traffic.ipynb

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        "outputId": "4390fd3c-6e80-4c5b-db11-6de33120915d"
      },
      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import matplotlib.pyplot as plt # data visualization\n",
        "\n",
        "link = 'https://raw.githubusercontent.com/dlsucomet/proj-traffic-incident/master/data_mmda_traffic_spatial.csv'\n",
        "\n",
        "# Central Question: What may cause incidents to occur more often? \n",
        "# Quesiton 1: Time (When do these incidents happen more?)\n",
        "# Question 2: Involved and Time (Does the type of vehicle affect if you'll get into accidents more?)\n",
        "# Question 3: City/Direction and Time (Where do these incidents happen more?)\n",
        "traffic_df = pd.read_csv(link)\n",
        "traffic_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Time</th>\n",
              "      <th>City</th>\n",
              "      <th>Location</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "      <th>Direction</th>\n",
              "      <th>Type</th>\n",
              "      <th>Lanes_Blocked</th>\n",
              "      <th>Involved</th>\n",
              "      <th>Tweet</th>\n",
              "      <th>Source</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8/20/2018</td>\n",
              "      <td>7:55 AM</td>\n",
              "      <td>Pasig City</td>\n",
              "      <td>ORTIGAS EMERALD</td>\n",
              "      <td>14.586343</td>\n",
              "      <td>121.061481</td>\n",
              "      <td>EB</td>\n",
              "      <td>VEHICULAR ACCIDENT</td>\n",
              "      <td>1.0</td>\n",
              "      <td>TAXI AND MC</td>\n",
              "      <td>MMDA ALERT: Vehicular accident at Ortigas Emer...</td>\n",
              "      <td>https://twitter.com/mmda/status/10313302019705...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8/20/2018</td>\n",
              "      <td>8:42 AM</td>\n",
              "      <td>Mandaluyong</td>\n",
              "      <td>EDSA GUADIX</td>\n",
              "      <td>14.589432</td>\n",
              "      <td>121.057243</td>\n",
              "      <td>NB</td>\n",
              "      <td>STALLED L300 DUE TO MECHANICAL PROBLEM</td>\n",
              "      <td>1.0</td>\n",
              "      <td>L300</td>\n",
              "      <td>MMDA ALERT: Stalled L300 due to mechanical pro...</td>\n",
              "      <td>https://twitter.com/mmda/status/10313462477459...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8/20/2018</td>\n",
              "      <td>9:13 AM</td>\n",
              "      <td>Makati City</td>\n",
              "      <td>EDSA ROCKWELL</td>\n",
              "      <td>14.559818</td>\n",
              "      <td>121.040737</td>\n",
              "      <td>SB</td>\n",
              "      <td>VEHICULAR ACCIDENT</td>\n",
              "      <td>1.0</td>\n",
              "      <td>SUV AND L300</td>\n",
              "      <td>MMDA ALERT: Vehicular accident at EDSA Rockwel...</td>\n",
              "      <td>https://twitter.com/mmda/status/10313589669896...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8/20/2018</td>\n",
              "      <td>8:42 AM</td>\n",
              "      <td>Mandaluyong</td>\n",
              "      <td>EDSA GUADIX</td>\n",
              "      <td>14.589432</td>\n",
              "      <td>121.057243</td>\n",
              "      <td>NB</td>\n",
              "      <td>STALLED L300 DUE TO MECHANICAL PROBLEM</td>\n",
              "      <td>1.0</td>\n",
              "      <td>L300</td>\n",
              "      <td>MMDA ALERT: Stalled L300 due to mechanical pro...</td>\n",
              "      <td>https://twitter.com/mmda/status/10313590696535...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8/20/2018</td>\n",
              "      <td>10:27 AM</td>\n",
              "      <td>San Juan</td>\n",
              "      <td>ORTIGAS CLUB FILIPINO</td>\n",
              "      <td>14.601846</td>\n",
              "      <td>121.046754</td>\n",
              "      <td>EB</td>\n",
              "      <td>VEHICULAR ACCIDENT</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2 CARS</td>\n",
              "      <td>MMDA ALERT: Vehicular accident at Ortigas Club...</td>\n",
              "      <td>https://twitter.com/mmda/status/10313711248424...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Date  ...                                             Source\n",
              "0  8/20/2018  ...  https://twitter.com/mmda/status/10313302019705...\n",
              "1  8/20/2018  ...  https://twitter.com/mmda/status/10313462477459...\n",
              "2  8/20/2018  ...  https://twitter.com/mmda/status/10313589669896...\n",
              "3  8/20/2018  ...  https://twitter.com/mmda/status/10313590696535...\n",
              "4  8/20/2018  ...  https://twitter.com/mmda/status/10313711248424...\n",
              "\n",
              "[5 rows x 12 columns]"
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        "outputId": "b78c565c-3b18-404a-a8e7-cf947fdf9ab9"
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      "source": [
        "traffic_df['Date'] = pd.to_datetime(traffic_df['Date']) \n",
        "traffic_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Time</th>\n",
              "      <th>City</th>\n",
              "      <th>Location</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "      <th>Direction</th>\n",
              "      <th>Type</th>\n",
              "      <th>Lanes_Blocked</th>\n",
              "      <th>Involved</th>\n",
              "      <th>Tweet</th>\n",
              "      <th>Source</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2018-08-20</td>\n",
              "      <td>7:55 AM</td>\n",
              "      <td>Pasig City</td>\n",
              "      <td>ORTIGAS EMERALD</td>\n",
              "      <td>14.586343</td>\n",
              "      <td>121.061481</td>\n",
              "      <td>EB</td>\n",
              "      <td>VEHICULAR ACCIDENT</td>\n",
              "      <td>1.0</td>\n",
              "      <td>TAXI AND MC</td>\n",
              "      <td>MMDA ALERT: Vehicular accident at Ortigas Emer...</td>\n",
              "      <td>https://twitter.com/mmda/status/10313302019705...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018-08-20</td>\n",
              "      <td>8:42 AM</td>\n",
              "      <td>Mandaluyong</td>\n",
              "      <td>EDSA GUADIX</td>\n",
              "      <td>14.589432</td>\n",
              "      <td>121.057243</td>\n",
              "      <td>NB</td>\n",
              "      <td>STALLED L300 DUE TO MECHANICAL PROBLEM</td>\n",
              "      <td>1.0</td>\n",
              "      <td>L300</td>\n",
              "      <td>MMDA ALERT: Stalled L300 due to mechanical pro...</td>\n",
              "      <td>https://twitter.com/mmda/status/10313462477459...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018-08-20</td>\n",
              "      <td>9:13 AM</td>\n",
              "      <td>Makati City</td>\n",
              "      <td>EDSA ROCKWELL</td>\n",
              "      <td>14.559818</td>\n",
              "      <td>121.040737</td>\n",
              "      <td>SB</td>\n",
              "      <td>VEHICULAR ACCIDENT</td>\n",
              "      <td>1.0</td>\n",
              "      <td>SUV AND L300</td>\n",
              "      <td>MMDA ALERT: Vehicular accident at EDSA Rockwel...</td>\n",
              "      <td>https://twitter.com/mmda/status/10313589669896...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-08-20</td>\n",
              "      <td>8:42 AM</td>\n",
              "      <td>Mandaluyong</td>\n",
              "      <td>EDSA GUADIX</td>\n",
              "      <td>14.589432</td>\n",
              "      <td>121.057243</td>\n",
              "      <td>NB</td>\n",
              "      <td>STALLED L300 DUE TO MECHANICAL PROBLEM</td>\n",
              "      <td>1.0</td>\n",
              "      <td>L300</td>\n",
              "      <td>MMDA ALERT: Stalled L300 due to mechanical pro...</td>\n",
              "      <td>https://twitter.com/mmda/status/10313590696535...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2018-08-20</td>\n",
              "      <td>10:27 AM</td>\n",
              "      <td>San Juan</td>\n",
              "      <td>ORTIGAS CLUB FILIPINO</td>\n",
              "      <td>14.601846</td>\n",
              "      <td>121.046754</td>\n",
              "      <td>EB</td>\n",
              "      <td>VEHICULAR ACCIDENT</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2 CARS</td>\n",
              "      <td>MMDA ALERT: Vehicular accident at Ortigas Club...</td>\n",
              "      <td>https://twitter.com/mmda/status/10313711248424...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "        Date  ...                                             Source\n",
              "0 2018-08-20  ...  https://twitter.com/mmda/status/10313302019705...\n",
              "1 2018-08-20  ...  https://twitter.com/mmda/status/10313462477459...\n",
              "2 2018-08-20  ...  https://twitter.com/mmda/status/10313589669896...\n",
              "3 2018-08-20  ...  https://twitter.com/mmda/status/10313590696535...\n",
              "4 2018-08-20  ...  https://twitter.com/mmda/status/10313711248424...\n",
              "\n",
              "[5 rows x 12 columns]"
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      "source": [
        "## Question 1: When do these incidents happen more?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tzTZuLkPAsh-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "0507a281-6482-4ea9-8b15-d6d36ed92730"
      },
      "source": [
        "traffic_df['Time'] = traffic_df['Time'].astype('string')\n",
        "traffic_df.dropna(subset=['Time'], inplace=True)\n",
        "traffic_df['Time'].isnull().any()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "2vO3RgBeBcZG",
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          "height": 54
        },
        "outputId": "3612b500-1003-443f-bdcf-944fd5c3a6ba"
      },
      "source": [
        "def get_hour(str):\n",
        "  hour = 0\n",
        "  if str[-2:] == 'PM' or str[-4:] == 'PM M':\n",
        "    hour += 12\n",
        "  \n",
        "  if str[1] == ':' and str[:2] != '12':\n",
        "    hour += int(str[:1])\n",
        "  elif str[:2] != '12':\n",
        "    hour += int(str[:2])\n",
        "\n",
        "  return hour\n",
        "\n",
        "traffic_df.loc[:, 'Hour'] = traffic_df['Time'].apply(get_hour)\n",
        "traffic_df['Hour'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 7,  8,  9, 10, 11, 12, 14, 16, 17, 18, 19, 20, 13, 15,  6,  0,  5,\n",
              "       21, 22, 23,  1,  3,  4,  2, 34])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
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    {
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          "height": 167
        },
        "outputId": "3d1c228b-3f44-4825-c2ef-a5ce57b72f65"
      },
      "source": [
        "traffic_df.loc[traffic_df['Hour'] == 34]"
      ],
      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Time</th>\n",
              "      <th>City</th>\n",
              "      <th>Location</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "      <th>Direction</th>\n",
              "      <th>Type</th>\n",
              "      <th>Lanes_Blocked</th>\n",
              "      <th>Involved</th>\n",
              "      <th>Tweet</th>\n",
              "      <th>Source</th>\n",
              "      <th>Hour</th>\n",
              "      <th>Day_of_Week</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>13547</th>\n",
              "      <td>2019-12-03</td>\n",
              "      <td>22:55 PM</td>\n",
              "      <td>Quezon City</td>\n",
              "      <td>EDSA SANTOLAN FLYOVER</td>\n",
              "      <td>14.609768</td>\n",
              "      <td>121.055583</td>\n",
              "      <td>NB</td>\n",
              "      <td>VEHICULAR ACCIDENT</td>\n",
              "      <td>1.0</td>\n",
              "      <td>SUV AND MC</td>\n",
              "      <td>MMDA ALERT: Vehicular accident at EDSA Santola...</td>\n",
              "      <td>https://twitter.com/mmda/status/12018924338411...</td>\n",
              "      <td>34</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Date      Time  ... Hour Day_of_Week\n",
              "13547 2019-12-03  22:55 PM  ...   34           1\n",
              "\n",
              "[1 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
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          "height": 35
        },
        "outputId": "371a0c93-265d-4e5e-c1c6-9bd07f56fe47"
      },
      "source": [
        "traffic_df.loc[13547, 'Hour'] = 22\n",
        "len(traffic_df['Hour'].unique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "24"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
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        },
        "outputId": "609a057b-307d-442b-9838-21111fe42a70"
      },
      "source": [
        "traffic_df.loc[:, 'Day_of_Week'] = traffic_df['Date'].dt.weekday\n",
        "traffic_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Time</th>\n",
              "      <th>City</th>\n",
              "      <th>Location</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "      <th>Direction</th>\n",
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              "      <th>Day_of_Week</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2018-08-20</td>\n",
              "      <td>7:55 AM</td>\n",
              "      <td>Pasig City</td>\n",
              "      <td>ORTIGAS EMERALD</td>\n",
              "      <td>14.586343</td>\n",
              "      <td>121.061481</td>\n",
              "      <td>EB</td>\n",
              "      <td>VEHICULAR ACCIDENT</td>\n",
              "      <td>1.0</td>\n",
              "      <td>TAXI AND MC</td>\n",
              "      <td>MMDA ALERT: Vehicular accident at Ortigas Emer...</td>\n",
              "      <td>https://twitter.com/mmda/status/10313302019705...</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018-08-20</td>\n",
              "      <td>8:42 AM</td>\n",
              "      <td>Mandaluyong</td>\n",
              "      <td>EDSA GUADIX</td>\n",
              "      <td>14.589432</td>\n",
              "      <td>121.057243</td>\n",
              "      <td>NB</td>\n",
              "      <td>STALLED L300 DUE TO MECHANICAL PROBLEM</td>\n",
              "      <td>1.0</td>\n",
              "      <td>L300</td>\n",
              "      <td>MMDA ALERT: Stalled L300 due to mechanical pro...</td>\n",
              "      <td>https://twitter.com/mmda/status/10313462477459...</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018-08-20</td>\n",
              "      <td>9:13 AM</td>\n",
              "      <td>Makati City</td>\n",
              "      <td>EDSA ROCKWELL</td>\n",
              "      <td>14.559818</td>\n",
              "      <td>121.040737</td>\n",
              "      <td>SB</td>\n",
              "      <td>VEHICULAR ACCIDENT</td>\n",
              "      <td>1.0</td>\n",
              "      <td>SUV AND L300</td>\n",
              "      <td>MMDA ALERT: Vehicular accident at EDSA Rockwel...</td>\n",
              "      <td>https://twitter.com/mmda/status/10313589669896...</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-08-20</td>\n",
              "      <td>8:42 AM</td>\n",
              "      <td>Mandaluyong</td>\n",
              "      <td>EDSA GUADIX</td>\n",
              "      <td>14.589432</td>\n",
              "      <td>121.057243</td>\n",
              "      <td>NB</td>\n",
              "      <td>STALLED L300 DUE TO MECHANICAL PROBLEM</td>\n",
              "      <td>1.0</td>\n",
              "      <td>L300</td>\n",
              "      <td>MMDA ALERT: Stalled L300 due to mechanical pro...</td>\n",
              "      <td>https://twitter.com/mmda/status/10313590696535...</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2018-08-20</td>\n",
              "      <td>10:27 AM</td>\n",
              "      <td>San Juan</td>\n",
              "      <td>ORTIGAS CLUB FILIPINO</td>\n",
              "      <td>14.601846</td>\n",
              "      <td>121.046754</td>\n",
              "      <td>EB</td>\n",
              "      <td>VEHICULAR ACCIDENT</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2 CARS</td>\n",
              "      <td>MMDA ALERT: Vehicular accident at Ortigas Club...</td>\n",
              "      <td>https://twitter.com/mmda/status/10313711248424...</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Date      Time  ... Hour Day_of_Week\n",
              "0 2018-08-20   7:55 AM  ...    7           0\n",
              "1 2018-08-20   8:42 AM  ...    8           0\n",
              "2 2018-08-20   9:13 AM  ...    9           0\n",
              "3 2018-08-20   8:42 AM  ...    8           0\n",
              "4 2018-08-20  10:27 AM  ...   10           0\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pjaeKbzEabX-",
        "colab_type": "text"
      },
      "source": [
        "#### One Year Overview\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SdaSB2AVwiVU",
        "colab_type": "text"
      },
      "source": [
        "-- add graphs and text"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T0d3cFkBbr2j",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "76d4de0a-26cf-4845-8bbd-146066a12351"
      },
      "source": [
        "traffic_df.groupby(['Date']).size().plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5918a88b00>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o2rfH_mocCf1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "outputId": "e18e192f-dc76-475b-f064-34d3d7203700"
      },
      "source": [
        "year_traffic_df = traffic_df.loc[traffic_df['Date'] < '2019-08-20']\n",
        "year_traffic_df.tail(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Date</th>\n",
              "      <th>Time</th>\n",
              "      <th>City</th>\n",
              "      <th>Location</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "      <th>Direction</th>\n",
              "      <th>Type</th>\n",
              "      <th>Lanes_Blocked</th>\n",
              "      <th>Involved</th>\n",
              "      <th>Tweet</th>\n",
              "      <th>Source</th>\n",
              "      <th>Hour</th>\n",
              "      <th>Day_of_Week</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10732</th>\n",
              "      <td>2019-08-19</td>\n",
              "      <td>7:32 PM</td>\n",
              "      <td>Quezon City</td>\n",
              "      <td>EDSA TIMOG FLYOVER</td>\n",
              "      <td>14.632910</td>\n",
              "      <td>121.044944</td>\n",
              "      <td>NB</td>\n",
              "      <td>STALLED ARMORED VAN DUE TO MECHANICAL PROBLEM</td>\n",
              "      <td>1.0</td>\n",
              "      <td>ARMORED VAN</td>\n",
              "      <td>MMDA ALERT: Stalled Armored van due to mechani...</td>\n",
              "      <td>https://twitter.com/mmda/status/11634166837231...</td>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10733</th>\n",
              "      <td>2019-08-19</td>\n",
              "      <td>8:32 PM</td>\n",
              "      <td>Mandaluyong</td>\n",
              "      <td>EDSA SHAW TUNNEL</td>\n",
              "      <td>14.581153</td>\n",
              "      <td>121.053565</td>\n",
              "      <td>NB</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>PICK-UP</td>\n",
              "      <td>MMDA ALERT: Stalled Pick-up due to mechanical ...</td>\n",
              "      <td>https://twitter.com/mmda/status/11634307352245...</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10734</th>\n",
              "      <td>2019-08-19</td>\n",
              "      <td>11:31 PM</td>\n",
              "      <td>Quezon City</td>\n",
              "      <td>MINDANAO AVE TUNNEL</td>\n",
              "      <td>14.690509</td>\n",
              "      <td>121.027812</td>\n",
              "      <td>SB</td>\n",
              "      <td>STALLED CLOSED VAN DUE TO MECHANICAL PROBLEM</td>\n",
              "      <td>1.0</td>\n",
              "      <td>CLOSED VAN</td>\n",
              "      <td>MMDA ALERT: Stalled closed van due to mechanic...</td>\n",
              "      <td>https://twitter.com/mmda/status/11634801419572...</td>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Date      Time  ... Hour Day_of_Week\n",
              "10732 2019-08-19   7:32 PM  ...   19           0\n",
              "10733 2019-08-19   8:32 PM  ...   20           0\n",
              "10734 2019-08-19  11:31 PM  ...   23           0\n",
              "\n",
              "[3 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l99sY-TUeigO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 632
        },
        "outputId": "5a4f694f-6302-4719-89cf-0a7fcb7c6b8a"
      },
      "source": [
        "year_traffic_df.loc[:, 'Week'] = year_traffic_df.loc[:, 'Date'] - year_traffic_df.loc[0, 'Date']\n",
        "def week(timedelta):\n",
        "  return timedelta.days // 7\n",
        "year_traffic_df.loc[:, 'Week'] = year_traffic_df['Week'].apply(week)\n",
        "year_traffic_df.tail(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:966: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self.obj[item] = s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Date</th>\n",
              "      <th>Time</th>\n",
              "      <th>City</th>\n",
              "      <th>Location</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "      <th>Direction</th>\n",
              "      <th>Type</th>\n",
              "      <th>Lanes_Blocked</th>\n",
              "      <th>Involved</th>\n",
              "      <th>Tweet</th>\n",
              "      <th>Source</th>\n",
              "      <th>Hour</th>\n",
              "      <th>Day_of_Week</th>\n",
              "      <th>Week</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10730</th>\n",
              "      <td>2019-08-19</td>\n",
              "      <td>6:39 PM</td>\n",
              "      <td>Quezon City</td>\n",
              "      <td>COMMONWEALTH TANDANG SORA</td>\n",
              "      <td>14.663710</td>\n",
              "      <td>121.068008</td>\n",
              "      <td>EB</td>\n",
              "      <td>VEHICULAR ACCIDENT</td>\n",
              "      <td>1.0</td>\n",
              "      <td>CAR AND TRUCK</td>\n",
              "      <td>MMDA ALERT: Vehicular accident at Commonwealth...</td>\n",
              "      <td>https://twitter.com/mmda/status/11634036857512...</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10731</th>\n",
              "      <td>2019-08-19</td>\n",
              "      <td>7:30 PM</td>\n",
              "      <td>Mandaluyong</td>\n",
              "      <td>EDSA SHAW SPLIT</td>\n",
              "      <td>14.581153</td>\n",
              "      <td>121.053565</td>\n",
              "      <td>NB</td>\n",
              "      <td>VEHICULAR ACCIDENT</td>\n",
              "      <td>1.0</td>\n",
              "      <td>CAR AND BUS</td>\n",
              "      <td>MMDA ALERT: Vehicular accident at EDSA NB, Sha...</td>\n",
              "      <td>https://twitter.com/mmda/status/11634165210009...</td>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10732</th>\n",
              "      <td>2019-08-19</td>\n",
              "      <td>7:32 PM</td>\n",
              "      <td>Quezon City</td>\n",
              "      <td>EDSA TIMOG FLYOVER</td>\n",
              "      <td>14.632910</td>\n",
              "      <td>121.044944</td>\n",
              "      <td>NB</td>\n",
              "      <td>STALLED ARMORED VAN DUE TO MECHANICAL PROBLEM</td>\n",
              "      <td>1.0</td>\n",
              "      <td>ARMORED VAN</td>\n",
              "      <td>MMDA ALERT: Stalled Armored van due to mechani...</td>\n",
              "      <td>https://twitter.com/mmda/status/11634166837231...</td>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10733</th>\n",
              "      <td>2019-08-19</td>\n",
              "      <td>8:32 PM</td>\n",
              "      <td>Mandaluyong</td>\n",
              "      <td>EDSA SHAW TUNNEL</td>\n",
              "      <td>14.581153</td>\n",
              "      <td>121.053565</td>\n",
              "      <td>NB</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>PICK-UP</td>\n",
              "      <td>MMDA ALERT: Stalled Pick-up due to mechanical ...</td>\n",
              "      <td>https://twitter.com/mmda/status/11634307352245...</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10734</th>\n",
              "      <td>2019-08-19</td>\n",
              "      <td>11:31 PM</td>\n",
              "      <td>Quezon City</td>\n",
              "      <td>MINDANAO AVE TUNNEL</td>\n",
              "      <td>14.690509</td>\n",
              "      <td>121.027812</td>\n",
              "      <td>SB</td>\n",
              "      <td>STALLED CLOSED VAN DUE TO MECHANICAL PROBLEM</td>\n",
              "      <td>1.0</td>\n",
              "      <td>CLOSED VAN</td>\n",
              "      <td>MMDA ALERT: Stalled closed van due to mechanic...</td>\n",
              "      <td>https://twitter.com/mmda/status/11634801419572...</td>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "      <td>52</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Date      Time         City  ... Hour  Day_of_Week  Week\n",
              "10730 2019-08-19   6:39 PM  Quezon City  ...   18            0    52\n",
              "10731 2019-08-19   7:30 PM  Mandaluyong  ...   19            0    52\n",
              "10732 2019-08-19   7:32 PM  Quezon City  ...   19            0    52\n",
              "10733 2019-08-19   8:32 PM  Mandaluyong  ...   20            0    52\n",
              "10734 2019-08-19  11:31 PM  Quezon City  ...   23            0    52\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OHAO6lWGf9lO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "050da2fd-2a0e-4e3e-8b07-fb6ad3d16e42"
      },
      "source": [
        "year_traffic_df.groupby(['Week']).size().plot()\n",
        "\n",
        "lines = [x for x in range(4, 52, 4)]\n",
        "for line in lines:\n",
        "  plt.axvline(x=line) #???? HELP"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qbjEFRK64hpe",
        "colab_type": "text"
      },
      "source": [
        "#### Day of the Week"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "srbU60b6UnEj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "56f9edd1-826f-429c-8c05-97473e3c0f7f"
      },
      "source": [
        "traffic_df.groupby(['Day_of_Week']).size().plot() # Map? Make x ticks clearer"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f591459d198>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 129
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C5xxQ1PNagHN",
        "colab_type": "text"
      },
      "source": [
        "#### Hourly"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MZI5_-B8ZNiM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "de8f7a2b-89ca-481d-9c06-53ab8a4c432e"
      },
      "source": [
        "traffic_df.groupby(['Hour']).size().plot()\n",
        "# Change to average amount of incidents per day in one hour instead?\n",
        "\n",
        "plt.axvline(x=7) # 7 AM\n",
        "plt.axvline(x=15) # 3 PM"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.lines.Line2D at 0x7f5915edcb70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 130
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KZTQwrCFwjKq",
        "colab_type": "text"
      },
      "source": [
        "-- add graphs and text"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jvB0EIciZywA",
        "colab_type": "text"
      },
      "source": [
        "## Question 2: Does the type of vehicle you're in affect if you'll get into accidents?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xKNM8VKIazmU",
        "colab_type": "text"
      },
      "source": [
        "#### Type of Vehicle to Accidents"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PY5GIXdAgBkI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "traffic_df = pd.read_csv(link)\n",
        "traffic_df.dropna(subset=['Involved', 'Type'])\n",
        "involved_df = traffic_df['Involved'].str.upper()\n",
        "\n",
        "\n",
        "inv_motorcycle = involved_df.str.contains('MOTOR|CYCLE|BIKE')\n",
        "inv_four_public = involved_df.str.contains('^UV|EXPRESS|TAXI|PUJ|JEEP|FX|MULTICAB|AMBULANCE')\n",
        "inv_four_private = involved_df.str.contains('MC|CAR|VIOS|HONDA CIVIC|INNOVA|VAN|STAREX|WIGO|MONTERO|PAJERO|HI-ACE|ACE|SUV|AUV|SCHOOL')\n",
        "inv_truck = involved_df.str.contains('TRUCK|TRUNCK|TRUK|TUCK|CANTER|L300|PICK|MIXER|TANKER|TRAILER|REVO|ELF|HI-LUX|LUX')\n",
        "inv_bus = involved_df.str.contains('BUS|COASTER|METROLINK')\n",
        "\n",
        "traffic_df['Bus'] = np.where(inv_bus, 1, 0)\n",
        "traffic_df['FourWheeled_Public'] = np.where(inv_four_public, 1, 0)\n",
        "traffic_df['FourWheeled_Private'] = np.where(inv_four_private, 1, 0)\n",
        "traffic_df['Truck'] = np.where(inv_truck, 1, 0)\n",
        "traffic_df['Motorcycle'] = np.where(inv_motorcycle, 1, 0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QjV9nztuww45",
        "colab_type": "text"
      },
      "source": [
        "-- add graphs and text"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y8OaW-YLyqKf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "c5f6e1cc-0af4-432d-9056-b65e7aa565cc"
      },
      "source": [
        "plt.bar(1, traffic_df['Bus'].sum(), label='Bus')\n",
        "plt.bar(2, traffic_df['FourWheeled_Public'].sum(), label='4 Wheel Public')\n",
        "plt.bar(3, traffic_df['FourWheeled_Private'].sum(), label='4 Wheel Private')\n",
        "plt.bar(4, traffic_df['Truck'].sum(), label='Truck')\n",
        "plt.bar(5, traffic_df['Motorcycle'].sum(), label='Motorcycle')\n",
        "plt.xticks(ticks=[])\n",
        "plt.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f5912dd6ef0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 167
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_IMCNWxVa6ur",
        "colab_type": "text"
      },
      "source": [
        "#### Checking type of Incident"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G_sbfHBJCraY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "outputId": "a0371389-c765-460a-ebd2-c17805684e34"
      },
      "source": [
        "type_df = traffic_df['Type'].str.upper()\n",
        "\n",
        "type_e = type_df.str.contains('RALLY|PARADE|SALE|WREATH|MARCH|CAMPAIGN|ACTIVITY|PHOTO|CARAVAN')\n",
        "type_sa = type_df.str.contains('SELF ACCIDENT')\n",
        "type_va = type_df.str.contains('VEHICULAR|ACCIDENT|FIRE')\n",
        "type_har = type_df.str.contains('HIT AND RUN')\n",
        "type_d = type_df.str.contains('ROAD|CLOSURE|WORK|DECLOGGING|PROJECT|DPWH|OPERATION|MMDA|WATER|CEMENT|LANE|MAYNILAD|DRAINAGE|DISMANTLING|MANHOLE|REPAIR|SPILL|TRAFFIC|FALLEN|BOX')\n",
        "type_mc = type_df.str.contains('MULTIPLE|COLLISION')\n",
        "type_si = type_df.str.contains('SHOOTING|STABBING')\n",
        "type_s = type_df.str.contains('STALL')\n",
        "\n",
        "traffic_df['Type'] = np.where(type_va, 'VEHICULAR ACCIDENT OR FIRE',\n",
        "                              np.where(type_s, 'STALLED VEHICLE', \n",
        "                                        np.where(type_mc, 'MULTIPLE COLLISION',\n",
        "                                                  np.where(type_sa, 'SELF ACCIDENT', \n",
        "                                                          np.where(type_e, \"RALLY, PARADE OR MISC. PUBLIC EVENT\", \n",
        "                                                                  np.where(type_har, \"HIT AND RUN\",\n",
        "                                                                          np.where(type_si, 'SHOOTING/STABBING INCIDENT',\n",
        "                                                                                  np.where(type_d, 'ROAD WORK/CLOSURE', type_df))))))))\n",
        "traffic_df['Type'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "VEHICULAR ACCIDENT OR FIRE             9823\n",
              "STALLED VEHICLE                        3151\n",
              "MULTIPLE COLLISION                      564\n",
              "ROAD WORK/CLOSURE                       259\n",
              "RALLY, PARADE OR MISC. PUBLIC EVENT     122\n",
              "HIT AND RUN                              32\n",
              "SHOOTING/STABBING INCIDENT               16\n",
              "Name: Type, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UGL2n6rKxBjB",
        "colab_type": "text"
      },
      "source": [
        "-- add graphs and text"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yC2C0fsKfeKG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Code to plot vehicular accidents/stalled vehicle for the categories of vehicles"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RT1ekvBdZ4-G",
        "colab_type": "text"
      },
      "source": [
        "## Question 3: Where do these incidents happen more often?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ehK4lZmbA1O",
        "colab_type": "text"
      },
      "source": [
        "#### City"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1cTKvTU0A5RU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "outputId": "6b004d97-c1a3-4a0f-c9c9-8d3ba36077b1"
      },
      "source": [
        "traffic_df['City'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Pasig City', 'Mandaluyong', 'Makati City', 'San Juan',\n",
              "       'Quezon City', 'Marikina', 'Pasay City', 'ParaÃ±aque', 'Manila',\n",
              "       nan, 'Kalookan City', 'Malabon', 'Taguig', 'Navotas', 'Valenzuela'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QuGae-8fA-EV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "outputId": "5e34b322-7024-430d-dcaf-0627830a7143"
      },
      "source": [
        "traffic_df.dropna(subset=['City'], inplace=True)\n",
        "traffic_df['City'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Pasig City', 'Mandaluyong', 'Makati City', 'San Juan',\n",
              "       'Quezon City', 'Marikina', 'Pasay City', 'ParaÃ±aque', 'Manila',\n",
              "       'Kalookan City', 'Malabon', 'Taguig', 'Navotas', 'Valenzuela'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SoI9wVq8kC1T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "outputId": "5f916df8-8e2a-4e50-cf8d-6f4e447cd9c9"
      },
      "source": [
        "traffic_df.groupby(['City']).size().plot(kind='bar')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f591427ff60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 136
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BJ4yMtEWwynv",
        "colab_type": "text"
      },
      "source": [
        "-- add graphs and text"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "USQholE7bFL-",
        "colab_type": "text"
      },
      "source": [
        "#### Direction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WR6po0qiAcuX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "1b02bce0-2b3e-45ed-a67e-7138d896113d"
      },
      "source": [
        "traffic_df = pd.read_csv(link)\n",
        "traffic_df['Direction'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['EB', 'NB', 'SB', 'EB.', 'WB', 'DAR', nan, 'CLOSED', 'PAX',\n",
              "       'CLARA'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LUjwcDwlBo9K",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "965615a5-b518-4252-9967-8350dd72544d"
      },
      "source": [
        "traffic_df.dropna(subset=['Direction'], inplace=True)\n",
        "traffic_df['Direction'].isnull().any()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5nQA7VDXAaD5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "e7c3e508-cb7f-45c2-c28a-24b7c507f98c"
      },
      "source": [
        "traffic_df['Direction'] = traffic_df['Direction'].map({'NB': 'NB', \n",
        "                                                      'EB': 'EB', \n",
        "                                                      'EB.': 'EB', \n",
        "                                                      'WB': 'WB', \n",
        "                                                      'SB': 'SB',\n",
        "                                                      'DAR': 'DAR',\n",
        "                                                      'CLOSED': 'CLOSED',\n",
        "                                                      'PAX': 'PAX',\n",
        "                                                      'CLARA': 'CLARA'},\n",
        "                                                      na_action = 'ignore')\n",
        "traffic_df['Direction'] = traffic_df['Direction'].astype('string')\n",
        "traffic_df['Direction'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<StringArray>\n",
              "['EB', 'NB', 'SB', 'WB', 'DAR', 'CLOSED', 'PAX', 'CLARA']\n",
              "Length: 8, dtype: string"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 145
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MJLeTrlZkQmd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 327
        },
        "outputId": "4a0c8fac-5863-4bfa-b82a-2ef96625f13d"
      },
      "source": [
        "traffic_df.groupby(['Direction']).size().plot(kind='bar')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5914039710>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 146
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lZD_5AIoxdCK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "outputId": "ff71ed60-86f7-45a9-f52f-ccb860a9d4d4"
      },
      "source": [
        "direction_df = traffic_df[(traffic_df['Direction'] == 'EB') | (traffic_df['Direction'] == 'NB') |\n",
        "                          (traffic_df['Direction'] == 'SB') | (traffic_df['Direction'] == 'WB')]\n",
        "direction_df.groupby(['Direction']).size().plot(kind='bar')                "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5913bd8390>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 147
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JdjB4/b6ztWnL3zG5YR0eD+8sU5KPAAu+aFX1o+MbzfRLcsXB1lfV745rLNMuyYcZvDfTSsPv0/jeXJ4krwI+BuwFvul/nVV179gHNQKGvnSUSHI2cF9V3d+WNwI/DXwO+J2q2jvB4U2dJH8E/BDwTOB2Bh8ANwE3TfNraegvU5JXV9UftPaLq+pvh9b9XlW9dnKjmz5Jfvsgq6uqXj+2wUy5JJ8Efqyq9ib5YQZXpvtl4Ezge6rq4okOcEq1XwRez+AD4Afb7cGqmspLvTplc/kuGWq/5oB1G8Y5kKPEl+e5AVwKOM1weVYM7YG+FNhcVe+tqt8CTj/I/XRwTwaeCjyt3eamcE4lv8hdvizQnm9Zi6iqP55rJ3kK8Erg5Qz2Uv94oftpXiuSrKyqx4Dz+L/LjoL/1pctyWbgDOB/GIT8TcCb2gypqeUbYflqgfZ8y1qCJE8Hfg34OWArcNa0/8OakHcD/5zkAeArwL8CJDkdeGiSA5tS3wY8CbibwTkkO4EHJzqiEfCY/jIl2c/gEEQY/Lfv4blVwLFV9YRJjW0aJflD4KcYXJzirVX1pQkPaaolOQc4GfhgVX251b4LOK6qPjnRwU2hdpLgGQyO5/8Q8L0MZvP8W1UddObZ45Whr4lK8nXgUeAxDphiyOCL3KdOZGDSkCSrgXMZBP9PAM+oquMnO6pDY+hL0jyS/Ar/t4f/Ndp0zXa7vaqm8qxcj+lL0vzWAH8LvGru3IejgXv6ktQR5+lLUkcMfUnqiKGvo16S/UluS3JHu+rRryc5pq1bn+TNI3qe1x6wfNMoHlcaJY/p66iX5EtVdVxrnwi8C/jYweZZD53ZekjPIz1euaevrlTVbgY/T/CKDDwvyfsBkvxOknck+RjwjiQzSd6b5JZ2O7f1Oy7JXya5PcmOJD+d5EoGv2d/W5J3tn5fan+T5A+TfKbd56Wt/rwkH0nyniT/nuSdc9e3lY4Up2yqO1V1T5IVwInzrF4HPLeqvpLkXcBVVfXRJN8G3AB8D/BbwENzl3JMsqqq3pvkFQtcQu+nGPzS5bOAE4BbkvxLW/dsBmd8/heDn+49F/joyDZWOoChL/1/11fVV1r7x4B1QzvfT01yXKt/49dWl/A7Qc8F3t0uBfmFJP8M/ACDS/DdXFU7YXDdVQZzww19HTGGvrqT5DuA/cBuBnvuw7481D4GOKeqHjng/qMczqND7f34b1JHmMf01ZUkM8CfA2+pxWcxfJDBRUjm7jt36GYbcNlQfVVrfi3JfD+496/AS5OsaM//w8DNh7gJ0mEx9NWDuS9Y7wD+iUGYL+Xau78CrG9f1t4J/FKrvwFY1b6Y/TQwd+3ZzcCOuS9yh7wP2AF8GvgQ8Oqq+vzhbZJ0aJyyKUkdcU9fkjpi6EtSRwx9SeqIoS9JHTH0Jakjhr4kdcTQl6SOGPqS1JH/BSuNCahLFYy8AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3yBnaQgKwzXW",
        "colab_type": "text"
      },
      "source": [
        "-- add graphs and text"
      ]
    }
  ]
}