NYPD_visualization_plotly.ipynb

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          "text": [
            "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (5.15.0)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly) (8.3.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from plotly) (24.0)\n"
          ]
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      "source": [
        "!pip install plotly"
      ]
    },
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      "cell_type": "code",
      "source": [
        "import calendar\n",
        "import datetime\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import plotly.express as px\n",
        "import plotly.offline as pyo\n",
        "import plotly.graph_objects as go"
      ],
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        "id": "7wQNUegO8ZS_"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/NYPD_Shooting.csv')\n",
        "df.head()"
      ],
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          "height": 389
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        "outputId": "1a4be719-488c-4925-e854-4cf63917ad8e"
      },
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      "outputs": [
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              "   INCIDENT_KEY  OCCUR_DATE OCCUR_TIME       BORO LOC_OF_OCCUR_DESC  PRECINCT  \\\n",
              "0     244608249  05/05/2022   00:10:00  MANHATTAN            INSIDE        14   \n",
              "1     247542571  07/04/2022   22:20:00      BRONX           OUTSIDE        48   \n",
              "2      84967535  05/27/2012   19:35:00     QUEENS               NaN       103   \n",
              "3     202853370  09/24/2019   21:00:00      BRONX               NaN        42   \n",
              "4      27078636  02/25/2007   21:00:00   BROOKLYN               NaN        83   \n",
              "\n",
              "   JURISDICTION_CODE LOC_CLASSFCTN_DESC LOCATION_DESC  \\\n",
              "0                0.0         COMMERCIAL   VIDEO STORE   \n",
              "1                0.0             STREET        (null)   \n",
              "2                0.0                NaN           NaN   \n",
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              "4                0.0                NaN           NaN   \n",
              "\n",
              "   STATISTICAL_MURDER_FLAG  ... PERP_SEX PERP_RACE VIC_AGE_GROUP VIC_SEX  \\\n",
              "0                     True  ...        M     BLACK         25-44       M   \n",
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              "1                    POINT (-73.88233 40.854402)  \n",
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    {
      "cell_type": "code",
      "source": [
        "df = df.drop(\n",
        "    labels=['INCIDENT_KEY', 'JURISDICTION_CODE', 'PERP_RACE', 'Lon_Lat',\n",
        "            'PERP_SEX', 'X_COORD_CD', 'Y_COORD_CD', 'PERP_AGE_GROUP'],\n",
        "    axis=1)\n",
        "df = df.replace(to_replace='(null)', value=np.nan)\n",
        "df.head()"
      ],
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        "id": "Pa-0THTE85A2",
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      },
      "execution_count": 4,
      "outputs": [
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          "data": {
            "text/plain": [
              "   OCCUR_DATE OCCUR_TIME       BORO LOC_OF_OCCUR_DESC  PRECINCT  \\\n",
              "0  05/05/2022   00:10:00  MANHATTAN            INSIDE        14   \n",
              "1  07/04/2022   22:20:00      BRONX           OUTSIDE        48   \n",
              "2  05/27/2012   19:35:00     QUEENS               NaN       103   \n",
              "3  09/24/2019   21:00:00      BRONX               NaN        42   \n",
              "4  02/25/2007   21:00:00   BROOKLYN               NaN        83   \n",
              "\n",
              "  LOC_CLASSFCTN_DESC LOCATION_DESC  STATISTICAL_MURDER_FLAG VIC_AGE_GROUP  \\\n",
              "0         COMMERCIAL   VIDEO STORE                     True         25-44   \n",
              "1             STREET           NaN                     True         18-24   \n",
              "2                NaN           NaN                    False         18-24   \n",
              "3                NaN           NaN                    False         25-44   \n",
              "4                NaN           NaN                    False         25-44   \n",
              "\n",
              "  VIC_SEX VIC_RACE   Latitude  Longitude  \n",
              "0       M    BLACK  40.754692 -73.993500  \n",
              "1       M    BLACK  40.854402 -73.882330  \n",
              "2       M    BLACK  40.710634 -73.767773  \n",
              "3       M    BLACK  40.832417 -73.890714  \n",
              "4       M    BLACK  40.688443 -73.910219  "
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              "      <th>0</th>\n",
              "      <td>05/05/2022</td>\n",
              "      <td>00:10:00</td>\n",
              "      <td>MANHATTAN</td>\n",
              "      <td>INSIDE</td>\n",
              "      <td>14</td>\n",
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              "      <td>True</td>\n",
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              "      <td>40.754692</td>\n",
              "      <td>-73.993500</td>\n",
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              "      <th>1</th>\n",
              "      <td>07/04/2022</td>\n",
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              "      <td>18-24</td>\n",
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              "      <td>40.854402</td>\n",
              "      <td>-73.882330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>05/27/2012</td>\n",
              "      <td>19:35:00</td>\n",
              "      <td>QUEENS</td>\n",
              "      <td>NaN</td>\n",
              "      <td>103</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>18-24</td>\n",
              "      <td>M</td>\n",
              "      <td>BLACK</td>\n",
              "      <td>40.710634</td>\n",
              "      <td>-73.767773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>09/24/2019</td>\n",
              "      <td>21:00:00</td>\n",
              "      <td>BRONX</td>\n",
              "      <td>NaN</td>\n",
              "      <td>42</td>\n",
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              "      <td>25-44</td>\n",
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              "      <td>40.832417</td>\n",
              "      <td>-73.890714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>02/25/2007</td>\n",
              "      <td>21:00:00</td>\n",
              "      <td>BROOKLYN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>83</td>\n",
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              "      <td>25-44</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 28562,\n  \"fields\": [\n    {\n      \"column\": \"OCCUR_DATE\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 6095,\n        \"samples\": [\n          \"02/22/2012\",\n          \"05/16/2010\",\n          \"04/07/2010\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"OCCUR_TIME\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1423,\n        \"samples\": [\n          \"12:04:00\",\n          \"05:24:00\",\n          \"14:08:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BORO\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"BRONX\",\n          \"STATEN ISLAND\",\n          \"QUEENS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LOC_OF_OCCUR_DESC\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"OUTSIDE\",\n          \"INSIDE\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PRECINCT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27,\n        \"min\": 1,\n        \"max\": 123,\n        \"num_unique_values\": 77,\n        \"samples\": [\n          83,\n          70\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LOC_CLASSFCTN_DESC\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"TRANSIT\",\n          \"STREET\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LOCATION_DESC\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 39,\n        \"samples\": [\n          \"CHECK CASH\",\n          \"PHOTO/COPY STORE\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"STATISTICAL_MURDER_FLAG\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"VIC_AGE_GROUP\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"25-44\",\n          \"18-24\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"VIC_SEX\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"M\",\n          \"F\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"VIC_RACE\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"BLACK\",\n          \"WHITE\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08761142362453814,\n        \"min\": 40.51158556600007,\n        \"max\": 40.91081808800004,\n        \"num_unique_values\": 13377,\n        \"samples\": [\n          40.83718807500002,\n          40.814178\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06596744945045428,\n        \"min\": -74.24930349899995,\n        \"max\": -73.70204616699993,\n        \"num_unique_values\": 13363,\n        \"samples\": [\n          -73.83969048299997,\n          -73.88093967499998\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_month():\n",
        "  months = []\n",
        "\n",
        "  for date in df['OCCUR_DATE']:\n",
        "    month_num = int(date.split(\"/\")[0])\n",
        "    months.append(calendar.month_name[month_num])\n",
        "\n",
        "  return months\n",
        "\n",
        "def time_ofday():\n",
        "\n",
        "  def time_in_range(start, end, current):\n",
        "    \"\"\"Returns whether current is in the range [start, end]\"\"\"\n",
        "    return start <= current <= end\n",
        "\n",
        "  time = []\n",
        "  time_dict = {\n",
        "      \"midnight\": {\n",
        "          \"start\": datetime.time(0, 0, 0),\n",
        "          \"end\": datetime.time(3, 59, 59)\n",
        "      },\n",
        "      \"early morning\": {\n",
        "          \"start\": datetime.time(4, 0, 0),\n",
        "          \"end\": datetime.time(7, 59, 59)\n",
        "      },\n",
        "      \"morning\": {\n",
        "          \"start\": datetime.time(8, 0, 0),\n",
        "          \"end\": datetime.time(11, 59, 59)\n",
        "      },\n",
        "      \"afternoon\": {\n",
        "          \"start\": datetime.time(12, 0, 0),\n",
        "          \"end\": datetime.time(16, 59, 59)\n",
        "      },\n",
        "      \"evening\": {\n",
        "          \"start\": datetime.time(17, 0, 0),\n",
        "          \"end\": datetime.time(20, 59, 59)\n",
        "      },\n",
        "      \"late evening\": {\n",
        "          \"start\": datetime.time(21, 0, 0),\n",
        "          \"end\": datetime.time(23, 59, 59)\n",
        "      }\n",
        "  }\n",
        "\n",
        "  for _, occur_time in enumerate(df['OCCUR_TIME']):\n",
        "    for key, value in time_dict.items():\n",
        "      if time_in_range(value.get(\"start\"), value.get(\"end\"), occur_time):\n",
        "        time.append(key.title())\n",
        "\n",
        "  return time"
      ],
      "metadata": {
        "id": "St65d5Yb9z9k"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# convert occur_time to datetime object\n",
        "df['OCCUR_TIME'] = pd.to_datetime(df['OCCUR_TIME'],format= '%H:%M:%S' ).dt.time\n",
        "df['OCCUR_TIME'] = time_ofday()\n",
        "df['OCCUR_DATE'] = get_month()"
      ],
      "metadata": {
        "id": "OgV5NY2K-ADK"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# victim age group modification\n",
        "age_map = {\n",
        "    '<18': '0-18',\n",
        "    '18-24': '18-25',\n",
        "    '25-44': '25-45',\n",
        "    '45-64': '45-65',\n",
        "    '65+': '65-100',\n",
        "    'UNKNOWN': 'UNKNOWN'\n",
        "}\n",
        "df['VIC_AGE_GROUP'] = df['VIC_AGE_GROUP'].map(age_map)"
      ],
      "metadata": {
        "id": "lbXcow-F-D0w"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df[df['VIC_AGE_GROUP'] != 'UNKNOWN']\n",
        "df = df[df['VIC_RACE'] != 'UNKNOWN']\n",
        "df = df[df['VIC_SEX'] != 'U']\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "MoXNGxZ3-GNb",
        "outputId": "1671a67d-d871-4dc5-d72e-939aac1fc152"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  OCCUR_DATE    OCCUR_TIME       BORO LOC_OF_OCCUR_DESC  PRECINCT  \\\n",
              "0        May      Midnight  MANHATTAN            INSIDE        14   \n",
              "1       July  Late Evening      BRONX           OUTSIDE        48   \n",
              "2        May       Evening     QUEENS               NaN       103   \n",
              "3  September  Late Evening      BRONX               NaN        42   \n",
              "4   February  Late Evening   BROOKLYN               NaN        83   \n",
              "\n",
              "  LOC_CLASSFCTN_DESC LOCATION_DESC  STATISTICAL_MURDER_FLAG VIC_AGE_GROUP  \\\n",
              "0         COMMERCIAL   VIDEO STORE                     True         25-45   \n",
              "1             STREET           NaN                     True         18-25   \n",
              "2                NaN           NaN                    False         18-25   \n",
              "3                NaN           NaN                    False         25-45   \n",
              "4                NaN           NaN                    False         25-45   \n",
              "\n",
              "  VIC_SEX VIC_RACE   Latitude  Longitude  \n",
              "0       M    BLACK  40.754692 -73.993500  \n",
              "1       M    BLACK  40.854402 -73.882330  \n",
              "2       M    BLACK  40.710634 -73.767773  \n",
              "3       M    BLACK  40.832417 -73.890714  \n",
              "4       M    BLACK  40.688443 -73.910219  "
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              "      <td>103</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 28443,\n  \"fields\": [\n    {\n      \"column\": \"OCCUR_DATE\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"January\",\n          \"October\",\n          \"May\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"OCCUR_TIME\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Midnight\",\n          \"Late Evening\",\n          \"Morning\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BORO\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"BRONX\",\n          \"STATEN ISLAND\",\n          \"QUEENS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LOC_OF_OCCUR_DESC\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"OUTSIDE\",\n          \"INSIDE\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PRECINCT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27,\n        \"min\": 1,\n        \"max\": 123,\n        \"num_unique_values\": 77,\n        \"samples\": [\n          83,\n          70\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LOC_CLASSFCTN_DESC\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"TRANSIT\",\n          \"STREET\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LOCATION_DESC\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 39,\n        \"samples\": [\n          \"CHECK CASH\",\n          \"PHOTO/COPY STORE\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"STATISTICAL_MURDER_FLAG\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"VIC_AGE_GROUP\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"18-25\",\n          \"0-18\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"VIC_SEX\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"F\",\n          \"M\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"VIC_RACE\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"BLACK\",\n          \"WHITE\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0876269952903804,\n        \"min\": 40.51158556600007,\n        \"max\": 40.91081808800004,\n        \"num_unique_values\": 13342,\n        \"samples\": [\n          40.86930060000003,\n          40.59677006300007\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06598893973495348,\n        \"min\": -74.24930349899995,\n        \"max\": -73.70204616699993,\n        \"num_unique_values\": 13328,\n        \"samples\": [\n          -73.94756439899999,\n          -74.022462\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "regions = df['BORO'].value_counts(sort=False).to_dict()\n",
        "region_count = {\n",
        "    'region': [ele for ele in list(regions.keys())],\n",
        "    'count': [ele for ele in list(regions.values())]\n",
        "}\n",
        "region_df = pd.DataFrame(data=region_count)\n",
        "region_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "sBvmR04b-I-D",
        "outputId": "1ec820c2-8fdd-4a64-d818-e4383b8cbe1d"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          region  count\n",
              "0      MANHATTAN   3752\n",
              "1          BRONX   8341\n",
              "2         QUEENS   4248\n",
              "3       BROOKLYN  11298\n",
              "4  STATEN ISLAND    804"
            ],
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              "      <th>count</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>MANHATTAN</td>\n",
              "      <td>3752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BRONX</td>\n",
              "      <td>8341</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>QUEENS</td>\n",
              "      <td>4248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BROOKLYN</td>\n",
              "      <td>11298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>STATEN ISLAND</td>\n",
              "      <td>804</td>\n",
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              "\n",
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              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
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              "  @keyframes spin {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "    60% {\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\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",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
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              "      .colab-df-generate:hover {\n",
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              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('region_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('region_df');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "region_df",
              "summary": "{\n  \"name\": \"region_df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"region\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"BRONX\",\n          \"STATEN ISLAND\",\n          \"QUEENS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4128,\n        \"min\": 804,\n        \"max\": 11298,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          8341,\n          804,\n          4248\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "area_count = px.bar(\n",
        "    region_df, x='region', y='count',\n",
        "    labels={'region': 'Region name', 'count': 'Number of victims'}\n",
        ")\n",
        "area_count.update_layout(\n",
        "    title_text='Shooting incidents reported in each region of NYC',\n",
        "    title_x=0.5\n",
        ")\n",
        "pyo.iplot(area_count, filename='Regionwise_Shooting_barplot')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "UFCY4nX0-kWA",
        "outputId": "7bd1ebc3-6d49-4ca5-c741-42f9250735e9"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    {
      "cell_type": "code",
      "source": [
        "area_pie = px.pie(\n",
        "    data_frame=region_df,\n",
        "    names = 'region',\n",
        "    values = 'count'\n",
        ")\n",
        "area_pie.update_layout(\n",
        "    title_text = 'Proportion of shooting cases reported in each region of NYC',\n",
        "    title_x = 0.5\n",
        ")\n",
        "pyo.iplot(area_pie, filename='Regionwise_Shooting_pie')"
      ],
      "metadata": {
        "id": "ez712b-w_foy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "9e195b0c-d0bc-48e6-bd6a-588a52d6c877"
      },
      "execution_count": 11,
      "outputs": [
        {
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Monthly and daily analysis\n",
        "\n",
        "**Useful but unimplemented**: [Line plot as plt.graph_object](https://plotly.com/python/line-charts/)"
      ],
      "metadata": {
        "id": "wIwcOy8lTq9R"
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      "source": [
        "month_to_num = {\n",
        "    'January': 1, 'February': 2, 'March': 3,\n",
        "    'April': 4, 'May': 5, 'June': 6, 'July': 7,\n",
        "    'August': 8, 'September': 9, 'October': 10,\n",
        "    'November': 11, 'December': 12\n",
        "}\n",
        "\n",
        "month_dict = df['OCCUR_DATE'].value_counts(sort=False).to_dict()\n",
        "month_data = {'months': [ele for ele in list(month_dict.keys())],\n",
        "              'count': [ele for ele in list(month_dict.values())]}\n",
        "month_df = pd.DataFrame(month_data)\n",
        "month_df['month_num'] = month_df['months'].map(month_to_num)\n",
        "month_df = month_df.sort_values(by='month_num')\n",
        "month_df = month_df.drop(labels=['month_num'], axis=1)\n",
        "month_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "oipcggkWRWDk",
        "outputId": "c9cf7b91-d66a-4f29-ed71-8f3e4a5cb7a9"
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      "execution_count": 12,
      "outputs": [
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            "text/plain": [
              "       months  count\n",
              "10    January   1802\n",
              "3    February   1441\n",
              "11      March   1788\n",
              "6       April   2063\n",
              "0         May   2673\n",
              "4        June   2948\n",
              "1        July   3382\n",
              "8      August   3249\n",
              "2   September   2663\n",
              "9     October   2366\n",
              "7    November   2004\n",
              "5    December   2064"
            ],
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "month_df",
              "summary": "{\n  \"name\": \"month_df\",\n  \"rows\": 12,\n  \"fields\": [\n    {\n      \"column\": \"months\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"November\",\n          \"October\",\n          \"January\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 614,\n        \"min\": 1441,\n        \"max\": 3382,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          2004,\n          2366,\n          1802\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "monthly_plot = px.line(\n",
        "    data_frame=month_df,\n",
        "    x='months',\n",
        "    y='count',\n",
        "    markers=True,\n",
        "    labels = {\n",
        "        'months': 'Month of the year',\n",
        "        'count': 'Number of victims'\n",
        "    }\n",
        ")\n",
        "monthly_plot.update_layout(\n",
        "    title_text='Monthly trend in reported shooting incidents over 10 years',\n",
        "    title_x = 0.5,\n",
        "    xaxis=dict(showgrid=False),\n",
        "    yaxis=dict(showgrid=True),\n",
        ")\n",
        "pyo.iplot(monthly_plot, filename='MonthlyTrend')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "DxteTPn6TqcI",
        "outputId": "8107700b-f7b8-4819-cdbc-4c1532eb21ef"
      },
      "execution_count": 13,
      "outputs": [
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    {
      "cell_type": "code",
      "source": [
        "time_to_num = {\n",
        "    'Early Morning': 1, 'Morning': 2, 'Afternoon': 3,\n",
        "    'Evening': 4, 'Late Evening': 5, 'Midnight': 6\n",
        "}\n",
        "\n",
        "time_dict = df['OCCUR_TIME'].value_counts(sort=False).to_dict()\n",
        "time_data = {'time': [ele for ele in list(time_dict.keys())],\n",
        "              'count': [ele for ele in list(time_dict.values())]}\n",
        "time_df = pd.DataFrame(time_data)\n",
        "time_df['time_num'] = time_df['time'].map(time_to_num)\n",
        "time_df = time_df.sort_values(by='time_num')\n",
        "time_df = time_df.drop(labels=['time_num'], axis=1)\n",
        "time_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "V9qWKtzVc4a8",
        "outputId": "6cbee6e6-5d08-4bf0-c788-a996bae149ae"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            time  count\n",
              "3  Early Morning   2853\n",
              "5        Morning   1215\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "time_df",
              "summary": "{\n  \"name\": \"time_df\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"time\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Early Morning\",\n          \"Morning\",\n          \"Midnight\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2510,\n        \"min\": 1215,\n        \"max\": 7924,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2853,\n          1215,\n          7924\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "time_plot = px.line(\n",
        "    data_frame=time_df,\n",
        "    x='time',\n",
        "    y='count',\n",
        "    markers=True,\n",
        "    labels = {\n",
        "        'time': 'Time of the day',\n",
        "        'count': 'Number of victims'\n",
        "    }\n",
        ")\n",
        "time_plot.update_layout(\n",
        "    title_text='Daily trend in reported shooting incidents over 10 years',\n",
        "    title_x = 0.5,\n",
        "    xaxis=dict(showgrid=False),\n",
        "    yaxis=dict(showgrid=True),\n",
        ")\n",
        "pyo.iplot(time_plot, filename='DailyTrend')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "NtkBg4_Mc5a6",
        "outputId": "634b42f5-28fd-40b8-f8c2-7d8cb308d040"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Age groups"
      ],
      "metadata": {
        "id": "_3avragEmufs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "age_to_num = {\n",
        "    '0-18': 1, '18-25': 2, '25-45': 3,\n",
        "    '45-65': 4, '65-100': 5\n",
        "}\n",
        "age_groups = df['VIC_AGE_GROUP'].value_counts(sort=False).to_dict()\n",
        "age_data = {'age': [ele for ele in list(age_groups.keys())],\n",
        "            'count': [ele for ele in list(age_groups.values())]}\n",
        "age_df = pd.DataFrame(age_data)\n",
        "age_df['age_num'] = age_df['age'].map(age_to_num)\n",
        "age_df = age_df.sort_values(by='age_num')\n",
        "age_df = age_df.drop(labels=['age_num'], axis=1)\n",
        "age_df.head()"
      ],
      "metadata": {
        "id": "tGXj-b8LdjnK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "5c45a065-8266-4772-8f25-cb9de949e2db"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      age  count\n",
              "4    0-18   2950\n",
              "1   18-25  10360\n",
              "0   25-45  12952\n",
              "2   45-65   1975\n",
              "3  65-100    205"
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        "age_radial = px.line_polar(\n",
        "    age_df,\n",
        "    r=\"count\",\n",
        "    theta=\"age\",\n",
        "    markers=True,\n",
        "    line_close=True,\n",
        "    start_angle=60\n",
        ")\n",
        "pyo.iplot(age_radial, filename='Radial')"
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        "outputId": "00f6dc2d-da34-4f60-aa29-273e31d35db9"
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      "execution_count": 17,
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    {
      "cell_type": "markdown",
      "source": [
        "# Ethnicity Analysis"
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      "metadata": {
        "id": "9MnA01YxNlHN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "victim_race = df['VIC_RACE'].value_counts(sort=False).to_dict()\n",
        "victim_race = {\n",
        "    \"race\": [ele for ele in list(victim_race.keys())],\n",
        "    \"count\": [ele for ele in list(victim_race.values())]\n",
        "}\n",
        "df_victim = pd.DataFrame(victim_race)\n",
        "df_victim"
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              "                             race  count\n",
              "0                           BLACK  20213\n",
              "1                           WHITE    714\n",
              "2                  WHITE HISPANIC   4275\n",
              "3                  BLACK HISPANIC   2791\n",
              "4        ASIAN / PACIFIC ISLANDER    439\n",
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              "        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",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-da19aa10-d5f6-42ad-b519-b640d4552a89')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-da19aa10-d5f6-42ad-b519-b640d4552a89 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_13c64e58-8faf-42e2-a222-0a92a6cd6217\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_victim')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_13c64e58-8faf-42e2-a222-0a92a6cd6217 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_victim');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_victim",
              "summary": "{\n  \"name\": \"df_victim\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"race\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"BLACK\",\n          \"WHITE\",\n          \"AMERICAN INDIAN/ALASKAN NATIVE\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7752,\n        \"min\": 11,\n        \"max\": 20213,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          20213,\n          714,\n          11\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "annotation = [\n",
        "    dict(\n",
        "        xref='paper', yref='paper',\n",
        "        x=0.5, y=0.5,\n",
        "        xanchor= 'center',\n",
        "        yanchor='middle',\n",
        "        text= '<b>RACE OF VICTIMS</b>',\n",
        "        font=dict(family=\"Arial\", size=12),\n",
        "        showarrow=False,\n",
        "  )\n",
        "]\n",
        "ethnicity_pie = go.Figure(\n",
        "    data = [\n",
        "        go.Pie(\n",
        "        labels=df_victim['race'].tolist(),\n",
        "        values=df_victim['count'].tolist(),\n",
        "        direction ='clockwise',\n",
        "        hole=.8,\n",
        "        sort=False\n",
        "    )]\n",
        ")\n",
        "ethnicity_pie.update_layout(annotations=annotation)\n",
        "pyo.iplot(ethnicity_pie, filename='Ethnicity_analysis_pie')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "u0DbUy50NuBf",
        "outputId": "091c8947-9851-42d1-86a3-42eb47805728"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "<head><meta charset=\"utf-8\" /></head>\n",
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              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Locality of reported incidents"
      ],
      "metadata": {
        "id": "E6PB3f9xz1go"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_new = df.loc[:, ['BORO', 'LOC_CLASSFCTN_DESC']].dropna()\n",
        "pivot = df_new.groupby(['BORO', 'LOC_CLASSFCTN_DESC']).size().unstack(fill_value=0)\n",
        "\n",
        "# Reset index to convert the pivot table to a regular DataFrame\n",
        "pivot_table = pivot.reset_index().drop(['STREET', 'OTHER', 'HOUSING'], axis=1)\n",
        "\n",
        "# Melt the DataFrame to long format\n",
        "df_locality = pivot_table.melt(id_vars='BORO', var_name='LOC_CLASSFCTN_DESC', value_name='Count')\n",
        "# Print the melted DataFrame\n",
        "df_locality"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 979
        },
        "id": "xl3cVq3jtbrA",
        "outputId": "42f4b86d-d6da-46be-c99d-8db8238e7696"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             BORO LOC_CLASSFCTN_DESC  Count\n",
              "0           BRONX         COMMERCIAL     56\n",
              "1        BROOKLYN         COMMERCIAL     75\n",
              "2       MANHATTAN         COMMERCIAL     42\n",
              "3          QUEENS         COMMERCIAL     31\n",
              "4   STATEN ISLAND         COMMERCIAL      3\n",
              "5           BRONX           DWELLING     65\n",
              "6        BROOKLYN           DWELLING     99\n",
              "7       MANHATTAN           DWELLING     18\n",
              "8          QUEENS           DWELLING     46\n",
              "9   STATEN ISLAND           DWELLING     14\n",
              "10          BRONX        PARKING LOT      4\n",
              "11       BROOKLYN        PARKING LOT      4\n",
              "12      MANHATTAN        PARKING LOT      2\n",
              "13         QUEENS        PARKING LOT      5\n",
              "14  STATEN ISLAND        PARKING LOT      0\n",
              "15          BRONX         PLAYGROUND      6\n",
              "16       BROOKLYN         PLAYGROUND     12\n",
              "17      MANHATTAN         PLAYGROUND     15\n",
              "18         QUEENS         PLAYGROUND      7\n",
              "19  STATEN ISLAND         PLAYGROUND      1\n",
              "20          BRONX            TRANSIT      4\n",
              "21       BROOKLYN            TRANSIT     12\n",
              "22      MANHATTAN            TRANSIT      3\n",
              "23         QUEENS            TRANSIT      4\n",
              "24  STATEN ISLAND            TRANSIT      0\n",
              "25          BRONX            VEHICLE      9\n",
              "26       BROOKLYN            VEHICLE      7\n",
              "27      MANHATTAN            VEHICLE      3\n",
              "28         QUEENS            VEHICLE      9\n",
              "29  STATEN ISLAND            VEHICLE      1"
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>BORO</th>\n",
              "      <th>LOC_CLASSFCTN_DESC</th>\n",
              "      <th>Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>BRONX</td>\n",
              "      <td>COMMERCIAL</td>\n",
              "      <td>56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BROOKLYN</td>\n",
              "      <td>COMMERCIAL</td>\n",
              "      <td>75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>MANHATTAN</td>\n",
              "      <td>COMMERCIAL</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>QUEENS</td>\n",
              "      <td>COMMERCIAL</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>STATEN ISLAND</td>\n",
              "      <td>COMMERCIAL</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>BRONX</td>\n",
              "      <td>DWELLING</td>\n",
              "      <td>65</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>BROOKLYN</td>\n",
              "      <td>DWELLING</td>\n",
              "      <td>99</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>MANHATTAN</td>\n",
              "      <td>DWELLING</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>QUEENS</td>\n",
              "      <td>DWELLING</td>\n",
              "      <td>46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>STATEN ISLAND</td>\n",
              "      <td>DWELLING</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>BRONX</td>\n",
              "      <td>PARKING LOT</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>BROOKLYN</td>\n",
              "      <td>PARKING LOT</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>MANHATTAN</td>\n",
              "      <td>PARKING LOT</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>QUEENS</td>\n",
              "      <td>PARKING LOT</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>STATEN ISLAND</td>\n",
              "      <td>PARKING LOT</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>BRONX</td>\n",
              "      <td>PLAYGROUND</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>BROOKLYN</td>\n",
              "      <td>PLAYGROUND</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>MANHATTAN</td>\n",
              "      <td>PLAYGROUND</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>QUEENS</td>\n",
              "      <td>PLAYGROUND</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>STATEN ISLAND</td>\n",
              "      <td>PLAYGROUND</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>BRONX</td>\n",
              "      <td>TRANSIT</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>BROOKLYN</td>\n",
              "      <td>TRANSIT</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>MANHATTAN</td>\n",
              "      <td>TRANSIT</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>QUEENS</td>\n",
              "      <td>TRANSIT</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>STATEN ISLAND</td>\n",
              "      <td>TRANSIT</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>BRONX</td>\n",
              "      <td>VEHICLE</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>BROOKLYN</td>\n",
              "      <td>VEHICLE</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>MANHATTAN</td>\n",
              "      <td>VEHICLE</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>QUEENS</td>\n",
              "      <td>VEHICLE</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>STATEN ISLAND</td>\n",
              "      <td>VEHICLE</td>\n",
              "      <td>1</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_locality",
              "summary": "{\n  \"name\": \"df_locality\",\n  \"rows\": 30,\n  \"fields\": [\n    {\n      \"column\": \"BORO\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"BROOKLYN\",\n          \"STATEN ISLAND\",\n          \"MANHATTAN\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LOC_CLASSFCTN_DESC\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"COMMERCIAL\",\n          \"DWELLING\",\n          \"VEHICLE\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25,\n        \"min\": 0,\n        \"max\": 99,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          56,\n          7,\n          12\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the stacked bar plot\n",
        "fig = px.bar(\n",
        "    df_locality,\n",
        "    x='BORO', y='Count',\n",
        "    color='LOC_CLASSFCTN_DESC',\n",
        "    labels={\n",
        "        'BORO': 'Regions of NYC',\n",
        "        'Count': 'Number of incidents reported'\n",
        "    },\n",
        "    barmode='stack'\n",
        ")\n",
        "fig.update_layout(\n",
        "    title_text='Incidents reported in different localities in different regions',\n",
        "    title_x=0.5,\n",
        "    legend=dict(\n",
        "        orientation=\"h\",\n",
        "        yanchor=\"bottom\",\n",
        "        title=\"Locality classification\",\n",
        "        y=-0.2,\n",
        "        xanchor=\"center\",\n",
        "        x=0.5,\n",
        "        traceorder=\"grouped\"\n",
        "    ),\n",
        ")\n",
        "# Show the plot\n",
        "pyo.iplot(fig, filename='Locality_classification_incident_count')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "yevgstVErER4",
        "outputId": "8a1c0b7d-a6a6-4c76-c6f7-7ade50ed0150"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Age group of male and female victims"
      ],
      "metadata": {
        "id": "6HajjuuRz781"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_age = df.loc[:, ['VIC_AGE_GROUP', 'VIC_SEX']].dropna()\n",
        "df_age = df_age.groupby(['VIC_AGE_GROUP', 'VIC_SEX']).size().unstack(fill_value=0)\n",
        "pivot_table = df_age.reset_index()\n",
        "df_age = pivot_table.melt(id_vars='VIC_AGE_GROUP', var_name='VIC_SEX', value_name='Count')\n",
        "df_age"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "BvX6VeqMl7ym",
        "outputId": "19b4aa42-65b8-4f4e-d52f-20f2f6f2f3fb"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  VIC_AGE_GROUP VIC_SEX  Count\n",
              "0          0-18       F    427\n",
              "1         18-25       F    826\n",
              "2         25-45       F   1072\n",
              "3         45-65       F    366\n",
              "4        65-100       F     63\n",
              "5          0-18       M   2523\n",
              "6         18-25       M   9534\n",
              "7         25-45       M  11880\n",
              "8         45-65       M   1609\n",
              "9        65-100       M    142"
            ],
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_age",
              "summary": "{\n  \"name\": \"df_age\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"VIC_AGE_GROUP\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"18-25\",\n          \"65-100\",\n          \"25-45\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"VIC_SEX\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"M\",\n          \"F\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4246,\n        \"min\": 63,\n        \"max\": 11880,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          1609,\n          826\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the stacked bar plot\n",
        "fig = px.bar(\n",
        "    df_age,\n",
        "    x='VIC_AGE_GROUP', y='Count',\n",
        "    color='VIC_SEX',\n",
        "    labels={\n",
        "        'VIC_AGE_GROUP': 'Age group of victims',\n",
        "        'Count': 'Number of victims'\n",
        "    },\n",
        "    barmode='stack'\n",
        ")\n",
        "fig.update_layout(\n",
        "    {\n",
        "        \"paper_bgcolor\": \"rgba(0, 0, 0, 0)\",\n",
        "        \"plot_bgcolor\": \"rgba(0, 0, 0, 0)\",\n",
        "    },\n",
        "    title_text='Gender wise representation of each age group',\n",
        "    title_x=0.5,\n",
        ")\n",
        "# Show the plot\n",
        "pyo.iplot(fig, filename='Age_Gender_count')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "IwLrfUJe0Rv_",
        "outputId": "e5539aef-2e29-4f22-e3b5-d1956a725be0"
      },
      "execution_count": 23,
      "outputs": [
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              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Bonus plots\n",
        "\n",
        "Histogram of the PRECINCT column"
      ],
      "metadata": {
        "id": "Z6jQsRyp3ZhX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hist = px.histogram(\n",
        "    df, x='PRECINCT',\n",
        "    labels={\n",
        "        'PRECINCT': 'Numeric values of PRECINCT',\n",
        "        'count': 'Frequency'\n",
        "    }\n",
        ")\n",
        "hist.update_layout(\n",
        "    title_text='Plot of PRECINCT column values',\n",
        "    title_x=0.5\n",
        ")\n",
        "pyo.iplot(hist, filename='Histogram')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "rmgFvDAh1VoK",
        "outputId": "fcc49d49-7e5c-4a41-a0d1-1e8810943a02"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
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              "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-2.24.1.min.js\"></script>                <div id=\"a4df6bc4-db44-4224-9711-f7914e6f2e28\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"a4df6bc4-db44-4224-9711-f7914e6f2e28\")) {                    Plotly.newPlot(                        \"a4df6bc4-db44-4224-9711-f7914e6f2e28\",                        [{\"alignmentgroup\":\"True\",\"bingroup\":\"x\",\"hovertemplate\":\"Numeric values of 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          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Sankey diagram for classification and description\n",
        "\n",
        "[Reference](https://stackoverflow.com/a/72764554)"
      ],
      "metadata": {
        "id": "j0Gfd8MaMEq6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hierarchy = df[['LOC_CLASSFCTN_DESC', 'LOCATION_DESC']].dropna()\n",
        "hierarchy = hierarchy[hierarchy['LOC_CLASSFCTN_DESC']!='OTHER']\n",
        "hierarchy.loc[hierarchy.LOCATION_DESC.str.contains(\"STORE\"),'LOCATION_DESC'] = \"STORE\"\n",
        "hierarchy.loc[hierarchy.LOCATION_DESC.str.contains(\"HOUS\"),'LOCATION_DESC'] = \"HOUSE\"\n",
        "hierarchy.loc[hierarchy.LOCATION_DESC.str.contains(\"CLUB\"),'LOCATION_DESC'] = \"CLUB\"\n",
        "hierarchy = hierarchy.groupby(['LOC_CLASSFCTN_DESC', 'LOCATION_DESC']).size().unstack(fill_value=0)\n",
        "pivot_table = hierarchy.reset_index()\n",
        "loc_df = pivot_table.melt(id_vars='LOC_CLASSFCTN_DESC', var_name='LOCATION_DESC', value_name='Count')\n",
        "loc_df = loc_df[loc_df['Count']!=0]\n",
        "loc_df"
      ],
      "metadata": {
        "id": "XYzuNYsZ4us8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "809e7473-792f-4258-b691-3266c4b798a6"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   LOC_CLASSFCTN_DESC            LOCATION_DESC  Count\n",
              "0          COMMERCIAL        BEAUTY/NAIL SALON      6\n",
              "4              STREET        BEAUTY/NAIL SALON      8\n",
              "5          COMMERCIAL                     CLUB     40\n",
              "9              STREET                     CLUB     45\n",
              "10         COMMERCIAL          COMMERCIAL BLDG      6\n",
              "13        PARKING LOT          COMMERCIAL BLDG      7\n",
              "14             STREET          COMMERCIAL BLDG     26\n",
              "15         COMMERCIAL      DRY CLEANER/LAUNDRY      1\n",
              "20         COMMERCIAL                FAST FOOD      1\n",
              "23        PARKING LOT                FAST FOOD      1\n",
              "24             STREET                FAST FOOD     29\n",
              "25         COMMERCIAL              GAS STATION      3\n",
              "29             STREET              GAS STATION      9\n",
              "30         COMMERCIAL           GROCERY/BODEGA     67\n",
              "34             STREET           GROCERY/BODEGA     60\n",
              "35         COMMERCIAL                 HOSPITAL      3\n",
              "39             STREET                 HOSPITAL      4\n",
              "40         COMMERCIAL              HOTEL/MOTEL      1\n",
              "41           DWELLING              HOTEL/MOTEL      1\n",
              "45         COMMERCIAL                    HOUSE      2\n",
              "46           DWELLING                    HOUSE     47\n",
              "47            HOUSING                    HOUSE    448\n",
              "49             STREET                    HOUSE     43\n",
              "50         COMMERCIAL  MULTI DWELL - APT BUILD      2\n",
              "51           DWELLING  MULTI DWELL - APT BUILD    172\n",
              "52            HOUSING  MULTI DWELL - APT BUILD      6\n",
              "54             STREET  MULTI DWELL - APT BUILD    110\n",
              "55         COMMERCIAL         RESTAURANT/DINER     12\n",
              "59             STREET         RESTAURANT/DINER      6\n",
              "60         COMMERCIAL           SMALL MERCHANT     17\n",
              "64             STREET           SMALL MERCHANT      2\n",
              "65         COMMERCIAL                    STORE     18\n",
              "69             STREET                    STORE     11\n",
              "74             STREET              SUPERMARKET      2"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-76cc18bf-93ce-45dc-9c98-c3bfcc61e5b2\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "        vertical-align: top;\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>LOC_CLASSFCTN_DESC</th>\n",
              "      <th>LOCATION_DESC</th>\n",
              "      <th>Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>COMMERCIAL</td>\n",
              "      <td>BEAUTY/NAIL SALON</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>STREET</td>\n",
              "      <td>BEAUTY/NAIL SALON</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>COMMERCIAL</td>\n",
              "      <td>CLUB</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>STREET</td>\n",
              "      <td>CLUB</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>COMMERCIAL</td>\n",
              "      <td>COMMERCIAL BLDG</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>PARKING LOT</td>\n",
              "      <td>COMMERCIAL BLDG</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>STREET</td>\n",
              "      <td>COMMERCIAL BLDG</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>COMMERCIAL</td>\n",
              "      <td>DRY CLEANER/LAUNDRY</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>COMMERCIAL</td>\n",
              "      <td>FAST FOOD</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>PARKING LOT</td>\n",
              "      <td>FAST FOOD</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>STREET</td>\n",
              "      <td>FAST FOOD</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>COMMERCIAL</td>\n",
              "      <td>GAS STATION</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>STREET</td>\n",
              "      <td>GAS STATION</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>COMMERCIAL</td>\n",
              "      <td>GROCERY/BODEGA</td>\n",
              "      <td>67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>STREET</td>\n",
              "      <td>GROCERY/BODEGA</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>COMMERCIAL</td>\n",
              "      <td>HOSPITAL</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>STREET</td>\n",
              "      <td>HOSPITAL</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>COMMERCIAL</td>\n",
              "      <td>HOTEL/MOTEL</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>DWELLING</td>\n",
              "      <td>HOTEL/MOTEL</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>COMMERCIAL</td>\n",
              "      <td>HOUSE</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>DWELLING</td>\n",
              "      <td>HOUSE</td>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>HOUSING</td>\n",
              "      <td>HOUSE</td>\n",
              "      <td>448</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>STREET</td>\n",
              "      <td>HOUSE</td>\n",
              "      <td>43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>COMMERCIAL</td>\n",
              "      <td>MULTI DWELL - APT BUILD</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>DWELLING</td>\n",
              "      <td>MULTI DWELL - APT BUILD</td>\n",
              "      <td>172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>HOUSING</td>\n",
              "      <td>MULTI DWELL - APT BUILD</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>STREET</td>\n",
              "      <td>MULTI DWELL - APT BUILD</td>\n",
              "      <td>110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>COMMERCIAL</td>\n",
              "      <td>RESTAURANT/DINER</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>STREET</td>\n",
              "      <td>RESTAURANT/DINER</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>COMMERCIAL</td>\n",
              "      <td>SMALL MERCHANT</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>STREET</td>\n",
              "      <td>SMALL MERCHANT</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>COMMERCIAL</td>\n",
              "      <td>STORE</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>STREET</td>\n",
              "      <td>STORE</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>STREET</td>\n",
              "      <td>SUPERMARKET</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "\n",
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              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "\n",
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              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('loc_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('loc_df');\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "loc_df",
              "summary": "{\n  \"name\": \"loc_df\",\n  \"rows\": 34,\n  \"fields\": [\n    {\n      \"column\": \"LOC_CLASSFCTN_DESC\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"STREET\",\n          \"HOUSING\",\n          \"PARKING LOT\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LOCATION_DESC\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"HOUSE\",\n          \"RESTAURANT/DINER\",\n          \"BEAUTY/NAIL SALON\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 81,\n        \"min\": 1,\n        \"max\": 448,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          448,\n          9,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classifications = list(set(loc_df['LOC_CLASSFCTN_DESC'].tolist()))\n",
        "descriptions = list(set(loc_df['LOCATION_DESC'].tolist()))\n",
        "classification_dict = {}\n",
        "description_dict = {}\n",
        "classification_idx = 0\n",
        "\n",
        "for idx, classification in enumerate(classifications):\n",
        "  classification_dict.update({classification: idx})\n",
        "  classification_idx = idx\n",
        "\n",
        "for idx, description in enumerate(descriptions):\n",
        "  description_dict.update({description: classification_idx+1+idx})\n",
        "\n",
        "print(classification_dict)\n",
        "print(description_dict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g43V03CoPr8v",
        "outputId": "53488fab-2214-4807-9b39-130096d12b68"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'DWELLING': 0, 'STREET': 1, 'COMMERCIAL': 2, 'PARKING LOT': 3, 'HOUSING': 4}\n",
            "{'SMALL MERCHANT': 5, 'FAST FOOD': 6, 'GAS STATION': 7, 'COMMERCIAL BLDG': 8, 'HOTEL/MOTEL': 9, 'SUPERMARKET': 10, 'CLUB': 11, 'GROCERY/BODEGA': 12, 'DRY CLEANER/LAUNDRY': 13, 'HOUSE': 14, 'BEAUTY/NAIL SALON': 15, 'MULTI DWELL - APT BUILD': 16, 'RESTAURANT/DINER': 17, 'STORE': 18, 'HOSPITAL': 19}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# add source and target columns to dataframe\n",
        "def get_source_target(df):\n",
        "  source_list, target_list = [], []\n",
        "  for row in df['LOC_CLASSFCTN_DESC']:\n",
        "    source_list.append(classification_dict.get(row))\n",
        "\n",
        "  for row in df['LOCATION_DESC']:\n",
        "    target_list.append(description_dict.get(row))\n",
        "\n",
        "  return source_list, target_list"
      ],
      "metadata": {
        "id": "ZfjAD5VTLhw5"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loc_df['Source'], loc_df['Target'] = get_source_target(df=loc_df)\n",
        "loc_df = loc_df.drop_duplicates()\n",
        "loc_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "BZzbgzLNQ0zG",
        "outputId": "ada4186e-9ce9-4eed-ee8b-5fb4f450e5a9"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   LOC_CLASSFCTN_DESC            LOCATION_DESC  Count  Source  Target\n",
              "0          COMMERCIAL        BEAUTY/NAIL SALON      6       2      15\n",
              "4              STREET        BEAUTY/NAIL SALON      8       1      15\n",
              "5          COMMERCIAL                     CLUB     40       2      11\n",
              "9              STREET                     CLUB     45       1      11\n",
              "10         COMMERCIAL          COMMERCIAL BLDG      6       2       8\n",
              "13        PARKING LOT          COMMERCIAL BLDG      7       3       8\n",
              "14             STREET          COMMERCIAL BLDG     26       1       8\n",
              "15         COMMERCIAL      DRY CLEANER/LAUNDRY      1       2      13\n",
              "20         COMMERCIAL                FAST FOOD      1       2       6\n",
              "23        PARKING LOT                FAST FOOD      1       3       6\n",
              "24             STREET                FAST FOOD     29       1       6\n",
              "25         COMMERCIAL              GAS STATION      3       2       7\n",
              "29             STREET              GAS STATION      9       1       7\n",
              "30         COMMERCIAL           GROCERY/BODEGA     67       2      12\n",
              "34             STREET           GROCERY/BODEGA     60       1      12\n",
              "35         COMMERCIAL                 HOSPITAL      3       2      19\n",
              "39             STREET                 HOSPITAL      4       1      19\n",
              "40         COMMERCIAL              HOTEL/MOTEL      1       2       9\n",
              "41           DWELLING              HOTEL/MOTEL      1       0       9\n",
              "45         COMMERCIAL                    HOUSE      2       2      14\n",
              "46           DWELLING                    HOUSE     47       0      14\n",
              "47            HOUSING                    HOUSE    448       4      14\n",
              "49             STREET                    HOUSE     43       1      14\n",
              "50         COMMERCIAL  MULTI DWELL - APT BUILD      2       2      16\n",
              "51           DWELLING  MULTI DWELL - APT BUILD    172       0      16\n",
              "52            HOUSING  MULTI DWELL - APT BUILD      6       4      16\n",
              "54             STREET  MULTI DWELL - APT BUILD    110       1      16\n",
              "55         COMMERCIAL         RESTAURANT/DINER     12       2      17\n",
              "59             STREET         RESTAURANT/DINER      6       1      17\n",
              "60         COMMERCIAL           SMALL MERCHANT     17       2       5\n",
              "64             STREET           SMALL MERCHANT      2       1       5\n",
              "65         COMMERCIAL                    STORE     18       2      18\n",
              "69             STREET                    STORE     11       1      18\n",
              "74             STREET              SUPERMARKET      2       1      10"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f707209d-ab19-4e9b-bb55-6463b6227ee6\" 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",
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              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>LOC_CLASSFCTN_DESC</th>\n",
              "      <th>LOCATION_DESC</th>\n",
              "      <th>Count</th>\n",
              "      <th>Source</th>\n",
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              "      <th>0</th>\n",
              "      <td>COMMERCIAL</td>\n",
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              "      <td>15</td>\n",
              "    </tr>\n",
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              "      <th>4</th>\n",
              "      <td>STREET</td>\n",
              "      <td>BEAUTY/NAIL SALON</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>COMMERCIAL</td>\n",
              "      <td>CLUB</td>\n",
              "      <td>40</td>\n",
              "      <td>2</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>STREET</td>\n",
              "      <td>CLUB</td>\n",
              "      <td>45</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>COMMERCIAL</td>\n",
              "      <td>COMMERCIAL BLDG</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>PARKING LOT</td>\n",
              "      <td>COMMERCIAL BLDG</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>STREET</td>\n",
              "      <td>COMMERCIAL BLDG</td>\n",
              "      <td>26</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>COMMERCIAL</td>\n",
              "      <td>DRY CLEANER/LAUNDRY</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>COMMERCIAL</td>\n",
              "      <td>FAST FOOD</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>PARKING LOT</td>\n",
              "      <td>FAST FOOD</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>STREET</td>\n",
              "      <td>FAST FOOD</td>\n",
              "      <td>29</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>COMMERCIAL</td>\n",
              "      <td>GAS STATION</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>STREET</td>\n",
              "      <td>GAS STATION</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>COMMERCIAL</td>\n",
              "      <td>GROCERY/BODEGA</td>\n",
              "      <td>67</td>\n",
              "      <td>2</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>STREET</td>\n",
              "      <td>GROCERY/BODEGA</td>\n",
              "      <td>60</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>COMMERCIAL</td>\n",
              "      <td>HOSPITAL</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>STREET</td>\n",
              "      <td>HOSPITAL</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>COMMERCIAL</td>\n",
              "      <td>HOTEL/MOTEL</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>DWELLING</td>\n",
              "      <td>HOTEL/MOTEL</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>COMMERCIAL</td>\n",
              "      <td>HOUSE</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>DWELLING</td>\n",
              "      <td>HOUSE</td>\n",
              "      <td>47</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>HOUSING</td>\n",
              "      <td>HOUSE</td>\n",
              "      <td>448</td>\n",
              "      <td>4</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>STREET</td>\n",
              "      <td>HOUSE</td>\n",
              "      <td>43</td>\n",
              "      <td>1</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>COMMERCIAL</td>\n",
              "      <td>MULTI DWELL - APT BUILD</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>DWELLING</td>\n",
              "      <td>MULTI DWELL - APT BUILD</td>\n",
              "      <td>172</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>HOUSING</td>\n",
              "      <td>MULTI DWELL - APT BUILD</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>STREET</td>\n",
              "      <td>MULTI DWELL - APT BUILD</td>\n",
              "      <td>110</td>\n",
              "      <td>1</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>COMMERCIAL</td>\n",
              "      <td>RESTAURANT/DINER</td>\n",
              "      <td>12</td>\n",
              "      <td>2</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>STREET</td>\n",
              "      <td>RESTAURANT/DINER</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>COMMERCIAL</td>\n",
              "      <td>SMALL MERCHANT</td>\n",
              "      <td>17</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>STREET</td>\n",
              "      <td>SMALL MERCHANT</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>COMMERCIAL</td>\n",
              "      <td>STORE</td>\n",
              "      <td>18</td>\n",
              "      <td>2</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>STREET</td>\n",
              "      <td>STORE</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>STREET</td>\n",
              "      <td>SUPERMARKET</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "variable_name": "loc_df",
              "summary": "{\n  \"name\": \"loc_df\",\n  \"rows\": 34,\n  \"fields\": [\n    {\n      \"column\": \"LOC_CLASSFCTN_DESC\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"STREET\",\n          \"HOUSING\",\n          \"PARKING LOT\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LOCATION_DESC\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"HOUSE\",\n          \"RESTAURANT/DINER\",\n          \"BEAUTY/NAIL SALON\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 81,\n        \"min\": 1,\n        \"max\": 448,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          448,\n          9,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Source\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1,\n          4,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 5,\n        \"max\": 19,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          14,\n          17,\n          15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sankey_node = dict(\n",
        "    label = classifications+descriptions,\n",
        "    line = dict(color = \"black\", width = 0.5),\n",
        "    pad = 15,\n",
        "    thickness = 20,\n",
        "    x = [0.001, 0.001, 0.001, 0.001, 0.001],\n",
        "    y = [0.001, 75/285, 160/285, 190/285, 0.001, 75/285, 130/285, 215/285],\n",
        "    color = [\"#305CA3\", \"#C9304E\", \"#C1DAF1\", \"#F7DC70\", \"#5CA330\"]\n",
        ")\n",
        "\n",
        "sankey_link = dict(\n",
        "    source = loc_df['Source'],\n",
        "    target = loc_df['Target'],\n",
        "    value = loc_df['Count'],\n",
        ")\n",
        "\n",
        "sankey = go.Figure(data=[go.Sankey(\n",
        "    arrangement='snap',\n",
        "    node = sankey_node,\n",
        "    link = sankey_link\n",
        "    )\n",
        "])\n",
        "sankey.update_layout(\n",
        "    title_text=\"Location Categories and Descriptions as a Sankey Diagram\",\n",
        "    title_x=0.5,\n",
        "    font_size=10\n",
        ")\n",
        "pyo.iplot(sankey, filename='Sankey')"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "qsRTeS2LTktC",
        "outputId": "ec824be0-b726-4cd9-b820-551dee305e69"
      },
      "execution_count": 29,
      "outputs": [
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              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Plotting on a map\n",
        "\n",
        "1. [Guide using GeoPandas](https://plotly.com/python/scattermapbox/)\n",
        "2. [All kind of maps](https://plotly.com/python/maps/)"
      ],
      "metadata": {
        "id": "pInFAFONW_BV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "px.set_mapbox_access_token(token=\"pk.eyJ1IjoiZGl2aS0wMDciLCJhIjoiY2x3dnRlMnU4MG5vOTJycXprMG1pOGZ3dCJ9.WULsINqBNsYBvpo11qP7kg\")\n"
      ],
      "metadata": {
        "id": "SP-Q30zPUJp1"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "murder_df = df[df['STATISTICAL_MURDER_FLAG']][['BORO', 'Latitude', 'Longitude']].dropna()\n",
        "murder_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "n6tXS9kUz6Nw",
        "outputId": "1f48ee9a-672d-4364-aa3f-f4dfa630eb9c"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            BORO   Latitude  Longitude\n",
              "0      MANHATTAN  40.754692 -73.993500\n",
              "1          BRONX  40.854402 -73.882330\n",
              "6         QUEENS  40.673306 -73.789887\n",
              "9          BRONX  40.860463 -73.865561\n",
              "11        QUEENS  40.769744 -73.909872\n",
              "...          ...        ...        ...\n",
              "28544   BROOKLYN  40.634916 -73.932963\n",
              "28549     QUEENS  40.700826 -73.881490\n",
              "28551   BROOKLYN  40.695772 -73.979528\n",
              "28557      BRONX  40.903785 -73.850098\n",
              "28559      BRONX  40.821404 -73.898938\n",
              "\n",
              "[5500 rows x 3 columns]"
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              "      <th></th>\n",
              "      <th>BORO</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>MANHATTAN</td>\n",
              "      <td>40.754692</td>\n",
              "      <td>-73.993500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BRONX</td>\n",
              "      <td>40.854402</td>\n",
              "      <td>-73.882330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>QUEENS</td>\n",
              "      <td>40.673306</td>\n",
              "      <td>-73.789887</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>BRONX</td>\n",
              "      <td>40.860463</td>\n",
              "      <td>-73.865561</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>QUEENS</td>\n",
              "      <td>40.769744</td>\n",
              "      <td>-73.909872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
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              "      <th>28544</th>\n",
              "      <td>BROOKLYN</td>\n",
              "      <td>40.634916</td>\n",
              "      <td>-73.932963</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28549</th>\n",
              "      <td>QUEENS</td>\n",
              "      <td>40.700826</td>\n",
              "      <td>-73.881490</td>\n",
              "    </tr>\n",
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              "      <th>28551</th>\n",
              "      <td>BROOKLYN</td>\n",
              "      <td>40.695772</td>\n",
              "      <td>-73.979528</td>\n",
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              "    <tr>\n",
              "      <th>28557</th>\n",
              "      <td>BRONX</td>\n",
              "      <td>40.903785</td>\n",
              "      <td>-73.850098</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28559</th>\n",
              "      <td>BRONX</td>\n",
              "      <td>40.821404</td>\n",
              "      <td>-73.898938</td>\n",
              "    </tr>\n",
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              "<p>5500 rows × 3 columns</p>\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\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",
              "      fill: #174EA6;\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        document.querySelector('#df-9031151c-2240-4723-9f82-35fc71ea6215 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-9031151c-2240-4723-9f82-35fc71ea6215');\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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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    40% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    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-dfea8b7b-fcfb-488e-a8e9-595224df5d2a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_28341aac-24a2-4b70-9fbd-5cd5b3ca87e1\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('murder_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_28341aac-24a2-4b70-9fbd-5cd5b3ca87e1 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('murder_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "murder_df",
              "summary": "{\n  \"name\": \"murder_df\",\n  \"rows\": 5500,\n  \"fields\": [\n    {\n      \"column\": \"BORO\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"BRONX\",\n          \"STATEN ISLAND\",\n          \"QUEENS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08864106617920434,\n        \"min\": 40.51158556600007,\n        \"max\": 40.90548732500002,\n        \"num_unique_values\": 3578,\n        \"samples\": [\n          40.59370785300007,\n          40.84304829600006,\n          40.67541832200004\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0671908623284575,\n        \"min\": -74.24930349899995,\n        \"max\": -73.70204616699993,\n        \"num_unique_values\": 3575,\n        \"samples\": [\n          -73.90799242199995,\n          -73.95843,\n          -73.96147901099994\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shooting_map = px.scatter_mapbox(\n",
        "    murder_df, lat=\"Latitude\", lon=\"Longitude\", color='BORO',\n",
        "    color_continuous_scale=px.colors.cyclical.IceFire, size_max=2, zoom=10\n",
        ")\n",
        "shooting_map.update_layout(\n",
        "    title_text=\"Map depicting shooting incidents in NYC\",\n",
        "    title_x=0.5,\n",
        "    legend=dict(\n",
        "        orientation=\"h\",\n",
        "        yanchor=\"bottom\",\n",
        "        y=-0.1,\n",
        "        xanchor=\"center\",\n",
        "        x=0.5,\n",
        "        traceorder=\"grouped\",\n",
        "        title=\"REGION\",\n",
        "        bgcolor=\"yellow\"\n",
        "    )\n",
        ")\n",
        "pyo.iplot(shooting_map, filename='Map')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "xhWTvkg70MY3",
        "outputId": "3b9cd10d-ce90-4be5-f032-4f30f7158f49"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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