main_nb.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "073f6ad3-ad8a-490c-a261-89645736ce34",
      "metadata": {
        "id": "073f6ad3-ad8a-490c-a261-89645736ce34",
        "outputId": "285ad4e7-035b-476d-f430-3d17b6c96861",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'JahnaviM-CloudHostedNotebook'...\n",
            "remote: Enumerating objects: 25, done.\u001b[K\n",
            "remote: Counting objects: 100% (10/10), done.\u001b[K\n",
            "remote: Compressing objects: 100% (7/7), done.\u001b[K\n",
            "remote: Total 25 (delta 1), reused 9 (delta 1), pack-reused 15 (from 1)\u001b[K\n",
            "Receiving objects: 100% (25/25), 46.78 MiB | 22.73 MiB/s, done.\n",
            "Resolving deltas: 100% (1/1), done.\n",
            "/root/JahnaviM-CloudHostedNotebook\n"
          ]
        }
      ],
      "source": [
        "# Clone Notebook to access mylib\n",
        "!rm -rf JahnaviM-CloudHostedNotebook/*\n",
        "!git clone https://github.com/nogibjj/JahnaviM-CloudHostedNotebook.git\n",
        "%cd JahnaviM-CloudHostedNotebook"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "3423c945-3b92-4feb-9ef0-15b769c2b16b",
      "metadata": {
        "id": "3423c945-3b92-4feb-9ef0-15b769c2b16b"
      },
      "outputs": [],
      "source": [
        "# import libraries/methods\n",
        "from IPython.display import display\n",
        "from mylib.lib import read_df, summarize_df, hist_time_occ"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "8f2fc907-ca25-48a9-8538-631032959d5c",
      "metadata": {
        "id": "8f2fc907-ca25-48a9-8538-631032959d5c",
        "outputId": "5c10178f-2395-4246-e6fe-e9f5b17cdd18",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Crime Dataset Loaded from zipped csv file.\n"
          ]
        }
      ],
      "source": [
        "# read data\n",
        "df = read_df(\"Crime_Data_from_2020_to_Present.csv.zip\")\n",
        "print('Crime Dataset Loaded from zipped csv file.')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "8fccdfd0-ab75-42b9-a60a-a6a5ca950321",
      "metadata": {
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        "outputId": "69153e1e-90a3-43d9-ef32-1b20d57e8121",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Summary Statistics:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "              DR_NO       TIME OCC           AREA    Rpt Dist No  \\\n",
              "count  9.744770e+05  974477.000000  974477.000000  974477.000000   \n",
              "mean   2.195654e+08    1338.771108      10.705048    1116.939579   \n",
              "std    1.285558e+07     651.717033       6.106287     610.729787   \n",
              "min    8.170000e+02       1.000000       1.000000     101.000000   \n",
              "25%    2.106058e+08     900.000000       5.000000     589.000000   \n",
              "50%    2.208087e+08    1420.000000      11.000000    1141.000000   \n",
              "75%    2.309064e+08    1900.000000      16.000000    1617.000000   \n",
              "max    2.499253e+08    2359.000000      21.000000    2199.000000   \n",
              "\n",
              "            Part 1-2         Crm Cd       Vict Age      Premis Cd  \\\n",
              "count  974477.000000  974477.000000  974477.000000  974463.000000   \n",
              "mean        1.405166     500.748719      29.168748     306.098905   \n",
              "std         0.490924     206.374691      21.954094     218.651133   \n",
              "min         1.000000     110.000000      -4.000000     101.000000   \n",
              "25%         1.000000     331.000000       0.000000     101.000000   \n",
              "50%         1.000000     442.000000      30.000000     203.000000   \n",
              "75%         2.000000     626.000000      44.000000     501.000000   \n",
              "max         2.000000     956.000000     120.000000     976.000000   \n",
              "\n",
              "       Weapon Used Cd       Crm Cd 1      Crm Cd 2     Crm Cd 3   Crm Cd 4  \\\n",
              "count   325782.000000  974466.000000  68760.000000  2309.000000   64.00000   \n",
              "mean       363.797727     500.501656    958.139311   984.192724  991.21875   \n",
              "std        123.664671     206.171549    110.261645    51.506344   27.06985   \n",
              "min        101.000000     110.000000    210.000000   310.000000  821.00000   \n",
              "25%        311.000000     331.000000    998.000000   998.000000  998.00000   \n",
              "50%        400.000000     442.000000    998.000000   998.000000  998.00000   \n",
              "75%        400.000000     626.000000    998.000000   998.000000  998.00000   \n",
              "max        516.000000     956.000000    999.000000   999.000000  999.00000   \n",
              "\n",
              "                 LAT            LON      TimeOccHr  \n",
              "count  974477.000000  974477.000000  974477.000000  \n",
              "mean       33.995066    -118.079971      13.506428  \n",
              "std         1.643523       5.696584       6.520273  \n",
              "min         0.000000    -118.667600       0.016667  \n",
              "25%        34.014600    -118.430600       9.000000  \n",
              "50%        34.058900    -118.322500      14.333333  \n",
              "75%        34.164900    -118.273900      19.000000  \n",
              "max        34.334300       0.000000      23.983333  "
            ],
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              "\n",
              "  <div id=\"df-007f7f72-1b15-451e-bb08-11894b803d82\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DR_NO</th>\n",
              "      <th>TIME OCC</th>\n",
              "      <th>AREA</th>\n",
              "      <th>Rpt Dist No</th>\n",
              "      <th>Part 1-2</th>\n",
              "      <th>Crm Cd</th>\n",
              "      <th>Vict Age</th>\n",
              "      <th>Premis Cd</th>\n",
              "      <th>Weapon Used Cd</th>\n",
              "      <th>Crm Cd 1</th>\n",
              "      <th>Crm Cd 2</th>\n",
              "      <th>Crm Cd 3</th>\n",
              "      <th>Crm Cd 4</th>\n",
              "      <th>LAT</th>\n",
              "      <th>LON</th>\n",
              "      <th>TimeOccHr</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>9.744770e+05</td>\n",
              "      <td>974477.000000</td>\n",
              "      <td>974477.000000</td>\n",
              "      <td>974477.000000</td>\n",
              "      <td>974477.000000</td>\n",
              "      <td>974477.000000</td>\n",
              "      <td>974477.000000</td>\n",
              "      <td>974463.000000</td>\n",
              "      <td>325782.000000</td>\n",
              "      <td>974466.000000</td>\n",
              "      <td>68760.000000</td>\n",
              "      <td>2309.000000</td>\n",
              "      <td>64.00000</td>\n",
              "      <td>974477.000000</td>\n",
              "      <td>974477.000000</td>\n",
              "      <td>974477.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.195654e+08</td>\n",
              "      <td>1338.771108</td>\n",
              "      <td>10.705048</td>\n",
              "      <td>1116.939579</td>\n",
              "      <td>1.405166</td>\n",
              "      <td>500.748719</td>\n",
              "      <td>29.168748</td>\n",
              "      <td>306.098905</td>\n",
              "      <td>363.797727</td>\n",
              "      <td>500.501656</td>\n",
              "      <td>958.139311</td>\n",
              "      <td>984.192724</td>\n",
              "      <td>991.21875</td>\n",
              "      <td>33.995066</td>\n",
              "      <td>-118.079971</td>\n",
              "      <td>13.506428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.285558e+07</td>\n",
              "      <td>651.717033</td>\n",
              "      <td>6.106287</td>\n",
              "      <td>610.729787</td>\n",
              "      <td>0.490924</td>\n",
              "      <td>206.374691</td>\n",
              "      <td>21.954094</td>\n",
              "      <td>218.651133</td>\n",
              "      <td>123.664671</td>\n",
              "      <td>206.171549</td>\n",
              "      <td>110.261645</td>\n",
              "      <td>51.506344</td>\n",
              "      <td>27.06985</td>\n",
              "      <td>1.643523</td>\n",
              "      <td>5.696584</td>\n",
              "      <td>6.520273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>8.170000e+02</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>101.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>110.000000</td>\n",
              "      <td>-4.000000</td>\n",
              "      <td>101.000000</td>\n",
              "      <td>101.000000</td>\n",
              "      <td>110.000000</td>\n",
              "      <td>210.000000</td>\n",
              "      <td>310.000000</td>\n",
              "      <td>821.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-118.667600</td>\n",
              "      <td>0.016667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.106058e+08</td>\n",
              "      <td>900.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>589.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>331.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>101.000000</td>\n",
              "      <td>311.000000</td>\n",
              "      <td>331.000000</td>\n",
              "      <td>998.000000</td>\n",
              "      <td>998.000000</td>\n",
              "      <td>998.00000</td>\n",
              "      <td>34.014600</td>\n",
              "      <td>-118.430600</td>\n",
              "      <td>9.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.208087e+08</td>\n",
              "      <td>1420.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>1141.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>442.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>203.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>442.000000</td>\n",
              "      <td>998.000000</td>\n",
              "      <td>998.000000</td>\n",
              "      <td>998.00000</td>\n",
              "      <td>34.058900</td>\n",
              "      <td>-118.322500</td>\n",
              "      <td>14.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.309064e+08</td>\n",
              "      <td>1900.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>1617.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>626.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>501.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>626.000000</td>\n",
              "      <td>998.000000</td>\n",
              "      <td>998.000000</td>\n",
              "      <td>998.00000</td>\n",
              "      <td>34.164900</td>\n",
              "      <td>-118.273900</td>\n",
              "      <td>19.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.499253e+08</td>\n",
              "      <td>2359.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>2199.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>956.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>976.000000</td>\n",
              "      <td>516.000000</td>\n",
              "      <td>956.000000</td>\n",
              "      <td>999.000000</td>\n",
              "      <td>999.000000</td>\n",
              "      <td>999.00000</td>\n",
              "      <td>34.334300</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>23.983333</td>\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b9c50fe3-8518-4d07-8ae7-022744a39413 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(summarize_df(df))\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"DR_NO\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 115390517.65811437,\n        \"min\": 817.0,\n        \"max\": 249925282.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          219565398.0640005,\n          220808651.0,\n          974477.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TIME OCC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 344097.5456302899,\n        \"min\": 1.0,\n        \"max\": 974477.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1338.7711079892085,\n          1420.0,\n          974477.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AREA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 344526.0709506324,\n        \"min\": 1.0,\n        \"max\": 974477.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          10.705047938535234,\n          11.0,\n          974477.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rpt Dist No\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 344157.78478301957,\n        \"min\": 101.0,\n        \"max\": 974477.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1116.9395788715383,\n          1141.0,\n          974477.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Part 1-2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 344529.19808513706,\n        \"min\": 0.4909244033959687,\n        \"max\": 974477.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.4051660531751904,\n          2.0,\n          0.4909244033959687\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Crm Cd\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 344369.53026877344,\n        \"min\": 110.0,\n        \"max\": 974477.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          500.7487185433828,\n          442.0,\n          974477.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Vict Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 344517.4709936701,\n        \"min\": -4.0,\n        \"max\": 974477.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          29.168747954030728,\n          30.0,\n          974477.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Premis Cd\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 344403.25854905305,\n        \"min\": 101.0,\n        \"max\": 974463.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          974463.0,\n          306.0989047300924,\n          501.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weapon Used Cd\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 115069.51962452765,\n        \"min\": 101.0,\n        \"max\": 325782.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          325782.0,\n          363.7977267006771,\n          400.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Crm Cd 1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 344365.6639373171,\n        \"min\": 110.0,\n        \"max\": 974466.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          500.50165629175365,\n          442.0,\n          974466.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Crm Cd 2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24047.027914487335,\n        \"min\": 110.26164478575902,\n        \"max\": 68760.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          68760.0,\n          958.1393106457242,\n          999.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Crm Cd 3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 662.675269746641,\n        \"min\": 51.50634410019204,\n        \"max\": 2309.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2309.0,\n          984.192724122997,\n          999.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Crm Cd 4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 431.2142291821074,\n        \"min\": 27.069850123602194,\n        \"max\": 999.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          64.0,\n          991.21875,\n          999.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LAT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 344520.9497501721,\n        \"min\": 0.0,\n        \"max\": 974477.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          33.995065719560344,\n          34.0589,\n          974477.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LON\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 344559.25316753594,\n        \"min\": -118.6676,\n        \"max\": 974477.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -118.07997062362686,\n          -118.3225,\n          974477.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TimeOccHr\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 344525.28564397705,\n        \"min\": 0.016666666666666666,\n        \"max\": 974477.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          13.506428114773362,\n          14.333333333333334,\n          974477.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# get summary stats\n",
        "print('Summary Statistics:')\n",
        "display(summarize_df(df))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "aef7b7dc-9b74-4d49-89f8-efd63c2f418a",
      "metadata": {
        "id": "aef7b7dc-9b74-4d49-89f8-efd63c2f418a",
        "outputId": "c797b9d1-094d-4089-c478-f1ea4148a1dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 600
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Histogram Plot:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Histogram Loaded'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "# show hist plot\n",
        "print('Histogram Plot:')\n",
        "hist_time_occ(df)"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9bBbflASaHCB"
      },
      "id": "9bBbflASaHCB",
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jxGRr-i4aRit"
      },
      "id": "jxGRr-i4aRit",
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kpo6vXi4aSq2"
      },
      "id": "kpo6vXi4aSq2",
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VmS2Y-psaykj"
      },
      "id": "VmS2Y-psaykj",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "",
      "name": ""
    },
    "language_info": {
      "name": ""
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}