Task_2_eda_starter.ipynb

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        "# Exploratory Data Analysis Starter\n",
        "\n",
        "## Import packages"
      ]
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      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "\n",
        "# Shows plots in jupyter notebook\n",
        "%matplotlib inline\n",
        "\n",
        "# Set plot style\n",
        "sns.set(color_codes=True)"
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      "source": [
        "---\n",
        "\n",
        "## Loading data with Pandas\n",
        "\n",
        "We need to load `client_data.csv` and `price_data.csv` into individual dataframes so that we can work with them in Python. For this notebook and all further notebooks, it will be assumed that the CSV files will the placed in the same file location as the notebook. If they are not, please adjust the directory within the `read_csv` method accordingly."
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      "source": [
        "client_df = pd.read_csv('./client_data.csv')\n",
        "price_df = pd.read_csv('./price_data.csv')"
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        "You can view the first 3 rows of a dataframe using the `head` method. Similarly, if you wanted to see the last 3, you can use `tail(3)`"
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              "                                 id                     channel_sales  \\\n",
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              "\n",
              "   cons_12m  cons_gas_12m  cons_last_month  date_activ    date_end  \\\n",
              "0         0         54946                0  2013-06-15  2016-06-15   \n",
              "1      4660             0                0  2009-08-21  2016-08-30   \n",
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              "  date_modif_prod date_renewal  forecast_cons_12m  ...  has_gas  imp_cons  \\\n",
              "0      2015-11-01   2015-06-23               0.00  ...        t       0.0   \n",
              "1      2009-08-21   2015-08-31             189.95  ...        f       0.0   \n",
              "2      2010-04-16   2015-04-17              47.96  ...        f       0.0   \n",
              "\n",
              "   margin_gross_pow_ele  margin_net_pow_ele  nb_prod_act  net_margin  \\\n",
              "0                 25.44               25.44            2      678.99   \n",
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              "                                 id  price_date  price_off_peak_var  \\\n",
              "0  038af19179925da21a25619c5a24b745  2015-01-01            0.151367   \n",
              "1  038af19179925da21a25619c5a24b745  2015-02-01            0.151367   \n",
              "2  038af19179925da21a25619c5a24b745  2015-03-01            0.151367   \n",
              "\n",
              "   price_peak_var  price_mid_peak_var  price_off_peak_fix  price_peak_fix  \\\n",
              "0             0.0                 0.0           44.266931             0.0   \n",
              "1             0.0                 0.0           44.266931             0.0   \n",
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              "      <th>price_mid_peak_fix</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>038af19179925da21a25619c5a24b745</td>\n",
              "      <td>2015-01-01</td>\n",
              "      <td>0.151367</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>44.266931</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>038af19179925da21a25619c5a24b745</td>\n",
              "      <td>2015-02-01</td>\n",
              "      <td>0.151367</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>44.266931</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>038af19179925da21a25619c5a24b745</td>\n",
              "      <td>2015-03-01</td>\n",
              "      <td>0.151367</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>44.266931</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "        document.querySelector('#df-ce01b2dc-107e-43e3-a14d-ff8061538f3e button.colab-df-convert');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ce01b2dc-107e-43e3-a14d-ff8061538f3e');\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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              "\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
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              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1524db49-6f4b-4d27-851f-1061f80a004e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "price_df"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "price_df.head(3)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "966c3948-41c0-4183-9118-dd665c58a93c",
      "metadata": {
        "id": "966c3948-41c0-4183-9118-dd665c58a93c"
      },
      "source": [
        "---\n",
        "\n",
        "## Descriptive statistics of data\n",
        "\n",
        "### Data types\n",
        "\n",
        "It is useful to first understand the data that you're dealing with along with the data types of each column. The data types may dictate how you transform and engineer features.\n",
        "\n",
        "To get an overview of the data types within a data frame, use the `info()` method."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "2dc75b07-c286-4852-bc33-e501782e67d9",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2dc75b07-c286-4852-bc33-e501782e67d9",
        "outputId": "8b92e84b-fa8e-44f5-b96c-5b36b11ab59e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 14606 entries, 0 to 14605\n",
            "Data columns (total 26 columns):\n",
            " #   Column                          Non-Null Count  Dtype  \n",
            "---  ------                          --------------  -----  \n",
            " 0   id                              14606 non-null  object \n",
            " 1   channel_sales                   14606 non-null  object \n",
            " 2   cons_12m                        14606 non-null  int64  \n",
            " 3   cons_gas_12m                    14606 non-null  int64  \n",
            " 4   cons_last_month                 14606 non-null  int64  \n",
            " 5   date_activ                      14606 non-null  object \n",
            " 6   date_end                        14606 non-null  object \n",
            " 7   date_modif_prod                 14606 non-null  object \n",
            " 8   date_renewal                    14606 non-null  object \n",
            " 9   forecast_cons_12m               14606 non-null  float64\n",
            " 10  forecast_cons_year              14606 non-null  int64  \n",
            " 11  forecast_discount_energy        14606 non-null  float64\n",
            " 12  forecast_meter_rent_12m         14606 non-null  float64\n",
            " 13  forecast_price_energy_off_peak  14606 non-null  float64\n",
            " 14  forecast_price_energy_peak      14606 non-null  float64\n",
            " 15  forecast_price_pow_off_peak     14606 non-null  float64\n",
            " 16  has_gas                         14606 non-null  object \n",
            " 17  imp_cons                        14606 non-null  float64\n",
            " 18  margin_gross_pow_ele            14606 non-null  float64\n",
            " 19  margin_net_pow_ele              14606 non-null  float64\n",
            " 20  nb_prod_act                     14606 non-null  int64  \n",
            " 21  net_margin                      14606 non-null  float64\n",
            " 22  num_years_antig                 14606 non-null  int64  \n",
            " 23  origin_up                       14606 non-null  object \n",
            " 24  pow_max                         14606 non-null  float64\n",
            " 25  churn                           14606 non-null  int64  \n",
            "dtypes: float64(11), int64(7), object(8)\n",
            "memory usage: 2.9+ MB\n"
          ]
        }
      ],
      "source": [
        "client_df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "b67f2628-351a-48ee-b8ba-88c54042af88",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b67f2628-351a-48ee-b8ba-88c54042af88",
        "outputId": "66e45620-84a1-41f9-a7e9-77a96651278a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 193002 entries, 0 to 193001\n",
            "Data columns (total 8 columns):\n",
            " #   Column              Non-Null Count   Dtype  \n",
            "---  ------              --------------   -----  \n",
            " 0   id                  193002 non-null  object \n",
            " 1   price_date          193002 non-null  object \n",
            " 2   price_off_peak_var  193002 non-null  float64\n",
            " 3   price_peak_var      193002 non-null  float64\n",
            " 4   price_mid_peak_var  193002 non-null  float64\n",
            " 5   price_off_peak_fix  193002 non-null  float64\n",
            " 6   price_peak_fix      193002 non-null  float64\n",
            " 7   price_mid_peak_fix  193002 non-null  float64\n",
            "dtypes: float64(6), object(2)\n",
            "memory usage: 11.8+ MB\n"
          ]
        }
      ],
      "source": [
        "price_df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3d7e80c5-ef07-47a7-908a-a081b3ebd09e",
      "metadata": {
        "id": "3d7e80c5-ef07-47a7-908a-a081b3ebd09e"
      },
      "source": [
        "### Statistics\n",
        "\n",
        "Now let's look at some statistics about the datasets. We can do this by using the `describe()` method."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "60f3010c-ecaf-4b8a-bb87-93bb54cc0c7e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "60f3010c-ecaf-4b8a-bb87-93bb54cc0c7e",
        "outputId": "763d5c2a-b01e-4e71-9d1f-de8a88634cf8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           cons_12m  cons_gas_12m  cons_last_month  forecast_cons_12m  \\\n",
              "count  1.460600e+04  1.460600e+04     14606.000000       14606.000000   \n",
              "mean   1.592203e+05  2.809238e+04     16090.269752        1868.614880   \n",
              "std    5.734653e+05  1.629731e+05     64364.196422        2387.571531   \n",
              "min    0.000000e+00  0.000000e+00         0.000000           0.000000   \n",
              "25%    5.674750e+03  0.000000e+00         0.000000         494.995000   \n",
              "50%    1.411550e+04  0.000000e+00       792.500000        1112.875000   \n",
              "75%    4.076375e+04  0.000000e+00      3383.000000        2401.790000   \n",
              "max    6.207104e+06  4.154590e+06    771203.000000       82902.830000   \n",
              "\n",
              "       forecast_cons_year  forecast_discount_energy  forecast_meter_rent_12m  \\\n",
              "count        14606.000000              14606.000000             14606.000000   \n",
              "mean          1399.762906                  0.966726                63.086871   \n",
              "std           3247.786255                  5.108289                66.165783   \n",
              "min              0.000000                  0.000000                 0.000000   \n",
              "25%              0.000000                  0.000000                16.180000   \n",
              "50%            314.000000                  0.000000                18.795000   \n",
              "75%           1745.750000                  0.000000               131.030000   \n",
              "max         175375.000000                 30.000000               599.310000   \n",
              "\n",
              "       forecast_price_energy_off_peak  forecast_price_energy_peak  \\\n",
              "count                    14606.000000                14606.000000   \n",
              "mean                         0.137283                    0.050491   \n",
              "std                          0.024623                    0.049037   \n",
              "min                          0.000000                    0.000000   \n",
              "25%                          0.116340                    0.000000   \n",
              "50%                          0.143166                    0.084138   \n",
              "75%                          0.146348                    0.098837   \n",
              "max                          0.273963                    0.195975   \n",
              "\n",
              "       forecast_price_pow_off_peak      imp_cons  margin_gross_pow_ele  \\\n",
              "count                 14606.000000  14606.000000          14606.000000   \n",
              "mean                     43.130056    152.786896             24.565121   \n",
              "std                       4.485988    341.369366             20.231172   \n",
              "min                       0.000000      0.000000              0.000000   \n",
              "25%                      40.606701      0.000000             14.280000   \n",
              "50%                      44.311378     37.395000             21.640000   \n",
              "75%                      44.311378    193.980000             29.880000   \n",
              "max                      59.266378  15042.790000            374.640000   \n",
              "\n",
              "       margin_net_pow_ele   nb_prod_act    net_margin  num_years_antig  \\\n",
              "count        14606.000000  14606.000000  14606.000000     14606.000000   \n",
              "mean            24.562517      1.292346    189.264522         4.997809   \n",
              "std             20.230280      0.709774    311.798130         1.611749   \n",
              "min              0.000000      1.000000      0.000000         1.000000   \n",
              "25%             14.280000      1.000000     50.712500         4.000000   \n",
              "50%             21.640000      1.000000    112.530000         5.000000   \n",
              "75%             29.880000      1.000000    243.097500         6.000000   \n",
              "max            374.640000     32.000000  24570.650000        13.000000   \n",
              "\n",
              "            pow_max         churn  \n",
              "count  14606.000000  14606.000000  \n",
              "mean      18.135136      0.097152  \n",
              "std       13.534743      0.296175  \n",
              "min        3.300000      0.000000  \n",
              "25%       12.500000      0.000000  \n",
              "50%       13.856000      0.000000  \n",
              "75%       19.172500      0.000000  \n",
              "max      320.000000      1.000000  "
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>cons_12m</th>\n",
              "      <th>cons_gas_12m</th>\n",
              "      <th>cons_last_month</th>\n",
              "      <th>forecast_cons_12m</th>\n",
              "      <th>forecast_cons_year</th>\n",
              "      <th>forecast_discount_energy</th>\n",
              "      <th>forecast_meter_rent_12m</th>\n",
              "      <th>forecast_price_energy_off_peak</th>\n",
              "      <th>forecast_price_energy_peak</th>\n",
              "      <th>forecast_price_pow_off_peak</th>\n",
              "      <th>imp_cons</th>\n",
              "      <th>margin_gross_pow_ele</th>\n",
              "      <th>margin_net_pow_ele</th>\n",
              "      <th>nb_prod_act</th>\n",
              "      <th>net_margin</th>\n",
              "      <th>num_years_antig</th>\n",
              "      <th>pow_max</th>\n",
              "      <th>churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.460600e+04</td>\n",
              "      <td>1.460600e+04</td>\n",
              "      <td>14606.000000</td>\n",
              "      <td>14606.000000</td>\n",
              "      <td>14606.000000</td>\n",
              "      <td>14606.000000</td>\n",
              "      <td>14606.000000</td>\n",
              "      <td>14606.000000</td>\n",
              "      <td>14606.000000</td>\n",
              "      <td>14606.000000</td>\n",
              "      <td>14606.000000</td>\n",
              "      <td>14606.000000</td>\n",
              "      <td>14606.000000</td>\n",
              "      <td>14606.000000</td>\n",
              "      <td>14606.000000</td>\n",
              "      <td>14606.000000</td>\n",
              "      <td>14606.000000</td>\n",
              "      <td>14606.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.592203e+05</td>\n",
              "      <td>2.809238e+04</td>\n",
              "      <td>16090.269752</td>\n",
              "      <td>1868.614880</td>\n",
              "      <td>1399.762906</td>\n",
              "      <td>0.966726</td>\n",
              "      <td>63.086871</td>\n",
              "      <td>0.137283</td>\n",
              "      <td>0.050491</td>\n",
              "      <td>43.130056</td>\n",
              "      <td>152.786896</td>\n",
              "      <td>24.565121</td>\n",
              "      <td>24.562517</td>\n",
              "      <td>1.292346</td>\n",
              "      <td>189.264522</td>\n",
              "      <td>4.997809</td>\n",
              "      <td>18.135136</td>\n",
              "      <td>0.097152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.734653e+05</td>\n",
              "      <td>1.629731e+05</td>\n",
              "      <td>64364.196422</td>\n",
              "      <td>2387.571531</td>\n",
              "      <td>3247.786255</td>\n",
              "      <td>5.108289</td>\n",
              "      <td>66.165783</td>\n",
              "      <td>0.024623</td>\n",
              "      <td>0.049037</td>\n",
              "      <td>4.485988</td>\n",
              "      <td>341.369366</td>\n",
              "      <td>20.231172</td>\n",
              "      <td>20.230280</td>\n",
              "      <td>0.709774</td>\n",
              "      <td>311.798130</td>\n",
              "      <td>1.611749</td>\n",
              "      <td>13.534743</td>\n",
              "      <td>0.296175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.300000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.674750e+03</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>494.995000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>16.180000</td>\n",
              "      <td>0.116340</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>40.606701</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.280000</td>\n",
              "      <td>14.280000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>50.712500</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>12.500000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.411550e+04</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>792.500000</td>\n",
              "      <td>1112.875000</td>\n",
              "      <td>314.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>18.795000</td>\n",
              "      <td>0.143166</td>\n",
              "      <td>0.084138</td>\n",
              "      <td>44.311378</td>\n",
              "      <td>37.395000</td>\n",
              "      <td>21.640000</td>\n",
              "      <td>21.640000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>112.530000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>13.856000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.076375e+04</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>3383.000000</td>\n",
              "      <td>2401.790000</td>\n",
              "      <td>1745.750000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>131.030000</td>\n",
              "      <td>0.146348</td>\n",
              "      <td>0.098837</td>\n",
              "      <td>44.311378</td>\n",
              "      <td>193.980000</td>\n",
              "      <td>29.880000</td>\n",
              "      <td>29.880000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>243.097500</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>19.172500</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>6.207104e+06</td>\n",
              "      <td>4.154590e+06</td>\n",
              "      <td>771203.000000</td>\n",
              "      <td>82902.830000</td>\n",
              "      <td>175375.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>599.310000</td>\n",
              "      <td>0.273963</td>\n",
              "      <td>0.195975</td>\n",
              "      <td>59.266378</td>\n",
              "      <td>15042.790000</td>\n",
              "      <td>374.640000</td>\n",
              "      <td>374.640000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>24570.650000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>320.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e64e4136-f334-4b3f-8916-e9e06175cae5')\"\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "        if (!dataTable) return;\n",
              "\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
              "\n",
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              "\n",
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              "\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    border-radius: 50%;\n",
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              "    fill: var(--fill-color);\n",
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              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2856429b-129b-4e93-a96e-7b376d20b396 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"client_df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"cons_12m\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2162448.0305851013,\n        \"min\": 0.0,\n        \"max\": 6207104.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          159220.2862522251,\n          14115.5,\n          14606.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cons_gas_12m\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1459534.9470120105,\n        \"min\": 0.0,\n        \"max\": 4154590.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          28092.375325208817,\n          4154590.0,\n          162973.05905732032\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cons_last_month\",\n      \"properties\": {\n   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\"number\",\n        \"std\": 5152.1170281562645,\n        \"min\": 0.0,\n        \"max\": 14606.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          14606.0,\n          43.13005552529372,\n          44.31137796\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"imp_cons\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6808.343059160503,\n        \"min\": 0.0,\n        \"max\": 15042.79,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          14606.0,\n          152.7868957962481,\n          193.98000000000002\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"margin_gross_pow_ele\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5141.0114665037945,\n        \"min\": 0.0,\n        \"max\": 14606.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          24.56512118307545,\n          21.64,\n          14606.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"margin_net_pow_ele\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5141.011647469969,\n        \"min\": 0.0,\n        \"max\": 14606.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          24.562517458578668,\n          21.64,\n          14606.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nb_prod_act\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5162.092826084596,\n        \"min\": 0.7097735191117632,\n        \"max\": 14606.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.2923456113925784,\n          32.0,\n          0.7097735191117632\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"net_margin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9384.014051878941,\n        \"min\": 0.0,\n        \"max\": 24570.65,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          189.26452211419968,\n          112.53,\n          14606.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"num_years_antig\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5162.203561441323,\n        \"min\": 1.0,\n        \"max\": 14606.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          4.997809119539915,\n          5.0,\n          14606.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pow_max\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5144.893454207611,\n        \"min\": 3.3,\n        \"max\": 14606.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          18.13513562919348,\n          13.856,\n          14606.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"churn\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5163.930460733404,\n        \"min\": 0.0,\n        \"max\": 14606.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.09715185540188963,\n          1.0,\n          0.2961745736931901\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "client_df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "f8b442e6-e186-42f9-b3c5-c5c15cd8c82a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "f8b442e6-e186-42f9-b3c5-c5c15cd8c82a",
        "outputId": "07f228b7-05a4-42a9-fb03-4c46b18e5b79"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       price_off_peak_var  price_peak_var  price_mid_peak_var  \\\n",
              "count       193002.000000   193002.000000       193002.000000   \n",
              "mean             0.141027        0.054630            0.030496   \n",
              "std              0.025032        0.049924            0.036298   \n",
              "min              0.000000        0.000000            0.000000   \n",
              "25%              0.125976        0.000000            0.000000   \n",
              "50%              0.146033        0.085483            0.000000   \n",
              "75%              0.151635        0.101673            0.072558   \n",
              "max              0.280700        0.229788            0.114102   \n",
              "\n",
              "       price_off_peak_fix  price_peak_fix  price_mid_peak_fix  \n",
              "count       193002.000000   193002.000000       193002.000000  \n",
              "mean            43.334477       10.622875            6.409984  \n",
              "std              5.410297       12.841895            7.773592  \n",
              "min              0.000000        0.000000            0.000000  \n",
              "25%             40.728885        0.000000            0.000000  \n",
              "50%             44.266930        0.000000            0.000000  \n",
              "75%             44.444710       24.339581           16.226389  \n",
              "max             59.444710       36.490692           17.458221  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>price_off_peak_var</th>\n",
              "      <th>price_peak_var</th>\n",
              "      <th>price_mid_peak_var</th>\n",
              "      <th>price_off_peak_fix</th>\n",
              "      <th>price_peak_fix</th>\n",
              "      <th>price_mid_peak_fix</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>193002.000000</td>\n",
              "      <td>193002.000000</td>\n",
              "      <td>193002.000000</td>\n",
              "      <td>193002.000000</td>\n",
              "      <td>193002.000000</td>\n",
              "      <td>193002.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.141027</td>\n",
              "      <td>0.054630</td>\n",
              "      <td>0.030496</td>\n",
              "      <td>43.334477</td>\n",
              "      <td>10.622875</td>\n",
              "      <td>6.409984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.025032</td>\n",
              "      <td>0.049924</td>\n",
              "      <td>0.036298</td>\n",
              "      <td>5.410297</td>\n",
              "      <td>12.841895</td>\n",
              "      <td>7.773592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.125976</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>40.728885</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.146033</td>\n",
              "      <td>0.085483</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>44.266930</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.151635</td>\n",
              "      <td>0.101673</td>\n",
              "      <td>0.072558</td>\n",
              "      <td>44.444710</td>\n",
              "      <td>24.339581</td>\n",
              "      <td>16.226389</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.280700</td>\n",
              "      <td>0.229788</td>\n",
              "      <td>0.114102</td>\n",
              "      <td>59.444710</td>\n",
              "      <td>36.490692</td>\n",
              "      <td>17.458221</td>\n",
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              "\n",
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              "  }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
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              "      }\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"price_df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"price_off_peak_var\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 68236.46752932726,\n        \"min\": 0.0,\n        \"max\": 193002.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.14102697259505084,\n          0.146033,\n          193002.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price_peak_var\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 68236.48515166197,\n        \"min\": 0.0,\n        \"max\": 193002.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          193002.0,\n          0.05463039689873681,\n          0.101673\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price_mid_peak_var\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 68236.49868993397,\n        \"min\": 0.0,\n        \"max\": 193002.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          193002.0,\n          0.030496007458938247,\n          0.114102\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price_off_peak_fix\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 68224.51247047177,\n        \"min\": 0.0,\n        \"max\": 193002.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          43.33447695717784,\n          44.26692996,\n          193002.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price_peak_fix\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 68232.25519092446,\n        \"min\": 0.0,\n        \"max\": 193002.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          193002.0,\n          10.622875247557124,\n          36.490692\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price_mid_peak_fix\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 68234.09414105103,\n        \"min\": 0.0,\n        \"max\": 193002.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          193002.0,\n          6.409984354197781,\n          17.45822136\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "price_df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6a7a8879-c483-4464-9057-c70e6b150e53",
      "metadata": {
        "id": "6a7a8879-c483-4464-9057-c70e6b150e53"
      },
      "source": [
        "---\n",
        "\n",
        "## Data visualization\n",
        "\n",
        "If you're working in Python, two of the most popular packages for visualization are `matplotlib` and `seaborn`. We highly recommend you use these, or at least be familiar with them because they are ubiquitous!\n",
        "\n",
        "Below are some functions that you can use to get started with visualizations."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "f350ea0a-dc45-43f5-a6b7-e5e816964419",
      "metadata": {
        "id": "f350ea0a-dc45-43f5-a6b7-e5e816964419"
      },
      "outputs": [],
      "source": [
        "def plot_stacked_bars(dataframe, title_, size_=(18, 10), rot_=0, legend_=\"upper right\"):\n",
        "    \"\"\"\n",
        "    Plot stacked bars with annotations\n",
        "    \"\"\"\n",
        "    ax = dataframe.plot(\n",
        "        kind=\"bar\",\n",
        "        stacked=True,\n",
        "        figsize=size_,\n",
        "        rot=rot_,\n",
        "        title=title_\n",
        "    )\n",
        "\n",
        "    # Annotate bars\n",
        "    annotate_stacked_bars(ax, textsize=14)\n",
        "    # Rename legend\n",
        "    plt.legend([\"Retention\", \"Churn\"], loc=legend_)\n",
        "    # Labels\n",
        "    plt.ylabel(\"Company base (%)\")\n",
        "    plt.show()\n",
        "\n",
        "def annotate_stacked_bars(ax, pad=0.99, colour=\"white\", textsize=13):\n",
        "    \"\"\"\n",
        "    Add value annotations to the bars\n",
        "    \"\"\"\n",
        "\n",
        "    # Iterate over the plotted rectanges/bars\n",
        "    for p in ax.patches:\n",
        "\n",
        "        # Calculate annotation\n",
        "        value = str(round(p.get_height(),1))\n",
        "        # If value is 0 do not annotate\n",
        "        if value == '0.0':\n",
        "            continue\n",
        "        ax.annotate(\n",
        "            value,\n",
        "            ((p.get_x()+ p.get_width()/2)*pad-0.05, (p.get_y()+p.get_height()/2)*pad),\n",
        "            color=colour,\n",
        "            size=textsize\n",
        "        )\n",
        "\n",
        "def plot_distribution(dataframe, column, ax, bins_=50):\n",
        "    \"\"\"\n",
        "    Plot variable distirbution in a stacked histogram of churned or retained company\n",
        "    \"\"\"\n",
        "    # Create a temporal dataframe with the data to be plot\n",
        "    temp = pd.DataFrame({\"Retention\": dataframe[dataframe[\"churn\"]==0][column],\n",
        "    \"Churn\":dataframe[dataframe[\"churn\"]==1][column]})\n",
        "    # Plot the histogram\n",
        "    temp[[\"Retention\",\"Churn\"]].plot(kind='hist', bins=bins_, ax=ax, stacked=True)\n",
        "    # X-axis label\n",
        "    ax.set_xlabel(column)\n",
        "    # Change the x-axis to plain style\n",
        "    ax.ticklabel_format(style='plain', axis='x')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6eab14d0",
      "metadata": {
        "id": "6eab14d0"
      },
      "source": [
        "Thhe first function `plot_stacked_bars` is used to plot a stacked bar chart. An example of how you could use this is shown below:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "448c05d8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "448c05d8",
        "outputId": "6f99931a-8182-4f32-91ff-6ec69f46d3db"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "churn = client_df[['id', 'churn']]\n",
        "churn.columns = ['Companies', 'churn']\n",
        "churn_total = churn.groupby(churn['churn']).count()\n",
        "churn_percentage = churn_total / churn_total.sum() * 100\n",
        "plot_stacked_bars(churn_percentage.transpose(), \"Churning status\", (5, 5), legend_=\"lower right\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d055f10e",
      "metadata": {
        "id": "d055f10e"
      },
      "source": [
        "The second function `annotate_bars` is used by the first function, but the third function `plot_distribution` helps you to plot the distribution of a numeric column. An example of how it can be used is given below:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "9a28757e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "9a28757e",
        "outputId": "4fb12006-82e6-486f-8256-1d9735025268"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "consumption = client_df[['id', 'cons_12m', 'cons_gas_12m', 'cons_last_month', 'imp_cons', 'has_gas', 'churn']]\n",
        "\n",
        "fig, axs = plt.subplots(nrows=1, figsize=(18, 5))\n",
        "\n",
        "plot_distribution(consumption, 'cons_12m', axs)"
      ]
    }
  ],
  "metadata": {
    "interpreter": {
      "hash": "152bf6e7dc8ee53edb5af21dc1a8faeab7f134840808a94079ed98d91ece7e0c"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.9"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}