygKeAe6WUvX6LSCkfkVIulFIuLCo6Pz6Emcb1f3clupH6lM5cPA1/VvcxvZKpY8kuCJ6P0M4DAzGdHX5cR/DO69kk4nDZymYMM7UK6IaLpg+dQvgsF8PofU4cR7DgilbefCmXRFzj3beycLsUO3NBB+kMfK/8YDONNUPv5RcN6+zeHMTpYfoeiwpefy6PW/+hboBKdnIh6gAAIABJREFULRldGuMvT6TuLZ/C8jusuDN1rzvcofGXJ/s+HkBoAxNfn9/k+k9cwfyrLuplFG34DK7426UDyud8MyChFkKYeCL9Bynl0+c2pJHLzfdcx0XLZ/a6aHIKs/iX33yuV3ohBP/x4v1pxf0UuqljWgaaLvrsDeimxvgZJdhBK41b+dnQt3O3aTmYPjfZu+ueRug9XbIH+kpX7tC8fvSVUhprTe6+r5ri0hiW3+m23/I7XHhJB5NmRJJCPvgyp8wOcdPH6zFMF8N08dkuPsvl3u+f4PEfF9NYZ+CzXXZsCIKESFjgJOArPzmOZfeOYdzEKHu3BpK91aE/Rw/eW0ZzvUGozZvdEmrXqDhkIaVk+c2tTJ0d6jcPTYNb/6GOdS/kAWBaBkIDw3SxAw6aLrEDDrMXd3D9hxs73dGjEc+RPRL2vhg2vdr3wz3d0Jkws5Qx5aN73R9CE533gz/LZsrcSdx+3y186VefpmBsPv5s29uXbTNu8hg++d27+ixruBjIrA8B/BZolFLeO5BM/1pnfYA3VrZj3R5e/s1awu0RFq2cz1UfuQyflb7n09Ea4rH/fIYDWw4RjyWIhqIYPoNlH7yE+dfOZvur3vS8JbddjK5rPPuzVRzbc5Ki0gIM06CxppkpcyZxzd3LKCobxa71+9i1fh/5xbk0VDXx4i9fIR5LUDxxNC11reiGzuS5E6k5WkswN8CsJRcQCUXZ/eY+juw8Rqgt0qmXmq6RPyabjuYWYhHILghQUFxIXUUDiXicybOizFnSwXs7JJtey+a0M7jXLSybHMUOuhg+SeVRH62NPnx+C9d1iYfj3dKmmp4XyHFJRAWxmAB5en/plBgFxVF8PpeWRp2Th2zC7Ua3Y08xbW6I8VNDbHo1n2hIA+HgOBqXXtvGuIkxtr0R5OCOAKDhsx0WXN5G6eQYW9YEkw/NPEomRZFAR4tOJKwRjwlcB051L4vLYkyaESHcoXFsv42TEDgJgRCSaFRj6uwQ02aHObTb9qYBXt5GbmGcV54sZM+WAFNmhbjw0jC1lSbSlYwpi3Nkr807b2ThDzo4CR3Ldhk/LcKBd4OE2rxBZH+WSyLuPXzUDS8mJwEIiW5IcCGREGgC8kbHCWa7eI7sLg21JuF2i1gE4nFB7qhcpCtx4iGmz29j5oJmTJ9Le6sOro4VADsYwJ+3kGd+fpymqg6EYaLpNm0N7biui2W7lM+KUzZ9HHF3JpPnTsR1XHy2ybIPXUrFe1W8+ttHMfQ6lt1WwpylMYSsp6lpOmufCWD7tjK61EfxtA9SNHEpz/zkRXat30fJ1DFMWzCZba/t4vCOo+iGzvSLp7D45oUsuG4O0pVseGEL65/eiOtKltx6MQuvn8PbL2ztnJ4376oLO8U8Hovz1nNbqDhYRfnsCVx8w1z0ngPz55FBuZALIS4D3gB2cvrqv19K+WK6Y/6ahVqhUCjOhr6Eut/Z3VLK9WTOYxeFQqH4q0P9C7lCoVBkOEqoFQqFIsNRQq1QKBQZjhJqhUKhyHCUUCsUCkWGo4RaoVAoMhwl1AqFQpHhKKFWKBSKDEcJtUKhUGQ4SqgVCoUiw1FCrVAoFBmOEmqFQqHIcDLCcldKybbXdrFl9XZyCrO4+q7lFJUWDls8VUdqeO3R9YTbI1x643xmLb2gX1cIKSVrH3+T5x5ahaZr3Pb5lSy5ZSFrHlvPcw+torm2lYkXljHn8llUHaqhvrKRotICLrlxYacz8nvbj7Dq169xaPtRWupacV1JwZg8xs8spWzaOOoqGvjL/11Ha0Mb0pUYlsGSWxay4Lq5vPaHN2ioakK6LpFQDNOnk1uUS7gjSkttC07cwXVdckcJJl4QQTpxYtEsDu81aa1vQ0qJZujYAYuCMXloVDBtTiMVhyyOHrAItXrLtApN9PKE9PAcw00ftDUbSVcOSSDLoaA4Tn2lSSSs03t9r1PrFwtOr0Xd1Tsx9aL5gOewLVzchIY/6FJcFqWoJE5dhcmRvYFkvBIhZGd+p1zDR42NkZWfoPKwnfRM9PLVdJJLl0KqWIUuEXhGvl4dvWVMcwviJByNSEhL+iymilkCGuWzwly2shkpBeteyOPYAV+3JVx1w8Xnd7H9LhLIK3AoKo3RXGtyeK+NE++Sv5BYtosQ0vOvjPcs+/Q51TRJ6ZQIsxZ28M4b2dScsDpTjS6NMn5alGBWgqUrWzEMyesveGtBJ2IaQvOcXQqKYwSyJKPGxigcHaex3uT4fptYTDBqTJxJMyK8uyGXqmM+AHLzJTd9IsK0eTbb1pvs3eyw6Kpa/EGoPJZL+cxGxpSGObTbzx9/WkQiYTJh1iTyxuQSamqipb6a1oYIhcWSBVdKDu6wqDiSRVFZEctvX8zJfRVseGErpmXywS9ex40fj3Ng8zbWv+BScTRAJOSi6zrls8eTW9DIpCnvMrZ8NGOn3QzOPnDrENal4FuGEBrSDUHkRWTiIMKcQVXlPF57bDPNta0I4ZkLzL1iFgtXeEuixqJx1j+9kQNbD1M6dSxX3rmUYE4gRfsPjn6XOT0bzmSZU8dx+NYHvs+7a3YT6Yh4C+RrGt944ktccuOCIY+tP1577A0e/OTPcRMOiYSDHbBYctsivva7z6cVa9d1+cLi+9m/+VC37T7bIBZJpDymM43fxwUXT2Hm0mn86cE/k4j1nX5weG1t+iTxmEZ3Eey54LrnWdX98uhvEcWuQit7vPd1TKr9Xbf3jLPnMV3ESHcxDG/9ZU+U08XRNdae23puP9NY05Un+NhXqvjQPXWYpsR14aFvlLD6sYK03o29v8RI8XfPmFPFc+pzMqUAKbvnk1uQ4BdrDtBYq/OVv5lCR4ue8tj0MQjPeT65uXxmhB889R66IbEDklhEYPgkTsL7khXC+7KT0nMef/H3Bfzi26Vp4pdd4u5ZXzrT5hYkiIR1ouGu+wSaLjF9kq8/coQFV7SjaYDQETggAmDMgpzvQNNHwA0DIRIJi6ZayWevn05Lw+n28WfZTJ47ka8//s986fJv0VTTTLg9gh20MC2TH7/5Hcqmn7mt7GBdyM8pax9/i3fX7CLS4bkBx6MJouEY//uuHxOLDr1xZ190tHTw4Cd/TiwcIxF3PKeNjihvPbuJTS9tS3vcq79f10ukgX5FGiAWjrFn436e/MEL51ik4VRPNd7Z4+vqGt0d6YrkjZw+Tbr8T3/u+t7XMf1t7xlDKiPdUz1mjVhU6+w5D+SY9OWcbaypy5swPcyH7qnD9kt0Aw5sD/Dqn/L7EOme+fU8B+nap6/PSYdw2TufULuOYbj89/8q7SHSqcpLvU26wjOPkIJ/fegY/iwXO+Apq8/2XF9MH2jJHrqme6JtByQrP9rIlItCaeLvGne6NhK0NBpEwz2d471fUtGwxve/ML6zbEHyp5MMQXwnNN0DbhMQAsAwouQWxkjEun8hhdsjHNx6mAduf5DaE/WdTuaRjijtTR18/+8eShHb4Bh2of7L718n0hFNuW/PW/vPayzvvLoLw+zt8BDpiPLao2+kPe65h1YPqtxE1MGJO/0nVIxollzf2s03cd2f87oMuww/pk+ydV02e7akcz4fOLrhMqYs5vVcB1q+JblsZcugyu0v7vFTo908KU8TAfcIXZ2BAE68Z+OkuDWj4Rj7Nr3Xq3MlpeTAlkOE28NnFnY/DPtVYpjph8l14/za4qTzLhQCTF/6OI3zHKdiZOI4ottQkm7IYXES7wvDdIcupjPMRyYtw84liYQ4o7B0Q6Z1Ue/ruZU4k2+oATDsQr3i76/CDlq9thumwczF085rLPOvmZ3yQZnPb3Htx65Ie9zt9908qHJN20RP0ZM/Nwz9M4nMIzPr+Mafc7sMycBVH2jG9GVOrIm4YO6yduYvb2Nw51DiJDSO7kvdG02HkxCsez5vEOV6ZfdFzQkTkVL1/N44dY/5FWMnxLDs3nnaQYs5V87C7OGFqukasy+fiR3orWmDYdiFesmtF3vmr34fpmXiz7LxZ9s88NxXz3uP2g5YfPNP92EHLeyghc828dkmt372euZcMSvtcUtuXcTiW3o/A8gpzOq3TNM2WXLLxXz6hx/H6KPXPjR4PTjL76BpPV2ju6fTdG8mgaa5adJ0T39mr7M9rv+XYboEs1PVb/hfVcd8PPLAWKIRQSQkKJ0c4Y7P1QxLrKnKtPwuLfUmn36gglFjY2eVr9CShrpC8p+fHk9Lg0FHm0YsKohFBI4D4Q5BPEbyIaLnNh6NCH7/X6M5ftDqco30zl833C7O8D0QkpLyKD7LxfS5Xa5dbygmkO1w/8+PE4uQ7CX7ki8brCsh7xHQS0EEAR+OY1N5xAbh6yxC0zWsgMXFK+bxjSe/zOS5E/Fn2Zg+A3+2n1ElBfzLbz7bO7ZBMuyzPk5xdPcJtr26k6z8IEtvW0Qg29//QeeI9uYO3nx2E+G2CAtXzKV06tgBHbd340Ge+fH/Q9M1bv/yzZTPmcjON/by/EOrqTtRz4xLpzLnygs5vreC+pMNFJUVMv/q2UyZNwmA+ooG1v7xLQ5tO0pTXQtuwqFofBETZpYwdlIxHa0hnv3pi1QeqiUej5Odl8XN91zHvKsv5MVfvUpDZRNCCNqbOzB8BsXjiwi3h6k8VI3ruCTiDtkFFpNnRnDi7aAXcWyfzsmD1STiDv6gRVZekLIZpTRWHGTc+GPUnDA5uj9AuEOg6zp2wCTUFsV1uo/lCQ1yC12C2dBQqxNp966rYF6C0klRao4bNDeYpOwbCBdNk7gSNHFqCl2qPoRXpqZJhJZ8OKW7REMaVkBSWh4lp8DhyF6L+kobcDFMiWl5U/QcR3gu5GjkFkYZXRaj8pBNR7uevJ8lll+SiAukC67bYyaMLtF1ia6L5M90OmeWjB4XRwpo7XyY1aOKGuimSyKqUTgmzuLrWnFd2LA6h/ZWDSeue2OnQhLMcbGS0/NiUW/qYcmkKO3NGnu3BXpMz4OsHAdNl8Qi3vTAnmi6xHUEmg4TLwhTPiPM1tdzaKozkvthdEmM0skx7IDDRZd2oGkuOzdmseOtLGJRDU3zTlFJeQLTgoJRUcaMj9HaHOTgDoN41KVkUowxEyXvrCuioUpHSvBn69xwl+DCpflsfU2ye2OEWQsrCObqVB8vpvyCk4wuaefgTj+rHstB0wIsuH4hWblZtDQ00l5/hJpjrRSO01m8wmLn2yY1JwKUXVDK0tsWUV/ZwOpfr8Hy+7jr6x9i3rJ2qg+9y9urE1Qfz8JJSBzHZer8SVhWG/n5WyibPpbCSTch4rvAbQDfxQhzJgBSOhB7AxJHwZhKW/uFvPXcVtqbO9B079xetGwGU+eXJ9NLdry+h0PbjzKmfDSXrJx/1h3MQbmQnw3KhVyhUCjOjIyenqdQKBSKvlFCrVAoFBmOEmqFQqHIcJRQKxQKRYajhFqhUCgyHCXUCoVCkeEooVYoFIoMRwm1QqFQZDhKqBUKhSLDUUKtUCgUGY4SaoVCochwlFArFApFhqOEWqFQKDKcjHAhzzQaq5v48y9eYdurO9FNnSU3L2TKgkn8+eGXOba3grLpJSz70CUc2HKYcHuY7PwsWupbKZ5QxOR5k3jnlR1IKbn8jiXMvNQzP6ivbOTl36yl7kQ9sy+fxZLbLual/3mV5/97FR0tYSbMLOG2z6/kkhvnU3+ykWd++iK71+8jmBfkmruXc/nti9E0jbee38Jbz26kvrKJwjH5lE4fS3NdK4ZpsPiWi9n26g42PL8FO8tC03X2bzpIPJbAn2Vz4WXjuXD+DkYVt3J4bx5rns0hETPwZ/loqW8m0tHboyhvVJysXIeWBoOEo+GzNEJtGvGYCyJpO+rClItCXPmBZnTDZd0L+ezZHKCrxYfld3CTRqZCQDyqddtvWAaJaALL7zBqTAwENNUZhNo87z5N98xgkb1dNQzTJZDtEshKUH3cJic/waSZEcZOiDJuQpRXnsqj+piN5XfRBITaNRJxz8PQcTTamnTiMQ0nIYl2OqVLps4JYdmSeEyQiGkEcxMsWdGCELDp1Rxy8uO0t5jUVvioPuYj2stWy6usaToYPgi3d68zQlJYHGfOknbsoENjjUk8qpGd7xCNCqqPGyxd0UZxaZyqYxYbXs7myJ6AZyDrQl5Rgus/3Mi0OWFaGgzWPJPH/u1+cgpcmut0EnENy++SUxCno9UgEtZwE11NjSU+v4vPJ4lGNGy/yzV3NNLaaNLRqlF9wkdDlQ9/lmTsxCi331NLc4PB9vVZtDbZ1FUX0droIxqO4ToOll9ywbwOak4IKo6aRENeg2s65BT6mTx7GsH8AMGcID6zidqj7zKuvJZdb/upPmYRzCvgxn+6mUUr5vHG02/TUNVM4Zg8Dm4/Qt3xBsrnTODWz93Asd0nePPZTTRVN6ObureGfdDG5/cxee5Erv3Y5eQUZPe6TqSUvLt2N+uf2Yjlt7jm7uVMunB8Wh1wYzug7UfgVoPvEsj6ZzQ9J236c8mAljkVQqwAfgzowK+klN/tK/1IXuZ0z4b9fPW6f0/r49gXmq556zQLz6bHZ5vc8pkVLL55Ifev/A9cxyUWiWNn2STiCRLRRK/jJ8wo5cSBym5ebEITlE0vwc6yObrrOLFw7KzqZgccJl4Q4ftPHcKJC15/Pof/uu/UhdpbAH22g5MQSfNV2SWN7HKM5MNfqOXOL9Zgmp4xQTQiePmPBTz09dIe6U8fk9qnSaZJky59z329r2WheV8kPfP11mjuWe/0dbz9M7V89Es1mD6vjq4LLz9RwM/+tQQnoWEnv4hinV9APWOWabZ7+4QAwyeTX2CS7LwEP3nxPfJGJQhkuVQeM/nybVNpqjW6uYcbpuTGj9Vzz79VEQlp3HvzFI7tt1Ocx67vqettmC5OQpxamruTT//7SW76eCPf+dQEtr2RTSSkoRsS1xXJcyvofe7Tt2EwJ4GmSRZc0cbaZ/NTxAOGqXsG0ykQmkjpxAQkzT58/OjN7zBhRmnndikl3/vEz1j/9EaioShC0zB8Bp/67l3c9vmVvfJx2x+B9h/02GpC4Yto5oSUZQ+WQS1zKoTQgf8GbgBmAncKIWYObYiZgZSS//zoT85KpIHTi+lLkK4kGorx3M9e4oE7HiTSESUW8VzVI+2RXiJ96vgju473Nsx0JSf2VfDeO4fPWqQBIiGdI3ttVj9WgB10efibpfTluh2LaF0cslM5UkNxWYyP3FvT6ayt6eAPymRPL9QjfX/O5OnS9OVy15dTtvBcsXvlK5ML/vd2Ck8VS1FJnLvvq8EOnK6jYcKVtzUxbY5nYhoJ68h+ndLT1cXzUvRE2vv7o1+uoWhsjECWd0397ntjaak3ermHJ+Iaq/4wisO7/ezf5k+KdKo4ejqH944tEde8/Ls4fZfPinDj3Y1seiUnKdLeLw4noSXPbc/z1V8bSmJRjUVXt/L68/lp4pFpRRpIK9IAsUicjpYOHvyHh7tt375mF+uf3kikI4qU3r0WC8f45Vd/T1NNc4/8m6D9hylyj0PLF9KWfS4ZyBj1IuA9KeVhKWUMeBy49dyGNTxUH62lqbq5/4RnQCKeoL2xfdD5SCl7uaqcDdGwzmtP51N5xJfSDaQ7/duAXnJNW0rzT9OSLFkxWEfpc8WZGaguuqq1S6/8NJYtWXrD6Tqe6g0PRUzLbmrB7GK79/bLOThO6rijEcGG1TmseqzwLMtOz6XXtmCYsPa5vKRId+dsjHDjUY3cQiflOU3meuaZdkFK2L/5PaLh0x2udX96O2UHTDN0Nq/a3n1j9C16upF3ktiLTB/4OWMgQl0CnOjy98nktm4IIf5RCLFFCLGlrq5uqOI7r5iWidvHt/XZoOnaWd+65wrTcofMVDUeFT16Vh6uA7FohllsnyXxWM/eo4frQqSX7dbQ1DkR6zkUkL69NN2zG7P8Qy8g8ZiGlOCzPQ/Nnpz1VXSOLw2hiW5O4Jbfh6b1LlRLDlF2P9jXK12XIzjnwacpdUiQUj4ipVwopVxYVFQ0VNmeV0aNK2DSRWVD2g6apjGuvLhPa/lupEmmGRrGEDiV2wGHG+9uYHRpnLyiBH3fav3fhm+uykVovdO5jmDtc/kpjsgETpmlDowNq3MQeu/0TkLw+nOnXbNN32CEsnv+Lz1aQCR8+mK45vYmTCt1/oYJl9/SzK1/Xz+I8lOz7vk8nARcf2cTPjtF+Ske7vaHZbtUH/ehpb2cB9eJ0A2dRSvm4eviEH7N3csxewoy4Loui1bO7xHgZaSdZ+FbMvB7eQgZiFBXAGVd/i5Nbntf8vU/fomCMfndv33TtIthGZ2Gl7qpd1rHWwELO8vGZ5t84eFP8cDzXyO/OJdAth8r4MPy+xhVUtArP83QuOKOJQRy/N3K1HSNxTct5Kq7lvWypz+13wr01QsAkJg+l2U3NbPo6lYiHYJPfLWyc1+qVyDLQTfctPtB0tqo873PlREJC0LtGuF2jWhE8PA3x1F5xOoz/1QxDvbl9fpkZ301PXW5hk+m2dfz5dLWrPPdz4wnEkrWscNz0/7N/xlDfZWJ5XcwfW4y57OL2We72IHT5/upXxSx6+0gkZDnWH7HZ2qYMC0Covuxuunyya9XkjcqQdnkKFNnh8763BmmZzJsB5xOp+/aSoOffq2EmQs7+NA9dfgsF8vv4A86Xc71QOsNILD8LlvWZHPd3zakTKPp3kyOVB0ToYnOe673TrCDFmMnF/PPv7yn264pcyfxsW//LT7bxA5a+LNtrIDFN5/8ci8jbSH8kPczesmjKETk/Vfqss8x/c76EEIYwAHgajyB3gx8REq5O90xI3nWB4CTcNi8ajs71u3BtEzmX30R5XMm8Nqj6zn4zmGmzp/EpTcvZPf6fcQicYrKCqk+UsuokgKmLZzMltXvIqXkkhvnkzvKm86TiCfYvGo7jVVNzFw8jUkXTeDwjmM8+eALNNU0MfuKWVz+N4spmTKWWDTOhuc3s3PdXvJG57Dk1kWUz/aeNB/fV8H2NbuoP9lAYUkBE2eVUX2kFk3TWHTjPA5uPczrT2wgOz9IUVkhf/7FKzTXtlA6fRzX3HUZoaY1GKKa9o7x7HzLQDd0RpUUcPLgAY7uqiUadjEsA9tveT8fRZxxE8Mk4j7iUUnx+AKqjhm01LehGzE0XdDa6GD7YyxdGcK0BBteClBfraHpAt3QcB0QIoYdcBEaOI5BPGbixF1cxyWY62fiheM5+M5h4tEIZeUhNF1SfdwiHBLYfs+Vu6NFJxb1ejpdZ3PkFEhGjY0RzHPYsymIz3K5+OoWxk2IYgc0Nv4ln+P7Bf7sBLbfobHOJBEVjBoXo7DYIRoRxKIa7c0a9dUmSB2QTJ8XwvC5yAQYliQaEcyYHyIS1tn4cg5jJ8awbIfaSh9VR00khheTBN1wO8U7O8fBcQVNtSanBUsDXErKo0yaGaGx1sT2uxgmFJdFSMR09m7zM312B6PGJair8PHevnIqDoZxnTCakPizXC69vpUxZTFqT/p4++VcNDPApJkuh3ZGaWvSCGa5jCuPUV9l0lhnIjQTgUMi7qLrkF/kYpgODdUGjitZflMr2XmSxlqdYwdyMEyd/GIfrU0Gy1cep6Q8xpF9WVSfGIOrzSIacmlpaMOJJ7CDMH95B4d2RDlxOEjtiQbi4Q782SbTFkznosvn4fMZ+Gwf2QUG+95cRUHRcU4ciHBkr5+S6Qu5/u+u44JLp7L5pW201LVSNqOE/Zve48T+Si64ZCqX376YqkM1bF+zi4bKJiy/j0CuH8M0MC2DkqnjmHvlLDQttZjXVzayZdV2fLbJJTctIJgTSKsDrtMOoV+DcwKsqxD2inPamx60C7kQYiXwI7zpeb+WUv5HX+lHulArFArF+aYvoR7QP7xIKV8EXhzSqBQKhUIxINS/kCsUCkWGo4RaoVAoMhwl1AqFQpHhKKFWKBSKDGdAsz7OOFMh6oBjZ3n4KGDoZ+6fX1QdMoP3Qx3g/VEPVYf+mSClTPnfgudEqAeDEGJLuikqIwVVh8zg/VAHeH/UQ9VhcKihD4VCochwlFArFApFhpOJQv3IcAcwBKg6ZAbvhzrA+6Meqg6DIOPGqBUKhULRnUzsUSsUCoWiC0qoFQqFIsPJGKEWQqwQQuwXQrwnhPjacMdztgghjgohdgohtgshRsQSgkKIXwshaoUQu7psKxBCvCKEOJh8z1QXACBtHb4thKhItsX25CqQGYsQokwIsUYIsUcIsVsI8cXk9hHTFn3UYaS1hS2E2CSEeDdZj39Lbp8khNiY1Kk/CtGnHczQxZMJY9RJA90DwLV4Vl+bgTullHuGNbCzQAhxFFgopRwxk/uFEMuBduB3UsoLk9u+BzRKKb+b/OLMl1J+dTjj7Is0dfg20C6l7GknnZEIIcYCY6WU7wghsoGtwG3AJxghbdFHHe5gZLWFAIJSynYhhAmsB74IfAl4Wkr5uBDi58C7UsqH+8prKMiUHvVfjYFuJiKlXAc09th8K/Db5Off4t1sGUuaOowopJRVUsp3kp/bgL14/qQjpi36qMOIQnqccqU2ky8JXAX8Kbn9vLVFpgj1gAx0RwgSeFkIsVUI8Y/DHcwgKJZSViU/VwPFwxnMIPicEGJHcmgkY4cMeiKEmAjMAzYyQtuiRx1ghLWFEEIXQmwHaoFXgENAs5QykUxy3nQqU4T6/cRlUsr5wA3AZ5M/yUc00hsfG/4xsjPnYWAyMBeoAh4c3nAGhhAiC3gKuFdK2dp130hpixR1GHFtIaV0pJRz8XxiFwEXDFcsmSLU7xsDXSllRfK9FngGr4FHIjXJ8cZT4461wxzPGSOlrEnebC7wS0ZAWyTHQ58C/iClfDq5eUS1Rao6jMS2OIWUshlYAywG8pI+snAedSpThHozMDX5RNUHfBh4fphjOmOEEMHkAxSEEEHgOmBX30dlLM8DH09+/jjw3DDGclacErckHyDD2yJs5iwAAAAA5ElEQVT5AOt/gL1Syh922TVi2iJdHUZgWxQJIfKSn/14Ex324gn23ySTnbe2yIhZH3DmBrqZiBCiHK8XDZ4f5aMjoR5CiMeAK/CWcawBvgU8CzwBjMdbsvYOKWXGPqxLU4cr8H5qS+Ao8E9dxnozDiHEZcAbwE4g6bHO/XhjvCOiLfqow52MrLaYjfewUMfr0D4hpXwgeY8/DhQA24CPSimj5zyeTBFqhUKhUKQmU4Y+FAqFQpEGJdQKhUKR4SihVigUigxHCbVCoVBkOEqoFQqFIsNRQq1QKBQZjhJqhUKhyHD+P5yp4nwwDPEsAAAAAElFTkSuQmCC\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"
          ]
        }
      ]
    }
  ]
}