EDA_on_US_Accidents_(2016_2021).ipynb

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  "metadata": {
    "colab": {
      "name": "EDA on US Accidents (2016 - 2021)",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyPYvhebcBqrLOsX4K4Q0dJ9",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hassaanhameed786/Machine-learning-/blob/main/EDA_on_US_Accidents_(2016_2021).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Download the dataset"
      ],
      "metadata": {
        "id": "NnhwRCqMV4tf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import opendatasets as od\n",
        "download_url = 'https://www.kaggle.com/datasets/sobhanmoosavi/us-accidents'\n",
        "\n",
        "od.download(download_url)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "izcePVwhT4fB",
        "outputId": "0a8a832d-dadc-4b7d-cb76-dde1137d42e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Please provide your Kaggle credentials to download this dataset. Learn more: http://bit.ly/kaggle-creds\n",
            "Your Kaggle username: muhammadhassaan786\n",
            "Your Kaggle Key: ··········\n",
            "Downloading us-accidents.zip to ./us-accidents\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 269M/269M [00:01<00:00, 157MB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_filenames = './us-accidents/US_Accidents_Dec21_updated.csv'"
      ],
      "metadata": {
        "id": "BW2wVuHWUvpm"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv(data_filenames)"
      ],
      "metadata": {
        "id": "t7Jc2W3lUvn_"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Preparation & Cleaning "
      ],
      "metadata": {
        "id": "z5Yyeo3cWCyp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 939
        },
        "id": "-GNPwBc8UvmW",
        "outputId": "94a0dd87-6014-42d0-d030-bfb33000dd66"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                ID  Severity           Start_Time             End_Time  \\\n",
              "0              A-1         3  2016-02-08 00:37:08  2016-02-08 06:37:08   \n",
              "1              A-2         2  2016-02-08 05:56:20  2016-02-08 11:56:20   \n",
              "2              A-3         2  2016-02-08 06:15:39  2016-02-08 12:15:39   \n",
              "3              A-4         2  2016-02-08 06:51:45  2016-02-08 12:51:45   \n",
              "4              A-5         3  2016-02-08 07:53:43  2016-02-08 13:53:43   \n",
              "...            ...       ...                  ...                  ...   \n",
              "2845337  A-2845338         2  2019-08-23 18:03:25  2019-08-23 18:32:01   \n",
              "2845338  A-2845339         2  2019-08-23 19:11:30  2019-08-23 19:38:23   \n",
              "2845339  A-2845340         2  2019-08-23 19:00:21  2019-08-23 19:28:49   \n",
              "2845340  A-2845341         2  2019-08-23 19:00:21  2019-08-23 19:29:42   \n",
              "2845341  A-2845342         2  2019-08-23 18:52:06  2019-08-23 19:21:31   \n",
              "\n",
              "         Start_Lat   Start_Lng    End_Lat     End_Lng  Distance(mi)  \\\n",
              "0        40.108910  -83.092860  40.112060  -83.031870         3.230   \n",
              "1        39.865420  -84.062800  39.865010  -84.048730         0.747   \n",
              "2        39.102660  -84.524680  39.102090  -84.523960         0.055   \n",
              "3        41.062130  -81.537840  41.062170  -81.535470         0.123   \n",
              "4        39.172393  -84.492792  39.170476  -84.501798         0.500   \n",
              "...            ...         ...        ...         ...           ...   \n",
              "2845337  34.002480 -117.379360  33.998880 -117.370940         0.543   \n",
              "2845338  32.766960 -117.148060  32.765550 -117.153630         0.338   \n",
              "2845339  33.775450 -117.847790  33.777400 -117.857270         0.561   \n",
              "2845340  33.992460 -118.403020  33.983110 -118.395650         0.772   \n",
              "2845341  34.133930 -117.230920  34.137360 -117.239340         0.537   \n",
              "\n",
              "                                               Description  ...  Roundabout  \\\n",
              "0        Between Sawmill Rd/Exit 20 and OH-315/Olentang...  ...       False   \n",
              "1                       At OH-4/OH-235/Exit 41 - Accident.  ...       False   \n",
              "2                         At I-71/US-50/Exit 1 - Accident.  ...       False   \n",
              "3                          At Dart Ave/Exit 21 - Accident.  ...       False   \n",
              "4                       At Mitchell Ave/Exit 6 - Accident.  ...       False   \n",
              "...                                                    ...  ...         ...   \n",
              "2845337                           At Market St - Accident.  ...       False   \n",
              "2845338    At Camino Del Rio/Mission Center Rd - Accident.  ...       False   \n",
              "2845339  At Glassell St/Grand Ave - Accident. in the ri...  ...       False   \n",
              "2845340     At CA-90/Marina Fwy/Jefferson Blvd - Accident.  ...       False   \n",
              "2845341              At Highland Ave/Arden Ave - Accident.  ...       False   \n",
              "\n",
              "        Station   Stop Traffic_Calming Traffic_Signal Turning_Loop  \\\n",
              "0         False  False           False          False        False   \n",
              "1         False  False           False          False        False   \n",
              "2         False  False           False          False        False   \n",
              "3         False  False           False          False        False   \n",
              "4         False  False           False          False        False   \n",
              "...         ...    ...             ...            ...          ...   \n",
              "2845337   False  False           False          False        False   \n",
              "2845338   False  False           False          False        False   \n",
              "2845339   False  False           False          False        False   \n",
              "2845340   False  False           False          False        False   \n",
              "2845341   False  False           False          False        False   \n",
              "\n",
              "        Sunrise_Sunset Civil_Twilight Nautical_Twilight Astronomical_Twilight  \n",
              "0                Night          Night             Night                 Night  \n",
              "1                Night          Night             Night                 Night  \n",
              "2                Night          Night             Night                   Day  \n",
              "3                Night          Night               Day                   Day  \n",
              "4                  Day            Day               Day                   Day  \n",
              "...                ...            ...               ...                   ...  \n",
              "2845337            Day            Day               Day                   Day  \n",
              "2845338            Day            Day               Day                   Day  \n",
              "2845339            Day            Day               Day                   Day  \n",
              "2845340            Day            Day               Day                   Day  \n",
              "2845341            Day            Day               Day                   Day  \n",
              "\n",
              "[2845342 rows x 47 columns]"
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              "      <td>At OH-4/OH-235/Exit 41 - Accident.</td>\n",
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              "      <td>2016-02-08 12:15:39</td>\n",
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              "      <td>39.102090</td>\n",
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              "      <td>0.055</td>\n",
              "      <td>At I-71/US-50/Exit 1 - Accident.</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "      <th>4</th>\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",
              "      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-77910ce2-00bd-4c9f-8543-033d8c4c3b88 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-77910ce2-00bd-4c9f-8543-033d8c4c3b88');\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": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns\n",
        "# len(df.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PXdb4QL5ufXJ",
        "outputId": "b948c22c-d2c9-4e21-c987-3e518bfc5906"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ID', 'Severity', 'Start_Time', 'End_Time', 'Start_Lat', 'Start_Lng',\n",
              "       'End_Lat', 'End_Lng', 'Distance(mi)', 'Description', 'Number', 'Street',\n",
              "       'Side', 'City', 'County', 'State', 'Zipcode', 'Country', 'Timezone',\n",
              "       'Airport_Code', 'Weather_Timestamp', 'Temperature(F)', 'Wind_Chill(F)',\n",
              "       'Humidity(%)', 'Pressure(in)', 'Visibility(mi)', 'Wind_Direction',\n",
              "       'Wind_Speed(mph)', 'Precipitation(in)', 'Weather_Condition', 'Amenity',\n",
              "       'Bump', 'Crossing', 'Give_Way', 'Junction', 'No_Exit', 'Railway',\n",
              "       'Roundabout', 'Station', 'Stop', 'Traffic_Calming', 'Traffic_Signal',\n",
              "       'Turning_Loop', 'Sunrise_Sunset', 'Civil_Twilight', 'Nautical_Twilight',\n",
              "       'Astronomical_Twilight'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "fJewWpCyUvkM",
        "outputId": "23075881-a511-4ecd-e482-c9e62455702a"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Severity     Start_Lat     Start_Lng       End_Lat       End_Lng  \\\n",
              "count  2.845342e+06  2.845342e+06  2.845342e+06  2.845342e+06  2.845342e+06   \n",
              "mean   2.137572e+00  3.624520e+01 -9.711463e+01  3.624532e+01 -9.711439e+01   \n",
              "std    4.787216e-01  5.363797e+00  1.831782e+01  5.363873e+00  1.831763e+01   \n",
              "min    1.000000e+00  2.456603e+01 -1.245481e+02  2.456601e+01 -1.245457e+02   \n",
              "25%    2.000000e+00  3.344517e+01 -1.180331e+02  3.344628e+01 -1.180333e+02   \n",
              "50%    2.000000e+00  3.609861e+01 -9.241808e+01  3.609799e+01 -9.241772e+01   \n",
              "75%    2.000000e+00  4.016024e+01 -8.037243e+01  4.016105e+01 -8.037338e+01   \n",
              "max    4.000000e+00  4.900058e+01 -6.711317e+01  4.907500e+01 -6.710924e+01   \n",
              "\n",
              "       Distance(mi)        Number  Temperature(F)  Wind_Chill(F)  \\\n",
              "count  2.845342e+06  1.101431e+06    2.776068e+06   2.375699e+06   \n",
              "mean   7.026779e-01  8.089408e+03    6.179356e+01   5.965823e+01   \n",
              "std    1.560361e+00  1.836009e+04    1.862263e+01   2.116097e+01   \n",
              "min    0.000000e+00  0.000000e+00   -8.900000e+01  -8.900000e+01   \n",
              "25%    5.200000e-02  1.270000e+03    5.000000e+01   4.600000e+01   \n",
              "50%    2.440000e-01  4.007000e+03    6.400000e+01   6.300000e+01   \n",
              "75%    7.640000e-01  9.567000e+03    7.600000e+01   7.600000e+01   \n",
              "max    1.551860e+02  9.999997e+06    1.960000e+02   1.960000e+02   \n",
              "\n",
              "        Humidity(%)  Pressure(in)  Visibility(mi)  Wind_Speed(mph)  \\\n",
              "count  2.772250e+06  2.786142e+06    2.774796e+06     2.687398e+06   \n",
              "mean   6.436545e+01  2.947234e+01    9.099391e+00     7.395044e+00   \n",
              "std    2.287457e+01  1.045286e+00    2.717546e+00     5.527454e+00   \n",
              "min    1.000000e+00  0.000000e+00    0.000000e+00     0.000000e+00   \n",
              "25%    4.800000e+01  2.931000e+01    1.000000e+01     3.500000e+00   \n",
              "50%    6.700000e+01  2.982000e+01    1.000000e+01     7.000000e+00   \n",
              "75%    8.300000e+01  3.001000e+01    1.000000e+01     1.000000e+01   \n",
              "max    1.000000e+02  5.890000e+01    1.400000e+02     1.087000e+03   \n",
              "\n",
              "       Precipitation(in)  \n",
              "count       2.295884e+06  \n",
              "mean        7.016940e-03  \n",
              "std         9.348831e-02  \n",
              "min         0.000000e+00  \n",
              "25%         0.000000e+00  \n",
              "50%         0.000000e+00  \n",
              "75%         0.000000e+00  \n",
              "max         2.400000e+01  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3a15b77a-f3a5-4250-827a-7b4572a30517\">\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>Severity</th>\n",
              "      <th>Start_Lat</th>\n",
              "      <th>Start_Lng</th>\n",
              "      <th>End_Lat</th>\n",
              "      <th>End_Lng</th>\n",
              "      <th>Distance(mi)</th>\n",
              "      <th>Number</th>\n",
              "      <th>Temperature(F)</th>\n",
              "      <th>Wind_Chill(F)</th>\n",
              "      <th>Humidity(%)</th>\n",
              "      <th>Pressure(in)</th>\n",
              "      <th>Visibility(mi)</th>\n",
              "      <th>Wind_Speed(mph)</th>\n",
              "      <th>Precipitation(in)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2.845342e+06</td>\n",
              "      <td>2.845342e+06</td>\n",
              "      <td>2.845342e+06</td>\n",
              "      <td>2.845342e+06</td>\n",
              "      <td>2.845342e+06</td>\n",
              "      <td>2.845342e+06</td>\n",
              "      <td>1.101431e+06</td>\n",
              "      <td>2.776068e+06</td>\n",
              "      <td>2.375699e+06</td>\n",
              "      <td>2.772250e+06</td>\n",
              "      <td>2.786142e+06</td>\n",
              "      <td>2.774796e+06</td>\n",
              "      <td>2.687398e+06</td>\n",
              "      <td>2.295884e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.137572e+00</td>\n",
              "      <td>3.624520e+01</td>\n",
              "      <td>-9.711463e+01</td>\n",
              "      <td>3.624532e+01</td>\n",
              "      <td>-9.711439e+01</td>\n",
              "      <td>7.026779e-01</td>\n",
              "      <td>8.089408e+03</td>\n",
              "      <td>6.179356e+01</td>\n",
              "      <td>5.965823e+01</td>\n",
              "      <td>6.436545e+01</td>\n",
              "      <td>2.947234e+01</td>\n",
              "      <td>9.099391e+00</td>\n",
              "      <td>7.395044e+00</td>\n",
              "      <td>7.016940e-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.787216e-01</td>\n",
              "      <td>5.363797e+00</td>\n",
              "      <td>1.831782e+01</td>\n",
              "      <td>5.363873e+00</td>\n",
              "      <td>1.831763e+01</td>\n",
              "      <td>1.560361e+00</td>\n",
              "      <td>1.836009e+04</td>\n",
              "      <td>1.862263e+01</td>\n",
              "      <td>2.116097e+01</td>\n",
              "      <td>2.287457e+01</td>\n",
              "      <td>1.045286e+00</td>\n",
              "      <td>2.717546e+00</td>\n",
              "      <td>5.527454e+00</td>\n",
              "      <td>9.348831e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2.456603e+01</td>\n",
              "      <td>-1.245481e+02</td>\n",
              "      <td>2.456601e+01</td>\n",
              "      <td>-1.245457e+02</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>-8.900000e+01</td>\n",
              "      <td>-8.900000e+01</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>3.344517e+01</td>\n",
              "      <td>-1.180331e+02</td>\n",
              "      <td>3.344628e+01</td>\n",
              "      <td>-1.180333e+02</td>\n",
              "      <td>5.200000e-02</td>\n",
              "      <td>1.270000e+03</td>\n",
              "      <td>5.000000e+01</td>\n",
              "      <td>4.600000e+01</td>\n",
              "      <td>4.800000e+01</td>\n",
              "      <td>2.931000e+01</td>\n",
              "      <td>1.000000e+01</td>\n",
              "      <td>3.500000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>3.609861e+01</td>\n",
              "      <td>-9.241808e+01</td>\n",
              "      <td>3.609799e+01</td>\n",
              "      <td>-9.241772e+01</td>\n",
              "      <td>2.440000e-01</td>\n",
              "      <td>4.007000e+03</td>\n",
              "      <td>6.400000e+01</td>\n",
              "      <td>6.300000e+01</td>\n",
              "      <td>6.700000e+01</td>\n",
              "      <td>2.982000e+01</td>\n",
              "      <td>1.000000e+01</td>\n",
              "      <td>7.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>4.016024e+01</td>\n",
              "      <td>-8.037243e+01</td>\n",
              "      <td>4.016105e+01</td>\n",
              "      <td>-8.037338e+01</td>\n",
              "      <td>7.640000e-01</td>\n",
              "      <td>9.567000e+03</td>\n",
              "      <td>7.600000e+01</td>\n",
              "      <td>7.600000e+01</td>\n",
              "      <td>8.300000e+01</td>\n",
              "      <td>3.001000e+01</td>\n",
              "      <td>1.000000e+01</td>\n",
              "      <td>1.000000e+01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4.000000e+00</td>\n",
              "      <td>4.900058e+01</td>\n",
              "      <td>-6.711317e+01</td>\n",
              "      <td>4.907500e+01</td>\n",
              "      <td>-6.710924e+01</td>\n",
              "      <td>1.551860e+02</td>\n",
              "      <td>9.999997e+06</td>\n",
              "      <td>1.960000e+02</td>\n",
              "      <td>1.960000e+02</td>\n",
              "      <td>1.000000e+02</td>\n",
              "      <td>5.890000e+01</td>\n",
              "      <td>1.400000e+02</td>\n",
              "      <td>1.087000e+03</td>\n",
              "      <td>2.400000e+01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3a15b77a-f3a5-4250-827a-7b4572a30517')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      </button>\n",
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              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-3a15b77a-f3a5-4250-827a-7b4572a30517 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3a15b77a-f3a5-4250-827a-7b4572a30517');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BIcvshdpUvhp",
        "outputId": "5805565e-a72e-4aa2-8f90-b437989d939f"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2845342 entries, 0 to 2845341\n",
            "Data columns (total 47 columns):\n",
            " #   Column                 Dtype  \n",
            "---  ------                 -----  \n",
            " 0   ID                     object \n",
            " 1   Severity               int64  \n",
            " 2   Start_Time             object \n",
            " 3   End_Time               object \n",
            " 4   Start_Lat              float64\n",
            " 5   Start_Lng              float64\n",
            " 6   End_Lat                float64\n",
            " 7   End_Lng                float64\n",
            " 8   Distance(mi)           float64\n",
            " 9   Description            object \n",
            " 10  Number                 float64\n",
            " 11  Street                 object \n",
            " 12  Side                   object \n",
            " 13  City                   object \n",
            " 14  County                 object \n",
            " 15  State                  object \n",
            " 16  Zipcode                object \n",
            " 17  Country                object \n",
            " 18  Timezone               object \n",
            " 19  Airport_Code           object \n",
            " 20  Weather_Timestamp      object \n",
            " 21  Temperature(F)         float64\n",
            " 22  Wind_Chill(F)          float64\n",
            " 23  Humidity(%)            float64\n",
            " 24  Pressure(in)           float64\n",
            " 25  Visibility(mi)         float64\n",
            " 26  Wind_Direction         object \n",
            " 27  Wind_Speed(mph)        float64\n",
            " 28  Precipitation(in)      float64\n",
            " 29  Weather_Condition      object \n",
            " 30  Amenity                bool   \n",
            " 31  Bump                   bool   \n",
            " 32  Crossing               bool   \n",
            " 33  Give_Way               bool   \n",
            " 34  Junction               bool   \n",
            " 35  No_Exit                bool   \n",
            " 36  Railway                bool   \n",
            " 37  Roundabout             bool   \n",
            " 38  Station                bool   \n",
            " 39  Stop                   bool   \n",
            " 40  Traffic_Calming        bool   \n",
            " 41  Traffic_Signal         bool   \n",
            " 42  Turning_Loop           bool   \n",
            " 43  Sunrise_Sunset         object \n",
            " 44  Civil_Twilight         object \n",
            " 45  Nautical_Twilight      object \n",
            " 46  Astronomical_Twilight  object \n",
            "dtypes: bool(13), float64(13), int64(1), object(20)\n",
            "memory usage: 773.4+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# count the numerical coloums \n",
        "numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n",
        "numerical_df = df.select_dtypes(include=numerics)"
      ],
      "metadata": {
        "id": "AyN1pDnMUvZX"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(numerical_df.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SVJ1O30RULfs",
        "outputId": "a603d1f8-2153-4420-c10f-540f6dd976ff"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# find the missing values \n",
        "df.isna().sum().sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cl_iHBaJwwgq",
        "outputId": "d3806e6b-1424-4b15-ff79-8ccc8921d273"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Number                   1743911\n",
              "Precipitation(in)         549458\n",
              "Wind_Chill(F)             469643\n",
              "Wind_Speed(mph)           157944\n",
              "Wind_Direction             73775\n",
              "Humidity(%)                73092\n",
              "Weather_Condition          70636\n",
              "Visibility(mi)             70546\n",
              "Temperature(F)             69274\n",
              "Pressure(in)               59200\n",
              "Weather_Timestamp          50736\n",
              "Airport_Code                9549\n",
              "Timezone                    3659\n",
              "Nautical_Twilight           2867\n",
              "Civil_Twilight              2867\n",
              "Sunrise_Sunset              2867\n",
              "Astronomical_Twilight       2867\n",
              "Zipcode                     1319\n",
              "City                         137\n",
              "Street                         2\n",
              "Country                        0\n",
              "Junction                       0\n",
              "Start_Time                     0\n",
              "End_Time                       0\n",
              "Start_Lat                      0\n",
              "Turning_Loop                   0\n",
              "Traffic_Signal                 0\n",
              "Traffic_Calming                0\n",
              "Stop                           0\n",
              "Station                        0\n",
              "Roundabout                     0\n",
              "Railway                        0\n",
              "No_Exit                        0\n",
              "Crossing                       0\n",
              "Give_Way                       0\n",
              "Bump                           0\n",
              "Amenity                        0\n",
              "Start_Lng                      0\n",
              "End_Lat                        0\n",
              "End_Lng                        0\n",
              "Distance(mi)                   0\n",
              "Description                    0\n",
              "Severity                       0\n",
              "Side                           0\n",
              "County                         0\n",
              "State                          0\n",
              "ID                             0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# create a nicer Visualization"
      ],
      "metadata": {
        "id": "Imf_9O4p1M4B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        " # calulating the percentages of the mising values \n",
        " missing_percentages = df.isna().sum().sort_values(ascending=False) / len(df)"
      ],
      "metadata": {
        "id": "SZpwLHOzxEAa"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "ksrIvJsg1kuu"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(missing_percentages)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sHVeCe_61mou",
        "outputId": "af0a9716-9396-47f4-faf9-16a91a008dc8"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "missing_percentages.plot(kind='barh')       # remove the values thats are not meaniful for us by using filter "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "3zM9eDzm1zvz",
        "outputId": "833fa9a2-efaf-49b5-eec7-e2792967a928"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f37464e0e50>"
            ]
          },
          "metadata": {},
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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ZS5IkSTqYNKB1sD3L9mDbZxAjxg4up5YmvMEziBmfuwHHEwVDp0p6nhBM2EvSsDJHtDKurKukmyQNL/99thQQvUZ4kvdLmi6pure0kus8s+x7OeGBfkZSdRXwQZJ6tvkXkSRJkjRKGtAaJG0sacOqQ72AF4k85IHAnYSQwQxgfcKwrQxcQcwbXZoQje8DXFbOLUOEfn9rexvCIP+mFBBdDHxAGOM1gFMkLVbzWKcBbxFj1H5MFCEdW0alfZHoD328Lb+HJEmSpGmyiGhuuhDjyJYnqmP/Q4RzDycmsPzH9uOSniDypS8Qod7JtqdLmkmZ+AKMKecuBtYDNpdU6cdctow8gzDItxBGeVlCbKHuaDLbr0j6CTGibWui0OjRemuziChJkqT9UM2c6aQRJK0AvEwYRBNeoIkw60m29ynrBgMn2x5RRqBNAcYTedLdy0i16n0HAFNsn1M+PwHsY3u8pCm2u0jqW/as3KMLcDYwCPgLcKztvzb1/L179/aIEdkumiRJMi9IGmm7bp9+hnBbziFEUc86trvZXovwPndq4poPgQds9yTE5f9UOVGTw2yO2l7PAcTc0quJ3tCr52GvJEmSpA1IA9pyDidCq9XcVI63hL8Cq0uaKulDYFhRJVofOKCq0ncjoiBpFNFvuhUROt5G0suSTiQGar9OqB51Bv5QJrMkSZIkHUT+T7eF2O5n+86aY+fZ3rQSWi3H+tquxEoXB9aU9AzwW0LwvTNRwXt36d38EjCqatsJwJhSYHQeUXl7MNHCspjt35Z1fYhw8rqEET6IJEmSpMNIA9q+VOaGbgLsBVwpqd4os1puKz/HAI/Yfs/2RGB6KW7as5w/1fZM4FqivWYOchpLkiRJ+5EGtIOw/TCwEtCVqO6t/u6XrFleEaSfXfV75fOiRPXvI7arRRTmqgZLJaIkSZL2Iw1oDZJWkXSNpOcljZT0sKQDJfWWdN587LsJUbn7JtFX+plyr4pH+RdJO1atn0j0lDZGH0nrltznoYQ6UpIkSdJBZB9oFSW8egtwhe0jyrF1gP1s38y8jw3rXIqBIHRtv2x7FvCSpBeI/s0nibznbGCHsnZ9wtC+38Tew4mh3RsQ7Sy1BU5JkiRJO5Ie6JzsCnxo++LKAdsv2j5fUl9Jt0taRNL44jkCIOm54k3OIdcH7FwZWWa7p+07qu71Z+AvtvckJrT8CNi+CNJvVo7dCGwq6dEiC/jdIkJ/NLCi7b1tb0wY2+/UvkzmQJMkSdqPNKBzsjmNqPoAKwBbAs8RrSP3S9pI0rbAi7ZfZ265vsuauNdQGjzOPoQHuZaky4DPAQ8B04ADbW9FTII5t3jJ/ySmsVBCuIcRbTJzkDnQJEmS9iNDuE0g6UKiuvVDYvbnm7Z7SNqBUAJahWgfGVh0aXcHNqsqtF1WUpcy4aWW4cCWkpYm2lOmFDH6M4FbCY9UwC8l7UyEeNcAVrF9naSvStqyPMNjZVh3kiRJ0kGkAZ2TJ2mYvILtb0laCXiCEC54qZx6mDBcKwDfIHKQpxAe/WjCU50JfLEYxs0Jyb3Fy5qDibyngLHAkpIOBf5NeJcrAc8CU4HnCY3caUTf55KS1ge6AfeU51qv7b+KJEmSpCkyhDsn9xEG6htVx5YCFiMMHQAOAeGbgW8Rf4R80/ZGwH+BTWx3JxSKrpG0JHAc8PsijtCb0NTdizDI04ATiCkvDxOh2THlHksA44oU4AvAWuURfg/8hMh9rkyMU0uSJEk6kDSgVRSjdQCwi6QXJA0jxpTdVmf5QGAP4DnbL5RjrwLvSxoN/K0c24gwjD+Q9H1gHdtTCZGEVQjvcUYZnP0oYTRHl2s/BFYqBUSrAu+U49sD1xGe71WNvU8WESVJkrQfaUBrsP2q7cNsr2u7j+1+RIHOKjWSfSOIwp5xVZd/CPzMdg/bmxGj0LB9DbAfEZL9h6RdbY8FNgG+BHxD0o9tTydE5y8v+82wvX3xaC8EbrU9vpwTsB1wZRPvkkVESZIk7UQa0JZxH7BEma8JgKQehAe4adW6TkBlLNlGwNrAs5LWA563fR5RINRD0urAB2UM2SvA/5We0d7A1dVtMnWYALwN3EvM+2yJPGCSJEnShqQBbQEltHsgsLukcZKeJCakzARWK4VGEK0nKiHXgUB/4AhCJegDSVMJg7cF0J2GiSy7AH8sOdIRwJG236FxDiIKnvYgxqlNa8v3TZIkSZonq3BbiO0JwBeqj0maAvwCOBE4nTCot9oeIKkbIZawEvA0cLTt/9Zse1fZZwAxeBvbfavO/0jSn21/hajKHSBpKSJ0PBy4htDFVTHEB9v+KKRcPOZjANZee+35+wKSJEmSOUgPdP65EDhS0nI1x88nJAF7EAOvm9PRPVHSqPLfoHLs98AGkg4k2mCOtf0B0cJyGHAxYXi/X9SOqvOxmQNNkiRpR9KAthJJswhFooeI4qFTa5ZsT3iIEJWyfSti9JL6S7qgZv1vq2T/+gHYnk2Ega8ChtgeWtaOBa4rxvlO4LU2fbkkSZKkWdKAtp6pxLzPLYCRwDdpenrKTNvHt+I+GxJe5uqtuDZJkiRpJ9KAtg1DgPHAV4E1JD1MiC88JWlj4EjgaUm3V18kqVOZygIh4DCryPYh6f4i1XcJ0V+6t6Rny34AB0rqBbwHLCPpQUk92/k9kyRJkkIa0PlEUidgN+AsomBoElEZ24PwHIcRvZ7n115bRps9SwzZPpEYnj1Q0uPAusSElQuAbQih+y7AueXyp4nw7nVEAdNW5X7Vz5ZCCkmSJO3EAm9Ai1c3StITkv7eTH/lvNCZEEp4jVAUusH2UoRe7bPAHUSV86u2dwPeqLr2AKBX+f0BQmv3T8AXgVGERODDpfr2OuAGQiP3LWAD25cTwgz7ANsSM0xPzSKiJEmSjmOBN6BEnrJXyVW+RRinNtsXWIcQMqjsexDwSrnfvsCSzexzP+Gx9gH+ASwP9CUMK8DPgEG1+5Vq3HuA04BDiErfJEmSpINY2PpAHyZCq5T84cWEWPw44Cu235Y0GDjZ9ogikDDCdjdJ/QmvbylgfWKyCrY/kPQvYlbnFwlx96fL/X5JCC08RowjW7b0a65LKBu9T4ggmNC/nU54tmcA4yW9ASwHLF/ypyPKs19Qfp9ChH8nE9q7/dr+K0uSJEnqsTB4oMAcucqKMPyVRP9kD0LY/YwWbNMLOJRQEVpU0lqSViOKh+4DLgLeBbYrRnMCEcLdspxfpHitgwjjtxKwMbAMEcY9iJADfJ/wSs8mQrtfL5/n+IPH9imEeMMxldaXJEmSpGNYGAxo5+L1VXKV9xTRg+VtDylrrgB2bsFe99qebHsaoSK0DpGDHGz7MyU3eRlwdTGaVwBji7TffsBzZZ9RwEW2p9qeRMj+3UcM7/6t7eVtv0ZU9y5CFCE9YPuHtrtVHqbo6UIY5LnIIqIkSZL2Y2EwoI3lKhtjJg3fS23+cnrV77No8Ai7S3qyjDE7FVhF0gmEStEFZZrKsTX7GaCs61T5DJxQp9Cp+pkqz7UD8AghKm/qkEVESZIk7cfCYECBj4pujgdOIkKkb0vaqZz+EuHtQfRzbl1+P6QFW88ENgN2L9e9RPRmnkAUBL1S1n255rr9y7Dt7xFi8sOJwqFXgfckdSW84mHAi8BmkpYoxnU34CHbawGvEyHgJEmSpANZqIqIbD9WvMTDCYN2cRFnfx44uiw7B7i+CLHf0YJtFyeKhoYQA69HEXM+K+HVByU9RXiZ65dJLhOJoqGngbUII3s1sCuhefsEYZiHAP8q+4wrx18FVgT6SzqF+CPoLkmvZB40SZKk41BM6kpai6QuxLiypQhjN9D2EEnjgd4lx4mkFWy/VYqZ7gWOtz26zrrxxEzQdYjB2tsRoedHiD7Rt4n+0962R0m6HritzBWtfbbqaSxbv/jii+3zJSRJkiygSBppu3e9cwtNCLe9sD2FCN0eQ3iWA0vLSy1fkPQo8BihKvSD4o2uDgyStG3Jh1aGY+8I3Gz7/XKPvxGVuAAv2B5Vfh9JTGep92yZA02SJGknFqoQbntRJPkGA4NLxe0c+U5J6wInA9uUXtM7CC9zc0K16GCirWUgDQa0KWqLmTrP7zskSZIk80Ya0PmkiLvPtl1pUelFFP10I4p7JgHLEoVLkyWtAnya8CKnS3oPmEEULFXyprcQBU83SzqIMKorA3uW85tK+m35vDhwc7u+ZJIkSTIXaUDnny7A+aU6diaRnzyGKFS6U9IE2/2KsMIzRJXuEGBLSWOJKtohRJHQBCKsfgBhGJcFPiC8zLeJvOjbZc0I2ydKupOQ/kuSJEk6kCwi+pgoxUQ7EfJ7xxKatgMoBUWS9gcOtn1UWf9VYHPb3yvDvJewPVPSesDfSq9r7T2yiChJkmQ+aKqIKD3Qj4nm8qbzul0j97iEmCdK79698y+lJEmSNuQTXYUracUyimyUpNckvVL1efEWXH+tpNGSTpS0SbnuMUnrS3qolc90sqRnyl7DJR3VzPrLJR1Sc2xjSRtWHarkTd8jekVvJAQUdpG0UvFWD6dB7GERGkQejiDaaJIkSZIO5BPtgdp+kzI3U9IAYIrtcyrnJS1qe2a9ayWtSlS9blA+nwbcaPvnZckO8/o8ko4D9gD62H5X0rLAgfO6D03nTa8EJth+tTzzIKKI6A7bt5br3wf6SPohMWf00FY8Q5IkSTIffKI90HoUj+5iSY8Av5bUR9LDxbN8qFTFAtwNrFE8xTMIab1vSBpU9plStef3JY2R9LikM5u4/Q+Ab9h+F8D2u7avKHv8uHikT0i6RNJc7SiSxkv6FTFhZXFCGOFVoCdwiO3zgc8QI8oAliAE6F8GDpL066rtnix7dAF+WUacJUmSJB3EJ9oDbYI1gR1szype4E6loGZ3YgbnwcT0k9srxTXFoM3hwZbjnwX2B7Ytsz1XqHfDcp9lbD/fyDNdYPunZe1VwD7A3+us+6/tXqUN5XKipWVJQqbv4jrrewFbEr2fz0o6n/BIfwRsRYR97wMer/PM1UVEjTx2kiRJ0hr+Vw3oDaUIB2Lg9BUlp2hgsXnca3fgL0VsHttvtfKZ+kk6lZD0W4HwEOsZ0Mo80jFAF9vvEeLx0zX3FBYoI9QAiqbuOsCRwIGVZ5V0A7BR7YVZRJQkSdJ+/M+FcAvvV/3+M0IndiYh2L6hpFeAfxAFOc0WGwFfb67YqIRtp5S2kTlQTFW5iAjDdgf+CXxbMYf0AOALZWlnomUFYDZzKgrNpv4fNI2NUKvmSGD9FrxnkiRJ0kb8rxrQapYDniuh2uuJkOZvgc8B42x/KKkpT3sE0B3YzvZviUKeG21vaXuc7epio18BF5ZwLpK6lCrcypzPSQpx+S8Bfy/PdCtQqfidCpzXBu88nKjQ/VR5t3XaYM8kSZJkHvhfDeFW82sihPtD5hw/dg6weik2GkrI6u0h6UjCkFWKfP6P+B7ekvR2WTdZ0m5FQWiK7S5l7TJETvKNIsH3MnCu7XckXUrkMV8raytFSi7rIAp+zgS+RhQKHSJpF8LIVpKU2wHrllaWTwPvSpJD8WJDIk86E/gv0eryVrnXB7VfTOZAkyRJ2o//GQNqe0Ajxx+mKv8nqdLWMoXw/PavKjb6oKrY6BtlXaXYaIty/QCaLjZav1JsVJ0vtf1D4Idl7Y+BEyVtANwJ3F6WnUyIyEMM0T7R9rWlPeaDokD0GmEgTyCk/YYShvRBYPuqvOe1wNlEuPgFQgqw9rvJHGiSJEk78T9jQJtD0orEnM1ViUKeToSQ+0hJfWh9sdESkjZjHoqNbP9U0tWE2PsRRFi4bzm9jKQngNWI/CjANYTHXGGY7ZfLe40ihOkfZM5CpW6EEX6XCFsPa+H7JEmSJG3AgpADBUJ0oeQcLwb+DTwFnGS7l+0PiWKjQcXT3JeGvGWnOtsdWAqJRhHf0TXE6LF5eZ5xtv8A7Ab0LAa+pcxVOFSnUOlc4BLbm5DGM0mSpMNZYDzQRviWJGzfSHigZxFFPAOAVSXdBmxBtIkOJjzWvkR+cUvbVgi3fw1YCbijhGX3Aj4E9rH9uqT1gauBpYl85knAUlV5y1nAOzXP9m+iX3UgcBjQSTGxZTnCYC5hezrR87ovcAoxnTk2AMsAACAASURBVOXdUqj0A2C4pIMJr3tE231tSZIkSXMsMB5oC/g1EY59jPA6Fwe+C+xazm9J5B0vJPo4P119se07ie/rcCIEPBP4ejn9e+D3xTN8mfjD5NniwV4FHFnVt1rhBOB7kkYDGxMh5UOBr5T7fKN4nbsBF9nenFAlGgfcRXip08o9nwH6176wpGMkjZA0YuLEifP2bSVJkiRNssAZ0FJs9BDh2b1YdfxholBnS0JKb4jtF2yPB46m5B1tn0EU5nQrlz5Qtf2HwDolVPy7qjXbAzeU368BptveqISPe9u+qzzD5cAZZd0rROtMD2Im6Du2x9oeTExm2ZkwrI/ZPqtccyJwv+1PA2/SYMB3Bdat811cUu7fu2vXrrWnkyRJkvlgQQ/hzqT8kSBpEcLrrPB+zdrGBAvWBgZKer8c6yNpe0LEfZ6+P0n9idwswNbABUVicAbwfFkzmDD+LWF1SdsRxjurbJMkSTqQBd2AjicM1fVEu8o8yfwVQ7kisJfth4sA/UuEgfsJsG0J0y4K/EfSO8CoRvbqRIRZzwWw/QAhIl9RMhpb8qsQIhD/Ap4FuknawPZ/CIGGIVXbHgWsTvyR8PC8vFuSJEkyfyzoBvRS4FZJjxP9mLVeZ3OsRniHM6qOHUIYrQHA4rZXk3QNYaBXLfeoaNeOJ4ztHsBviB7Q3wHrSXq5as8TiTDyDUTR0b3AxbanSzq6vEM3QgCip6Th5bq9yz13JdSX5iCFFJIkSdoPRaFoUo9S7fog0Xf5L2Cg7SHFMPa2PamsW4MQPViEEIl/vagYjScKgH5d1g0GTrbdaMVsvTWSlgJm255W+livJaqCvwt83fY+zb1L7969PWJEFuomSZLMC5JG2u5d79wCV0TUltieQoSAjwEmErnQ/nWWnkh4nlOADYBbqs61NJ/ZFIsBl0oaQ3ipm7XBnkmSJMl8sKCHcOeZ0vc5purQdbbPBAYXA/blmvXrEopCa9h+u+RJZ0nqToR6by97diIM39WSliMKlSq9JccAX7R9fCOPdSJRqduT+KNnmu1ukvrO/xsnSZIkrSEN6NxMrRrCvTExZqxCL6I1phshLD+JEDd4nxCgX4UYWYbtMZImAP2qQr1/J3KhuzC33m5TakLLAS/bni3pyzSoJ71XniNJkiTpYDKE2zRdCP3cpyR9SCgH7URU5t4naRAhnLASUeDzKHO2w9RyOSE1eBxVf7xI6iupIjjfDXhAMWB7pmIeaRfg55LeJbzY9yVtTRQObSnp3SJgPwcppJAkSdJ+pAGdm85VOrh/IhSGNiOKhC6y3RP4OXCv7X6EMMIfbS9BhGI7A9cB2O5W8T7L55tsb0zDSLJ6XA6MJIzm1oTX+zfbnYnq3KHAp4DzgYNtL0sIKnSr3SiFFJIkSdqPhTqEK+l0YlrKLCJUeyxh2HaoTF2p4W/l50jgoPL7zsBBpbjobuDtRu51M6EW1IXQt31T0heBbwIXED2rFf5pe0bJuXYiWnAgcrPdCIWiLYB7QoeBTsCrLX/zJEmSZH5ZaA1oEUnYB9iq9FuuRCgVLUa0rdQzoJXwbLVSEYQn358YqF0X2weW+/YlZnRW9rqImAyzk6TPVN+n5DxnuKHXaHa5r4AnbW/f0vdNkiRJ2paFOYS7GjCpTDyhhFoPIYzToJLfRNIfJI0gKmpPrbp+O0lnleO/IkQSbiHCq0vSPFcUrdxewBqE5u50whD3l/S8pDOJySzDijf6qXLtm0D3kpsdLmlnSfM0bi1JkiSZPxZmA3o3sJaksZIukrSL7fOqzn+q5EGnlybaCcCnJfWoWvMmsClRJStgODEKbdp8PNeqwE1l3y8Bi9juA1wGbFvWnAN8m6gCXhq4B9ihdqMsIkqSJGk/FloD2oRIwotE60nFO3xG0qPAu8BGxNSWEcBrhDLRm7b3JIznL2yvU1041AhjKy0skn4AzKpSE5pm+4ziGY8D+pXjY4DXynW7E+PQliUmxEwi1Ilq3zGLiJIkSdqJhTYHClBmdA6maZGEk4FtikjC5cTw6wrzqq1bjx/UfH6w6vfZNORKK/lPiD98trM9P55ukiRJMh8stAa0IpJg+7lyqCUiCZ8t1/YFVq7a6wIijLtM0b+9ngixiqiunU4YvSeA7YCVJB0MbEMRXpB0NSF+v1f5LGA94DpJUykVwOXes4BhkhYnKoLPtl13CkySJEnSPiy0IVzmFEkYTQgUDCAqZO+UNMj248BjwDPEoOyhTez3INHfuToRal0KOAkYWkLB/yRmiH4FuB+4z/ZphADDVNtH1ux3UHnGI4iQ7XHAEuVcZ8IwfwgcWJ57LjIHmiRJ0n4stB6o7ZHUKbwhBArOr1rXv/pk0bpdgej3fKTMAF0R+IPtjatGmMGc/aK7A4cVj3dwzbN0KT8HS5pWvMzPE1NZhpX73k0IyQM8Ynu/cvwPNGLYbV9C/EFA7969c+xOkiRJG7LQGtD55KfA27a3AJD0NyKEW6GxftGW0pfwZOvRiTnlAlt7jyRJkmQ+yP/xzjudiLzoKpKWIMKpWwK3STqbMHzPS3qLMHRrS5pMqAl9S9KiwAjgViI83KlU+S5GeJ0iwrWLAb+TNK583gfYHPgP0E9SV9uVuOxZku6o+pwkSZK0M2lA54Fi/GYDDwEziKKgFwhDuDWwCdEv+lngLuB44CxCcP6rwIWEIdyPaIuByJ2uSUxWORkwkUudAqxSrlkDGAv0IcTsewJHAr8D1gJeSuOZJEnSsSzMRUStYXOiOhfbp9re0Paetg8iNHSvLQLyTwNDiKKfE8r6Kba/TPRr/th2RVe3v+1Nge8A3Sr50HLNKSVMfCtwnu1ZtgcT+dSjyrKpZBFRkiRJh5MGtIVIOo4wfhcS3mZL+DzwF2DnMuFl23Lt4lVrWpov/ajn1PZLwOuSdiWM6eB6F6SQQpIkSfuRBrSF2L64jDU7G1hC0jGVc0Xe7x3gUEmdJHUlDNum5edbwOeAyYRm7uK1+9fQkkHZlwF/JXKlLdHeTZIkSdqQzIHOI7Yt6UCiwOf7hO7teCJU2wV4nMhjXgNsZHucpOuJkO4sogjp+0VIAWC8pD8C+xKDsr9IKCJtKOlbxBBvgJ6STiLCwuOIGaArEH8EDZI0qcwnTZIkSToANUzKStoSSV2IAqGlgH8RurlDSp9o74periQDh9q+XtKmwK+Bg8o80IuAfwP/IJSIPmv7/WK41yVysmtV79cYvXv39ogRI9rlXZMkSRZUJI0sA0XmIj3QdsL2FElbE1Wz/Qix+tPqLJ1FTF8B2I3IkQ4vg7I7E+pF2xFKSUPL8dWIKSx7EWHcupQw8zEAa6+99vy/VJIkSfIRaUDbkebE6gvTgK6SfkcY0JlEK8yZwPG2B0g6HnjCdt/ai4tBbez+qUSUJEnSTmQRUTshaWNJG1YdqojV1ysQupkwtLsQ/Z+/JvKox0hap/y+taQNyt5LS9qoXNuSgqMkSZKkjUkPtP3oApwvaXnCq/wPEU49nBCrn1CKfjoBM2xfDCDph4RAwhLEZJg9gFOIUO9oSROJodtfJcQVLgWelTTM9o4d+H5JkiQLNVlE9DFTwrPr2j6x5ng34HbbW5RB371tf7ucOwOYbPt3kvYEjrV9MDXU5EC3fvHFF9v1XZIkSRY0mioiyhDuPCLpwCKKUP3fbElHSrqxne89pfz6ZxqUiL5CiDXMRQopJEmStB9pQOcR2zfb7lX5D7gIeICQ8TukFVs+ScuVjSrPUK1E1IeYNZokSZJ0IGlA54NSyPNj4EvE1JUnyvH+km6VNFjScyXkWrnmKEmjJT0u6SrgPmAZSc+U4/eWsOxawGKSHgZ+QrTDVDORmPDSpTxDkiRJ0oFkEVErkbQYoTZ0ku3/lpxlNX2ALYAPiL7OOwjh9x8CO9ieJGmFomw0kZgnuhIxnPtKwmCuBvwCuJ2Y+NJZ0qHEMO/pRAvMLsDZkna2fX97vnOSJEnSQBrQ1vMz4EnbAxs5f4/tN+Gjgds7EpW0N1RUg2y/VdZuBaxW1IcWA161/ZykGURoeIak7sAE2wMlnQPsTUQQriW80A2BOQxoCikkSZK0H2lAW4GkvoRG7VZNLKstb25tuXO96/oQbS572X6w0QtTSCFJkqTdaFEOVNIBkixpk2bW/aBtHmv+kbR6a6tiJV0uqW5BkKTbiWHZnYGXK5W4zG1M95C0gqTORO/n9kS+86uSLit7fU/SUcSA7sPKdUcSRUkAQ4ELJV1Qjlf4OdFXunvZZw1JK7fmXZMkSZLW0dIiosMJYfTDm1lX14Aq6NCCJdsTWlkV2xxDCWGEaTXHN6z5PIzQuB0N/NX2YbafBP4OHCjpcaCH7SuJYdpHSxpNFCR9t+zxXULe73BgjcrGtu8m8q8/KhKBN9KMGtGYVybP63smSZIkTdCsUStTRXYklG8OK8dWk3R/8b6ekLSTpDOJIpdRkq6W1E3Ss5KuBJ4A1pJ0dlk/phTDIKlvqVa9sVSiXq0i8CppN0mPlfV/lrREOT5e0q/KvUZI2krSXZLGKQZfU+5fqYrtJOmccu/Rkr5Tjv9Y0vBy/JLKfZvC9q9sLw18DXigtLJ0Av4IdJf0JrAD8DLwEvBNYEjxXCEM8LW2exKjzE62/SLw/XJ+ReA7kp6w/QLhbQ4h5oi+KunXZd1q5ecs4Hnb4+r82x1Tvp8Rsz5IA5okSdKWtMQr3B+40/ZY4E3FhJEjgLuK8egJjLJ9GjC19EdWwo0bAhfZ3pwwAJX1uxOVoxUjsCUxT3MzYD3g05KWBC4nRn11J/K136h6rv+W+z9Q1h1CTC35SZ13OIaQxetluwdwdTl+ge1tbG9BhGT3acH3UY+hwKeJ8WLPAxWd2u2J8GxL+AuhKNSLMIrV9AIOBboTQ7vXauT7noNqIYVOSy03b2+UJEmSNElLDOjhwHXl9+vK5+FEyHEA0N32e41c+6Ltf5ffdyQ8r1m2Xye8qm3KuWG2X7Y9GxhFGLuNgReK4Qa4Ati5au/bys8xwCO237M9EZiu0J+tZnfgj7ZnwhzVr/0kPVLCoLsSBrA1PFCebWfgD8CSwK+At22/39zF5XmXsf1wOXRNzZJ7bU+2PQ14ClhnXh+w+xppQJMkSdqSJqtwJa1AGJbuisHPnYiq0FMIY7E3cLmk35RcXi3NGo/C9KrfZ1U/l6TTCY+3M7CCpG1rrpldc/3s5t6r7HsnUfizte2Xyh8DS7bweWt5jwjVzgTeLO9wCA3FQPNLY9/PIpI+Z/sfbXSfJEmSpIU054EeAlxlex3b3WyvBbxAGM/XbV8KXEZDBWqlj7EeDxDhx06SupY9hjVx72eJUOghZf/7iTFfL7XgvWq5BzhW0qKloGlFIp9rYFLJ885PwdHZhOEcR3jOtwMnU9OX2Ri23wHeq/rj4LCm1ldfSgvDzllElCRJ0rY0Z0APJ2ZVVnMTkXN8XNJjRG7u9+XcJcTIrauZm5uJitTHiXaOU22/1tiNS7jyD0ROdAThWZ5rewKwJrBCWdoNOBCgeJErArcSxquy5h7Ck55EqAF9kwgV/5XQov0vsDrwzUpxE7CepCGSRpYCpUq+th4rE38MjC1DtG8iqmZ3kHRyZVEpalqJkO57GtgXOFnS3eWZLpX0MnAssK6kSuh80VJENYxQKPq0pMUJb/QYSW9VPTdV98sioiRJknbiEz3OrHiGDwJLAf8CBtoeImk8Md5rkqTewDm2+xYDuifQj2jreJaYnbkGUdyzQyUnW9mDkMLby/bXy/HlCPm9IcD+ticW4/QZ219p5Dl/DJxIDMW+E7jC9rTyPFNsn1PWPUGDx/if8g6jJF1PFGX9SdIE4ELCKJ9h+x1JvwSesv3Xki8dRhRefZ6qMWdN0bt3b48YMaK5ZUmSJEkVamKc2Sdaicj2lFL1uxNhFAdKOq2Zy+4APkUMpV4GGEmEV18jwqq1odoxwLmSziI0ZiFCsFsDoxQ6tZ2AVysXSLqQqLpdHFiXMNSvEgLvRxCee9/qm0hanRCIh6gWnl6M537lOfco7TXLAN8GfkrkVCFUj75d5c0uCVS0+XpKWsr2B818L0mSJEkb8ok2oAAlJDoYGFyqZb9MGJZK+Lm28Gc6ES6+ghBz3w/YFLiwnrCC7bGStgI+B3wduBcQ8AbwTlk2C1hZoTj0fdvfgjmGXveq7CdpUWBiybN+9Jy2JxRjTNWe2L5NMdWli+3DJHUi8sP7EiL03cv6kbb7VT97yZn2JDz0NKBJkiQdyCd6nJmkjSVVK/z0Al4ExtMwQ/Pgmss2AGbYvrjq2NNEgVNFWOHfwGLl99WBW4BngEeB/oRHuTQwuBjHbYAjS8/lXXWec29Jd0jqQfS+VrzI8cBRkr4uaW9g/TrX9gcOKL9vQOR7zyOUjjYmhOKHAxupQWziH0X0YafynIMkDWrse4QsIkqSJGlrPtEGlDAeV0h6SiFztxkwgBBL+L2kEcwtOrAqEbZtioGE4YHIgfYhqon3B4bb/hC4HthbIbk3ilAXaowvlT3+QfRwjivrbyKKk04lPOfpjW1Q+D3RrkNZP6NU6F5JFB9tTrzv9kQI+RQawrwXU0MWESVJkrQfn+gQru2R1DdcD9Cg9lO9foCk44m8JEVhCABJnyFymxDG8WulCGllorr39OINVpLFrxEqSue04DkPk/Rp4HgidNwH2IMIBb9re8MS7t3M9vjye3WLy6jy7G8Cq9ieKWlZYEI5/yFwv+09yrv8Aehm+0FJrwL9KiPSap4rp7EkSZK0Ex+7AS0CDb+xfVL5fDKRDxzQir2WJ8KkW5bPqwPn1eY+bb8i6c0Scj0UOK7OdgcQLTf17nMzYaSXJzR+RxHGckWi2vcewmP8Og3ecHca9Gv3okH1qB/hpTbFXkRoukK1mEJKDCVJknwMfBJCuNOBgySt1AZ7LU9ML1lC0jGViSzFUK5Vs3YgEVpdzvbolmwu6cJiLNetOvxhyY32JFpTPg88THjJ1WIKY6iq5K1iEJFzBfg3DTndloopLEszk1ggc6BJkiRtzSfBgM4kwown1p6QtK9Cq/YxSf+StEo5PqBWoKCERc8kPNClge9JelHSNEKX9g1g1bJ2NOG5HQa8rjKRhSggahTb3yrGslcpLjoNmFGeYQzRn/kGMYllFULwYQ9JexCtK2vX2fYAojgK4E/AXyRNJaa9LFG1bklJd0p6jhLWVkzAEfC0pLlEKTIHmiRJ0n58EgwohHDAkUXEoJoHge1sb0kI2Z/azD6nAeNsb2F7E6JA6D+29yaUiAbTMJHlYtuLAodUTWR5Hfhc8TI3BvZRjEybIumiOvd7jQYPcyghvnAMEcbtbltln4dq1j5D9KtCFCjdUn7/GbCb7c6Etzq1av1UGiayrEgIzJ8GfGB7Sdur1j5cTmNJkiRpPz72HCiA7XcVc0OPp8FoQHhwAxUyeosTOrytZXfCaNabyHIq0Uu5AnC+7TOLEX3c9pdbuH9lIsuLhAThMZLWoExkUTOjRkv+dkXg4tKiMoMwuhXutT25rK1MZGmxLnBOY0mSJGlbPikeKIRy0FdpaC8BOJ+Y2dmd0IetiCZUCylAK6eoKGaOXkR4od2BS2v3KnnP/0iaXLzR1yQ9KOkBwiteVTHk+keE97kz4el+SHik60i6izDOSxTD/FNCWH8WEUr+FDGebQVCr3cf4Gigq6TzgNOBL0iqFEPNIjz24cQQ83ozUJMkSZJ25BNjQItHeD1hRCssB7xSfq/2BMdTJsAUFaFKUc97NF5Q89FElnLdCjQYy0YnshTVoa8BD5S8Z6XfclfCsG0JDCoh40WJNpiXiHxnZ8Ko/rnsMb3s8WNirudNwGQiD/onYCwR7j2PhiKi1YBfEi04Z5ZjKxHeeR/gXaC3pOpZqXORRURJkiRtyyfGgBbOJYxDhQHADZJGEp5ZhZuI2aBPEoo/YwFsvwkMLYVCZ9fsfRkxdWV0EUc4oogUXAo8AdxFKP60hH/ankHkOiEE5CFCzJOJvGdXoj3l58APy+cKGxCiEBVx+m6EAMNXCTGHfQhP/EMiP2pCp3eVsr4roY70WFmzBzHqbQ6yiChJkqT9+ERPY/k4UdUkFUl9gZNt76O5J6xMsd2l+hrCGF9ie/s6+65GtK7sZ3tsOTaJ8DSXINp6XgXOAY4Cfmz7xup7STqXGJ32x5a+T05jSZIkmXfUxDSWT5oH2iZIOkCSJW1SPq8u6cZ2vN8JkpaqOvQskb/cvpxfTFJvSZcSXuuiwCVqGKD9EBGy3ZuQAVyc0Ll9vOoeAyj6vYSB/koJOyNpjaKolCRJknQQC6QBJcaJPVh+UhFUqF1UyYc2RVEdugzYqRQAHcfcMoInEFW8lPt9SORTz6rS0r2IyImK8FJXBq4taknfIYqGTgeeA7YorTd1tXNt302EfB8u/ac30oyYwphXJtPttDuaWpIkSZLMAwucAS1e2Y5EPvGwcqybGiax9Jd0m6T7gHsl9ZV0v2KayrOSLpa0SJESfIXIV04jWmB6lX7LIySdW4zjr4lc5yBgl0po1/Yo2zsXhaL9iLzll0vPZi/bm9lez/YEQn1oZeLf4++2/1te5zngl5IeJPKqp5d3WB/4LJH/fAc42va4dvtSkyRJkrn4RPSBtjH7A3eWOZ9vKgZyv1mzZiugh+23Sn6zD1HU8yJREHSQpIeAs4ixaW8Dd0s6wPYtRIHPI1X6vV+hEUH3wuaEYHzt5BjK8x0NbEt4p49IGkIY08MIlaJFiVFrFV3dS4DjbD9XwsAXEVXBtXsfQ1QB02nZrrWnkyRJkvlgQTSghxNjwSD6NA8HLqhZc0+VkALAMNvPA0i6lvBgZxDzQCeW41cTPZ63EH2YN7XR8+4I3Gz7/XKfvxH5z0XK8Q/K8dvKzy6ElN8NVeIMS9RuCnNPYxlx5t5t9MhJkiRJqwyopN8CL9r+Xfl8F/CS7a+Vz+cCr9j+zTzseQBRWfpU+TyYqHxtcelo6e3cFeiumPLSiWgBuRDoVHKYKwArStoTmEjMHH1X0nm2jy9bNVeaPK2eN9kEXYA+kjrN43X1WAR4p/STJkmSJB8TrfVAhwJfAH4naRGid3PZqvM7UEccvhkOIMQCnmrlM0EU7lwFfKsi2VfCoWsBs2z3Usz8/CZwfVWLyj+Bg8u7HEp4bcOA8xRTYt4mPNnzG7lvRcChsRDuRoSx/omkH9m2Qvx+c0IC8HI1CMMfSAzoVjn+K+LfaV/gj0X28AVJn7d9Q5H962G77ui1Ci0pIhqfHmqSJEmLaW0R0UNApcdxc0KI4D1Jn5K0BLApYElDJI2UdFfpf0TS1xXTTx6XdJOkpSTtQBTanF3k8tYve39e0jBJYyXtVK7vJOnsssdoSceW432Jgp4ezGmEbwL+r5n3GUuMEnua0KPdj1BFWorIO74G9AS+IWmxcr+tK+9HGLh7JA2SdLykp8qzXVcM5XFEkdA3gJclvQiMBn5TnvlGwmC/AHxAKBHdTIxAe6Xcf2ka/r3WJip8p5b1R9d7KaWQQpIkSbvRKg/U9gRJMyWtTXibDwNrEEZ1MmGIfgvsb3uipEOBXxDKO3+zfSmApJ8DX7V9fsnx3V4lGgCwqO0+kj4HnEEIwn8VmGx7m2Ksh0q6uzzaYoTC0Eei87bPIwxS5fPlxahV8wEwvEooYXdi0PVm5d0Otv3P0tKyN6FdO6Tm/T5j+yuSJgDr2p4uaXnb70i6mDnFFz5FhGEt6WvApra3qLr3ro3ce2x53pnAn2z/QtJRRDSg3r9T5kCTJEnaifkpInqIMJ47EJ7UGuX3yYTXtCfhlUHkIivDpLcohnN5Ijd4VxP3+Fv5OZKQu6Ps20MNwurLARsSLR3Dqo0ngEKwfQzxrk8zp6ZuY/zT9ozSY9mJBqm+MeU5Nga2aOT9RgNXS7qFhjFltTQ2ZWZVYMly727lXO29K1wrqStwJKG/myRJknQg82NAhxIGszsRwn0JOIkQNx8MrFFPyg64HDjA9uMlH9m3iXtUhARmVT2rgO/YnsPwlhDu+3X2mFopuCmVtMfVnH+QCPGeXHVsOoDt2ZJmuEHvcHZ5DgFPNvJ+nyJ6R3cGLpP0DPHHxhRJi5bc7PnAb2zfVp57QLl2J4rBtH2rpGl17l3Bxft9lTDgSZIkSQcyvx7oycDzpbL0LcVMy82J0WPfkbS97YdL3nAj208SxTavlmNH0jBtpalJKtXcReQi7yue2kZVezTHA0SOdClgZ8UEk00Io7qpYjzY2mVdRbe2c6neXZTIUz4NnAJsJWkc0SJzAfEHxbeIIqQpwAgi1HpJOf5ZYN8Sju5OCCT8hNKjKmkZQiz+1fK5P+GBIulyIq+8iqRvluc/lJjO8hYhptAk86JElMVESZIkzTM/SkRjiOrbf9ccm2z7DeaWstuhrPkR8AhhcJ6puvY64BRJj1UVEdXjMqJI6FGFutAfacEfAgrZvs+WZ4QIOX/X9kbA54AZtrchDN62ktYFjgBmFg+2J1HMszoh/L4tYbi/Ut5vOeL7/Ctwf1l3HpFfXbH8XI4Yh3YFYQRFiDosQoRh32jiFboQ7Tj7EF7upySNJvKldSX/sogoSZKk/fhET2ORtCJwb/m4KhHKnVg+9ymas83tUcmBQniWJxHG/Azb/cqaGwnP9IOybjnCi55GzPL8K3CL7VGlAGgE8A/gDuDuEuodBZxn+8+l9WWE7W7Fk9zF9tHlXiOIOaQzy71WAD5DzBXd2fZxZV1/Igy8MtEe8zKhmfsCYczvt72LpOOBX1YmwjRGTmNJkiSZd/T/7Z17uF3jtcZ/74kEScSlKU2jlSLqFoIISoly9HLSoKKtUlWiKL1TlKN6O1qtS1s91Uor9CEuqVZo656UaiQSQkiOBlVUEEHiEgnJOH+MMe25V9beWXtnr73XTsbvedaz12WuOb9vLTHW+L53vKOVbiwN7UQU/T2L/cuzKSlZ28Dbe6AF0IMbBwAAGUxJREFUIfwp75dW3VeNY/fGlbfjJF0YKt4d8aB3PK6APRqvg90x3rZOxWkqr3WImT1S2hNF3jmm8n1Dge/gtaFv4k29R0pajvvngmfubf1MkiRJklWkoQNoNeTesefjS5ovAEeZ2Ty5c9H9uBCnH+4mtAnQR9Kz+PLrUXjAeQrYPTLPI/F91dMknYHXW76Oq3WX4cutM/Fl06MlLQW+jgt33gC2DMXsu4DPSdoHX8LtX1IKl/uGzgEmRSa6taTDcM/bjwCbS5pS6vO5BS4qOrjiY3i95Lv7HuAlScPNbFpLn1t7urHkXmiSJEnLdLduLMIVrKPNbBd8efUHpdeXRqr9C1yQtA2e/b0F7IeLbd4PXI/XcS7CXYnG4aUw/XFf2Y3wzG8EsCsudnoSD6pP4p+b8H3UR8zsiRjL63jgXtTKHC7HO7NshWeVl+L7xkPw5dnjYv+1P7DEzMr7m0VLtXUj2IPXq07Dfzg0/7ByDzRJkqRudLcAujZN9ZczgTPxmsqCifF3Fl5mMi+yvsfxTA3cs/ciMxuJ723uhQezfngt6Vt4ycgmZnYZcC8esD8YNaYL8cy3B7AYz1iJ5843s53M7EzcXQgzG2dmJ5XGuBT4m5ltbWbb4eYIR8Z8NoxxDsZVzvfFOY7CA/5d5q3QephZ8cNhFHAbLlpqhpn92syGmdmwHr3Xr+kDTpIkSWqjuy3htlZ/CU1q1PPwQFWwHPeX/SS+9FvGivPiIp0jzezLks6WVNSGlvcwf4XXYA6R9FVcVQye2fYDfiI3xu9L/ECRe+z2Kp2jfL7dgTPM7DJJ6wCHmtktknZixT1RJA0rjfEzeNnLcjyYt8iQgeuTTkRJkiQdR3fLQJcA75S0B4CknpK2q3Lc7ayYkR2AK3A3KN6Pl6n8DXgEX1btGYGpZzyuxrpA0bXlw6Xnp8cN3Bj/TbyXKHiW2LPyRDH2+cDBknqa2Rt4P9A+eGY6qPI9Zja91DVmPJ49b4ubWSRJkiSdRHfLQJfj9aU/k7Q+Pv4L8eyxzF9xI4deUeqyDh4Q34vvg54o6Vp8n/RfeBPu0cBlsf/4FPAYnlHuBFwn6Xvh4fu/wJSw+ZtbuuZacd7DcEHRS8AoSaPwpdki6xxI037l4Xit6GC8rnVbvLb0tfg7X9LUGPtYeNtx6WQzGxleupNj/Ge29sGliChJkqRj6TYB1MzOLj3cu8rrI0r3b5B0K26ccD3uSds/Xl5uZkdEANzNzP5dMn3/ChGcomzmYDwY9gHul/Qn4Fa8b+kuEcyKJeHngKvM7KRwDiob40+iqb3bdjQFuz2B8WZ2KfAteQ/T40rm8f3xetOrgcvM27G9Pc9gPi6eWlD5mUj6AvAFgB79WkqokyRJkvbQbQJoOxiPl4dcH3+PwRWrBXfjtZ3X0GRaX8n1ZrYYWBxBcDhe0tJWxgKfl/R13IJveDw/gCZjCPB927J5/HQ8MFcayZdZRpMZfTOyG0uSJEn9qMseqKQLQmBTPL5Z0tjS4/MimLTlnAfFEmfxeHIIalriemA/STsDvc1sBq6UHQ8Qjj9n4qrXGeF6BLC2pKvwfc4xkv4cfrvggqP28HvgE7jt4QxgT0mn4cKfUaV5vYk33d4fX65eYmZjzazSSL7MbMJPN0mSJOk86iUiKjq1FArU/vjSZcEH8DKNtnAQLpapCTN7FZiE12eOL78maS1JW5jZVDM7C88CizKXYXg3mZ/h3rTfxvcoR+AlLbXQzBg/xEH34ibyl5rZRDP7IW6qcEh5XmZ2lpndVus8cRFRCoiSJEk6mbp44Up6NzDVzN4jaQjetWUAvnz5Or4s+WHgXFZ0FDoW37frBTyKl58MBW7EazAX4kHnN7gp/b54b9FjzOwuST3wLiUjcPHNZrihwrvwEpReeKb3EB4Yhat2vxq3M83sHbEHujlNBgvn4sFqVJzzczGPc3B1cB+8jdmfcaHRJXEtxZgfBT4e8/0sHqgXAN/EXZIWxrWujbluH2PeDc8+t4737hHX+HGcb0vgcDO7obXvZO0Bg23A5y5s7ZAVSBFRkiRrOp3uhWtmz0h6S9J78WxzCq4+3QMPFHOAC4ADo6flp3BHoaOB60Ltirzx9jFm9nNJE2kuzAFYy8yGy1uEfRvYH9/rXGhmu0paG8+Gi/rQTYHtraLpdkEYz18eczi74rVD8EC+LR5Q78WD2+n4cvF2wDNxvaLLyly8W8qjuM3gU2a2LMYOHui/CJxqZtfEdcaVXpsL7Gdm/5B0OXCCmV0o6Xn8R8dHccXxgcAKATRFREmSJPWjniKiv+PB8wO4d+3AuL8QL9E4AHcUAnf1mRfv2z4C5wZ4xrWCwXuJQvwzgyaRzQHADvJem7PwziqD8UD2UkvBswb2whWzyyQdjJe67Irb9k0zs6fDE3erGMt7gf8ws7mS7sJ9eR8sn9DMFku6H7cOLBiOl9DMxTPYCZJuxD15T5TUF2+Fdl1c40Z8f3UFUkSUJElSP+oZQIt90CH4culTuJHBInyPcWALjkLjgIPM7AF5S68RrVyjyCyX0TQX4Y5Cvy93YZH0IXzptjUexutBW8XMLpa0Z5VxFKyFq3Ufi8e34wFx4yqnewbPJov2be/EM/TBeMnNDlGScw9ez7obvgy+xMzuLUpfJPU2s9ernD9JkiSpA/XOQE8GHjezZcCLkjbAlzqPw40O9jCzKeH8s5WZPYyLb+bFc4fj2SpUCHNa4WbghOJBKGj/XT6gSp1m0SllGTA0fGn7AdfgxgtH4nucu0i6DN/3HAmcgu+J7i1vHH5L6TIbA7vK26EdH+d7U9IH8f3La+O4N4BfSroe39stxjoX6CdpMO5i9Ek8Kz+JFctuXo7xXNPSh9IeIwXIfdAkSZKWqKeV3yx8r/CeiucWmtnzeKb3owg8MwnVLu4tOxXPYP+v9N6rgFMk3S9pi1auOxYv7egjaTG+vDsF+HyN4za844rhGfPR+B7j+fjS6QO4gOhGM3sWOBV4yMx2rDjPUnzJ9tdxrhm4GfxdMa/N4riF+PcwDRdWvQBgZg/gNaEP4D67ffHOL/dVGfMrZDeWJEmSTqVuGWhknf0qnjuqdH8m1R2Ffgn8ssrzd9O8jGVE6bUXiD3QqJn8lqQvR1YJQCwHt1Y3WjDNzEbFe+4ETo99zFnA02Z2UCh0X42MuqeZFcH/d8BHzZtujwDmm9luWrEZ+Gm4+pb4DPYxs4ck3YKrgO+JuRRNs5F0A3CGvI3ZNNwl6RIzmx4OSsdX+cze3gNde8DgjpdbJ0mSrME0vBORpAuAf5nZhfH4ZlzNOiYen4dncUujtrIW3gJOCgHTdTTvlLIkzjsZD7jjQp07h+bdUU4i/GnbipndLWlQBNlNgBfjpcXAf0s6xcxmF8dLOhDPYPsCW5jZJ8Oc4orY91yH7MaSJEnSqXSHbiy1mDLc0obgCV6/uRu+F/kY0LO071lmDnAYsAO+JLtr6bWLzOxl4GVJe8VzR7RwvWr7t5cDV+LBvOgcMwf4Q0Xw7InXp56Ld4IpMskeNAX+NFNIkiTpZBo+A8XFSBfE/e3wQDFA0oa4GnUbvGzliJKR+yJgXUmPA98sxEK42OcRPIC+gSt2rwZObMGoHQAzWyrpV8DHJRV7nT8AfgJcBNwUWeoy4BlJhcnBuyUdh9doTgiTiMXAq8CdwDvwoH1F7Nd+A7hS0sxYmj0M7226HK9zPQ3oHdeaCvw13rco3tsiKSJKkiTpWBo+AzWzZ4BKU4apuCnDMFyYtLTibQNw5epI3JAAPMg8h++jfhoPnieb2anFXqmZTTazkaXzHGdm0+O1O3CV7dZhslCUxPwjzjXUzDbCLQAXmtnWeEnKsXHsqcATwJAQHM3AVbPTcSehoWZ2O74c2zfcnH4E7Ijv7+6KG08chn9v58R5puPLurMqP7sUESVJktSP7pCBQuumDHdXOf6PISaaLWmTeG5vwggBzxLvaMc41MLz00oGDYWRQ1FPWhg57I/74L4u6ee4i9DHCJFPiUdxC7/1gclmNh9A0hUxhz/iPxhujOP/jSt1VyCNFJIkSepH3TNQ1daZ5azoTtISlaYM9+DlJaOpbkq/RNLIcPnpLWl2vHdV5tEjzjGnysuvSTpb0sk0GTlMBr5sZu8zs3J9KGb2JTPb0sz+UeVcr+BBtHztVyuOedPMTNJJuD3honZNKkmSJGk3nZGB3o2bAFxYEgGVxTofAL5WlG60QDVThl64eObv+FJtmR545jUcr7ncCV9K/VQYIWyMm9BfWcsEQsjzA1z9++BKDr8Zb4XW38y+WjJyuBU4q1DOStrIzF6kZYOIacDPJBWNwA8Dfl5xzG9x96S/rmwO7d0DXRVy/zRJktWZzgig7RUBDcOXMr8J/AEPvAtCBPQUIQIysxdK5uwF6+JzWwBgZksk/QLvaPJinF94gHrDzG5U8y4ua8ftCklLYhwGzJf0HTP7NkDUZB6PuxS9gu9rjsVbrw2S9BDeKm1zPFgPijk8iSt/DbgNuDiu/2x8HhNwE4XT8ZZs6+ImDt+VdHqMnQjEz8dnkyRJknQidQ+gNXRmaUkEtBce8Caa2YQwQjgB2B2vnZwNjIlrjMM9dN82a4jSkn8BN0g6HN//PEluxv4ufP9xC2CSpC1xu77KLi6H4vuXo3H7QQETw55vn7jm+/HP8T5ghpktlzQfGFO0GJP0BPCkmb076lr3w38grIO7GG0SCuCbcJHTv+L+bWY2JALtOWZ2g6Rzadr/BJiI/5hYAWU3liRJkrrRWSKiThcBmdkYeS/S/fHl3/8EjoqXr4nzz41Sl61pWfxzQNzuj+f7xvPr4TWbrwPI260VDMAzzzLF67OAvmb2CvCKpCXhaAQuRno8zjce/xExgeaioRkxl4LnY/zVPoMUESVJktSJzipjqSYC2iOeqyoCKt1vSfnajGpiJeArZnYBHnA+I+msGEPZ1m5b3Gd3f3zvFLw/ZyH++Qhwc5SZDA3xz29K1+kp6Ye41+5XJU3Bg+s6FeKfYk7LgS0lHRmPN8Jt+cADeNlu8BOS+uFlMG+F2cMP4/lB8QPhGFbiQpQkSZJ0PJ2ZgbbUmeVYVhQBVeNOvG1XSyKgslhpPXy/sRArDcUdf27B9yMPjfO8D1/+HIov4X4MONTM3iyJf54BPiSpr5m9KmkgHtDuxJdwe+MZ5wLgYtwP91fAlq3M5WEzu7zK8+vh5gv/AZwIPGBmi2KPd3GYPYwGRprZEwAxnnlVztWMrhARrQmkUCpJ1lw6KwNtrTPLCzWe4w94i6/ZuA3elIrX/45nteBZZR88o5sFfBd3CdoBt/B7El/6nI6Lf2YDL8Xf+yQtwDvE3IgH3ruAKXGuCcB60RXlOuBreFCeBmBmz+HuQSMAJP0At+q7qbQcPTxKXip5BRdNzcGD//k1fjZVNzjTSCFJkqR+dEoGWkNnlnGURECSlkWwWgv4i5qaRZ/UyjXKYqWhwPfxvdYp+F7rlTRlhbfhAp4+wKcIsRJuzXcHHoCvxIPoJFwQdHSVy16HL90eEo5JRI3r+XgG3Ae4x8x6hfjnWDP7vqRBpXNMwAMneJCfihsjfA/4sKQeZtY3PpOZcdw/41o74T8EPgOcXeUzyT3QJEmSOtGoVn6LY79xe1xA06xVl6SWAn9ZrDQlbsXj8XiGWeaPZrY8zNs3MbOL8f3R8XjPzx54QG2NjWgyg8fMxsT5voEv9ZbFP4NWci7wBt1XAhuZ2Wlmdls8X3wmQ82s2DPtH9fZVFLvGs6dJEmSdBDdwcrvLnwpdgSelb0EbC1pG1xQMwbPJoul4ANw4dHO8bcvrqDtF/dvxzPNV4B95b00PwNI3rdzZ9xHdxhwBV4yMyEESB/HazL/jpe1bB/nHC/pNXwJ+S+4x+5UScuBB+WbmI8CL8cYvwXcKemzePC9K0p1nsbLXd6KbHoc3rh7Au6o9J0YQ098r/bWEBK9hO8jX7PKn3aSJElSEw0dQCPT/CheEwke3LY3s39GjeNCM9uwVLd5FvAbPMjtFkYDM/HANqni9G/i2Vtv4MLS84/jS73TgXNwhe5SvH3Zd2Ncv8OFPFeGAncOMDq6tvQE9pP0DN5u7EN4gLufptKWnnig3D+WqvePca9Pk1XgI6zoUPSCme0s6Yu4KGsM7sY0B/ggrQTQFBE1HilASpLuTaMu4a4bgW86LvgpykYqTduPjOOm4q3BluNLqn8r6jPxfcyFNC+NAQ+U4Mu1e5SefwgXKw3HxUeFWGlfSVMj4H2Ipp6k/yQESOE8NAR4De+esszM5ptZIUQqRETLaFpOXkCTCKgXTVnqn3DDhzLXxd/ycvC+uJXfuyuOTRFRkiRJHWnUDHSxmQ0tPxGlHK+VnwK+ZGY3Vxx3Ee5/CzRzJhqHdzeZIGkkcEpksj39MOsbS7iEY9H2+FLsdEnr4O5Aw/CAPgVfNgY3c3/EzLaM60zG1cYD8eBcMIMmx6A3zOwncf8imsp4XqYpEI7FnZcKnqTpR8AyYK3IvIfhmfL7qSBFREmSJPWjUTPQWrgZOCECIJK2ktQHN23/fCGqkbRRC+//VOlvZUkMNDd5L4LlC3gQG4Yv/YIHxV5V3j8N2EdSf7nP7WGs3PR9IZF1mtk8vNfouq0c/168yfaWrCiQSpIkSepIo2agtTAWX8a8L0Q684GDzOwmSUOB6ZKWAn/GRTuVbCjpQTwgHlbl9XG4yftifIn3JTxD7IU7/3xR0u34MvPIWEqeRSzHmtm8qPuchAuNegBHhxduL7k/71fwZd3pcc2ngT0lTcdFT08CiyMAbwjcGvcLZe9AXFg1GA/k/9PSh5V7oEmSrInUU2sgM1v5UasZcnP3YW0wcSi/91V8mfVBYEfcSamvmZ1doZpF0quxNDwCb4S9Dd4N5nFgrJl9O1TA74vWZ+Nw0dQH8XKaSXh2eSSwcdSQlo3uN8P3Smea2Z5Vxlo2k99l0xMubet0kyRJujWrGkAlzTCzYdVe685LuF2GmS3C3ZC+3Ia33Wtm88xsCfAYbisInrUOKh33U+BdZjYXD7SF0X2lYGpwHD8Ht/2rNs5fm9kwMxvWo/f6bRhqkiRJsjK68xJuuzGzQR1wmgvxFmbltO4t4kdJ+NmW90bLKuDlNDeXL38P88zszvJwaVkwNQJ41sxmshKGDFyfFBElSZJ0HGtkAO0IzOxFSdfg3VB+G08/AeyC12OOwus920rZ6H5zvB60EEzdUWF0XzMzZsx4Vd6MfHWhP03mGasDOZ/GJufT2NRzPpu19EIG0FXjPJr7814CXC/pAdz84bWq72qdJ3EFbz/geDN7I/x1B1EhmGrjeR9paR2/OyJpes6nccn5NDY5nw667pooIloTyX8wjU3Op7HJ+TQ2XTWfFBElSZIkSTvIALrm8OuuHkAHk/NpbHI+jU3OpwPIJdwkSZIkaQeZgSZJkiRJO8gAupoh6SOSHpH0qKTTqry+tqSr4/WpkgZ1/ihrp4b57C3pvuifOrorxtgWapjP1yXNlvSgpNsltSihbwRqmM/xkmZJminpb5K27Ypx1srK5lM67hBJJqmhhTg1fD9HSZof389MSWO6Ypy1Usv3I+mT8W/oYUlX1nVAZpa31eSG++0+hteP9gIeALatOOaLwMVx/9PA1V097lWczyBgB9wZanRXj7kD5rMv0Dvun7AafD/9SvdHATd19bhXZT5x3HrAnXjXpWFdPe5V/H6Ownsdd/l4O2g+g/HeyxvG443rOabMQFcvhgOPmtnjZrYUbwZ+YMUxBwKXxf0JePNvdeIY28JK52NmT5jZg7ijU6NTy3wmWVMv23vwdnmNSi3zWVR62Ad31mpUavn3A97A4Uc0tSdsVGqdT3ehlvkcC/zCzF4CMLPn6zmgDKCrFwOBp0qPn47nqh5j3uh7Ie6t24jUMp/uRFvncwzwl7qOaNWoaT6STpT0GHAubfOP7mxWOh9JOwPvMbPu0Nqo1v/eDoktgwmS3tM5Q2sXtcxnK2ArSXdLukfSR+o5oAygSdKASDoC7zv7464ey6piZr8wsy2AU4Ezu3o87SX8rc8HvtHVY+lAbgAGmdkOeC/ly1ZyfKOzFr6MOwJvU3mJpA3qdbEMoKsX/wbKvyA3ZUXP3LePkbQWsD6woFNG13ZqmU93oqb5SNofOAMYZd69p1Fp6/dzFW23oOxMVjaf9YDtgcnREnF3YGIDC4lW+v2Y2YLSf2NjcS/vRqWW/96eBiaa2Ztm9k/gHzR1rupwMoCuXtwLDJb0Pkm9cJHQxIpjJgKfi/ujgTssdtsbkFrm051Y6Xwk7QT8Cg+edd2/6QBqmU/5f17/BcztxPG1lVbnY2YLzay/mQ0y7+h0D/49Te+a4a6UWr6fAaWHo/D2iI1KLf8/+COefSKpP76k+3jdRtTVyqq8dewN+Bj+q+sx4Ix47rv4P3SAdYBrgUdx0/rNu3rMqzifXfFfna/hmfTDXT3mVZzPbcBzwMy4TezqMa/ifH4KPBxzmQRs19VjXpX5VBw7mQZW4db4/ZwT388D8f1s3dVjXsX5CF9mn433Wv50PceTTkRJkiRJ0g5yCTdJkiRJ2kEG0CRJkiRpBxlAkyRJkqQdZABNkiRJknaQATRJkiRJ2kEG0CRJkiRpBxlAkyRJkqQdZABNkiRJknbw//DNyrwl5YGCAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " ## users[users!=0]\n",
        "missing_percentages[missing_percentages !=0].plot(kind='barh')\n",
        "#   missing_percentages = df.isna().sum().sort_values(ascending=False) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "nAkaFwce1_f1",
        "outputId": "2b6a8b4f-7dbe-491c-cf1c-cd98d21f2384"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f372d37acd0>"
            ]
          },
          "metadata": {},
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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+fKqkw3J836wKxy0+p6RNc9tmSvqBpHmFov0lXS/pz5KuUHI0sD4wTtK4CnVHlF8IIbRJX+xAtyYl+9RyDfAZAKWM24G2uzpFdQKwa34FZQFLnpXuCtxVZx0/Iz1T3Qb4W9m+7UmjzS2B9wA72z4HeBbY3fbu5ZVFlF8IIbRPX+xA63EtS26nfgao+Wy0ThNICUI7AzeTRoxrApuUJwjVsCNwXf58Zdm+Sbb/ZnsRMI0l0X8hhBB6QF/sQGezJKWoItvPAC/lW64H0JqFrCeT8nBLI84HgcPofDRcr2oRhXWJKL8QQmitvtiB3gGsJunw0obcUW5UVu4a4JvAOrZndPWktt8AngY+DdxHGpEeR/23bwHuZ0k04YF1HhNxfiGE0AP6XAfqFK20P7BHfo1lNnAa8PeyoteTOqlrW3j6CcDztl/PnzfMP+t1LPB1STOAzYB6Zv6MJi3BtswkohBCCO0TUX69SH5m+rptSzoQOMj2vq2oO6L8QgihcbWi/Prce6DLuWHAefld1leAL/Zwe0IIIVQRHWgnujO+z/YEYLtW1wsR5RdCCK3W489AJVnSmYXvx0k6pcm61pX0P4Xvi+PzmqjrUkkjS/F9xT/APjm676+S5hai/HaqUV+HpHPy52J4wpGSDumkLVUDHiR9p5nrCyGE0DU93oGSXs/4pKQBLahrXWBxB2r72c7i+5phe//ckX4JmFDoXO+tccwU20dX2H6+7cu70JzoQEMIoQf0hg50AWkm6dfKd0j6hKSJORrvT5LembcvFfQuaVbOpT0d2DSPBs8oxudJ6ifpJ7nsDElH5e0nS5qct4/Ozx8bluP31s0Rey+VRpWSLpf0kWqB8MVrkTQ8t63U/mL03/qSbpH0F0k/zuVPB9bI5a+oUHdE+YUQQpv0hg4U4OfAwTkGr+hu4AO2tweuJr23WcsJwGN5NHh82b7DSek9Q2xvC5Q6nPNsD7e9NbAGsHeT13AP6VnpVsDjpEAFSOlCVUemZS4Bjsij24Vl+4aQQh+2AQ6QtJHtE0izdofYPri8sojyCyGE9ukVHajtfwGXA+W3ODcExkqaCRxP6pyatQdwge0F+Zwv5+2751HuTOBDXThHKcpvN+CXwDaSNgD+afvfnR0saV1gbdv35U3lUX63255r+z/AQ8C7m2xnCCGEFugVHWh2NnAosFZh27mkEeI2wBHA6nn7ApZu++o0QdLqwC+AkfkcFzZbFylxaNf8ZzzwAilvt5EghVoiyi+EEHqRXtOB5hHhtaROtGQd4Jn8+fOF7XOAoQCShgKb5O21Yu1uA46QtHI+7m0s6SxflNSfCut1NtD+p4EBwGDbj5NuP9cd5Wf7FeBVSe/Pm+qN8ntT0iqNtjeEEELX9JoONDuT1AmVnAJcJ2kq8GJh+w3A23JM31eBRwFsvwTckycEnVFW90XAU8AMSdOB/86d1oXALGAsKRC+KyaW2kIaeW5A6kjrdShwoaRppJF4vVF+MypNIgohhNA+EeXXi0jqb3te/nwCaZ3SY1pRd0T5hRBC4yLKb/mxl6Rvk/5engRG9WxzQgghVBMj0AJJbwduz1/fRZqssxbpueo/gb/lfd+yPbb7W9i81QYO9vzn/tLTzQghhOVKjEDrlJ+hDoEUcADMs/2THm1UCCGEXqm3TSLqlYopQjk56DJJEyQ9KemTkn6ck4huKc2IlTRM0p2SpkoaK2lgzuadVvizUNK7c2LSHTmF6HZJG+c6LpV0jqR7JT0uaWShTcfnBKUZkr7XM7+ZEEJYcUUH2pxNSaEL+wC/Bsbl90hfJz3HXIX0DutI28OAi4Ef5mzeUiD9hcANtp/MZS8rJCSdUzjXQGAXUkLS6QCS9gQGAzuQRszDJO1W3siI8gshhPaJW7jN+aPtN3N6UT/glrx9Jiku8L3A1sBtOVq3H/Bc6WBJOwOHkTpGSHF/n8yffwX8uHCu39peBDxUygIG9sx/Hszf+5M61KXeObU9mvSaC6sNHBwPu0MIoYWiA23OfADbiyS96SUzsRaRfqcCZtvesfxASQOB/wP2Kb2yUs+5SocXfp5m+4JmLyCEEELXxC3c9ngEWE/SjgCSVpG0Vb61ex1pFu+jhfL3siR56GA6j/8bC3wxpychaQNJ76h1QET5hRBCa8UItA1sv5En/JyTV5hZmZT1OwDoAL5XmPjzceAo4BJJx5MydL/QSf23SnofcF++RTwP+CzwfDuuJ4QQwrLiPdAVRCQRhRBC42q9Bxq3cEMIIYQmRAcaQgghNCE60BXEzGfmMuiEm3u6GSGE0Gf0yQ5U0n6SLGmL/H19Sde38XzHSlqzkzL9JV0g6bGcTjS+sPZnPec4RdJxXW9tCCGEVuiTHShwEGkdzoMAcgLQMotllxbX7gpJ/YBjgZodKGk90pdJC24PI820HVD7kBBCCL1Vn+tA87uRu5AWpz4wbxskaVb+PErSGEl3ALfnnNu7JN0s6RFJ50taKZc9KGfczpL0o8I55kk6My/MfSKwPjBO0rgqbdoUeD9wUk4VwvYTtm/O+7+ezzFL0rGF406U9Kiku0npRovry7m7U3Mm7xZVzhtRfiGE0CZ98T3QfYFbbD8q6SVJw4CXysoMBba1/bKkEaRM2S1Ja3DeAnxS0r3Aj4BhpKXMbpW0n+3fkpY4m2j7GwCSvgjsbvvFKm3aCphme2H5jty+L5A6WAETJd1J+sfNgaSs25WBB4Cp+bDRwJG2/5JvA/+ClM27lIjyCyGE9umLHehBwM/y56vz9/PKytxm++XC90m2HweQdBVpBPsmMN72C3n7FcBuwG9J64Te0KL27gLcaPvf+Ty/AXYldaA32n4tbx+Tf/YHdgKuyyEKAKt1dpJtNliHKafv1aImhxBC6FMdqKS3kUZi20gyKcTdwM/Liv677Hv56Kyz0dp/Ko0ma5gNbCepX4PHVbIS8Epe0SWEEEIP6WvPQEcCv7L9btuDbG8EPAFs1MlxO0jaJD/7PIA0AWkS8EFJA/JEoYOAO6sc/yqwdrXKbT8GTCFF+AkWP5fdi5R7u5+kNSWtBeyft92Vt68haW3gE7mufwFPSPp0rkeStuvsFxNCCKG1+loHehBwY9m2G4Bvd3LcZNJt3odJHe6Ntp8DTgDGAdOBqbZ/V+X40cAt1SYRZV8C3gn8NU9ouhR43vYD+fMkYCJwke0H8/Zr8rn/mNtYcjBwaJ7ENJv03DeEEEI3WuGzcPMkouNs793TbWmnyMINIYTGRRZuCCGE0GJNTSKSdBbwpO2z8/exwNO2v5S/nwk8Y/unDdS5H/Co7Yfy9/GkkWFLhk2S3g7cnr++izST9oX8/XBJ59g+ugXnmciys2J/Bjxi+96u1t+seqL85sQs3RBCqFuzs3DvAT4DnJ0n3gwA3lLYvxPwtQbr3A+4CXioyTYtJmll2wuK22y/RHqnEkmnAPNs/6RQZFJXz5vPs0w8Xz7f20kLZ4cQQugDmr2Fey+wY/68FTALeFXSWyWtBrwPsKQ7c1rOWEkDASQdJmmypOmSbsizT3cC9gHOkDQtJ/cAfFrSpJzGs2s+vp+kM3IdMyQdkbePyKk8Y2iwE87H3pQ/nyLpslzXk5I+KenHOZHoFkmr5HLDqlzf0ZIeym27WtIg4Ejga/nadpX0CUkTJT0o6U+S3tnguecUtk+StFnjf4UhhBC6oqkO1PazwAJJG5NGm/eRZpDuCHSQZrOeBYzMua8XAz/Mh//G9nDb2+Vyh+Zbm2OA420Pya99AKxsewdS1uz/5m2HAnNtDweGA4dJ2iTvGwocY3vzZq6rYFPS+6T7AL8GxtneBngd2Ct3ZOdWub4TgO1tb0tKC5oDnA+cla9tAuk1mQ/Y3p4U9vDNes9dKDc3bz8POLvSRSii/EIIoW26EqRwL6nz3An4KbBB/jwXeAbYE7gtv/bYD3guH7e1pB8A6wL9gbE1zvGb/HMqMCh/3hPYVlIpHH4dYDDwBilR6IkuXFPJH22/KWlmbvstefvM3I73AltT+fpmAFdI+i0ptaiSDYFr8qh1VdKrM/Weu+Sqws+zKp0kovxCCKF9utKB3kPqMLch3cJ9GvgG8C9gPLCB7R0rHHcpsJ/t6ZJGASNqnGN+/rmw0FYBR9lequPNr6OUJww1az6A7UWS3vSSd30W5XYImF3l+vYiRf59AjhR0jYVypwL/NT2mNzuUxo4d4mrfK4oovxCCKG1uvIay73A3sDLthfmbNl1SbdxrwLWk7QjgKRVJG2Vj1sbeC7fBj24UF/NNJ+CscCXC88DN88JPt3pESpcX55QtZHtccC3SKPj/ix7beuQRukAn2+yDQcUft7XZB0hhBCa1JUR6EzS7Nsry7b1t/18vsV6jqR18nnOJqXmfJf0vPSF/LPUsVwNXCjpaFIkXzUXkW5lPqB0//QF0gzebmP7jSrX9yjw67xNwDm2X5H0e+B6SfsCR5FGnNdJ+idwB7BJpfN04q2SZpBGrAd1+aJCCCE0ZIVPIloeSZoDdNRYPm0ZkUQUQgiNUyQRhRBCCK3Vp5YzKylLHSr6cA5UWK7ZHtTTbQghhBVdn7+FK2kh6dnsyqT3Tj9fWqS6t5G0PfBV24dK2gfY0vbpNcqvR1q+7b86q3u1gYM98PMVXxddRkT6hRBCsqLfwn09BxhsTXpX9MjiTkndNgqv41zfAc4BsD2mVueZy7xAmtG8c4uaGEIIoU4rQgdaNAHYrDz2r0Y84EBJd+UIvlk5hq+fpEvz95mSvpbLjpfUkT8PyBN9kDRK0hhJdwC3S1pL0sU5gu/BPDMXpUWzt7U9vXDcefnzpZLOkXSvpMcLIRKQwhqKrwOFEELoBn3yGWglefT3MZYk+wwFtrb9hKTDyfGASlm+90i6FfgkMNb2DyX1A9YkBdJvkEe0SFq3jtMPJXWOL0v6f8Adtr+Yj50k6U+kCMRZNeoYCOwCbEGKPbw+b58C/KDKNR8OHA7Q7y3r1dHMEEII9VoROtA1JE3LnycA/0dKUCrG/lWLB5wMXJxDG35re5qkx4H3SDoXuBm4tY423JaDJkrn2kfScfn76sDGpA7yhUoHZ7+1vYg0Yn5nYfvzwPqVDogovxBCaJ8VoQN93faQ4oacX1uM/asYD5jL7kaK57tU0k9tXy5pO+CjpOepnwG+CCxgyS3x1cuqKT/Xp2w/UnaeLSocVzS/8FmFz6uTguZriii/EEJorRXtGWg1FeMBJb0b+IftC0kJSEMlDQBWsn0DcBLp9izAHGBY/lwrSWkscFROUSrNvIU0Q7iZZck2p/at3xBCCG2wIoxA61EtHnAEcLykN4F5wCGkVWcuybm3AN/OP38CXJufO95c41zfJ8X+zch1PAHsbfvPktaRtLbtVxto++6dnC+EEEIb9Pn3QJcneUbvq7YvauCYu4B9bf+zVrmI8gshhMat6O+BLk9+ydLPOmvKQQo/7azzDCGE0Hq9ugOV9Pb8DuY0SX+X9Ezh+6o93b6i/G7pTl2pw/Z/bP+qUOdASTcV6p9buP4/5SCFDSV9sYvNDyGE0KBe/Qw059YOAZB0CjDP9k96qj2SVra9oMruEaTnpPe2qD6ArwMXFr5PsL13WZmLSYubX1zrXDOfmcugExp7VBqRfiGEUF2vHoFWImmYpDslTZU0VtLAvH28pLMkTZH0sKThkn4j6S+SfpDLDJL0Z0lX5DLXS1qzjnrPljQFOEbSJyRNzClCf5L0TkmDSK+0fC2PDnfN6UEjC+2el3/WlYKUfYolwQ8V5VzfOZJ2aNGvOIQQQh2Wtw5UwLnASNvDSKOuHxb2v5Ef9p4P/A74CrA1MEpphRaA9wK/sP0+4F/A/+TXV2rVu6rtDttnAncDH7C9PWkR8G/anpPPeVbO3Z3QyXUMBY6xvTlwKDkFCRgOHCZpE0mbAP+0XXwmumvhFu6Jhe1TgF2X+WVJh+d/UExZ+NrcTpoUQgihEb36Fm4Fq5E6xNvya5T9gOcK+8fknzOB2bafA8jpQRsBrwBP274nl/s1cDRplFer3msKnzcErskj1FVJr6E0qp4UpHksm0xU6RYupDSiLco3RhJRCCG0z/LWgYrUMe5YZX9ptLaIpWezLmLJtZZ3JK6j3mKS0Lmkma9jJI0AThTZEYIAABuFSURBVKlyzOJkovy+Z3HSU6cpSDlgoVYyUVFdaUQhhBBaZ3nrQOcD60na0fZ9+dbr5rZnN1DHxqXjgf8m3ZJ9pIF61wGeyZ8/X9j+KvCWwvc5pGSia4F9gFWqtKeUgnSH7TclbZ7rf5QU7lCPzUkTiaqKKL8QQmit5e0Z6CJSTN6PJE0HppGC4RvxCPAVSQ8DbwV+afuNBuo9BbhO0lTgxcL23wP7lyYRkWbPfjDXtyNLjzqLLgIeIqUgzQIuAFa2/W/gMUn1xPvtDNxWR7kQQggtskIlEeXZsjeVliLr7STtDwyzfVKNMtsDX7f9uVp1RRJRCCE0rlYS0fJ2C3eFYvvGwuzhagYA3+2O9oQQQlhihepA8+smy8Xos6SzXFzbces2hBB6wPL2DLQuksZJ+mjZtmMlPSHphBrHdUg6J38+RUsWvS6WWV/S9fnziELU3j6luiXtJ2nLOtt6vaT3NHBtxTbuLenUeo8NIYTQOn11BHoVcCBphmvJgcDnbd9V7SDbU0ihBFXZfpYK633aHsOS91D3A24iTQ6qStJWQD/bj9cqV6ONNwPfl3R6TiSqKqL8QgihtfrkCBS4HtirFDifJw+tD2wq6by87dOSZkmarrQk2FIjymw7SfflOMDDSnXl2bJLkTRK0nlKgfL7AGfkGbmbSnqgUG5w4fvBpMSk0r55OdZvdo4J3CFHCT4uaZ/yNjrNABsPVApXCCGE0EZ9sgO1/TIwCfhY3nQg6X3M4pTjk4GP2t6O1OFVsi3wIdJrKCdLWr+Oc99LGoken2P9HgPmShqSi3wBuCR/3hmYWjh8LeAO21uR3iv9AfARYH+g2q3aijF+EFF+IYTQTn2yA81Kt3HJP68q238PcGkeWfarUsfvbL9u+0VgHNBsYPtFwBck9QMOAK7M2weydFzfGywJj58J3Gn7zfx5UJW6nyeNrpdhe3TO8O3ot+Y6TTY9hBBCJX25A/0d8GFJQ4E1bRdHetg+EjiJlJE7tcrrIpVi/5pxA2k0vDcwNS/TBil+rxjX96aXvJi7OI7QdjGKsFzE+IUQQg/oq5OIsD1P0jjSyirlo08kbWp7IjBR0sdIHWm5fSWdRrq1OgI4gaUzbat5FVi70Jb/SBoL/JK0+krJw8BmpNi/Zm0OLPNMtlxE+YUQQmv15REopI5zOyp0oKRJPjPzhKB7gekVyswg3bq9H/h+noFbj6uB45XWDN00b7uCNKq8tVDuZlLH3BW753pCCCF0oxUqyq8n5XdK17H93cK2NUgd9M62FzZR5zuBK21/uLOyEeUXQgiNiyi/HibpRmBT0ozexWy/Lul/gQ2Ap5qoemPgG11vYQghhEZFB9oNbO9fY9/YavvqqHdys8eGEELomuhAVxCRRBRCCK3VlklEks6SdGzh+1hJFxW+nynp6w3WuVS+bE7oqXhfuiskvUvS1ZIekzRV0h/yItddrbeu3FxJp0rao6vnCyGE0F7tmoV7D3lBakkrkZbc2qqwfyfSzNdG7AfUFdDeGUkVR96SBNwIjLe9qe1hwLeBd7bivCW2x9g+PX9d6rpsn2z7T608XwghhNZrVwd6Lyn+DlLHOQt4VdJbJa0GvA+wpDvzKG+spIEAkg6TNDln1N4gac1K+bK57k9LmiTpUUm75uP75TzZyZJmSDoibx8haYKkMVQPed+dFGZwfmmD7em2Jyg5I+fnzpR0QKHe8UqrqvxZ0hW5I0bSf+VtDwCfLNXZSW7upZJG5nIfzq/CzJR0cf7dIWmOpO9JeiDv26LSxUSUXwghtE9bOtD8vuQCSRuTRpv3ARNJnWoHKUDgLGBkHuVdDPwwH/4b28NzRu3DwKFV8mUBVra9A3As8L9526HAXNvDgeHAYZI2yfuGAsfYrnZLdmuWzqYt+iQwhPRe6R6kTm9g3rd9bsOWwHuAnSWtDlwIfAIYBryrwu+p2nWRj78UOMD2NqTn1V8uHP6i7aGkcIZlll3L9UeUXwghtEk7gxTuJXWepQ70vsL3Z0id1W2SppEi9TbMx22dR4ozSauVbFVeccFv8s+pLMmK3RM4JNc7EXg7MDjvm2T7iSavZxfgKtsLbf8DuJPUQZfq/VuO3JuW27IF8ITtv+R4vl83eL735uMfzd8vA3Yr7K907SGEELpJO2fhlp6DbkO6hfs06Z3Ff5GW4NrA9o4VjrsU2M/2dEmjqJ3UMz//XMiSaxFwVPnrIZJGAP/upM2zqbDWZx3mFz4X29JOla69qojyCyGE1mr3CHRv4OU8ansZWJd0G/cqYD1JOwJIWkVpcWlIGbLPSVqFNAItWSpftoaxwJfz8UjaXNJadbb5DmA1SYeXNkjaNj9fnQAckJ+xrkcaDU6qUdefgUGF57UHVSlX7boeycdvlr9/jjTqDSGE0Au0swOdSZp9e3/Ztrm2nyeN9H4kaTrptudOucx3Sbde7yF1QiWV8mUruYg0SegBpZzbC6hzRJhvte4P7JFfY5kNnAb8nTQ7dwYpM/cO4Ju2/16jrv8AhwM350lEz1cpWvG68vFfAK7Lt7MXAedXqSOEEEI3iyzcFURk4YYQQuNUIwu3r6/GEkIIIbRFn43ykzTPdv/C91FAh+2vKi2efXuFwz5cWOy6Vt1HAq/Zvrxs+yDgJttb55SkQ2wfnScwvZFfW+ms7rNJr/LcJekK0iSsm2x/J+8/CZhl+7f5+97ADrZPrlVvM1F+EHF+IYRQTZ/tQGvJneSQLhzf6bNI21OA0j3TEcA8Oklfyh37B2wfK2lb4HXb20q6TdI6wJrA+23/oHDYzcD3JZ1u+7UmLieEEEITVshbuMW0n/x9Xv45Iqcj/U7S45JOl3RwTjuaWZrkI+kUpfU9kTQspyZNB75SqHOEpJvyqPRI4Gs5bWhXSU8UZgm/pfD9U8AtuYo3gTWUohBXIb2ucipLAiOAxROfxpNmPIcQQugmfbkDXSN3WNNyqMKpdR63HanDex/p1ZHNc9rRRcBRFcpfQnrvdLtKldmeQ5o9e1ZOG5pA6vBK90YPJN2yfRPYmZyEZPth4AXgAeD3wGbASrYfqHCaKcCu5Rsjyi+EENqnL9/Cfd324tu0pWegdRw32fZz+ZjHgFvz9pmkrNzFJK0LrGv7rrzpV8DH6jjHRcA3gd+SXlU5LG8fSOo0AbBdXNHm98ARkk4kdfK32b4w734eWL/8JLZHA6MBVhs4OKZbhxBCC/XlEWgtC8jXnm+RrlrYV0wVWlT4vogW/YPD9j2kkIQRQD/bs/Ku14HVy8tL2pc0Mu0PbGr7M8BISWvmIqvnY0MIIXSTvjwCrWUOKeD9WtJqKKs0U4ntVyS9ImkX23ezdHJS0avAW8q2XQ5cCXy/sO1h0q3a8aUN+dnosaRbvoOB0kiyH6njfw3YnBSXWFVE+YUQQmutqCPQC4EP5ok/O9J5Rm4tXwB+np+zqkqZ3wP7lyYR5W1XAG8lxRqW3Myy2b9fAS7LM2xnAGvmZKKptl/JZXbPx4YQQugmkUTUQ/Is4H1tf65s+93A3oXOsbN63glcafvDtcpFElEIITSuVhLRinoLt0dJOpc02ejjFXZ/A9gYqKsDzWW/0aKmhRBCqFN0oD3AdqXXYUr7JjZY1+SutyiEEEKjogNdQUSUXwghtFavn0Qk6SxJxfchx0q6qPD9TEknSzqhwXqXSiOqsH+8pEckzZD0Z0nn5fc+S/s7zbWtsx2jJK1f+H6RpC1bUXcIIYT26fUdKGld0J1g8TubA4CtCvt3Am61fXobzn2w7W2BbUnvg/6utMP2TuWFJTUzoh9FIQTB9pdsP9REPSGEELrR8tCB3kt61QRSxzkLeFXSWyWtRorc21bSebB4ZHmOpHtznu3IvF15FPmIpD8B76i3AbbfICUHbSxpu1xfMT93gqQxwEOS+kk6Q9LkPHo9olSPpG/lTN3pOWd3JCkd6Yr8issaeeTbkcsflMvPkvSjQj3zJP0w13N/nom7jIjyCyGE9un1HajtZ4EFkjYmjTbvAyaSOtUOUsTeG2WHDQR2IQWsl0am+wPvBbYEDsl1NdKOhcB0YIsKu4cCx9jeHDgUmGt7ODAcOEzSJpI+BuxLWk1lO+DHtq8n5dgenHNyF6cJ5du6PwI+RFo5Zrik/fLutYD7cz13sSQKsLzNo2132O7ot+Y6jVxuCCGETvT6DjS7l9ThlTrQ+wrf76lQ/re2F+VboaXR2W7AVbYX5k75jibaUS0oYZLtJ/LnPYFDcrDCRODtpAShPYBLSkuO2X65k3MNB8bbfsH2AlLwwm553xvATfnzVGBQE9cSQgihC5aXWbil56DbkG7hPk169/FfpNVQ3lZWvphnW63Ta4ikfvn8D1fYXUwyEml1lrFlx3+0Fe3I3vSSBIyF1PH3GFF+IYTQWsvTCHRv4OU8gnwZWJd0G7fe2bB3AQfkZ5QDKVtZpZacR3sa8LTtGZ0UHwt8ubDe5+aS1gJuA75QCoCXVOr0XwXWrlDPJFLc4IDceR8E3Flvm0MIIbTX8jICnUmafXtl2bb+tl+U6hpk3kh6nvgQ8BTpNnBnrpA0H1gN+BPpGWZnLiLdUn1AqWEvAPvZvkXSEGCKpDeAPwDfAS4Fzpf0OksmS2H7ufxqzjjSqPZm278jhBBCrxBZuCuIyMINIYTG1crCXV5u4YYQQgi9Sttv4Uo6C3jS9tn5+1jSs8Qv5e9nAnOBNxoJQ5B0KXBTfhWk0v69SWttrkRa7/Nnti+oUO5GYJOyzd8qnwRUR3tOAebZ/kn+fjbwG9t3NVJPlbrn2e5fYftXgddsX9xZHc1G+XVFxACGEPqy7ngGeg/wGeDsQpJQcXHpnYCv2b6/VSfME3hGAzvY/lsOXBhUqazt/Vt13sL53w58wPaxnRbumotJv99OO9AQQgit1R23cHsiSWht0j8OXgKwPd/2I4X6z88JPY/mkSqdJAgdX9j+vcL2E3Mdd5NCGko+BdxSKDdH0mk5bWiKpKFKmb6PSToylxkh6S5JN+drPD//g6NUxzLJQ/md0jmSdqj/ryOEEEIrtL0D7YkkofyayxjgSUlXSTq42BmRRqM7AHuRZsCuTvUEoT1JQQg7kBKBhknaTdIw4MC87eP5mJKdSQEHRU/ZHgJMIM28HQl8APheocwOwFH5GjcFPpm310oemgLsWun3EFF+IYTQPt31GksxSeinwAb581xqJAmRsmWXSRICnpVUM0nI9pckbUNKADoO+AgpuB3g2lz/XyQ9Torn25M0Ei6t0LIOqePcM/95MG/vn7evDdxYShZSysItGUh6faWotL/0+s2rpJH4fC1Z5WWS7cdzfVeR/hFxPcsmD32kUO/zVI4XxPZo0q1sVhs4OKZbhxBCC3VXB9ojSUK2ZwIzJf0KeIIlHWh5Z2JqJwidVj4BSYUl1ip4HVi9bFvpmhax9PUtYsnfQ6V2Qe3kodXz+WqKJKIQQmit7nqNpVuThCT1lzSisGkI8GTh+6clrSRpU+A9wCNUTxAaC3xRUv+8fQNJ78jt2U9pBZW1gU8U6n8Y2KzO6yraId82Xgk4ALi7jmM2J/2jJIQQQjfqrhFodycJCfimpAtIo7N/s2T0ST5+Emk28JG2/6O0SPcglk0QulXS+4D7cjvnAZ+1/YCka0grtDwPTC7UfzNwBCmVqBGTgfNIne+4fM2d2Rk4pcHzhBBC6KIVLomos/dHW3ieu4G9bb9SZ/kRwHG2927gHNsDX7f9uc7KRhJRCCE0LpKIesY3gI3bfI4BwHfbfI4QQggVLC9h8lU1miRke1TbG5XOM7HB8uOB8Q0ec1sj5UMIIbTOct+BlpKEejAycBVSZOCnSEuTzQdOtf3HGhF8R5Ii+C4v1i9pPOk27pT8HPZ20nPYf0laSHpuXLIf6VWab9Tzj4KeiPJbEURcYQgrruW+Ay3o9sjA7Puk9z63tj0/v7f6wVoH2D6/jno/Dky3/a/8/fUcxLAUSRtK2tj2U402PIQQQvP60jPQbo8MVFoc+zDS+6PzAWz/w/a1hTLLRPBJOkXScZ1cz8FAPet//p6UiBRCCKEb9ZkOtCciA0mvmzxVGCWWqxXB15nyOMA1cpbutPzctySi/EIIoQf0pVu40AORgZ2oFcHXmbfluL+SirdwSe+grl+pgojyCyGE9ulrHWh3Rwb+FdhY0luqjEJrRfB1ZoGklXIHX0tE+YUQQg/oM7dws26NDMxB8v8H/EzSqgCS1pP06a5cRPYIKWawMxHlF0IIPaCvdaClyMD7y7bNtf1inXXcCPyFFBl4ObUjAwFOIsX+PSRpFumWbbVnoo24GRhRR7ndc9kQQgjdaIWL8lte5NHv5barPjfNs4vvBHaxvaBWfRHlF0IIjYsov+WQ7eeACyW9pUaxjYETOus8QwghtF6v7EAlLcyva8ySdF1+37KrdZ4qaY9Oyhwp6ZD8eZSk9Qv7biy8RlL689EK5S6StGUX2nlsqQ3A1sAO1cra/gvQX9KpzZ4vhBBCc3rlLdxiBJ6kK4Cptn9a2L9yu0ddxVi9VpSr85wrAw8AQ+u9vhz59wCwc57UVNFqAwd74OfP7moTQwtFDGAIvd/yfgt3ArCZpBGSJkgaQ5qw00/SGZImS5oh6YjSAZK+JWlmTgA6PW+7tJA2NEfSj3OZSZI2y9tPkXRcLtcBXJFHmmtIOjmfa5ak0TmxqFK58ZI6cn0H5XPMkvSjQvvmVUooIq13+kCp86zQ5u9JeiDXuQVAfk1mPGn2cQghhG7SqzvQPCL7GEtC1IcCx9jeHDiUNLt2ODAcOEzSJpI+BuwLvD8nAP24SvVzbW9DWsB6qaFZDo6fAhxse4jt14HzbA+3vTWwBmmtz0rlSm1fH/gRqVMcAgyXtF/eXS2hqDx9qNyLtocCvwSKUYAV04giiSiEENqnt3aga0iaRuoYniK9awkwyfYT+fOewCG53ETg7cBgYA/gktLtzPwuaCVXFX7uWKVM0e6SJkqaSeoUt+qk/HBgvO0X8ojyClLKESybUDQofx5IeiWmmt9UOAaqpBHZHm27w3ZHvzXX6aS5IYQQGtFbk4iWia1Lj/r4d3ETKcR9bFm5j9Z5Dlf5vAxJqwO/ADpsPy3pFFICULOqJRS93km9peSk8lSjutKIQgghtE5v7UDrMRb4sqQ7bL8paXPgGeA24GRJV9h+TdLbqoxCDyAFyB9A5bCEV0nrbcKSTu1FSf2BkcD1FcoVTQLOkTQA+CdwEHBuJ9f0MCmgvlGdphFFlF8IIbTW8tyBXkS6jflAnon6Amnx6VskDQGmSHoD+APwnQrHv1XSDNKo7qAK+y8Fzpf0OukW74WkTurvwOQa5YD0HqekE4BxpNHyzbY7W57sj8CvOilTye7At5s4LoQQQpN65Wss7SZpDul2bL3xft1Gaamyb+Z3POsp/07gStsfrlUukohCCKFxy/trLCuaE0iTieq1MWnFmRBCCN1oeb6F2zTbg3q6DdXYfoS0Eku95Sd3XiqEEEKrxQi0QZIs6czC9+PyrNxW1L04OCGEEELvtkKOQLtoPvBJSaf1pmeoncUbznxmLoNOiFXPQggrlnZGZsYItHELgNHA18p3lI8gJc3LP0dIulPS7yQ9Lul0SQfnGMGZkjYtVLNHTg96VNLe+fiKsYXl8YbtvOgQQghLixFoc34OzJBULSawku2A9wEvA48DF9neQdIxwFHAsbncINIKLJsC43JO7yHk2EKlNUDvkXRrLj8U2LqQ0LSYpMOBwwH6vWW9Bi8xhBBCLTECbYLtfwGXA0c3cNhk28/Zng88BpQ6wJksHct3re1F+TWWx4EtqB5bCEvHG5a3M6L8QgihTWIE2ryzScuIXVLYtoD8jxJJKwGrFvbNL3xeVPi+iKX/HspfzDXVYwtHsHS8YQghhG4SHWiTbL8s6VrSqjAX581zgGHAtcA+wCpNVP1pSZcBmwDvIb3SUi22sG4R5RdCCK0Vt3C75kxgQOH7hcAHJU0nxfo1Mzp8ipSj+0fgSNv/IcUWPkSKLZwFXED84yeEEHrUChnltyKKKL8QQmhcrSi/6EBXEJJepYGEo+XAAKDXvIfbAnE9vVtcT+/Wzut5t+2KrzHEbcAVxyPV/hW1PJI0Ja6n94rr6d3ielojnoGGEEIITYgONIQQQmhCdKArjtE93YAWi+vp3eJ6ere4nhaISUQhhBBCE2IEGkIIITQhOtAQQgihCdGB9jGS/kvSI5L+KumECvtXk3RN3j9R0qDub2X96rie3SQ9IGnB8rAYeR3X83VJD+Vl626X9O6eaGe96rieI/OSfdMk3S1py55oZ706u55CuU9JsqRe/SpIHX8/oyS9kP9+pkn6Uk+0s171/P1I+kz+39BsSVe2tUG2408f+QP0I6308h5SkP10YMuyMv8DnJ8/Hwhc09Pt7uL1DAK2Ja2OM7Kn29yC69kdWDN//nIf+Pt5S+HzPsAtPd3urlxPLrc2cBdwP9DR0+3u4t/PKOC8nm5rC69nMPAg8Nb8/R3tbFOMQPuWHYC/2n7c9hvA1cC+ZWX2BS7Ln68HPixJ3djGRnR6Pbbn2J5BWtWmt6vnesbZfi1/vR/YsJvb2Ih6rudfha9rsexqQ71JPf/7Afg+8CPgP93ZuCbUez3Li3qu5zDg57b/CWD7+XY2KDrQvmUD4OnC97/lbRXL2F4AzCWtL9ob1XM9y5NGr+dQ0qICvVVd1yPpK5IeA35MY2vodrdOr0fSUGAj2zd3Z8OaVO9/b5/Kjwyul7RR9zStKfVcz+bA5pLukXS/pP9qZ4OiAw2hF5L0WaADOKOn29JVtn9ue1PgW8BJPd2eZuU1fn8KfKOn29JCvwcG2d4WuI0ld6eWVyuTbuOOAA4CLpS0brtOFh1o3/IMUPwX5IYsu27o4jKSVgbWAV7qltY1rp7rWZ7UdT2S9gBOBPaxPb98fy/S6N/P1cB+bW1R13R2PWsDWwPjJc0BPgCM6cUTiTr9+7H9UuG/sYtI6xn3VvX89/Y3YIztN20/ATxK6lDbIjrQvmUyMFjSJpJWJU0SGlNWZgzw+fx5JHCH89P2Xqie61medHo9krYnrfe6T7uf37RAPddT/D+vvYC/dGP7GlXzemzPtT3A9iDbg0jPqPex3VvXCazn72dg4es+wMPd2L5G1fP/B78ljT6RNIB0S/fxtrWop2dWxZ/W/gE+TvpX12PAiXnbqaT/oQOsDlwH/JW0cPd7errNXbye4aR/df6bNJKe3dNt7uL1/An4BzAt/xnT023u4vX8DJidr2UcsFVPt7kr11NWdjy9eBZunX8/p+W/n+n572eLnm5zF69HpNvsDwEzgQPb2Z6I8gshhBCaELdwQwghhCZEBxpCCCE0ITrQEEIIoQnRgYYQQghNiA40hBBCaEJ0oCGEEEITogMNIYQQmvD/AcL7/NN8b30DAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "remove the columns thats not use in analysis "
      ],
      "metadata": {
        "id": "JinaNDkw9AWq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Analyzing the intrested columns\n",
        "\n",
        "\n",
        "*   city\n",
        "*   tempeature \n",
        "*   start_time\n",
        "*   weather conditions\n",
        "\n"
      ],
      "metadata": {
        "id": "iZyZSj_I9KMk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wt1Uni5i6HCY",
        "outputId": "b7a6b222-14ea-4489-f7b6-e0a23458ff9a"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ID', 'Severity', 'Start_Time', 'End_Time', 'Start_Lat', 'Start_Lng',\n",
              "       'End_Lat', 'End_Lng', 'Distance(mi)', 'Description', 'Number', 'Street',\n",
              "       'Side', 'City', 'County', 'State', 'Zipcode', 'Country', 'Timezone',\n",
              "       'Airport_Code', 'Weather_Timestamp', 'Temperature(F)', 'Wind_Chill(F)',\n",
              "       'Humidity(%)', 'Pressure(in)', 'Visibility(mi)', 'Wind_Direction',\n",
              "       'Wind_Speed(mph)', 'Precipitation(in)', 'Weather_Condition', 'Amenity',\n",
              "       'Bump', 'Crossing', 'Give_Way', 'Junction', 'No_Exit', 'Railway',\n",
              "       'Roundabout', 'Station', 'Stop', 'Traffic_Calming', 'Traffic_Signal',\n",
              "       'Turning_Loop', 'Sunrise_Sunset', 'Civil_Twilight', 'Nautical_Twilight',\n",
              "       'Astronomical_Twilight'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# cities=df.City"
      ],
      "metadata": {
        "id": "heI5KVwNLlYu"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Analyzing the City"
      ],
      "metadata": {
        "id": "an6K8KgqzRi8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cities=df.City.unique()\n"
      ],
      "metadata": {
        "id": "I_AWLF9O846D"
      },
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cities_by_accidents =df.City.value_counts() \n",
        "cities_by_accidents"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Sm5Mt229WOO",
        "outputId": "ee973aff-bb91-427a-b824-2893322e6cae"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Miami                           106966\n",
              "Los Angeles                      68956\n",
              "Orlando                          54691\n",
              "Dallas                           41979\n",
              "Houston                          39448\n",
              "                                 ...  \n",
              "Ridgedale                            1\n",
              "Sekiu                                1\n",
              "Wooldridge                           1\n",
              "Bullock                              1\n",
              "American Fork-Pleasant Grove         1\n",
              "Name: City, Length: 11681, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cities[:100]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "epf53FcUrBAc",
        "outputId": "357488f5-4708-4fc2-8fef-3b72677c2d55"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Dublin', 'Dayton', 'Cincinnati', 'Akron', 'Williamsburg',\n",
              "       'Cleveland', 'Lima', 'Westerville', 'Jamestown', 'Freeport',\n",
              "       'Columbus', 'Toledo', 'Roanoke', 'Ft Mitchell', 'Edinburgh',\n",
              "       'Fairborn', 'Shelbyville', 'Greensburg', 'Saint Paul',\n",
              "       'Parkersburg', 'Indianapolis', 'Dundee', 'Jeffersonville',\n",
              "       'Pittsburgh', 'Lewis Center', 'Dunkirk', 'Redkey', 'Milton',\n",
              "       'Willshire', 'Straughn', 'Cambridge Springs', 'Fremont',\n",
              "       'Louisville', 'South Charleston', 'Edinboro', 'Buckhannon',\n",
              "       'Lockbourne', 'Painesville', 'Washington', 'Dunbar', 'Angola',\n",
              "       'Edon', 'Medina', 'De Mossville', 'New Albany', 'Charleston',\n",
              "       'Fort Wayne', 'Burnsville', 'Bedford', 'Clarksville', 'Lakewood',\n",
              "       'Richfield', 'Sewickley', 'Independence', 'Westlake', 'Erlanger',\n",
              "       'Grove City', 'Monroe', 'West Middlesex', 'Gaston', 'Economy',\n",
              "       'Fairmount', 'Hagerstown', 'Walton', 'Crittenden', 'Coraopolis',\n",
              "       'Holland', 'Greenfield', 'Anderson', 'Englewood', 'Knightstown',\n",
              "       'Bentleyville', 'Memphis', 'Henryville', 'Kendallville', 'Avilla',\n",
              "       'Ohio City', 'Van Wert', 'Rocky River', 'Sturgis', 'West Chester',\n",
              "       'Orient', 'Madison', 'Deputy', 'Keystone', 'Mercer', 'Bryant',\n",
              "       'Pennville', 'Kimbolton', 'Thornville', 'Wexford', 'Fishers',\n",
              "       'Noblesville', 'Macedonia', 'Youngstown', 'Fairdale', 'Sutton',\n",
              "       'Mount Sterling', 'Northwood', 'Huntington'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cities_by_accidents[:20].plot(kind='barh')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "PZs97oveGwbk",
        "outputId": "62a2b721-28f4-47ff-ecd3-ff29ace1da97"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f36da100790>"
            ]
          },
          "metadata": {},
          "execution_count": 94
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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bt26lb9++AHTu3JmNGzdqH9+dPXuWJk2a8OKLL3Lnzh0yMzOpVasWrVu3Zt26dQQEBDx1XrVazWeffcaJEyd47bXXSE9PJzg4+KlHgnnp3LkzmzZtIiAgAJVKxZkzZ2jatCmpqanUqFEDExMTtm/fjkajKfA8aWlppKen4+LiQuvWrQu1hER5jQPKIbFAQgh9UqQ4oLfffjvXKqmzZs0iKCgItVqNRqPB2dlZmyvXokULsrOzAXB2dmbZsmW0adPmqXNaWVmxevVqgoODCQwMJDs7Gx8fH4YNG/bM8YwfP56FCxfSq1cvsrOzcXR0ZM2aNQwZMoT33nuPHTt20KVLl2c+GkxLS2P8+PHagNScJSieReKAhBBCNyQOqJgkDshwSb2Gy5hqBf2sV+KAhBBC6C1pRkIIIcpcqTWj0NBQvLy8UKvV+Pj48NtvvxW4//Llyzlx4kSB+0RFRfHLL7/k+Vl4eDgdOnTAx8cHT09PtmzZUqxxh4eH51pPSQghROkrlfWMYmJiOHLkCNu3b8fCwoKkpCQePnxY4DGTJk165nl/+uknKlSoQOvWrfP83NPTkzlz5nD79m28vLxwdXWlatWqxaqhMMpzHFAOiQUSQuiDUmlGt27dwt7eXvtOz+OZditXruTw4cNkZGTg5OREUFAQKpUKf39/unbtioeHB66urvj6+nL48GGysrL49NNPsbS05Ntvv8XExIRdu3YREBCQ53tLAFWqVOHll18mMTGRkJAQ/vjjDzIyMujRowcTJ04EHr1Iu3XrVhwcHPjjjz9YsmQJGzduLHSN5T0OKIfEAgkh9EGpNKNOnTqxatUqevToQceOHfH09KRdu3bAo2XE/fz8AJg6dSqHDx/G1dX1qXPY29uzfft2wsLCWLduHQsWLGDQoEFUqFCBUaNGFXj9q1evcvXqVV5++WUmT56MnZ0dGo2GN998k7/++ovGjRvrvuhy7FlxIeUpUkQXpF7DZUy1Qvmqt1SaUcWKFQkPD+f06dNERUUxefJkpkyZQp8+fYiKiuKLL74gPT2d5ORkGjRokGcz6t69OwCvvvoqP/zwQ6Guu2fPHqKjo7GwsCAoKAg7Ozu++eYbtmzZQlZWFrdu3eLixYvSjJ7wrKmf+jg9tDRJvYbLmGoF/ay3xHFARWVqakr79u1p3749DRs2ZMeOHXh5eREYGMi2bdt48cUXCQkJ0b5o+iRzc3MATExMnpmgkCPnN6McV69eZd26dWzdupXKlSvj7++vvZ6pqal2zaT8xiCEEOL5KJVm9M8//2BiYkLt2rWBRzFBNWvW1P6jb29vT1paGvv376dHjx6FPm/FihW1Sd6FkZaWhrW1NZUqVeLff//l2LFj2seFL730ErGxsbi4uPD9998Xvrj/r7zHAeWQWCAhhD4olWZ0//59goODSUlJwdTUlFdeeYWgoCBsbW3p378/3t7eVK1alebNmxfpvG+88QYTJ07k4MGDBU5gyNG4cWOaNm1Kz549qVGjRq5ZeH5+fsyaNYvly5fTvn37YtUpcUBCCKEbEgdUTBIHZLikXsNlTLWCftYrcUBCCCH0ljQjIYQQZa7UZtMVVpMmTWjYsCEajYa6deuyePFibt++zdixY4mIiCi16964cYMFCxawYsWKYp/DEBIYQFIYhBBlr8ybkZWVFTt37gRgypQpfPvtt3Tr1q3Ur1u9evUSNSJDSWAASWEQQpQ9vXpM5+zszJUrVwDQaDTMnj0bLy8v3n77bdLT04FH08QHDBiAWq1mwoQJ3L17F4C4uDhGjRpFnz59GDJkCBcvXgQeLZQXHBzMoEGDcHNzY9++fQDEx8fj7e0NwFdffcWMGTMAOHfuHN7e3jx4IHcKQgjxvJT5nVGOrKwsjh07RpcuXQC4cuUKy5YtIzg4mEmTJrF//358fHyYNm0aAQEBtGvXjuXLl7Ny5UpmzZpFQEAAgYGB1K5dm99++43AwEA2bNgAwM2bN/n666/5559/GDduHB4eHrmuPWLECIYPH84PP/xAaGgogYGBWFtbP/fvoCwVFBlSniJFdEHqNVzGVCuUr3rLvBmlp6fj4+MDPLoz6tevHzdv3sTR0ZEmTZoA0KxZMxISEkhNTSU1NVX74mrv3r2ZNGkSaWlpxMTE5Er+zszM1P5nd3d3TExMqF+/Pv/+++9TYzAxMeHDDz+kV69eDBw4MM/l0Q1dQdM/9XF6aGmSeg2XMdUK+lnvc48DKqzHfzN6XE7iNzyK7ikoskdRFGxtbfM8z5Pnys/ly5epUKECN2/eLMSohRBC6FKZN6OiqFSpEra2tpw+fRpnZ2d27txJ27ZtsbGxwdHRkb1799KzZ08UReHcuXOFDkRNTU0lODiYTZs2MX/+fPbt2/fUo7wnGUocEEgkkBCi7JWrZgSwePFi5s6dy4MHD6hVqxaLFi0C4KOPPmLevHmEhoaSlZWFp6dnoZvRwoULGTp0KHXq1GHBggWMGDGCtm3bUqVKlQKPkzggIYTQDYkDKiaJAzJcUq/hMqZaQT/rlTggIYQQekuakRBCiDL3zN+McuJ6FEXB1NSUgICAXEsxPCklJYXdu3czdOhQnQzQ39+fn376iUqVKqEoCjNmzKBjx446OXdJGUoc0OMkGkgIURae2Ywen3p9/Phxli1bxqZNm/LdPyUlhW+++UZnzQhg2rRpeHh4cOrUKebMmVOsxfB0zZDigB4n0UBCiLJQpMd09+7dw9bWFni0iurIkSPp3bs3arWaAwcOAPDxxx8TFxeHj48PixcvRlEUFi9ejLe3N2q1mj179gAQFRXF8OHDmThxIh4eHkyZMoVnzaVwcnLixo0bwKOlwmfMmIFarcbX15dTp04BEB4eTlBQkPaYMWPGEBUVBcB3331Hjx496NevH7Nnz9bul5SUxHvvvUffvn3p27cv0dHRRflahBBClNAz74xyEhIyMjK4desW69evB8DS0pJVq1ZhY2NDUlISAwcOxM3NjSlTpnD+/Hnt3dT+/fv566+/2LlzJ3fu3KFfv37aFVrPnDlDZGQk1apVY/DgwURHRxe4euvx48dxd3cHICwsDIDdu3dz8eJFRo0axf79+/M99saNG4SGhhIeHk7FihUZOXKkdur3ggULGDlyJM7OziQmJjJq1Cj27t1bmO/PID0ZH1KeIkV0Qeo1XMZUK5Sveov0mC4mJobp06cTERGBoigsW7aMn3/+GRMTE27cuJFn1E50dDReXl6YmppStWpV2rZtyx9//IGNjQ0tWrSgRo0awKMlwhMSEvJsRkuWLOGTTz7h+vXrbN68WXveYcOGAVCvXj1q1qzJpUuX8q3jjz/+oG3bttjZ2QHg4eHB5cuXAThx4gQXLlzQ7nvv3j3S0tKoWLHis74eg/TkVFB9nB5amqRew2VMtYJ+1quTOCAnJyfu3LlDUlISR48eJSkpifDwcMzNzXF1dS0wsicvT0b+aDSaPPfL+c1o48aNzJw5k/Dw8HzPaWpqSnZ2tvbPhRlTdnY2W7ZswdLSsgijF0IIoStFakYXL15Eo9FgZ2dHamoqVapUwdzcnFOnTpGQkABAxYoVSUtL0x7j7OzM5s2b6d27N3fv3uX06dNMmzaNf/75p8iDHTZsGNu2beP48eM4Ozuze/duOnbsyKVLl7h27Rp169bl3r17fPPNN2RnZ3Pjxg1+//13AJo3b87ChQu5e/cuFStW5Pvvv6dhw4YAdO7cmY0bN/LOO+8Aj5apyAlpzY8hxQE9TqKBhBBlodC/GQHayQimpqao1WrGjRuHWq3m1VdfpW7dugDY29vTunVrvL296dKlC9OmTSMmJgYfHx9UKhVTp07lhRdeKFYzUqlUjBs3ji+++ILPP/+cefPmoVarMTU1ZdGiRVhYWNCmTRteeuklPD09qVevHs2aNQMeLaY3ZswY+vfvT+XKlalbty6VKj26XZw1axZBQUGo1Wo0Gg3Ozs65JkHkR+KAhBBCN4wqDijnd6CsrCz8/Pzo27dvsVeVlTggwyX1Gi5jqhX0s169XULieVq5ciUnTpwgIyODzp07a2fmCSGEKFtG1YymT59e1kMQQgiRh3LRjA4cOMCECRPYs2cP9erVK9bxtWvXpn79+gAsX76ctm3b8tprr5VoXBIHJIQQulEumlFERARt2rQhMjKSiRMnFvn4AwcO0LVrV20zenx58uKSOCAhhNAdvU/tTktLIzo6mgULFhAZ+egf/6ioKMaMGaPdJygoSPvu0dKlS/H09EStVrN48WJ++eUXDh06xJIlS/Dx8SEuLg5/f3/27dsHgKurKytWrNDGGl28ePH5FymEEEZO7++MDh48SJcuXahTpw729vbExsbmu++dO3f44Ycf2LdvHyqVipSUFGxtbXF1daVr1675LiVub2/P9u3bCQsLY926dSxYsKC0yikXJA5I6jVUxlQrlK969b4ZRUZGMmLECAA8PT2JjIyka9euee5bqVIlLC0tmTlzJm+88Ua++z2pe/fuALz66qv88MMPuhh2uSZxQFKvoTKmWkE/6y2XU7uTk5M5deoUf//9NyqVCo1Gg0qlws3NLc/IHzMzM7Zu3crJkyfZt28fmzZtYsOGDc+8jrm5OQAmJib5RhIJIYQoPXrdjPbv34+Pj0+uNIRhw4aRnZ3NxYsXyczMJD09nZMnT9KmTRvS0tJIT0/HxcWF1q1ba98jejKiSBckDkgIIXRHr5tRREQE7777bq5t3bt3JzIyEg8PD7y9vXF0dKRp06bAo8kO48eP194p+fv7A48e7wUEBLBx40ZWrFihs/FJHJAQQuiGUcUB6ZLEARkuqddwGVOtoJ/15vebkd5P7RZCCGH4pBkJIYQoc8/tN6NGjRrx1ltvaX/HWbt2Lffv3+e9994r0nmioqJYt24da9asKfQx7777Lh9//DG2trY4OTkRExNDfHw8Y8eOJSIiokjXf5whxgHlx8bWWmKChBCl5rk1IwsLC77//ntGjx6Ng4PD87osAP/3f/+n83MaahxQfiQmSAhRmp7bYzozMzMGDhzI+vXrn/rs0KFD9O/fH19fX958803+/fdfAH766Sd8fHzw8fHB19eXe/ce/XN4//59Jk6ciIeHB1OmTEFRFI4dO5Yrt+7xyCBXV1eSkpLyHZtGo2Hx4sX07dsXtVrNt99+q8vShRBCPMNzndo9dOhQevXqpV3eO0ebNm3YsmULKpWK7777ji+++AJ/f3/WrVvHnDlztO8QWVpaAnDmzBkiIyOpVq0agwcPJjo6mtdee405c+Zw//59KlSowJ49e/D09CzUuLZu3UqlSpXYtm0bmZmZDBo0iE6dOlGrVi2dfwflWXmJFSmp8hShogvGVK8x1Qrlq97n2oxsbGzw8fFhw4YNWFlZabdfv36dyZMnc+vWLTIzM3F0dASgdevWfPjhh6jVarp3707FihUBaNGiBTVq1ACgcePGJCQk4OzsTJcuXTh8+DA9evTg6NGjTJ06tVDj+u9//8u5c+fYv38/AKmpqVy5ckWa0RP0bYpoadHH6bClyZjqNaZaQT/r1Zs4oJEjR9KnTx/69Omj3RYcHMybb76Jm5sbUVFRrFy5EoDRo0fj4uLC0aNHGTx4MF988QXw6PenHKamptoIH09PT8LCwqhcuTKvvvoqNjY2hRqToijMnj2bLl26FLoOQ01gyI8kMwghStNzb0Z2dnZ4eHiwdetW+vbtCzy6E6levToAO3bs0O4bFxdHo0aNaNSoEbGxsVy6dIlKlfKfwdauXTtmzZrFli1bCv2IDqBz58588803dOjQAXNzcy5dukT16tWpUKHg21tjSWCws6sgM+mEEKWqTN4zevvtt7lz5472z35+fkyaNIk+ffpgZ2en3b5+/Xq8vb1Rq9WYmZnx+uuvF3heU1NTunbtyvHjx3njjTcKPZ7+/ftTv359+vTpg7e3N3PmzJHAVCGEeI4kDqiYJA7IcEm9hsuYagX9rFfigIQQQuitMmtGTZo0wcfHB29vbyZOnMiDB4X/TeLs2bMcPXpU++fw8PBcy0yUREhICGvXrtXJuYQQQhROmS0hYWVlxc6dOwGYMmUK3377LW+99dYzj8vKyuLs2bPExsbi4uJS2sMskDHFAeXU+iAjS65OYXAAACAASURBVCYzCCF0Ti/WM3J2dubcuXMkJyczc+ZMrl69irW1NUFBQTRu3JiQkBDi4uK4evUqNWvW5JdffiE9PZ3o6GhtykKOQ4cOERoaysOHD7Gzs2Pp0qVUrVqVkJAQEhMTiY+PJzExkZEjR2qXMw8NDWXHjh04ODjw4osv0qxZs2eO2djigHJILJAQojSUeTPKysri2LFjdOnShZCQEJo2bcrq1as5efIk06dP1949Xbx4ka+//horKyvCw8OJjY1lzpw5wKPHdDnyS3MAuHTpEhs2bODevXv07NmTwYMHc+7cOfbs2cOOHTvQaDT07t27UM1ICCGE7pRZM0pPT8fHxwd4dGfUr18/BgwYQEhICAAdO3YkOTlZm0fn6uqaK7UhP/mlOQC4uLhgYWGBg4MDDg4O3L59m9OnT+Pu7o61tbX2OqJg5SVepLjKU4SKLhhTvcZUK5SvevXiN6PCyGkWz5JfmgM8ndyQlSWpAsWhb1NFdU0fp8OWJmOq15hqBf2sV2/igAri7OzMrl27mDBhAlFRUdjb2+cZ6VOxYkXS0tLyPEd+aQ75adu2Lf7+/owZM4asrCwOHz7MwIEDn3mcscUB5ZBYICFEadCrZuTn58fMmTNRq9VYW1vz4Ycf5rlf+/bt+fzzz/Hx8XlqAkNOmkPlypVp37498fHxBV6zWbNmeHp64uPjg4ODA82bNy/0eI0pDkjf/r8rIYRhkQSGYpIEBsMl9RouY6oV9LNeSWAQQgiht6QZCSGEKHNFbkaNGjXiP//5j/bPWVlZdOjQQfvbzcGDB/n88891N0Ide3w5cn0fqxBCGIsiT2CoUKEC58+fJz09HSsrK/773/9qZ68BuLm54ebmptNBlpaSjtUY44DyIzFBQoiSKNZsOhcXF44cOYKHhweRkZF4eXkRHR0NkCsdwd/fHxsbG2JjY7l16xZTp07Fw8ND+/6Pvb09f//9N82aNWPp0qWoVCpiY2P58MMPuX//Pvb29ixatIhq1aqxZcsWNm/ezMOHD3nllVdYsmQJ1tbW+Pv7Y2FhQWxsLGlpafj7+/PGG2+QkZHBvHnziI2NxdTUFH9/fzp06JCrjsfHunfvXlatWoWJiQmVKlUiLCyswO/AWOOA8iMxQUKIkijWb0aenp7s2bOHjIwMzp07R8uWLfPd9+bNm3z99desWbOGjz/+WLv9zJkzzJw5kz179hAfH090dDQPHz4kODiYFStWEB4eTt++ffnkk08A6NatG9u2bWPXrl3UrVuXrVu3as+VkJDA1q1bWbNmDXPnziUjI0PbTHbv3s3HH3+Mv78/GRkZ+Y5z9erVrF27ll27dhEaGlqcr0UIIUQxFevOqHHjxsTHxxMREfHM5Gx3d3dMTEyoX78+//77r3Z7ixYtqFGjhvZ8CQkJ2Nra8vfff2vTu7Ozs3nhhRcAOH/+PJ9++impqamkpaXRuXNn7bl69uyJiYkJtWvXplatWvzzzz9ER0czbNgwAOrVq0fNmjW5dOlSvuN0cnLC39+fnj170q1bt+J8LUavvMSOPEt5ilDRBWOq15hqhfJVb7FfenV1dWXJkiVs2LCB5OTkfPd7PIInv+2mpqZoNBoURaFBgwZs3rz5qf39/f1ZvXo1jRs3Jjw8nJ9++kn7mUqlyrXvk38ujKCgIH777TeOHDlC37592bZtG/b29kU+jzHTt/cZiksf380oTcZUrzHVCvpZr87jgPr164etrS2NGjUiKiqq2AN7XJ06dUhKSiImJgYnJycePnzI5cuXadCgAWlpabzwwgs8fPiQ3bt355o0sW/fPnr37k18fDxXr16lTp06ODs7s3v3bjp27MilS5e4du0adevWJSYmJs9rx8XF0bJlS1q2bMmxY8e4fv16gc3IWOOA8iMxQUKIkih2M6pRo4Z2PSBdsbCwYMWKFQQHB5OamopGo2HkyJE0aNCASZMm0b9/fxwcHGjZsmWubLoXX3yRfv36kZaWRmBgIJaWlgwZMoR58+ahVqsxNTVl0aJF+d6lASxZsoQrV66gKAodOnSgcePGzxyvxAEJIYRulPs4IH9/f7p27YqHh8dzva7EARkuqddwGVOtoJ/1ShyQEEIIvaVXqd3FkV+ytxBCiPJDb+6MmjRpgo+PD97e3owdO5aUlJQC93881ic/f/zxB8HBwQXuEx8fj7e3d5HHK4QQQnf05s7o8ZVfp0+fTlhYGOPGjSvROZs3b16k9YmKSuKA/kfigIQQJaE3zehxrVq14ty5cwD8/vvvLFiwgIyMDKysrFi4cCF169bNtf/9+/eZP38+58+fJysrCz8/P9zd3YmKimLdunWsWbOGpKQkpkyZws2bN2nVqhUnTpxg27ZtAGg0GmbPnk1MTAzVq1dn9erVWFlZFThGiQPKTeKAhBAloXfNSKPRcPLkSfr16wdA3bp1CQsLw8zMjBMnTvDJJ58QEhKS65jPPvuMDh06sGjRIlJSUujfvz+vvfZarn1WrlypTRc/duxYrjihK1eusGzZMoKDg5k0aRL79+/Hx8en9Is1MOXlTe9nKU9vreuCMdVrTLVC+apXb5pReno6Pj4+3Lhxg3r16tGpUycAUlNTmT59OleuXEGlUvHw4cOnjv3xxx85dOgQ69atAyAjI4Nr167l2ic6OpqVK1cC8Prrr1O5cmXtZ46OjjRp0gR4tAx5QkJCqdRo6PRtCmlx6eN02NJkTPUaU62gn/XqPIFB13J+M3rw4AGjRo0iLCyMESNGsHz5ctq3b8+qVauIj4/P90XbFStWPPX47vEsvII8GU1UUKCqEEII3dObZpTD2tqa2bNnM2HCBIYMGUJqaqo2+mf79u15HtO5c2c2bdpEQEAAKpWKM2fO0LRp01z7tG7dmr179zJ69Gh+/PFH7t69W6JxShxQbhIHJIQoCb1rRgBNmzalUaNGRERE8M477+Dv709oaGi+CeHjx49n4cKF9OrVi+zsbBwdHVmzZk2uffz8/Pjggw/YtWsXrVq14oUXXsDGxob794t/CytxQEIIoRvlPg6osDIzMzExMcHMzIyYmBjmzZunnUpeHBIHZLikXsNlTLWCftar978ZlbbExETef/99srOzMTc3Z/78+WU9JCGEEP+f0TSj2rVrs2PHjrIehhBCiDyUSjMKDQ0lIiICExMTTExMCAoKKnBp8sKIj4/H09OTunXrkpGRQcWKFRkyZAh9+vQB4ODBg1y8eJHRo0frogQhhBDPkc6bUUxMDEeOHGH79u1YWFiQlJSU57tBxfHyyy9r726uXr2Kn58fiqLQt29f3NzccHNz08l1CkvigIpHooOEEE/SeTO6desW9vb22nd3HBwctJ+tXLmSw4cPk5GRgZOTE0FBQahUKoYPH06LFi2IiooiNTWVBQsW4OzsXOB1atWqhb+/P4sXL6Zv376Eh4cTGxvLnDlzSEpKYu7cuSQmJgIwc+ZM2rRpk28kkIODA19++aU2Hqhfv368+eabBV5f4oCKT6KDhBBP0nkz6tSpE6tWraJHjx507NgRT09P2rVrB8CwYcPw8/MDYOrUqRw+fBhXV1fgUQzQ1q1bOXr0KCtXruSrr7565rWaNWvGP//889T2BQsWMHLkSJydnUlMTGTUqFHs3bs330ig2NhYwsPD2bJlC4qiMGDAANq1a/fUu0pCd/Q5oqQ8RajogjHVa0y1QvmqV+fNqGLFioSHh3P69GmioqKYPHkyU6ZMoU+fPkRFRfHFF1+Qnp5OcnIyDRo00Dajbt26AUWL48lvVvqJEye4cOGC9s/37t0jLS0t30ig6Oho3N3dqVChgnYsp0+flmZUivRtuunj9HE6bGkypnqNqVbQz3qf69RuU1NT2rdvT/v27WnYsCE7duzAy8uLwMBAtm3bxosvvkhISEiu2J2cx3omJiZoNJpCXefMmTPUq1fvqe3Z2dls2bIFS0tL3RQkhBCiVOm8Gf3zzz+YmJhQu3ZtAM6ePUvNmjW1jcfe3p60tDT2799Pjx49in2d+Ph4lixZwrBhw576rHPnzmzcuJF33nlHO4YmTZrkGwnk7OyMv78/o0ePRlEUDhw4wJIlSwq8vsQBFZ9EBwkhnqTzZnT//n2Cg4NJSUnB1NSUV155haCgIGxtbenfvz/e3t5UrVq1WIvexcXF4evrq53aPXz4cO3U7sfNmjWLoKAg1Go1Go0GZ2dngoKC8o0EatasGX369KF///7AowkMhXlEJ3FAQgihG0YTBwS6jQSSOCDDJfUaLmOqFfSzXqOPAwKJBBJCCH1lVM1IIoGEEEI/FbkZlUbUz/P21VdfMXDgQKytrUt0HklgKFuS5CCE4ShSM9J11E9WVhZmZs//5mzDhg306tWrRM1IEhjKniQ5CGE4itQJ8ov6yS/m58qVK8ydO5ekpCRMTU1Zvnw5165dY/ny5dja2nLp0iX279/P+PHjuX79OhkZGYwYMYKBAwcC4OTkxKBBgzh27BgvvPACH3zwAR999BGJiYnMnDkTNzc3NBoNS5cu5aeffiIzM5OhQ4cyaNAgoqKiWLlyJfb29vz99980a9aMpUuXsnHjRm7evMnIkSOxs7Nj48aNREREsGbNGhRFwcXFhalTp+r4axZCCFGQIs2mS0tLY8iQIaSnp+eK+klOTsbOzg54FPPTs2dPXF1d6d+/P6NHj6Zbt25kZGSQnZ3N77//zpgxY9i9eze1atUC0B6fnp5Ov3792LhxI/b29jRq1IjPP/8cFxcXJkyYwIMHD1izZg0XL15k+vTp7Ny5k82bN3P79m3Gjx9PZmYmgwYNYvny5SQmJjJ+/HgiIyOpVq0agwcPZurUqTg7O+Pq6srWrVtxcHDgxo0bDBw4kPDwcGxtbXn77bcZMWIE7u7uz/w+5M6obF3+0IuHDwv3gnRRmJqaoNFk6/y8+sqY6jWmWkE/6zU3N81ze5HujPKL+qlYseJTMT/t2rXjxo0b2pifx9MQmjdvrm1EABs3buSHH34A4Nq1a1y5cgV7e3vMzc15/fXXAWjYsCEWFhaYm5vTsGFDbWTQf//7X86dO8f+/fsBSE1N5cqVK5ibm9OiRQtq1KgBQOPGjUlISHgqgPWPP/6gXbt22rs8tVrNzz//XKhmJMpeaUxb1cfpsKXJmOo1plpBP+vV2dTuJ6N+Nm/ezLlz5/KN+clLTgYcQFRUFCdOnGDz5s1YW1szfPhw7fHm5uaoVCrgUUxQXpFBiqIwe/ZsunTpkusaUVFR2v1zxl3YmCEhhBDPV5GaUV5RP3Xq1OHcuXNPxfzY2NhQo0YNDhw4gLu7O5mZmXk2g9TUVCpXroy1tTUXL17k119/LVIBnTt35ptvvqFDhw6Ym5tz6dIlqlevXuAxFStWJC0tDQcHB1q0aMGCBQtISkqicuXKREZG5hkx9CSJAyp7EiskhOEoUjPKL+qnUqVKecb8LFmyhDlz5rB8+XLMzc1Zvnz5U+d8/fXX+fbbb+nZsyd16tShVatWRSqgf//+JCQk0KdPHxRFwd7entWrVxd4zIABA3jnnXeoVq0aGzduZMqUKYwcOVI7gaGwj+gkDkgIIXTDqOKAdEnigAyX1Gu4jKlW0M968/vNyOQ5j0MIIYR4ijQjIYQQZe65xR/cunWLhQsX8scff2Bra0uVKlVwd3fn0KFDrFmzptjnDQkJoUKFCowaNSrffcLDw+nUqZN2YoPEARWdPtcqsUBClH/PpRkpioKfnx++vr588sknAPz1118cPHiwROfNyircbKrt27fToEEDbTOSOCDDIrFAQpR/z6UZnTp1CjMzMwYPHqzd1rhxY+7evcupU6eYOHFirsgelUqVb8TQ8OHDady4MdHR0Xh7e+e6ztmzZ5k7dy4PHjzg5ZdfZuHChZw8eZLY2Fj+85//YGVlRZ8+fZ6KA/rxxx8JCQkhMzOTWrVqsWjRIipWrPg8vhohhBA8p2Z0/vx5mjVrludnZ86cyRXZEx0djbOzM8OGDcPPzw94FDF0+PBhXF1dAXj48CHh4eHAo8d0OaZNm0ZAQADt2rVj+fLlrFy5klmzZhEWFsa0adO0087Xr1/P+vXrcXBwICkpidDQUL788ksqVKjA559/zpdffqm9tigf7OwqPHunQjI1NdHp+fSdMdVrTLVC+aq3zNczyi+yJyoq6qmIoZxm5Onp+dR5UlNTSU1NpV27dgD07t2bSZMmPfP6v/32GxcuXNDetT18+LDI7zqJsqfL6av6OB22NBlTvcZUK+hnvWW60muDBg202XFPyiuyJyMjg8DAwHwjhko68eBxiqLQqVMnli1bprNzCiGEKJrn0ow6dOjAsmXL2Lx5s3Z5iL/++ovTp0/nuX9O43kyYqgglSpVwtbWltOnT+Ps7MzOnTtp27Yt8L/4nxyPxwG1atWKoKAgrly5wiuvvML9+/e5ceMGderUKfB6EgekPyQWSIjy77k0o5wJCQsXLuT//u//sLS05KWXXso3dsfW1pb+/fvnGTFUkMWLF2snMORMRIBHj+zmzp2LlZUVmzdvfioOaNGiRXzwwQdkZmYC8P777z+zGYHEAQkhhK5IHFAxSRyQ4ZJ6DZcx1Qr6Wa/EAQkhhNBb0oyEEEKUuTKb2u3k5ERMTIz2z+Hh4cTGxjJnzhydXeOzzz5j7NixOjvfk/Q5IkfXjKFWiRUSouyU+XtGpWnNmjWl1owkDsjwSKyQEGVHLx/TxcfHM2LECNRqNSNHjiQxMREAf39/9u3bp93PyckJgJs3bzJ06FB8fHzw9vbm9OnTLF26lPT0dHx8fJgyZQoAX375Jd7e3nh7e/PVV19pr9WzZ09mz56Nl5cXb7/9Nunp6c+3YCGEMHJldmeU0yhy3L17V5uwEBwcTO/evenduzdbt24lODi4wNVbIyIi6Ny5M+PGjUOj0fDgwQOcnZ0JCwtj586dAMTGxhIeHs6WLVtQFIUBAwbQrl07bG1tuXLlCsuWLSM4OJhJkyaxf//+XGMTxsPOrkK5ilDRBWOq15hqhfJVb5k1IysrK22jgP/9ZgQQExOjzZzz8fHho48+KvBczZs3Z+bMmWRlZeHu7k6TJk2e2ic6Ohp3d3cqVHj0F9OtWzdOnz6Nq6srjo6O2mOaNWtGQkKCTmoU5U9y8n29nA5bmoypXmOqFfSzXoOY2m1qakp2djYA2dnZPHz4EIC2bduyadMmqlevjr+/Pzt27CjSefOKJBJCCPH86OUEBicnJyIjI/H19WX37t04OzsD8NJLL/Hnn3/i6enJoUOHtM0oISGBGjVqMGDAADIzM/nzzz/x9fXFzMyMhw8fYm5ujrOzM/7+/owePRpFUThw4ABLliwp9hglDsjwSKyQEGVHL5tRQEAAM2bMYO3atTg4OGhjfQYMGMD48ePp1asXXbp00T5y++mnn1i7di1mZmZUqFCBxYsXa/fv1asXTZs25eOPP6ZPnz70798fgH79+tG0aVPi4+OLPU6JAxJCCN2QOKBikjggwyX1Gi5jqhX0s16D+M1ICCGEYZJmJIQQoszp5W9GT2rSpAkNGzYkKysLU1NTfH19efPNNzExyb+XxsfHM3bsWCIiIoiKimLdunWsWbNGp+MyhoicHMZUK+Rdr8QFCVF6ykUzevydpNu3bzNlyhTu3bvHxIkTy2xMEgdkfCQuSIjSU+4e01WpUoX58+cTFhaGoijEx8czZMgQbWLDL7/8UuDxv//+OwMHDsTX15dBgwbxzz//AHD+/Hn69euHj48ParWay5cvP4dqhBBCQDm5M3pSrVq10Gg03L59mypVqvDll19iaWnJ5cuX+eCDDwgPD8/32Lp16xIWFoaZmRknTpzgk08+ISQkhG+//ZYRI0bQq1cvMjMztS/XCvG48hKtUhTlKTKmpIypVihf9ZbLZvS4rKwsgoKC+OuvvzAxMXnmHU1qairTp0/nypUrqFQq7YuzrVq14rPPPuP69et0796d2rVrl/7gRbmjb9NkdUEfp/+WFmOqFfSz3vx+fy6Xzejq1auYmppSpUoVVq5cSdWqVdm5cyfZ2dm0aNGiwGOXL19O+/btWbVqlTYdHECtVtOyZUuOHDnC6NGjCQwMpGPHjvmeRxIYjI8kNAhRespdM0pKSmLu3LkMHToUlUpFamoqNWrUwMTEhO3btz8zVy41NZXq1asDsH37du32q1evUqtWLUaMGMG1a9c4d+5cgc0IJIHBUBlbvULog3LRjHKWm8iZ2u3j48Nbb70FwJAhQ3jvvffYsWNHroig/Lzzzjv4+/sTGhqKi4uLdvvevXvZuXMnZmZmVK1alTFjxpRqTUIIIf5H4oCKSeKADJfUa7iMqVbQz3olDkgIIYTe0otmdP36dcaNG0f37t1xd3cnODiYzMzMp/aLj4/H29tbJ9eMioqSR3FCCKEnyvw3I0VR8PPzY/DgwYSGhqLRaAgICOCTTz5h+vTp2v2ysvRvJpMxReQYU61QvuqVmCJhCMq8GZ06dQpLS0v69u0LPFppdebMmbi5ueHo6Mjx48e5f/8+2dnZfPjhh9rj4uPjmTZtGg8ePPofYUBAAK1btyYqKoqVK1dib2/P33//TbNmzVi6dCkqlYpjx46xcOFCrK2tadOmjfZcycnJzJw5k6tXr2JtbU1QUBCNGzcucNwSByT0hcQUCUNQ5s3o/PnzNGvWLNc2GxsbXnzxRTQaDWfOnGHXrl3Y2dnlWgivoOSFM2fOEBkZSbVq1Rg8eDDR0dE0b96cgIAA1q9fzyuvvML777+vPVdISAhNmzZl9erVnDx5kunTp2uz8IQQQpS+Mm9Gz9KpUyfs7Oye2l5Q8kKLFi2oUaMGAI0bNyYhIYGKFSvi6OioTVbo1asXW7ZsASA6OpqQkBAAOnbsSHJyMvfu3cPGxqZ0ixNCR0oS+VKeImNKyphqhfJVb5k3o/r167N///5c2+7du8e1a9cwNTXF2to6z+O++uqrfJMXLCwstP/Z1NT0mS/CClHelWT6rj5O/y0txlQr6Ge9ehsH1LFjR5YuXcqOHTvw9fVFo9Hw4Ycf0rt373wbEVDk5IW6deuSkJBAXFwcL7/8MpGR//u9x9nZmV27djFhwgSioqKwt7d/5l2RxAEJfSExRcIQlHkzUqlUrFq1isDAQFavXk12djYuLi588MEHRERE5HtcUZMXLC0tCQoKYvTo0doJDGlpaQD4+fkxc+ZM1Go11tbWuSZKFETigAyTsdUrhD6QBIZikgQGwyX1Gi5jqhX0s15JYBBCCKG3pBkJIYQoc0VuRk5OTjofxIIFC+jSpUupra4aEhLC2rVrS+XcQgghSq7MJzBkZ2dz4MABXnzxRX766Sc6dOhQ1kMqtPIUGVNSxlQrSL2lQWKLREF00ozOnj3L3LlzefDgAS+//DILFy6kcuXKbNiwgW+//RZTU1Pq16/PJ5988tSxUVFR1K9fH09PTyIjI7XNKCQkhMTEROLj40lMTGTkyJHaVVlXrVrFrl27cHBw4MUXX6RZs2aMGjWKuLg4AgMDuXPnDlZWVsyfP5969erlul5+++zdu5dVq1ZhYmJCpUqVCAsLK7BmiQMSomgktkgURCfNaNq0aQQEBNCuXTuWL1/OypUrmTVrFp9//jmHDh3CwsKClJSUPI+NjIzEy8sLd3d3li1bxsOHDzE3Nwfg0qVLbNiwgXv37tGzZ08GDx7M2bNn+f7779m1axcPHz6kT58+2jihgIAAAgMDqV27Nr/99huBgYFs2LAh1/Xy22f16tWsXbuW6tWr5ztWIUTJlHUaQHlKJNCF8lRviZtRamoqqamptGvXDoDevXszadIkABo1asR//vMf3NzccHd3f+rYzMxMjh49ir+/PzY2NrRs2ZIff/yRN954AwAXFxcsLCxwcHDAwcGB27dv88svv+Dm5oalpSWWlpbafdPS0oiJidFeO+f8jytoHycnJ/z9/enZsyfdunUr6dcihMhDWU8z1sepzqVJH+stkwSGzz//nJ9//pnDhw/z2WefsXv3bszM/nfJH3/8kdTUVHr16gXAgwcPcjWYJ2N9ClpGQlEUbG1tCww4LWifoKAgfvvtN44cOULfvn3Ztm0b9vb2Ra5ZCCFE0ZW4GVWqVAlbW1tOnz6Ns7MzO3fupG3btmRnZ3Pt2jU6dOhAmzZtiIyM5P79+9ja2mqPjYyMJDg4WLtg3v3793Fzc9MuC5GX1q1bM3fuXMaMGUNWVhZHjhxhwIAB2NjY4OjoyN69e+nZsyeKonDu3LlcS0EUtE9cXBwtW7akZcuWHDt2jOvXrxfYjCQOSIiikdgiUZAiN6MHDx7w+uuva//81ltvsXjxYu0Ehlq1arFo0SI0Gg1Tp07l3r17KIrCiBEjcjWiBw8ecPz4cQIDA7XbKlSoQJs2bTh8+HC+12/RogWurq706tWLKlWq0LBhQypVenTb99FHHzFv3jxCQ0PJysrC09PzqXWJ8ttnyZIlXLlyBUVR6NChwzPXMwKJAzJUUq8Qz1+5jANKS0ujYsWKPHjwgKFDhzJ//vyn1kQqbRIHZLikXsNlTLWCftart6ndxTFnzhwuXLhARkYGvXv3fu6NSAghhG6Vy2b08ccfl/UQhBBC6FC5yabLmSaeIysriw4dOjBmzBgADh48yOeff66Ta82aNYsLFy7o5FxCCCGerdz8ZuTk5MTLL7/M5s2bsbKy4ujRoyxbtowaNWqwZs2a5z6e7GwFExPVc7+uEEKUpZLGOhnEb0YuLi4cOXIEDw8PbXJDdHQ0AOHh4cTGxjJnzhwOHTpEaGgoDx8+xM7OjqVLl1K1alVCQkKIj4/n6tWrXLt2jRkzZvDrr79y/PhxqlWrxmeffYa5uTnDhw9n2rRpNG/ePN+xSByQEMIYlVasU7l5TAfg6enJnj17yMjI4Ny5c7Rs2TLP/dq0acOWLVvYsWMHXl5efPHFF9rP4uLiWL9+PaGhoUydOpX2XC5eYQAACCVJREFU7duze/du7d2WEEKIgtnZVSj2/+WnXN0ZNW7cmPj4eCIiInBxccl3v+vXrzN58mRu3bpFZmYmjo6O2s9ef/11zM3NadiwIRqNRvvOVMOGDYmPjy/1GoQQorwryXRxg1np1dXVlSVLluDllX/6QXBwMEOHDmX37t0EBQXlyqjLiRgyMTHB3NwclUql/bNGoyndwQshhMhTubozAujXrx+2trY0atSIqKioPPdJTU2levXqAOzYsaNUxiFxQEIIY1RasU7lrhnVqFFDu65Rfvz8/Jg0aRKVK1emffv2pfb4TeKADJPUa7iMqVYoX/WWm6nd+kbigAyX1Gu4jKlW0M968/vNSJqREEKIMlfuJjAIIYQwPNKMhBBClDlpRkIIIcqcNCMhhBBlTpqREEKIMifNSAghRJmTZiSEEKLMSTMqomPHjtGjRw+6deums8X8npdr164xfPhwPD098fLyYv369QAkJyfz1ltv0b17d9566y3u3r0LgKIoBAcH061bN9RqNX/++af2XNu3b6d79+50796d7du3a7fHxsaiVqvp1q0bwcHBlPVrbBqNBl9fX+0ijFevXqV///5069aN999/X5tbmJmZyfvvv0+3bt3o379/rtSONWvW0K1bN3r06MHx48e12/XtvwspKSlMnDgRDw8PevbsSUxMjMH+3X711Vd4eXnh7e3NBx98QEZGhkH93c6YMYOOHTvi7e2t3fY8/i7zu8ZzoYhCy8rKUtzc3JS4uDglIyNDUavVyvnz58t6WIV248YNJTY2VlEURUlNTVW6d++unD9/Xlm8eLGyZs0aRVEUZc2aNcqSJUsURVGUI0eOKKNGjVKys7OVmJgYpV+/foqiKMqdO3cUV1dX5c6dO0pycrLi6uqqJCcnK4qiKH379lViYmKU7OxsZdSoUcqRI0fKoNL/WbdunfLBBx8oo0ePVhRFUSZOnKhEREQoiqIoAQEBSlhYmKIoirJp0yYlICBAURRFiYiIUCZNmqQoiqKcP39eUavVSkZGhhIXF6e4ubkpWVlZevnfhWnTpilbtmxRFEVRMjIylLt37xrk3+3169eVN954Q3nw4IGiKI/+Trdt22ZQf7c//fSTEhsbq3h5eWm3PY+/y/yu8TzInVER/P7777zyyivUqlULCwsLvLy8OHjwYFkPq9CqVatGs2bNALCxsaFu3brcuHGDgwcP4uvrC4Cvry8HDhwA0G5XqVS0atWKlJQUbt68yY8//kinTp2ws7OjcuXKdOrUiePHj3Pz5k3u3btHq1atUKlU+P6/9u7nl5UujAP41w2SmxRRaWcIkSAWfnUlJGphplpJSzRsbAg7CxK/FtiwYEdi44/AwqYLi1aDaBOJ0EXtRJDoSKgSVJXnLsQkXmnCu5hxe5/PbqbTnnPme9Kn6ZzkdHbqen8ikQj8fj+6u7sBvP2CDAaDcDgcAAC32632z+fzwe12AwAcDgcCgQCICF6vF06nE9nZ2SgpKUFpaSlCodCPmwt3d3fY29tTx5qdnY3c3Ny0zfbl5QXxeBzJZBLxeBwmkymtsq2vr0deXt6Hc1pkmaoNLXAx+gZFUSCKonosCAIURdGxR//f+fk5jo6OYLFYcHV1BbPZDAAwmUy4uroC8Hm8oihCUZSU9yHV9XqZn5/HxMQEfv16m+bRaBS5ubnIzMz81D9FUVBYWAgAyMzMRE5ODqLR6JfHqvdcOD8/h9FoxOTkJDo7OzE9PY2Hh4e0zFYQBAwMDKClpQVWqxUGgwHV1dVpm+07LbJM1YYWuBj9g+7v7zE8PIypqSkYDIYPr2VkZKh7PP3NNjc3YTQaUVNTo3dXNJFMJhEOh9HT04P19XX8/v3707OOdMk2FovB6/XC6/Vie3sbj4+PH573/Au0yFLr+cLF6BsEQUAkElGPFUVR9036Wzw/P2N4eBjt7e2w2+0AgIKCAlxeXgIALi8vYTQaAXwebyQSgSAIKe9Dquv1sL+/D5/PB0mSMDo6imAwiLm5Odze3iKZTH7qnyAIuLi4APD2xX53d4f8/Pwvj1XvuSCKIkRRhMViAQC0tbUhHA6nZba7u7soLi6G0WhEVlYW7HY79vf30zbbd1pkmaoNLXAx+oba2lqcnJzg7OwMiUQCHo8HkiTp3a0vIyJMT0+jrKwM/f396nlJktRNCNfX1yHL8ofzRISDgwPk5OTAbDbDarViZ2cHsVgMsVgMOzs7sFqtMJvNMBgMODg4ABF9+CytjY2NYWtrCz6fD4uLi2hsbMTCwgIaGhqwsbEB4G2l0Xt+kiSpq402NjbQ2NiIjIwMSJIEj8eDRCKBs7MznJycoK6u7sfNBZPJBFEUcXx8DAAIBAIoLy9Py2yLiopweHiIx8dHEBECgQAqKirSNtt3WmSZqg1NaLZUIk34/X6y2+0kyzItLy/r3Z1v2dvbo8rKSnK5XNTR0UEdHR3k9/vp+vqaent7qbW1lfr6+igajRIR0evrK83MzJAsy+RyuSgUCqmftbq6SjabjWw2G62trannQ6EQOZ1OkmWZZmdn6fX1VfNx/lcwGFRX052enlJXVxfZbDYaGhqip6cnIiKKx+M0NDRENpuNurq66PT0VH3/8vIyybJMdrv9wwqynzYXwuEwud1ucrlcNDg4SDc3N2mb7dLSEjkcDnI6nTQ+Pq6uiEuXbEdGRqipqYmqqqqoubmZVlZWNMkyVRta4P2MGGOM6Y7/pmOMMaY7LkaMMcZ0x8WIMcaY7rgYMcYY0x0XI8YYY7rjYsQYY0x3XIwYY4zp7g89MVhTHHdSnwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "for this graph we analyzed higher rate of accidents in the miami city"
      ],
      "metadata": {
        "id": "5zD_9Xd-HC_t"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# "
      ],
      "metadata": {
        "id": "nDI9IjZpHs5-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "\n",
        "sns.set_style(\"darkgrid\")"
      ],
      "metadata": {
        "id": "3mKlkV-GGwdG"
      },
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(cities_by_accidents)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "fLXQ3GKOGwwr",
        "outputId": "b71aa0c0-4d55-4968-b90c-49d7101ffd88"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f36d9c19390>"
            ]
          },
          "metadata": {},
          "execution_count": 96
        },
        {
          "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": [
        ""
      ],
      "metadata": {
        "id": "hlGc6-KsGwy5"
      },
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "high_accident_cities = cities_by_accidents[cities_by_accidents >= 1000]\n",
        "low_accident_cities  = cities_by_accidents[cities_by_accidents < 1000]"
      ],
      "metadata": {
        "id": "15aeJ9JbGw0u"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(high_accident_cities) / len(cities) # less than 4 % cities have higer accidents"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DMT04eeyGw2a",
        "outputId": "83ce9447-fed5-44e3-f2dd-a5f5b5a4a3f8"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.04245848313644924"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(low_accident_cities) / len(cities)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wsEl30j-JmLH",
        "outputId": "185b951d-12c0-4c78-d836-6f174c34cd28"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9574559150830337"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "'New York' in df.City"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "479eV43vJmlY",
        "outputId": "645eaa65-257a-4486-ece8-07d705637d57"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "'NY' in df.State"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d9ngZCZZJmpa",
        "outputId": "59b05e92-0504-4fe4-802b-157470d9bca3"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(high_accident_cities) # not much helpful its just show the data is exponential "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "nVL0gkD7Jmra",
        "outputId": "3d1d5974-4772-41ae-cad2-324cc3abc8d3"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f36d994c910>"
            ]
          },
          "metadata": {},
          "execution_count": 105
        },
        {
          "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": [
        "sns.distplot(low_accident_cities)# not much helpful its just show the data is exponential "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "UeRdhMXpJmtk",
        "outputId": "b5928cbd-8c74-4f17-e0ca-28bce1c8d926"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f36f3b6a810>"
            ]
          },
          "metadata": {},
          "execution_count": 106
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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rr72GL7/8Eu+++y6OHDmCI0eOYOfOnTh69Chee+01L5VIRET+ZMjg2LNnD5577jnEx8e72uLj4/GXv/wFu3fvlr04IiLyP0MGh81mG/ScRlRUFC/JJSK6RA0ZHP0nNJTyHhER/XINeXL85MmT+NWvfjWgXRRFWCwW2YoiIiL/NWRwnDhxwlt1EBHRGDGsGwCJiIj6MDiIiEgSBgcREUnC4CAiIkkYHEREJAmDg4iIJJE1OEpLS5GWlgaDwYBt27YNeN9isWDdunUwGAzIzs5GXV0dAMBsNuPOO+/EVVddhYKCArd1KioqsGzZMhgMBjz11FOuyReJiMg7ZAsOu92OgoICvPrqqzAajdi7dy+qqqrcltm5cyfCw8Oxb98+rFq1CoWFhQCAwMBA3H///diwYcOA7f7pT3/Ck08+iY8//hg1NTUoLS2VqwtERDQI2YKjvLwcCQkJiI+Ph1qtRnp6OoqLi92WKSkpQWZmJgAgLS0NZWVlEEURISEhuOaaaxAYGOi2fGNjIzo6OjBv3jwIgoCMjIwB2yQiInkN60FOI2EymaDT6VyvtVotysvLBywTFxfnLESlQlhYGMxm8wUfFvXzbep0OphMpovWolQK0GhCRtINj+tp70VIsBqnm7ugCQ5AeHAAggIDoIkI8nVpI6ZUKvzm9+sp7NPYwD75hmzB4U/sdhEtLV2+LgMA0GUV8c4Xp/F1fTsigwOw6rp49PRa0dLi8HVpI6bRhPjN79dT2KexgX2SV0xM2KDtsg1VabVaNDQ0uF6bTCZotdoBy9TX1wNwTuHe3t6OyMjIYW+zoaFhwDb93ZtHnKExd2I4Wnus+KDCxBP8RDSmyBYcSUlJqKmpQW1tLSwWC4xGI/R6vdsyer0eu3btAgAUFRUhOTkZgiBccJuxsbEIDQ3FsWPHIIoidu/ejZSUFLm64HEOUcRHlQ24PDoES2ZpcfPU8ag614mvf2zzdWlERMMm21CVSqVCfn4+cnJyYLfbsXLlSiQmJmLLli2YPXs2UlJSkJWVhby8PBgMBkRERGDTpk2u9fV6PTo6OmC1WvHJJ59g+/btmDp1Kp544gk8/PDD6OnpwYIFC7BgwQK5uuBxX5xuQWN7L5YnOc/TXDUpAqXfNeHTU+ew8PLBz+sQEfkbQbwExkmsVrtfjBk+/uFJHKxuxh9vSIBK6TzY21Vej4a2Xnz0h2SoFBc+2vJn/jQm6yns09jAPsnL6+c4yJ3N7sD+U+dwy/QYV2gAwCxtGFq6rfiytsWH1RERDR+Dw0u+PduJHpsD1yS4n/yfMj4EwQEKfPLtWR9VRkQkDYPDS8rPnwC/ckK4W3uAUoF5kzT452kecRDR2MDg8JLyH9ugCwtEbNjAG/2SJoajtqUHZzt6fVAZEZE0DA4vKf+xDXN+drTRZ87ECADA0bpWb5ZERDQiDA4vaGjrgam994LBccX4cRinVjI4iGhMYHB4Qd/5jaQLBIdSIWDOhHAcPcPgICL/x+DwghOmDqiVAqbFjLvgMldNisB357rQ0mX1YmVERNIxOLyg6lwnLosKcbt/4+fmnT/PUdHA6UeIyL8xOLzgu3OdmDrE0QYAzNCGQiEAxxvavVQVEdHIMDhk1tptxdkOC6aOHzo4ggOUmBI9DpUMDiLycwwOmVWd6wTgvHLqYmbpQnG8oYPTrBORX2NwyOy788FxsSMOAJilc85b9WNbj9xlERGNGINDZlXnOhEepEJMqPqiy16pc85EebyhQ+6yiIhGjMEhs6qzXbhi/LghH1DVZ+r4cVArBVTW8zwHEfkvBoeMRFFEdVPnsIapAEClVGB6bCiOmxgcROS/GBwyauywoNNix5TokGGvM0sXhpOmdtgdPEFORP6JwSGjmmbnU7wulxgc3VYHvm/2jyeAERH9HINDRj+c//BPiJIWHABvBCQi/8XgkFFNczdCA5WIDgkY9jqTI4MxTq1kcBCR32JwyKimuQuXRYUM64qqPgpBwCxdGIODiPwWg0NGPzR3SRqm6jNLF4Zvz3ai1+aQoSoiotFhcMik02JDY4cFl0UGS173Sl0Y7A4Rp87yRkAi8j8MDpn80NwNALhshEccAHgjIBH5JQaHTPouxR1JcMSGqhE9Ts0bAYnILzE4ZPJDcxeUCgGTNEGS1xUEAVfqwnjEQUR+icEhk++buzEpImjIp/4NZZYuFD+Yu9HRa/NwZUREo8PgkEnfpbgj1TdT7gkOVxGRn2FwyMDmEFFr7h72pbiCIMBssbt9XRHLE+RE5J9Uvi7gl+jH1h7YHCIuixrepbjdNgfKTp11a7tlRiwmaYL4KFki8juyHnGUlpYiLS0NBoMB27ZtG/C+xWLBunXrYDAYkJ2djbq6Otd7W7duhcFgQFpaGg4cOOBq1+v1WLZsGZYvX44VK1bIWf6IjeaKqv5mx4Xj6/p2PkqWiPyKbEccdrsdBQUF2LFjB7RaLbKysqDX6zF16lTXMjt37kR4eDj27dsHo9GIwsJCbN68GVVVVTAajTAajTCZTFi9ejWKioqgVCoBAK+//jqioqLkKn3UfprcUPrNf/1dNTEc/3uiEbUtPZg8ghsJiYjkINsRR3l5ORISEhAfHw+1Wo309HQUFxe7LVNSUoLMzEwAQFpaGsrKyiCKIoqLi5Geng61Wo34+HgkJCSgvLxcrlI9rqa5C1EhAQgPGv7khoOZNykCAHCsrtUTZREReYRsRxwmkwk6nc71WqvVDvjwN5lMiIuLcxaiUiEsLAxmsxkmkwlz5851W9dkMrle33333RAEAbfffjtuv/32i9aiVArQaEY3bCRFXVsvpsaGDvoze9p7ERLs/vxxlVIxoC0oMABXxYQiMiQAlWc7cZcX65dKqVR49ffrDezT2MA++caYOzn+1ltvQavVoqmpCatXr8aUKVNw7bXXDrmO3S6ipcU7D0YSRRFVjR0wTI8Z9GeKggJd3Ra3NpvdMaCtp9eK1lYH5k4Ix+fVTV6rfyQ0mhC/rm8k2KexgX2SV0xM2KDtsg1VabVaNDQ0uF6bTCZotdoBy9TX1wMAbDYb2tvbERkZOeS6fd+jo6NhMBj8bgjL3G1FW4/NY+ck5k2MwJnWHjS293pke0REoyVbcCQlJaGmpga1tbWwWCwwGo3Q6/Vuy+j1euzatQsAUFRUhOTkZAiCAL1eD6PRCIvFgtraWtTU1GDOnDno6upCR4dzxtiuri4cOnQIiYmJcnVhRKrPOf9SuCJ6nEe2d9X58xz/qmvxyPaIiEZLtqEqlUqF/Px85OTkwG63Y+XKlUhMTMSWLVswe/ZspKSkICsrC3l5eTAYDIiIiMCmTZsAAImJibj11luxZMkSKJVK5OfnQ6lUoqmpCffeey8A51VbS5cuxYIFC+TqwohUN3UCAKaM98wY5fTYUEQEqfBZjRm3ztRefAUiIpkJ4iVwk4DVavfamOEz+05h3zdnUXzv/EGf/NctKPDRV2fc2uYnxgx6A2Ck2nn58WPGE/jidAs++kMyFBKeJugt/jQm6yns09jAPsnL6+c4LlXVTZ2YEi3tcbEXM/+yKDR3WfFtIx/sRES+x+DwIFEUUd3U5bFhqj7XXRYJACirMXt0u0REI8Hg8KCmTgvaemweOzHeZ/w4NabHhuLw980e3S4R0UgwODzouybnuKSnjzgAYMEVUfjqTBvOdvCyXCLyLQaHB1X3BYeHjzgAwDA9FiKA4m/PeXzbRERSMDg86LtznYgIUiEqZHRzVA3m8ugQJMaMw8cnz158YSIiGTE4POgbUwemx4Z69Iqq/gzTY/B1fRvq23pk2T4R0XAwODzEaneg6lwnZmhDPbK9wZ4KeN2UaACAsdJ0kbWJiOQz5iY59FfV57pgc4iYoR38hhmpBnsqIABcM1mD976qx6pfx0OlZO4Tkffxk8dDTpicj3idEeuZI44LWT4nDuc6LSg5xZPkROQbDA4POdnYgXFqJSZqgmT9OddeFolJmiD8vy/P8JGyROQTDA4P+aaxAzO0obLPJaUQBPz26kn4ur4dh7/nneRE5H0MDg+wOUScOtuJ6TIPU/VZnqTDJE0QXjzwPewOHnUQkXcxODzgu7Od6LU5MNNDJ8YvJkCpwB9vuAxV5zqxt7Lh4isQEXkQg8MD+h6yNG9iuNd+5qLpMbhqYjg27a9GA+/rICIvYnB4wNG6VkyICIIuXN4T4/0pBAH5i6fD7hDxZNG3HLIiIq9hcIySQxRxtK4Vvzr/iFe59b8xcFyIGn+46XIcOd2CF0qrvfLziYh4A+AoVTd1obXH5rXg+PmNgcEqBTLmxuHNf51BbGggfnvNJK/UQUSXLgbHKB2tawUA/CreO8ExmN/feDnauqzY/Gk1uq123J08Wbb5soiIOFQ1Sl+cbkFsqBoTvHh+4+eUCgEbl85E+qxYbD38Ax7ZewKdFpvP6iGiXzYGxyh0W+04/H0zFk4d7/O/8FUKAU8sno77brocJafO4Tev/QsHq5t4dzkReRyDYxTKvm9Gr82BlGnjfVpH3wnzFqsDy+ZNwPMrkxAUoMQDuyrx+7e/wpEfzAwQIvIYnuMYheJvzyEyOADzJvru/AYw+Ey6L/5mLj492YjXj9Ti3ne/RlJcODLn6KCfNh7j1NztRDRy/AQZoV6bAwerm5E2MwZKhf+diA5UKWG4UoeF02Pxv8dNeP/Yjygo+hbPFlfh5qnRWHBFNH6dEAlNsOefVkhEv2wMjhEqOtGILqsdhukxvi5lUP2PQsLUStx17SScae2BuceGA6fOoejkWSgEYJYuDFdNjMDcieGYMyEckSFqH1dORP6OwTECDlHEf39Ri+mxobgmXuPrcoZFEARM0gTjrpla3LtgCr5t7MAXNWb8q7YFb315Bv/3n3UAgMmRwZgdF4ZpMaGYFjsO02JCEcGjEiLqh8ExAqVVTfjB3I2N6TN8fjWVVP2PROI1QYjX6HBD4nh8a+rA8fo2VNa34/MfWvDh8UbXOtqwQEyLGYfEmHFIiArB5MhgTI4MRngQA4XoUsTgkKjX5sDLh2owISII+mn+OUwllQMCzrb1IGacGjdPjcbNU6NxVUIkvjvbie/OdeC7s52oPteJw983w97v4ixNcAAmRwbjithQRAWpoAsLhC48ENqwIOjCAxEcoPRdp4hINgwOif568HtUN3Vh84rZUPnhSXFPCVKr0NZlQUyIGjEJaiQnROKmaTGob+1GXUsPzpi7UdfSjTMt3Tj0XRPOdfTi5/MsRgSpoA0LdH1FhagRERyAyJAAaIJV0AQHQBMcgNBAFYJUijF39EZ0qWJwSLDn63q8+a8zyJobhxsuj/J1OV5nE4EqUwcAIDokANEhAZg7IRw3zdDi0+MNaO+1oa3HhrYeK2LCg9HY3oPG9l6cae3BsTNtaO+98N3sAoAQtRIhaiWCA5QYN+S/VQgJUDi/q5UICVC61u37d3CA0i+vdiP6JZA1OEpLS7Fx40Y4HA5kZ2djzZo1bu9bLBZs2LABlZWV0Gg02LRpEyZNck7St3XrVrz77rtQKBR47LHHcNNNNw1rm3Lo6LXhb5+dxhv/rEPyZZG4f+EU2X/mWKNQCIgIDjh/Ij0Y8xNjnOdSdD893Grh9Fi0dlvQ2u0Ml5ZuK9q6rbDYRbT3WNFltaPH4kCX1Y5emwOdvTa09vSiy2JHj9XufN/qGHZNKoWAQJUCgSoF1EqF6999XwFKBVQKASqlAkpBgEopQKkQoFIICAkKgMNmd71WKfreU7jaLvS9//ZUfdv92faH2k7/n8ejMPJHsgWH3W5HQUEBduzYAa1Wi6ysLOj1ekydOtW1zM6dOxEeHo59+/bBaDSisLAQmzdvRlVVFYxGI4xGI0wmE1avXo2ioiIAuOg2PekbUwd2HvsR+6vOobXHhowkHf5PylSolLzhfiQsDhHHTre4tY0LUGLRrJgBNzC6gudnbYe/bYTFLsJic8Bid37NnhiBLosd3Va767vNLqLLYncuc35Zq11Ej9XZ1mNzoL3XCgcAm90Bm0OE/fyXzSHCIYqw2X96bXM4BgzFeYMAZyirFAIUgvM5LEqFM4gUCgHK/m2DhI9KIUB5PiCD1CrA4XCFklIhQBSdVwk6vwMiROd30fn9p/d++u4A3GYiEAPpW68AAApCSURBVADgfMAJfV9C33vC+QV+ahfON/TPRAFwC9C+Pip/Fqx9bX2vQ0PUsFpsEATn7wdw/j6cP0uAIAAK4fy/cf7f52uSK5IFAW5/ZPT/I2JAfwb5o6FXoUBnl8XVV4Xw075XCM7/HhS+nuJIrg2Xl5cjISEB8fHxAID09HQUFxe7fciXlJRg7dq1AIC0tDQUFBRAFEUUFxcjPT0darUa8fHxSEhIQHl5OQBcdJue9LfPT+PID2Zcf3kU7rp2EmZ46dGwdGGCICBQ5TyS6DNNF+4KmRCVAiEqxQWDZzhtAHDTDC0OnDS5td0yIxY2uwN2UXQFjUJQoNtqc4VOX9AoBAHdVrtbm9Uhovy0GQ4RsIvOcJquCx+wTUBAr825rkMU4XA4P7ABwGp3uNa3O0QIAKx20fXa7nB+wFtdYeiA5XwdYrcVFlu/kBRFKM9/APV9uPZ9QLl/8AquYT9BABT9PngFAXA4AGfkiHBWL0AUnf/uCxhBEOBwONtcS4vObfcPJWefAZvD4arR7gAc/Wru+31e6vqHSV8w9unLlWkxoXj1jnke/9myBYfJZIJOp3O91mq1rg///svExcU5C1GpEBYWBrPZDJPJhLlz57qtazI5/ye+2DYHExCgREyM9A/9HXdfJ3md4fh9yrQBbXMmRw6rTcqycqx/oW3OGmTalV9in0bthss9v00iL+OYCxERSSJbcGi1WjQ0NLhem0wmaLXaAcvU19cDAGw2G9rb2xEZGXnBdYezTSIikpdswZGUlISamhrU1tbCYrHAaDRCr9e7LaPX67Fr1y4AQFFREZKTkyEIAvR6PYxGIywWC2pra1FTU4M5c+YMa5tERCQv2c5xqFQq5OfnIycnB3a7HStXrkRiYiK2bNmC2bNnIyUlBVlZWcjLy4PBYEBERAQ2bdoEAEhMTMStt96KJUuWQKlUIj8/H0ql8y7kwbZJRETeI4h8wg8REUnAk+NERCQJg4OIiCRhcHhJaWkp0tLSYDAYsG3bNl+XM2z19fW48847sWTJEqSnp+P1118HALS0tGD16tVITU3F6tWr0draCsB5w9dTTz0Fg8GAZcuWobKy0pflD8lutyMjIwO///3vAQC1tbXIzs6GwWDAunXrYLFYADinxlm3bh0MBgOys7NRV1fny7IvqK2tDbm5uVi8eDFuvfVWHD16dMzvp9deew3p6elYunQp1q9fj97e3jG5nx5++GHMnz8fS5cudbWNZN/s2rULqampSE1NdV1Y5BMiyc5ms4kpKSni6dOnxd7eXnHZsmXiqVOnfF3WsJhMJrGiokIURVFsb28XU1NTxVOnTonPPvusuHXrVlEURXHr1q3in//8Z1EURXH//v3i3XffLTocDvHo0aNiVlaWz2q/mO3bt4vr168X16xZI4qiKObm5op79+4VRVEUH3/8cfHvf/+7KIqi+MYbb4iPP/64KIqiuHfvXvH+++/3TcEXsWHDBvGdd94RRVEUe3t7xdbW1jG9nxoaGsRbbrlF7O7uFkXRuX/ee++9Mbmfjhw5IlZUVIjp6emuNqn7xmw2i3q9XjSbzWJLS4uo1+vFlpYW73dGFEUecXhB/+lX1Gq1a6qUsSA2NhZXXnklACA0NBRTpkyByWRCcXExMjIyAAAZGRn45JNPAMDVLggC5s2bh7a2NjQ2Nl5w+77S0NCA/fv3IysrC4Dzr7zPPvsMaWlpAIDMzEzXPiopKUFmZiYA59Q4ZWVlbnM1+YP29nZ88cUXrv6o1WqEh4eP+f1kt9vR09MDm82Gnp4exMTEjMn9dO211yIiwn0mAqn75uDBg7jhhhug0WgQERGBG264AQcOHPB6XwAOVXnFYNOv9E2hMpbU1dXhxIkTmDt3LpqamhAbGwsAiImJQVNTE4CBfdXpdH7Z16effhp5eXlQKJz/C5jNZoSHh0Olcl6h3r/uC02N40/q6uoQFRWFhx9+GBkZGXj00UfR1dU1pveTVqvF7373O9xyyy248cYbERoaiiuvvHJM76f+pO4bf/ocYXDQsHR2diI3NxePPPIIQkND3d5zToY3dqb//sc//oGoqCjMnj3b16V4jM1mw/Hjx3HHHXdg9+7dCA4OHnAubaztp9bWVhQXF6O4uBgHDhxAd3e3z/7ClttY2zcMDi8Y61OlWK1W5ObmYtmyZUhNTQUAREdHu4Y2GhsbERXlfLDVz/va0NDgd3398ssvUVJSAr1ej/Xr1+Ozzz7Dxo0b0dbWBpvN+bCp/nVfaGocf6LT6aDT6VyTgy5evBjHjx8f0/vp8OHDmDRpEqKiohAQEIDU1FR8+eWXY3o/9Sd13/jT5wiDwwvG8lQpoiji0UcfxZQpU7B69WpXu16vx+7duwEAu3fvRkpKilu7KIo4duwYwsLCXIfj/uLBBx9EaWkpSkpK8PzzzyM5ORnPPfccrrvuOtdzX3bt2uXaRxeaGsefxMTEQKfTobq6GgBQVlaGK664YkzvpwkTJuCrr75Cd3c3RFFEWVkZpk6dOqb3U39S982NN96IgwcPorW1Fa2trTh48CBuvPFGn9TOO8e95NNPP8XTTz/tmirlj3/8o69LGpZ//vOf+O1vf4tp06a5zgesX78ec+bMwbp161BfX48JEyZg8+bN0Gg0EEURBQUFOHDgAIKDg/H0008jKSnJx724sM8//xzbt2/H1q1bUVtbiwceeACtra2YOXMmCgsLoVar0dvbi7y8PJw4ccI1NU7fM2H8yYkTJ/Doo4/CarUiPj4ezzzzDBwOx5jeTy+88AI+/PBDqFQqzJw5Exs3boTJZBpz+2n9+vU4cuQIzGYzoqOjcd9992HRokWS9827776LrVu3AgD+8Ic/YOXKlT7pD4ODiIgk4VAVERFJwuAgIiJJGBxERCQJg4OIiCRhcBARkSQMDiIZnT17Fg888AAWLVqEFStW4D//8z/xxRdfIDc3F4DzEtpPP/3Ux1USSSPbo2OJLnWiKGLt2rXIyMhwPRb55MmT6OjowAsvvADAGRwVFRVYuHChL0slkoRHHEQy+eyzz6BSqXDHHXe42mbMmAGdToelS5fCYrG4bnBbvnw5PvzwQ6SmpqK5uRkA4HA4YDAYXK+J/AWPOIhkcurUKdeU9INRq9XIzc1FRUUF8vPzAQDV1dX4n//5H6xatQqHDx/GjBkzXHMYEfkLHnEQ+ZGVK1diz549AID33nsPK1as8HFFRAMxOIhkkpiYKPmRrHFxcYiOjkZZWRnKy8uxYMECmaojGjkGB5FMkpOTYbFY8Pbbb7vaTp486TY19rhx49DZ2em2XnZ2NvLy8rB48WIolUqv1Us0XAwOIpkIgoAXX3wRhw8fxqJFi5Ceno7nn38e48ePdy1z3XXXoaqqynVyHHBOq93V1cVhKvJbnB2XyM98/fXXeOaZZ/Dmm2/6uhSiQfGqKiI/sm3bNrz11lv4y1/+4utSiC6IRxxERCQJz3EQEZEkDA4iIpKEwUFERJIwOIiISBIGBxERSfL/AdZ0+ICNHQARAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(high_accident_cities, log_scale=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "Y-mmJvt1s-DU",
        "outputId": "da0df51d-0b9d-4611-f6db-124faed52f8e"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f36f386fe10>"
            ]
          },
          "metadata": {},
          "execution_count": 109
        },
        {
          "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": [
        "len(cities_by_accidents[ cities_by_accidents == 2  ])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "16I4GWojs-Bt",
        "outputId": "ed3802c5-d17c-480d-c2b9-74460e7a9fe4"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "819"
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Start_time "
      ],
      "metadata": {
        "id": "4LOjCFG9zeIc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# df.Start_Time\n",
        "\n",
        "df.Start_Time=pd.to_datetime(df.Start_Time)"
      ],
      "metadata": {
        "id": "4MsAIeAPs9_d"
      },
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# calculate the percentage of the accidents in the hours \n",
        "sns.distplot(df.Start_Time.dt.hour,bins=24, kde=False,norm_hist = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "Jip5YINCzZ1P",
        "outputId": "ebb1ffc9-d5a0-49be-a1c6-43f1996b77c9"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f36f2b53bd0>"
            ]
          },
          "metadata": {},
          "execution_count": 135
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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h1+vx7LPPor6+HvX19REVpNGoYDRO/i8FjUY9pf6iiHQehk4PIzlJF/HrajXqqNpPtk+iPgFGQ2JUfS7mUvMQ7diB6McyU/MVro9arQrZP1vqmo5jRPNema/5ENMPY3//+9/jrrvuwoIFCyLuI8sK+vsHJ31MozF5Sv1FEek8DPtlDA75I37dgDwaVfvJ9vH5A/j0xEDE7Se6lvpS8xDt2IHoxzJT8xWuT3KSLmT/bKlrOo4x7BtBf/9oRG1Fzof09JQJ94UNekmS0NfXF9z2eDyQJGlcm97eXphMJgQCAQwMDCAtLQ2HDx9Gc3MzKisrcebMGajVauj1etx5551TGA7NB9FeT81rqYkmFjboc3Jy0NXVhe7ubkiSBJfLhaeeeiqkjdVqRWNjI66++mo0NzcjNzcXKpUKf/7zn4Ntnn76aSQnJzPkiYhmWNig12q1qKioQElJCWRZRkFBAcxmM6qrq5GdnY28vDwUFhaivLwcNpsNBoMBVVVVM1E7ERFFIKI1eovFAovFEvK70tLS4M96vR41NTWXfI377rtvEuURRWaie6QMnR7G8AT3TuGlojRf8JuxJISJ1vQv/BDy63ipKM0XvNcNEZHgGPRERIJj0BMRCY5BT0QkOAY9EZHgGPRERIJj0BMRCY7X0RPRnBbNA8XPf4Fuvj1QnEFPRHNaNDfAO/8Fuvl2Ezwu3RARCY5BT0QkOAY9EZHgGPRERIJj0BMRCY5X3RDRvBPNJZnnzeVLMhn0RDTvRPtMYmBuP5c4oqWb9vZ22O122Gw21NbWjtvv9/tRVlYGm82GoqIi9PT0AADcbjc2bNiADRs2YP369WhpaZne6omIKKywZ/SyLMPpdOK5556DJEkoLCyE1WrFihUrgm0aGhqQmpqKlpYWuFwuVFZWYteuXTCbzfjb3/4GrVaLEydOYMOGDbjlllug1fIfEkREMyXsGb3b7UZWVhYyMzOh0+ngcDjQ2toa0qatrQ35+fkAALvdjo6ODiiKgqSkpGCo+3w+qFRzdIGLiGgOC3tq7fF4YDKZgtuSJMHtdo9rk5GRMfaCWi1SUlLg9XqxaNEiHD58GI888gi++OIL7Ny5M+zZvEajgtGYPJmx/K+/ekr9RRHpPAydHkZyki7i19Vq1FG1n6k+E7VXq1UTvk4865rpPhfOw2ypa6aPcX4eJnOcRH0CjIbEqPrMFjFfQ7nqqqvgcrnw8ccf4+GHH8bNN98MvV4/YXtZVtDfPzjp4xmNyVPqP5f5FGBwZOxKgkR9AoZ9I2H7yAomfHj2xQTk0ajaz1Sfidpf6uHg8axrpvtcOA+zpa6ZPsb5eZjMcXz+AD49MRBx+5m+Sic9PWXCfWGDXpIk9PX1Bbc9Hg8kSRrXpre3FyaTCYFAAAMDA0hLSwtp881vfhPJycn46KOPkJOTE+0YKAKDIzJe//AEgEsH3NetNqfHuiwiIUR7pc5sukon7Bp9Tk4Ourq60N3dDb/fD5fLBavVGtLGarWisbERANDc3Izc3FyoVCp0d3cjEAgAAD7//HN88sknWLp0aQyGQUREEwl7Rq/ValFRUYGSkhLIsoyCggKYzWZUV1cjOzsbeXl5KCwsRHl5OWw2GwwGA6qqqgAA7733Hp599llotVqo1Wr85je/waJFi2I+KCIi+kpEa/QWiwUWiyXkd6WlpcGf9Xo9ampqxvXbuHEjNm7cOMUSiYhoKnivGyIiwfGbS0REMTCb7qfDoCciioHZdD8dLt0QEQmOQU9EJDgGPRGR4IRboz8zPBLVByBz+WECRESREC7oz/m+ug1AJGbT15SJiGKBSzdERIJj0BMRCY5BT0QkOAY9EZHgGPRERIJj0BMRCY5BT0QkOOGuo4/WbLrDHBFRLEQU9O3t7dixYwdGR0dRVFSETZs2hez3+/3YunUrjh49CqPRiKqqKixbtgxvvfUWnnrqKYyMjCAhIQHl5eVYvXp1TAYyWbPpDnNERLEQdulGlmU4nU7U1dXB5XKhqakJnZ2dIW0aGhqQmpqKlpYWFBcXo7KyEgCQlpaGZ555Bq+++iqeeOIJbN26NTajICKiCYUNerfbjaysLGRmZkKn08HhcKC1tTWkTVtbG/Lz8wEAdrsdHR0dUBQFV1xxBSRJAgCYzWb4fD74/f4YDIOIiCYSdunG4/HAZDIFtyVJgtvtHtcmIyNj7AW1WqSkpMDr9YY8CLy5uRlXXHEFdDrddNUeN1zXJ6K5ZEY+jD1+/DgqKytRX18ftq1Go4LRmDzpYw0P+JCcFPlfJlqNOqr2ADCiAO/9pz+qPpaV6TAaEqPqE62h08PBsajVqojGFe34JzNfM9FnovaXmod41jXTfS6ch9lS10wf4/w8zNaxJOoTYpITYYNekiT09fUFtz0eT3A55uttent7YTKZEAgEMDAwgLS0NABAX18fNm/ejCeffBLLly8PW5AsK+jvH4x2HEGKSo3BociXhwLyaFTtJ9tn2DeC/v7RqPpEa9gvB+tKTtJFVGO0Y5mp+Zquui41D/Gsa6b7XDgPs6WumT7G+XmYrWOZSk6kp6dMuC/sGn1OTg66urrQ3d0Nv98Pl8sFq9Ua0sZqtaKxsRHA2BJNbm4uVCoVzpw5g02bNuHBBx/EtddeO6ni5yufAnj9clR/ZCXeVRPRbBT2jF6r1aKiogIlJSWQZRkFBQUwm82orq5GdnY28vLyUFhYiPLycthsNhgMBlRVVQEA/vSnP+Gzzz7D7t27sXv3bgBAfX09Fi9eHNtRCWBwJLr76gPAanN6jKohorksojV6i8UCi8US8rvS0tLgz3q9HjU1NeP63XPPPbjnnnumWCIREU3FvP9m7EyJ9kodLsMQ0XRh0M+QaL+By2UYIpouvKkZEZHgGPRERIJj0BMRCY5BT0QkOAY9EZHgGPRERIJj0BMRCY5BT0QkOAY9EZHgGPRERIJj0BMRCY5BT0QkOAY9EZHgGPRERIJj0BMRCS6ioG9vb4fdbofNZkNtbe24/X6/H2VlZbDZbCgqKkJPTw8AwOv14uc//zmuvvpqOJ3O6a2ciIgiEjboZVmG0+lEXV0dXC4Xmpqa0NnZGdKmoaEBqampaGlpQXFxMSorKwGMPWKwtLQUW7dujU31REQUVtigd7vdyMrKQmZmJnQ6HRwOB1pbW0PatLW1IT8/HwBgt9vR0dEBRVGQnJyM6667Dnq9PjbVExFRWGEfJejxeGAymYLbkiTB7XaPa5ORkTH2glotUlJS4PV6sWjRoqgL0mhUMBqTo+533vCAD8lJuojbazXqqNrPVJ+pHkOtVkXUf6brilWfidpfah7mwn/H6epz4TzMlrpm+hjn52G2jiVRnwCjITGqPhHVMu2vOEWyrKC/f3DS/RWVGoND/ojbB+TRqNrPVJ+pHiM5SRdR/5muK1Z9Jmp/qXmYC/8dp6vPhfMwW+qa6WOcn4fZOpZh3wj6+0ej6nNeenrKhPvCLt1IkoS+vr7gtsfjgSRJ49r09vYCAAKBAAYGBpCWljapYomIaHqFDfqcnBx0dXWhu7sbfr8fLpcLVqs1pI3VakVjYyMAoLm5Gbm5uVCpVLGpmIiIohJ26Uar1aKiogIlJSWQZRkFBQUwm82orq5GdnY28vLyUFhYiPLycthsNhgMBlRVVQX7W61WnD17FiMjIzhw4ADq6+uxYsWKmA6KiIi+EtEavcVigcViCfldaWlp8Ge9Xo+ampqL9m1ra5tCeURENFX8ZiwRkeAY9EREgmPQExEJjkFPRCQ4Bj0RkeAY9EREgmPQExEJjkFPRCQ4Bj0RkeAY9EREgmPQExEJjkFPRCQ4Bj0RkeAY9EREgmPQExEJjkFPRCQ4Bj0RkeAiCvr29nbY7XbYbDbU1taO2+/3+1FWVgabzYaioiL09PQE9+3Zswc2mw12ux1vvvnm9FVOREQRCRv0sizD6XSirq4OLpcLTU1N6OzsDGnT0NCA1NRUtLS0oLi4GJWVlQCAzs5OuFwuuFwu1NXV4bHHHoMsy7EZCRERXVTYoHe73cjKykJmZiZ0Oh0cDgdaW1tD2rS1tSE/Px8AYLfb0dHRAUVR0NraCofDAZ1Oh8zMTGRlZcHtdsdmJEREdFFhHw7u8XhgMpmC25IkjQtrj8eDjIyMsRfUapGSkgKv1wuPx4OrrroqpK/H47nk8RISNEhPT4lqEBf6v7yVUbW/cnla1MeYiT6si3XFsg/rmp1jiQV+GEtEJLiwQS9JEvr6+oLbHo8HkiSNa9Pb2wsACAQCGBgYQFpaWkR9iYgotsIGfU5ODrq6utDd3Q2/3w+XywWr1RrSxmq1orGxEQDQ3NyM3NxcqFQqWK1WuFwu+P1+dHd3o6urC1deeWVsRkJERBcVdo1eq9WioqICJSUlkGUZBQUFMJvNqK6uRnZ2NvLy8lBYWIjy8nLYbDYYDAZUVVUBAMxmM9auXYvbbrsNGo0GFRUV0Gg0MR8UERF9RaUoihLvIoiIKHb4YSwRkeAY9EREggu7Rj9XtLe3Y8eOHRgdHUVRURE2bdoU75Liwmq1YsGCBVCr1dBoNNi3b1+8S5ox27ZtwxtvvIHFixejqakJANDf348HHngAn3/+OZYuXYpdu3bBYDDEudLYutg8PP3009i7dy8WLVoEANiyZQssFks8y4y53t5ebN26FSdPnoRKpcKPf/xj3HXXXfPyPQFFAIFAQMnLy1M+++wzxefzKevWrVOOHz8e77Li4pZbblFOnjwZ7zLi4p133lGOHDmiOByO4O+efPJJZc+ePYqiKMqePXuUnTt3xqu8GXOxeaipqVHq6uriWNXM83g8ypEjRxRFUZSBgQHl1ltvVY4fPz4v3xNCLN1EcpsGEt/1118/7systbUVGzduBABs3LgRBw4ciEdpM+pi8zAfLVmyBN/5zncAAAsXLsTll18Oj8czL98TQgT9xW7TEO5WCyK7++67cfvtt+Oll16Kdylxd/LkSSxZsgQAkJ6ejpMnT8a5ovh58cUXsW7dOmzbtg2nT5+OdzkzqqenB8eOHcNVV101L98TQgQ9feUvf/kLGhsb8eyzz+LFF1/EoUOH4l3SrKFSqaBSqeJdRlz89Kc/RUtLC/bv348lS5bgiSeeiHdJM+bcuXO4//778cgjj2DhwoUh++bLe0KIoOetFr5yftyLFy+GzWab93cLXbx4MU6cOAEAOHHiRPDDyPnmG9/4BjQaDdRqNYqKivDBBx/Eu6QZMTIygvvvvx/r1q3DrbfeCmB+vieECPpIbtMwHwwODuLs2bPBn9966y2YzeY4VxVfVqsVr7zyCgDglVdeQV5eXpwrio/zwQYABw4cmBfvC0VR8Oijj+Lyyy/HL37xi+Dv5+N7Qphvxv7rX//Cb3/72+BtGn71q1/Fu6QZ193djXvvvRfA2ANjfvSjH82rediyZQveeecdeL1eLF68GPfddx9+8IMfoKysDL29vbjsssuwa9cuGI3GeJcaUxebh3feeQcffvghAGDp0qVwOp3BdWpRvfvuu7jjjjuwcuVKqNVj57RbtmzBlVdeOe/eE8IEPRERXZwQSzdERDQxBj0RkeAY9EREgmPQExEJjkFPRCQ4Bj0RkeCEuU0x0TPPPIOmpiao1Wqo1Wo4nU68//77+MlPfoKkpKSoXmvfvn248cYbJ/yG9b333ouenh4MDg7i1KlTWLZsGQBg+/bt2LlzJ/76179OeTxE04VBT0J4//338cYbb6CxsRE6nQ6nTp3CyMgInn/+eaxfvz6qoJdlGY2NjTCbzRMG/e7duwEABw8eRH19Pfbs2RPcx5Cn2YZBT0L48ssvkZaWBp1OBwBYtGgRnn/+eZw4cQJ33XUXjEYjXnjhBWzfvh0ffPABfD4f7HY77r//fgBjX4tfu3Yt3n77bRQXF+PIkSN46KGHkJiYiJdeegmJiYkR13L11Vfj/fffx8GDB/H0008jJSUFH330EdauXYuVK1fi+eefh8/nw+7du7F8+XKcOnUK27dvxxdffAEAeOSRR3DttddO/yTR/BXXu+ETTZOzZ88q69evV2699VZl+/btysGDBxVFGf8gFq/XqyjK2MNq7rzzTuXYsWPBdrW1tcF2d955p+J2u8Me99///reyadOmkN9990t0zu0AAAHpSURBVLvfDe679tprFY/Ho/h8PmXNmjVKdXW1oiiK8sc//lF5/PHHFUVRlC1btiiHDh1SFEVRPv/8c+WHP/zhpOaAaCI8oychLFiwAPv27cO7776LgwcP4oEHHsCDDz44rt1rr72GvXv3IhAI4Msvv8THH3+MVatWAQBuu+22aa8rJycneE+Z5cuX48YbbwQArFy5EgcPHgQAvP322+js7Az2OXv2LM6dO4cFCxZMez00PzHoSRgajQY33HADbrjhBqxcuTJ4h8Lzuru7UV9fj5dffhkGgwG//vWv4fP5gvuj/cA2EueXkgBArVYHt9VqNWRZBgCMjo5i79690Ov10358IoCXV5IgPvnkE3R1dQW3jx07hssuuwwLFizAuXPnAIw9gCIpKQkpKSn473//i/b29glf7+v9Ym3NmjV44YUXgtvHjh2bkePS/MEzehLC4OAgHn/8cZw5cwYajQZZWVlwOp1wuVwoKSnBkiVL8MILL+CKK67A2rVrYTKZcM0110z4evn5+di+ffukPoyN1qOPPgqn04l169ZBlmVcd911cDqdMTsezT+8TTERkeC4dENEJDgu3RCFcf5bsF/30EMP4aabbopTRUTR4dINEZHguHRDRCQ4Bj0RkeAY9EREgmPQExEJ7v8BA/5Ca3uFpGAAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " - A higer percentage of accidents occur in the hour of almost 1pm to 5 pm  & \n",
        " * next higer percentage is 5 am to 10 am(these are the office times morning and evening)"
      ],
      "metadata": {
        "id": "LnlLT-pB5FIC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "VaxnbsEVzZ3C",
        "outputId": "df491811-3a6d-4fca-8409-da0297225469"
      },
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f36f3870290>"
            ]
          },
          "metadata": {},
          "execution_count": 139
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* almost Friday has higer number of accident(need to investigate)\n",
        "* and less number of accident on weekends"
      ],
      "metadata": {
        "id": "NlQWLqik9d4U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# weekday\n",
        "sns.distplot(df.Start_Time.dt.dayofweek,bins=7, kde=False,norm_hist = True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "6Xkf08-ozZ5R",
        "outputId": "793132e2-8a24-4397-b523-6b2e96e9ebc2"
      },
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f36f2d46690>"
            ]
          },
          "metadata": {},
          "execution_count": 143
        },
        {
          "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": [
        "sunday_start_time =   df.Start_Time[df.Start_Time.dt.dayofweek == 6]\n",
        "saturaday_start_time = df.Start_Time[df.Start_Time.dt.dayofweek == 5]\n",
        "friday_start_time =   df.Start_Time[df.Start_Time.dt.dayofweek == 4]\n",
        "thursday_start_time = df.Start_Time[df.Start_Time.dt.dayofweek == 3]\n",
        "wed_start_time =   df.Start_Time[df.Start_Time.dt.dayofweek == 2]\n",
        "tuesday_start_time =   df.Start_Time[df.Start_Time.dt.dayofweek == 1]\n",
        "monday_start_time =   df.Start_Time[df.Start_Time.dt.dayofweek == 0]\n"
      ],
      "metadata": {
        "id": "8rcjJyT_zZ9A"
      },
      "execution_count": 153,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(sunday_start_time.dt.hour,bins=24, kde=False,norm_hist = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "VS__IgdzzZ_V",
        "outputId": "f851f14b-dad9-48fb-cae2-0a12e5ff3782"
      },
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f36f37fd1d0>"
            ]
          },
          "metadata": {},
          "execution_count": 158
        },
        {
          "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": [
        "sns.distplot(saturaday_start_time.dt.hour,bins=24, kde=False,norm_hist = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "uu9LlHrkzaBy",
        "outputId": "09183c01-e226-4da8-db35-d8e60e400bc0"
      },
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f36f3950310>"
            ]
          },
          "metadata": {},
          "execution_count": 159
        },
        {
          "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": [
        "'''I wannt to figure out in which month has more accidents'''\n",
        "sns.distplot(df.Start_Time.dt.month,bins=12, kde=False,norm_hist = True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "EHdWNogSCCQ-",
        "outputId": "42ddedd1-92bd-42f7-d34b-45c20e7ccd1f"
      },
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f36f2243350>"
            ]
          },
          "metadata": {},
          "execution_count": 162
        },
        {
          "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_year = df[df.Start_Time.dt.year == 2018]"
      ],
      "metadata": {
        "id": "fb6HI-ClCCTR"
      },
      "execution_count": 172,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## 2sample_df.Start_Lng018\n",
        "sns.distplot(df_year.Start_Time.dt.month,bins=12, kde=False,norm_hist = True)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "MCUjCbNyCCV8",
        "outputId": "f37018aa-e044-4c2c-a88c-fa11e1f1fd38"
      },
      "execution_count": 173,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f36f4752190>"
            ]
          },
          "metadata": {},
          "execution_count": 173
        },
        {
          "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": [
        "## 2019\n",
        "df_year = df[df.Start_Time.dt.year == 2019]\n",
        "sns.distplot(df_year.Start_Time.dt.month,bins=12, kde=False,norm_hist = True)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "B372-rF0CCYA",
        "outputId": "ad2b5cac-a118-4d23-856b-1c6b8928d7cc"
      },
      "execution_count": 174,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f36f1667390>"
            ]
          },
          "metadata": {},
          "execution_count": 174
        },
        {
          "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": [
        "## 2020\n",
        "df_year = df[df.Start_Time.dt.year == 2020]\n",
        "sns.distplot(df_year.Start_Time.dt.month,bins=12, kde=False,norm_hist = True)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "OirD99j2FSPi",
        "outputId": "51c71be0-af56-4b3e-b100-09dbc0ea57b0"
      },
      "execution_count": 175,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f36f4097ed0>"
            ]
          },
          "metadata": {},
          "execution_count": 175
        },
        {
          "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": [
        "## 2016## 2020\n",
        "df_year = df[df.Start_Time.dt.year == 2020]\n",
        "sns.distplot(df_year.Start_Time.dt.month,bins=12, kde=False,norm_hist = True)\n",
        "\n",
        "\n",
        "df_year = df[df.Start_Time.dt.year == 2016]\n",
        "sns.distplot(df_year.Start_Time.dt.month,bins=12, kde=False,norm_hist = True)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "XKo-j3gBFSUz",
        "outputId": "3c797063-01f3-4f78-d58d-c30e60c9c4ed"
      },
      "execution_count": 177,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f36f0d3cdd0>"
            ]
          },
          "metadata": {},
          "execution_count": 177
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "i can't find about month wise trend becaue of most of the data points are missing hard to figure outable"
      ],
      "metadata": {
        "id": "7MKPJGgyF1Qb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "Fd7KUArVFSbj"
      },
      "execution_count": 184,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Analyzing the latitude and longitude"
      ],
      "metadata": {
        "id": "DsT7fgBhPmP3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.Start_Lat"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gU_MOFvXG4GR",
        "outputId": "d06af686-3052-475b-c652-7dd0b18c9192"
      },
      "execution_count": 185,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0          40.108910\n",
              "1          39.865420\n",
              "2          39.102660\n",
              "3          41.062130\n",
              "4          39.172393\n",
              "             ...    \n",
              "2845337    34.002480\n",
              "2845338    32.766960\n",
              "2845339    33.775450\n",
              "2845340    33.992460\n",
              "2845341    34.133930\n",
              "Name: Start_Lat, Length: 2845342, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 185
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.Start_Lng"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q2_g9h3wG4If",
        "outputId": "e26386a3-d5d1-449a-c969-3e025ca9693c"
      },
      "execution_count": 186,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0          -83.092860\n",
              "1          -84.062800\n",
              "2          -84.524680\n",
              "3          -81.537840\n",
              "4          -84.492792\n",
              "              ...    \n",
              "2845337   -117.379360\n",
              "2845338   -117.148060\n",
              "2845339   -117.847790\n",
              "2845340   -118.403020\n",
              "2845341   -117.230920\n",
              "Name: Start_Lng, Length: 2845342, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 186
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sample_df = df.sample(int(0.1* len(df)))\n",
        "sns.scatterplot(x=sample_df.Start_Lng, y=sample_df.Start_Lat,size = 0.0001 )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "4jadOnXaG4Kv",
        "outputId": "80897d4b-3239-4dff-d76c-c96cbc681107"
      },
      "execution_count": 190,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f36f0dbcb90>"
            ]
          },
          "metadata": {},
          "execution_count": 190
        },
        {
          "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": [
        " "
      ],
      "metadata": {
        "id": "bxgHiDDeG4M-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "rZM_8z3EG4Rj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "i5kJMOJhG4Wf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "Lnj8hkaoG4bY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "jgVO_7m9G4eL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "TW4niknYG4gb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "ngsEx3PLG4ik"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "ADj_UhaoG4ky"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "WdNtXCYFG4nP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "0BcqYmsfFSd3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Insight from this dataset\n",
        "\n",
        "* No New york data in this dataset\n",
        "* almost the 1100 cities have only one accident and 2 accident have 800 cities\n",
        "* Number of accidents have exponential graph\n",
        "* less than 4 % cities have higer accidents\n",
        "* Alot to investigate \n"
      ],
      "metadata": {
        "id": "BKhfzymAJ6HW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ask questions and then answers \n",
        "\n",
        "\n",
        "1.   Which city have more number of accidents\n",
        "2.   How many cities have only 1 accidents\n",
        "3.   What time of the day are more accidents most frequent in \n",
        "4.   Which time has higer percentage  of accidents \n",
        "5.   and find out which day has higer higer percentage ?\n",
        "\n"
      ],
      "metadata": {
        "id": "8ZbFIVSKJocY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "jmydFoFD0v2r"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}