sqlite3-geospatial-analysis-on-wildfire-data.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"\n","metadata":{}},{"cell_type":"markdown","source":"<a id='table'></a>\n\n<div style=\"color:white;display:fill;border-radius:8px;\n            background-color:#8F00FF;font-size:150%;\n            font-family:Nexa;letter-spacing:0.5px\">\n    <p style=\"padding: 5px;color:white;\"><b> Table of contents </b></p>\n</div>\n\n* [1. Introduction](#Introduction)\n* [2. GeoSpatial analysis for wild fire in different forests](#Importingdataset)\n  * [2.1. Intial Explanatory analysis on the sqlite3 dataset ](#Meta)\n  * [2.2. sqlite3 queries](#imshow)\n* [Conclusion](#Conclusion)\n\n<div style=\"color:white;display:fill;border-radius:8px;\n            background-color:#8F00FF;font-size:150%;\n            font-family:Nexa;letter-spacing:0.5px\">\n    <p style=\"padding: 5px;color:white;\"><b></b></p>\n</div>\n<a id='Introduction'></a>\n\n#  <b><span style='color:#8F00FF'>| 1. </span> Introduction </b>\n\n[Table of contents](#table)\n\nIn this Notebook The Wild Fire Geospatial data is used and the aim is to feature fire locations and combine the given information to provide a more detailed statics for this phenomena in the north america forest sites.  \n\nIn this Notebook, first an analysis and executation of 7 SQL queries is done. Later a Visual Explanatory analysis is performed on the data and finally there GeoSpatial analysis based on varied folium map sets are presented.   \n\n##  <b><span style='color:#8F00FF'>| 1.1. </span> What is sqlite3 dataset in the geospatial context?  </b>\n\n\n[sqlite3](https://docs.python.org/3/library/sqlite3.html) is a C library that provides a lightwieght disk-based database. \n\nSqlite3 has some advantages. One of them is that it does not require a separate server process and the other one is that it allows accessing the database with any nonstandarrd variant of the SQL query Language. \n\nHere we reading a sqlite3 file and it is possible to execute PostgreSQL or Oracle queries beside the standard versions. \n\nTo do so first we create a database or here dataset connection and then we need to use a database cursor. ","metadata":{}},{"cell_type":"code","source":"import sqlite3\nimport pandas as pd\nimport altair as alt \n\n# Create a SQL connection to our SQLite database\nconn = sqlite3.connect(\"../input/188-million-us-wildfires/FPA_FOD_20170508.sqlite\")\n\ncur = conn.cursor()\n\ndf = pd.read_sql( \"\"\" \n                        SELECT *\n                        from fires \n                        \n                       \"\"\", con=conn)\n\n","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:46:08.022520Z","iopub.execute_input":"2023-10-05T11:46:08.022879Z","iopub.status.idle":"2023-10-05T11:46:46.264184Z","shell.execute_reply.started":"2023-10-05T11:46:08.022850Z","shell.execute_reply":"2023-10-05T11:46:46.263017Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"\npd.set_option('display.float_format', lambda x: '%.2f'  % x) \n\nconn = sqlite3.connect(\"../input/188-million-us-wildfires/FPA_FOD_20170508.sqlite\")\n\nfires = pd.read_sql(\"\"\"SELECT * FROM fires\"\"\", con=conn) \n","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:46:46.266451Z","iopub.execute_input":"2023-10-05T11:46:46.267193Z","iopub.status.idle":"2023-10-05T11:47:22.121535Z","shell.execute_reply.started":"2023-10-05T11:46:46.267154Z","shell.execute_reply":"2023-10-05T11:47:22.120517Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"data = pd.read_sql(\"\"\"\n\nSELECT *\nFROM fires\nLIMIT 100\n\n\"\"\",con = conn) \n\npd.set_option('display.max_columns', None)\n\ndata.head()","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:47:22.122748Z","iopub.execute_input":"2023-10-05T11:47:22.123053Z","iopub.status.idle":"2023-10-05T11:47:22.257908Z","shell.execute_reply.started":"2023-10-05T11:47:22.123028Z","shell.execute_reply":"2023-10-05T11:47:22.256774Z"},"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"   OBJECTID  FOD_ID      FPA_ID SOURCE_SYSTEM_TYPE SOURCE_SYSTEM  \\\n0         1       1  FS-1418826                FED   FS-FIRESTAT   \n1         2       2  FS-1418827                FED   FS-FIRESTAT   \n2         3       3  FS-1418835                FED   FS-FIRESTAT   \n3         4       4  FS-1418845                FED   FS-FIRESTAT   \n4         5       5  FS-1418847                FED   FS-FIRESTAT   \n\n  NWCG_REPORTING_AGENCY NWCG_REPORTING_UNIT_ID  NWCG_REPORTING_UNIT_NAME  \\\n0                    FS                USCAPNF    Plumas National Forest   \n1                    FS                USCAENF  Eldorado National Forest   \n2                    FS                USCAENF  Eldorado National Forest   \n3                    FS                USCAENF  Eldorado National Forest   \n4                    FS                USCAENF  Eldorado National Forest   \n\n  SOURCE_REPORTING_UNIT SOURCE_REPORTING_UNIT_NAME LOCAL_FIRE_REPORT_ID  \\\n0                  0511     Plumas National Forest                    1   \n1                  0503   Eldorado National Forest                   13   \n2                  0503   Eldorado National Forest                   27   \n3                  0503   Eldorado National Forest                   43   \n4                  0503   Eldorado National Forest                   44   \n\n  LOCAL_INCIDENT_ID FIRE_CODE FIRE_NAME ICS_209_INCIDENT_NUMBER ICS_209_NAME  \\\n0            PNF-47      BJ8K  FOUNTAIN                    None         None   \n1                13      AAC0    PIGEON                    None         None   \n2               021      A32W     SLACK                    None         None   \n3                 6      None      DEER                    None         None   \n4                 7      None  STEVENOT                    None         None   \n\n  MTBS_ID MTBS_FIRE_NAME COMPLEX_NAME  FIRE_YEAR  DISCOVERY_DATE  \\\n0    None           None         None       2005      2453403.50   \n1    None           None         None       2004      2453137.50   \n2    None           None         None       2004      2453156.50   \n3    None           None         None       2004      2453184.50   \n4    None           None         None       2004      2453184.50   \n\n   DISCOVERY_DOY DISCOVERY_TIME  STAT_CAUSE_CODE STAT_CAUSE_DESCR  CONT_DATE  \\\n0             33           1300             9.00    Miscellaneous 2453403.50   \n1            133           0845             1.00        Lightning 2453137.50   \n2            152           1921             5.00   Debris Burning 2453156.50   \n3            180           1600             1.00        Lightning 2453189.50   \n4            180           1600             1.00        Lightning 2453189.50   \n\n   CONT_DOY CONT_TIME  FIRE_SIZE FIRE_SIZE_CLASS  LATITUDE  LONGITUDE  \\\n0     33.00      1730       0.10               A     40.04    -121.01   \n1    133.00      1530       0.25               A     38.93    -120.40   \n2    152.00      2024       0.10               A     38.98    -120.74   \n3    185.00      1400       0.10               A     38.56    -119.91   \n4    185.00      1200       0.10               A     38.56    -119.93   \n\n   OWNER_CODE       OWNER_DESCR STATE COUNTY FIPS_CODE  FIPS_NAME  \\\n0        5.00              USFS    CA     63       063     Plumas   \n1        5.00              USFS    CA     61       061     Placer   \n2       13.00  STATE OR PRIVATE    CA     17       017  El Dorado   \n3        5.00              USFS    CA      3       003     Alpine   \n4        5.00              USFS    CA      3       003     Alpine   \n\n                                               Shape  \n0  b'\\x00\\x01\\xad\\x10\\x00\\x00\\xe8d\\xc2\\x92_@^\\xc0...  \n1  b'\\x00\\x01\\xad\\x10\\x00\\x00T\\xb6\\xeej\\xe2\\x19^\\...  \n2  b'\\x00\\x01\\xad\\x10\\x00\\x00\\xd0\\xa5\\xa0W\\x13/^\\...  \n3  b'\\x00\\x01\\xad\\x10\\x00\\x00\\x94\\xac\\xa3\\rt\\xfa]...  \n4  b'\\x00\\x01\\xad\\x10\\x00\\x00@\\xe3\\xaa.\\xb7\\xfb]\\...  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>OBJECTID</th>\n      <th>FOD_ID</th>\n      <th>FPA_ID</th>\n      <th>SOURCE_SYSTEM_TYPE</th>\n      <th>SOURCE_SYSTEM</th>\n      <th>NWCG_REPORTING_AGENCY</th>\n      <th>NWCG_REPORTING_UNIT_ID</th>\n      <th>NWCG_REPORTING_UNIT_NAME</th>\n      <th>SOURCE_REPORTING_UNIT</th>\n      <th>SOURCE_REPORTING_UNIT_NAME</th>\n      <th>LOCAL_FIRE_REPORT_ID</th>\n      <th>LOCAL_INCIDENT_ID</th>\n      <th>FIRE_CODE</th>\n      <th>FIRE_NAME</th>\n      <th>ICS_209_INCIDENT_NUMBER</th>\n      <th>ICS_209_NAME</th>\n      <th>MTBS_ID</th>\n      <th>MTBS_FIRE_NAME</th>\n      <th>COMPLEX_NAME</th>\n      <th>FIRE_YEAR</th>\n      <th>DISCOVERY_DATE</th>\n      <th>DISCOVERY_DOY</th>\n      <th>DISCOVERY_TIME</th>\n      <th>STAT_CAUSE_CODE</th>\n      <th>STAT_CAUSE_DESCR</th>\n      <th>CONT_DATE</th>\n      <th>CONT_DOY</th>\n      <th>CONT_TIME</th>\n      <th>FIRE_SIZE</th>\n      <th>FIRE_SIZE_CLASS</th>\n      <th>LATITUDE</th>\n      <th>LONGITUDE</th>\n      <th>OWNER_CODE</th>\n      <th>OWNER_DESCR</th>\n      <th>STATE</th>\n      <th>COUNTY</th>\n      <th>FIPS_CODE</th>\n      <th>FIPS_NAME</th>\n      <th>Shape</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>FS-1418826</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USCAPNF</td>\n      <td>Plumas National Forest</td>\n      <td>0511</td>\n      <td>Plumas National Forest</td>\n      <td>1</td>\n      <td>PNF-47</td>\n      <td>BJ8K</td>\n      <td>FOUNTAIN</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>2005</td>\n      <td>2453403.50</td>\n      <td>33</td>\n      <td>1300</td>\n      <td>9.00</td>\n      <td>Miscellaneous</td>\n      <td>2453403.50</td>\n      <td>33.00</td>\n      <td>1730</td>\n      <td>0.10</td>\n      <td>A</td>\n      <td>40.04</td>\n      <td>-121.01</td>\n      <td>5.00</td>\n      <td>USFS</td>\n      <td>CA</td>\n      <td>63</td>\n      <td>063</td>\n      <td>Plumas</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00\\xe8d\\xc2\\x92_@^\\xc0...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2</td>\n      <td>FS-1418827</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USCAENF</td>\n      <td>Eldorado National Forest</td>\n      <td>0503</td>\n      <td>Eldorado National Forest</td>\n      <td>13</td>\n      <td>13</td>\n      <td>AAC0</td>\n      <td>PIGEON</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>2004</td>\n      <td>2453137.50</td>\n      <td>133</td>\n      <td>0845</td>\n      <td>1.00</td>\n      <td>Lightning</td>\n      <td>2453137.50</td>\n      <td>133.00</td>\n      <td>1530</td>\n      <td>0.25</td>\n      <td>A</td>\n      <td>38.93</td>\n      <td>-120.40</td>\n      <td>5.00</td>\n      <td>USFS</td>\n      <td>CA</td>\n      <td>61</td>\n      <td>061</td>\n      <td>Placer</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00T\\xb6\\xeej\\xe2\\x19^\\...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>3</td>\n      <td>FS-1418835</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USCAENF</td>\n      <td>Eldorado National Forest</td>\n      <td>0503</td>\n      <td>Eldorado National Forest</td>\n      <td>27</td>\n      <td>021</td>\n      <td>A32W</td>\n      <td>SLACK</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>2004</td>\n      <td>2453156.50</td>\n      <td>152</td>\n      <td>1921</td>\n      <td>5.00</td>\n      <td>Debris Burning</td>\n      <td>2453156.50</td>\n      <td>152.00</td>\n      <td>2024</td>\n      <td>0.10</td>\n      <td>A</td>\n      <td>38.98</td>\n      <td>-120.74</td>\n      <td>13.00</td>\n      <td>STATE OR PRIVATE</td>\n      <td>CA</td>\n      <td>17</td>\n      <td>017</td>\n      <td>El Dorado</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00\\xd0\\xa5\\xa0W\\x13/^\\...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>4</td>\n      <td>FS-1418845</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USCAENF</td>\n      <td>Eldorado National Forest</td>\n      <td>0503</td>\n      <td>Eldorado National Forest</td>\n      <td>43</td>\n      <td>6</td>\n      <td>None</td>\n      <td>DEER</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>2004</td>\n      <td>2453184.50</td>\n      <td>180</td>\n      <td>1600</td>\n      <td>1.00</td>\n      <td>Lightning</td>\n      <td>2453189.50</td>\n      <td>185.00</td>\n      <td>1400</td>\n      <td>0.10</td>\n      <td>A</td>\n      <td>38.56</td>\n      <td>-119.91</td>\n      <td>5.00</td>\n      <td>USFS</td>\n      <td>CA</td>\n      <td>3</td>\n      <td>003</td>\n      <td>Alpine</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00\\x94\\xac\\xa3\\rt\\xfa]...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>5</td>\n      <td>FS-1418847</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USCAENF</td>\n      <td>Eldorado National Forest</td>\n      <td>0503</td>\n      <td>Eldorado National Forest</td>\n      <td>44</td>\n      <td>7</td>\n      <td>None</td>\n      <td>STEVENOT</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>2004</td>\n      <td>2453184.50</td>\n      <td>180</td>\n      <td>1600</td>\n      <td>1.00</td>\n      <td>Lightning</td>\n      <td>2453189.50</td>\n      <td>185.00</td>\n      <td>1200</td>\n      <td>0.10</td>\n      <td>A</td>\n      <td>38.56</td>\n      <td>-119.93</td>\n      <td>5.00</td>\n      <td>USFS</td>\n      <td>CA</td>\n      <td>3</td>\n      <td>003</td>\n      <td>Alpine</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00@\\xe3\\xaa.\\xb7\\xfb]\\...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"data.columns","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:47:22.261073Z","iopub.execute_input":"2023-10-05T11:47:22.261522Z","iopub.status.idle":"2023-10-05T11:47:22.268884Z","shell.execute_reply.started":"2023-10-05T11:47:22.261482Z","shell.execute_reply":"2023-10-05T11:47:22.267563Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"Index(['OBJECTID', 'FOD_ID', 'FPA_ID', 'SOURCE_SYSTEM_TYPE', 'SOURCE_SYSTEM',\n       'NWCG_REPORTING_AGENCY', 'NWCG_REPORTING_UNIT_ID',\n       'NWCG_REPORTING_UNIT_NAME', 'SOURCE_REPORTING_UNIT',\n       'SOURCE_REPORTING_UNIT_NAME', 'LOCAL_FIRE_REPORT_ID',\n       'LOCAL_INCIDENT_ID', 'FIRE_CODE', 'FIRE_NAME',\n       'ICS_209_INCIDENT_NUMBER', 'ICS_209_NAME', 'MTBS_ID', 'MTBS_FIRE_NAME',\n       'COMPLEX_NAME', 'FIRE_YEAR', 'DISCOVERY_DATE', 'DISCOVERY_DOY',\n       'DISCOVERY_TIME', 'STAT_CAUSE_CODE', 'STAT_CAUSE_DESCR', 'CONT_DATE',\n       'CONT_DOY', 'CONT_TIME', 'FIRE_SIZE', 'FIRE_SIZE_CLASS', 'LATITUDE',\n       'LONGITUDE', 'OWNER_CODE', 'OWNER_DESCR', 'STATE', 'COUNTY',\n       'FIPS_CODE', 'FIPS_NAME', 'Shape'],\n      dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"data.info()","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:47:22.270431Z","iopub.execute_input":"2023-10-05T11:47:22.270911Z","iopub.status.idle":"2023-10-05T11:47:22.306256Z","shell.execute_reply.started":"2023-10-05T11:47:22.270881Z","shell.execute_reply":"2023-10-05T11:47:22.305391Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 100 entries, 0 to 99\nData columns (total 39 columns):\n #   Column                      Non-Null Count  Dtype  \n---  ------                      --------------  -----  \n 0   OBJECTID                    100 non-null    int64  \n 1   FOD_ID                      100 non-null    int64  \n 2   FPA_ID                      100 non-null    object \n 3   SOURCE_SYSTEM_TYPE          100 non-null    object \n 4   SOURCE_SYSTEM               100 non-null    object \n 5   NWCG_REPORTING_AGENCY       100 non-null    object \n 6   NWCG_REPORTING_UNIT_ID      100 non-null    object \n 7   NWCG_REPORTING_UNIT_NAME    100 non-null    object \n 8   SOURCE_REPORTING_UNIT       100 non-null    object \n 9   SOURCE_REPORTING_UNIT_NAME  100 non-null    object \n 10  LOCAL_FIRE_REPORT_ID        100 non-null    object \n 11  LOCAL_INCIDENT_ID           99 non-null     object \n 12  FIRE_CODE                   88 non-null     object \n 13  FIRE_NAME                   100 non-null    object \n 14  ICS_209_INCIDENT_NUMBER     3 non-null      object \n 15  ICS_209_NAME                3 non-null      object \n 16  MTBS_ID                     2 non-null      object \n 17  MTBS_FIRE_NAME              2 non-null      object \n 18  COMPLEX_NAME                0 non-null      object \n 19  FIRE_YEAR                   100 non-null    int64  \n 20  DISCOVERY_DATE              100 non-null    float64\n 21  DISCOVERY_DOY               100 non-null    int64  \n 22  DISCOVERY_TIME              100 non-null    object \n 23  STAT_CAUSE_CODE             100 non-null    float64\n 24  STAT_CAUSE_DESCR            100 non-null    object \n 25  CONT_DATE                   99 non-null     float64\n 26  CONT_DOY                    99 non-null     float64\n 27  CONT_TIME                   99 non-null     object \n 28  FIRE_SIZE                   100 non-null    float64\n 29  FIRE_SIZE_CLASS             100 non-null    object \n 30  LATITUDE                    100 non-null    float64\n 31  LONGITUDE                   100 non-null    float64\n 32  OWNER_CODE                  100 non-null    float64\n 33  OWNER_DESCR                 100 non-null    object \n 34  STATE                       100 non-null    object \n 35  COUNTY                      91 non-null     object \n 36  FIPS_CODE                   91 non-null     object \n 37  FIPS_NAME                   91 non-null     object \n 38  Shape                       100 non-null    object \ndtypes: float64(8), int64(4), object(27)\nmemory usage: 30.6+ KB\n","output_type":"stream"}]},{"cell_type":"markdown","source":"<div style=\"color:white;display:fill;border-radius:8px;\n            background-color:#8F00FF;font-size:150%;\n            font-family:Nexa;letter-spacing:0.5px\">\n    <p style=\"padding: 5px;color:white;\"><b></b></p>\n</div>\n<a id='Introduction'></a>\n\n#  <b><span style='color:#8F00FF'>| 2. </span> Expanatory data analysis With sqlite Queries </b>\n[Table of contents](#table)\n\nHere in order to gain a better insight in regard to data, this query group <ins>SOURCE REPORTING UNIT NAME</ins> based on the number of repetition and incident occurrence. Each query is limited so that RAM usage remains optimum.\n\n\n\n","metadata":{}},{"cell_type":"code","source":"pd.read_sql(\"\"\"\n                SELECT SOURCE_REPORTING_UNIT_NAME,count(*) as [count]\n                FROM fires\n                GROUP BY SOURCE_REPORTING_UNIT_NAME\n                ORDER BY count\n                LIMIT 30;\n\"\"\",con = conn)","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:47:22.307154Z","iopub.execute_input":"2023-10-05T11:47:22.307453Z","iopub.status.idle":"2023-10-05T11:47:33.328642Z","shell.execute_reply.started":"2023-10-05T11:47:22.307425Z","shell.execute_reply":"2023-10-05T11:47:33.327776Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"                           SOURCE_REPORTING_UNIT_NAME  count\n0                                             356 VFD      1\n1                                     ADAIR CO RFD #3      1\n2                    AMISTAD NATIONAL RECREATION AREA      1\n3                   APOSTLE ISLANDS NATONAL LAKESHORE      1\n4                                         ARAPAHO NWR      1\n5                                       ARROWWOOD NWR      1\n6                                     ASH MEADOWS NWR      1\n7   ATTWATER PRAIRIE CHICKEN NATIONAL WILDLIFE REFUGE      1\n8                                         AVA CITY FD      1\n9                                        AZS Prescott      1\n10  Abraham Lincoln Birthplace National Historic Site      1\n11                    Agate Volunteer Fire Department      1\n12                            Alabama-coushatta Tribe      1\n13             Alaska Interagency Coordination Center      1\n14          Alibates Flint Quarries National Monument      1\n15               Andersonville National Historic Site      1\n16                          Arizona - Tucson District      1\n17                       Arizona-Flagstaff District 2      1\n18               Atchafalaya National Wildlife Refuge      1\n19                   Augusta Fire Protection District      1\n20                               Azle Fire Department      1\n21                  BACK BAY NATIONAL WILDLIFE REFUGE      1\n22                                   BANDON MARSH NWR      1\n23                          BARBER CO FIRE DEPARTMENT      1\n24                                       BEAR VLY NWR      1\n25                                        BENT COUNTY      1\n26            BENTON LAKE WETLAND MANAGEMENT DISTRICT      1\n27                                    BENTON LAKE WMD      1\n28                 BIG STONE NATIONAL WILDLIFE REFUGE      1\n29                                     BITTER CRK NWR      1","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>SOURCE_REPORTING_UNIT_NAME</th>\n      <th>count</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>356 VFD</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>ADAIR CO RFD #3</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>AMISTAD NATIONAL RECREATION AREA</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>APOSTLE ISLANDS NATONAL LAKESHORE</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>ARAPAHO NWR</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>ARROWWOOD NWR</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>ASH MEADOWS NWR</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>ATTWATER PRAIRIE CHICKEN NATIONAL WILDLIFE REFUGE</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>AVA CITY FD</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>AZS Prescott</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Abraham Lincoln Birthplace National Historic Site</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Agate Volunteer Fire Department</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Alabama-coushatta Tribe</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Alaska Interagency Coordination Center</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Alibates Flint Quarries National Monument</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Andersonville National Historic Site</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Arizona - Tucson District</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Arizona-Flagstaff District 2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Atchafalaya National Wildlife Refuge</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Augusta Fire Protection District</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Azle Fire Department</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>BACK BAY NATIONAL WILDLIFE REFUGE</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>BANDON MARSH NWR</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>BARBER CO FIRE DEPARTMENT</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>BEAR VLY NWR</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>BENT COUNTY</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>BENTON LAKE WETLAND MANAGEMENT DISTRICT</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>BENTON LAKE WMD</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>BIG STONE NATIONAL WILDLIFE REFUGE</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>BITTER CRK NWR</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"query = pd.read_sql(\"\"\" SELECT *\n                         FROM fires\n                         Order BY FIRE_CODE\n                         LIMIT 10; \"\"\", conn) \n\nquery","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:47:33.329766Z","iopub.execute_input":"2023-10-05T11:47:33.330718Z","iopub.status.idle":"2023-10-05T11:47:42.382174Z","shell.execute_reply.started":"2023-10-05T11:47:33.330654Z","shell.execute_reply":"2023-10-05T11:47:42.380785Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"   OBJECTID  FOD_ID      FPA_ID SOURCE_SYSTEM_TYPE SOURCE_SYSTEM  \\\n0         4       4  FS-1418845                FED   FS-FIRESTAT   \n1         5       5  FS-1418847                FED   FS-FIRESTAT   \n2         6       6  FS-1418849                FED   FS-FIRESTAT   \n3         7       7  FS-1418851                FED   FS-FIRESTAT   \n4        10      10  FS-1418859                FED   FS-FIRESTAT   \n5        11      11  FS-1418861                FED   FS-FIRESTAT   \n6        12      12  FS-1418863                FED   FS-FIRESTAT   \n7        13      13  FS-1418865                FED   FS-FIRESTAT   \n8        14      14  FS-1418872                FED   FS-FIRESTAT   \n9        15      15  FS-1418874                FED   FS-FIRESTAT   \n\n  NWCG_REPORTING_AGENCY NWCG_REPORTING_UNIT_ID  NWCG_REPORTING_UNIT_NAME  \\\n0                    FS                USCAENF  Eldorado National Forest   \n1                    FS                USCAENF  Eldorado National Forest   \n2                    FS                USCAENF  Eldorado National Forest   \n3                    FS                USCAENF  Eldorado National Forest   \n4                    FS                USCAENF  Eldorado National Forest   \n5                    FS                USCAENF  Eldorado National Forest   \n6                    FS                USCAENF  Eldorado National Forest   \n7                    FS                USCAENF  Eldorado National Forest   \n8                    FS                USCAENF  Eldorado National Forest   \n9                    FS                USCAENF  Eldorado National Forest   \n\n  SOURCE_REPORTING_UNIT SOURCE_REPORTING_UNIT_NAME LOCAL_FIRE_REPORT_ID  \\\n0                  0503   Eldorado National Forest                   43   \n1                  0503   Eldorado National Forest                   44   \n2                  0503   Eldorado National Forest                   54   \n3                  0503   Eldorado National Forest                   58   \n4                  0503   Eldorado National Forest                   61   \n5                  0503   Eldorado National Forest                   64   \n6                  0503   Eldorado National Forest                   71   \n7                  0503   Eldorado National Forest                   91   \n8                  0503   Eldorado National Forest                   99   \n9                  0503   Eldorado National Forest                  102   \n\n  LOCAL_INCIDENT_ID FIRE_CODE   FIRE_NAME ICS_209_INCIDENT_NUMBER  \\\n0                 6      None        DEER                    None   \n1                 7      None    STEVENOT                    None   \n2                 8      None      HIDDEN                    None   \n3                 9      None        FORK                    None   \n4                10      None  TANGLEFOOT                    None   \n5                11      None     FORK #2                    None   \n6                14      None     MOKE #2                    None   \n7                26      None     WRIGHTS                    None   \n8                21      None       TIGER                    None   \n9                28      None       BROWN                    None   \n\n  ICS_209_NAME MTBS_ID MTBS_FIRE_NAME COMPLEX_NAME  FIRE_YEAR  DISCOVERY_DATE  \\\n0         None    None           None         None       2004      2453184.50   \n1         None    None           None         None       2004      2453184.50   \n2         None    None           None         None       2004      2453186.50   \n3         None    None           None         None       2004      2453187.50   \n4         None    None           None         None       2004      2453187.50   \n5         None    None           None         None       2004      2453188.50   \n6         None    None           None         None       2004      2453188.50   \n7         None    None           None         None       2004      2453251.50   \n8         None    None           None         None       2004      2453276.50   \n9         None    None           None         None       2004      2453281.50   \n\n   DISCOVERY_DOY DISCOVERY_TIME  STAT_CAUSE_CODE STAT_CAUSE_DESCR  CONT_DATE  \\\n0            180           1600             1.00        Lightning 2453189.50   \n1            180           1600             1.00        Lightning 2453189.50   \n2            182           1800             1.00        Lightning 2453187.50   \n3            183           1800             1.00        Lightning 2453188.50   \n4            183           1800             1.00        Lightning 2453188.50   \n5            184           1830             1.00        Lightning 2453189.50   \n6            184           1730             1.00        Lightning 2453189.50   \n7            247           1600             9.00    Miscellaneous 2453251.50   \n8            272           1200             4.00         Campfire 2453276.50   \n9            277           0630             1.00        Lightning 2453281.50   \n\n   CONT_DOY CONT_TIME  FIRE_SIZE FIRE_SIZE_CLASS  LATITUDE  LONGITUDE  \\\n0       185      1400       0.10               A     38.56    -119.91   \n1       185      1200       0.10               A     38.56    -119.93   \n2       183      1600       0.10               A     38.64    -120.10   \n3       184      1400       0.10               A     38.69    -120.15   \n4       184      1800       0.10               A     38.55    -120.15   \n5       185      1900       0.10               A     38.69    -120.16   \n6       185      2000       0.10               A     38.53    -120.11   \n7       247      1630       0.10               A     38.79    -120.19   \n8       272      1800       6.00               B     38.43    -120.51   \n9       277      1500       0.20               A     38.68    -120.28   \n\n   OWNER_CODE            OWNER_DESCR STATE COUNTY FIPS_CODE  FIPS_NAME  \\\n0        5.00                   USFS    CA      3       003     Alpine   \n1        5.00                   USFS    CA      3       003     Alpine   \n2        5.00                   USFS    CA      5       005     Amador   \n3        5.00                   USFS    CA     17       017  El Dorado   \n4        5.00                   USFS    CA      5       005     Amador   \n5        5.00                   USFS    CA     17       017  El Dorado   \n6        5.00                   USFS    CA      5       005     Amador   \n7        5.00                   USFS    CA     17       017  El Dorado   \n8       14.00  MISSING/NOT SPECIFIED    CA      5       005     Amador   \n9        5.00                   USFS    CA     17       017  El Dorado   \n\n                                               Shape  \n0  b'\\x00\\x01\\xad\\x10\\x00\\x00\\x94\\xac\\xa3\\rt\\xfa]...  \n1  b'\\x00\\x01\\xad\\x10\\x00\\x00@\\xe3\\xaa.\\xb7\\xfb]\\...  \n2  b'\\x00\\x01\\xad\\x10\\x00\\x00\\xf0<~\\x90\\xa1\\x06^\\...  \n3  b'\\x00\\x01\\xad\\x10\\x00\\x00$o\\x996\\xd0\\t^\\xc0h\\...  \n4  b'\\x00\\x01\\xad\\x10\\x00\\x00dS\\\\\\xf2\\x8b\\t^\\xc0\\...  \n5  b'\\x00\\x01\\xad\\x10\\x00\\x00\\x0c\\xd6\\x8b\\xe38\\n^...  \n6  b'\\x00\\x01\\xad\\x10\\x00\\x00L\\xcc@\\x86\\xca\\x06^\\...  \n7  b'\\x00\\x01\\xad\\x10\\x00\\x00\\xe8d\\xc2\\x92_\\x0c^\\...  \n8  b'\\x00\\x01\\xad\\x10\\x00\\x00p=\\n\\xd7\\xa3 ^\\xc0\\x...  \n9  b'\\x00\\x01\\xad\\x10\\x00\\x00P\\xb7\\x06\\xf8\\xe6\\x1...  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>OBJECTID</th>\n      <th>FOD_ID</th>\n      <th>FPA_ID</th>\n      <th>SOURCE_SYSTEM_TYPE</th>\n      <th>SOURCE_SYSTEM</th>\n      <th>NWCG_REPORTING_AGENCY</th>\n      <th>NWCG_REPORTING_UNIT_ID</th>\n      <th>NWCG_REPORTING_UNIT_NAME</th>\n      <th>SOURCE_REPORTING_UNIT</th>\n      <th>SOURCE_REPORTING_UNIT_NAME</th>\n      <th>LOCAL_FIRE_REPORT_ID</th>\n      <th>LOCAL_INCIDENT_ID</th>\n      <th>FIRE_CODE</th>\n      <th>FIRE_NAME</th>\n      <th>ICS_209_INCIDENT_NUMBER</th>\n      <th>ICS_209_NAME</th>\n      <th>MTBS_ID</th>\n      <th>MTBS_FIRE_NAME</th>\n      <th>COMPLEX_NAME</th>\n      <th>FIRE_YEAR</th>\n      <th>DISCOVERY_DATE</th>\n      <th>DISCOVERY_DOY</th>\n      <th>DISCOVERY_TIME</th>\n      <th>STAT_CAUSE_CODE</th>\n      <th>STAT_CAUSE_DESCR</th>\n      <th>CONT_DATE</th>\n      <th>CONT_DOY</th>\n      <th>CONT_TIME</th>\n      <th>FIRE_SIZE</th>\n      <th>FIRE_SIZE_CLASS</th>\n      <th>LATITUDE</th>\n      <th>LONGITUDE</th>\n      <th>OWNER_CODE</th>\n      <th>OWNER_DESCR</th>\n      <th>STATE</th>\n      <th>COUNTY</th>\n      <th>FIPS_CODE</th>\n      <th>FIPS_NAME</th>\n      <th>Shape</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>4</td>\n      <td>4</td>\n      <td>FS-1418845</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USCAENF</td>\n      <td>Eldorado National Forest</td>\n      <td>0503</td>\n      <td>Eldorado National Forest</td>\n      <td>43</td>\n      <td>6</td>\n      <td>None</td>\n      <td>DEER</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>2004</td>\n      <td>2453184.50</td>\n      <td>180</td>\n      <td>1600</td>\n      <td>1.00</td>\n      <td>Lightning</td>\n      <td>2453189.50</td>\n      <td>185</td>\n      <td>1400</td>\n      <td>0.10</td>\n      <td>A</td>\n      <td>38.56</td>\n      <td>-119.91</td>\n      <td>5.00</td>\n      <td>USFS</td>\n      <td>CA</td>\n      <td>3</td>\n      <td>003</td>\n      <td>Alpine</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00\\x94\\xac\\xa3\\rt\\xfa]...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>5</td>\n      <td>5</td>\n      <td>FS-1418847</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USCAENF</td>\n      <td>Eldorado National Forest</td>\n      <td>0503</td>\n      <td>Eldorado National Forest</td>\n      <td>44</td>\n      <td>7</td>\n      <td>None</td>\n      <td>STEVENOT</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>2004</td>\n      <td>2453184.50</td>\n      <td>180</td>\n      <td>1600</td>\n      <td>1.00</td>\n      <td>Lightning</td>\n      <td>2453189.50</td>\n      <td>185</td>\n      <td>1200</td>\n      <td>0.10</td>\n      <td>A</td>\n      <td>38.56</td>\n      <td>-119.93</td>\n      <td>5.00</td>\n      <td>USFS</td>\n      <td>CA</td>\n      <td>3</td>\n      <td>003</td>\n      <td>Alpine</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00@\\xe3\\xaa.\\xb7\\xfb]\\...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>6</td>\n      <td>6</td>\n      <td>FS-1418849</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USCAENF</td>\n      <td>Eldorado National Forest</td>\n      <td>0503</td>\n      <td>Eldorado National Forest</td>\n      <td>54</td>\n      <td>8</td>\n      <td>None</td>\n      <td>HIDDEN</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>2004</td>\n      <td>2453186.50</td>\n      <td>182</td>\n      <td>1800</td>\n      <td>1.00</td>\n      <td>Lightning</td>\n      <td>2453187.50</td>\n      <td>183</td>\n      <td>1600</td>\n      <td>0.10</td>\n      <td>A</td>\n      <td>38.64</td>\n      <td>-120.10</td>\n      <td>5.00</td>\n      <td>USFS</td>\n      <td>CA</td>\n      <td>5</td>\n      <td>005</td>\n      <td>Amador</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00\\xf0&lt;~\\x90\\xa1\\x06^\\...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>7</td>\n      <td>7</td>\n      <td>FS-1418851</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USCAENF</td>\n      <td>Eldorado National Forest</td>\n      <td>0503</td>\n      <td>Eldorado National Forest</td>\n      <td>58</td>\n      <td>9</td>\n      <td>None</td>\n      <td>FORK</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>2004</td>\n      <td>2453187.50</td>\n      <td>183</td>\n      <td>1800</td>\n      <td>1.00</td>\n      <td>Lightning</td>\n      <td>2453188.50</td>\n      <td>184</td>\n      <td>1400</td>\n      <td>0.10</td>\n      <td>A</td>\n      <td>38.69</td>\n      <td>-120.15</td>\n      <td>5.00</td>\n      <td>USFS</td>\n      <td>CA</td>\n      <td>17</td>\n      <td>017</td>\n      <td>El Dorado</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00$o\\x996\\xd0\\t^\\xc0h\\...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>10</td>\n      <td>10</td>\n      <td>FS-1418859</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USCAENF</td>\n      <td>Eldorado National Forest</td>\n      <td>0503</td>\n      <td>Eldorado National Forest</td>\n      <td>61</td>\n      <td>10</td>\n      <td>None</td>\n      <td>TANGLEFOOT</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>2004</td>\n      <td>2453187.50</td>\n      <td>183</td>\n      <td>1800</td>\n      <td>1.00</td>\n      <td>Lightning</td>\n      <td>2453188.50</td>\n      <td>184</td>\n      <td>1800</td>\n      <td>0.10</td>\n      <td>A</td>\n      <td>38.55</td>\n      <td>-120.15</td>\n      <td>5.00</td>\n      <td>USFS</td>\n      <td>CA</td>\n      <td>5</td>\n      <td>005</td>\n      <td>Amador</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00dS\\\\\\xf2\\x8b\\t^\\xc0\\...</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>11</td>\n      <td>11</td>\n      <td>FS-1418861</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USCAENF</td>\n      <td>Eldorado National Forest</td>\n      <td>0503</td>\n      <td>Eldorado National Forest</td>\n      <td>64</td>\n      <td>11</td>\n      <td>None</td>\n      <td>FORK #2</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>2004</td>\n      <td>2453188.50</td>\n      <td>184</td>\n      <td>1830</td>\n      <td>1.00</td>\n      <td>Lightning</td>\n      <td>2453189.50</td>\n      <td>185</td>\n      <td>1900</td>\n      <td>0.10</td>\n      <td>A</td>\n      <td>38.69</td>\n      <td>-120.16</td>\n      <td>5.00</td>\n      <td>USFS</td>\n      <td>CA</td>\n      <td>17</td>\n      <td>017</td>\n      <td>El Dorado</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00\\x0c\\xd6\\x8b\\xe38\\n^...</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>12</td>\n      <td>12</td>\n      <td>FS-1418863</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USCAENF</td>\n      <td>Eldorado National Forest</td>\n      <td>0503</td>\n      <td>Eldorado National Forest</td>\n      <td>71</td>\n      <td>14</td>\n      <td>None</td>\n      <td>MOKE #2</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>2004</td>\n      <td>2453188.50</td>\n      <td>184</td>\n      <td>1730</td>\n      <td>1.00</td>\n      <td>Lightning</td>\n      <td>2453189.50</td>\n      <td>185</td>\n      <td>2000</td>\n      <td>0.10</td>\n      <td>A</td>\n      <td>38.53</td>\n      <td>-120.11</td>\n      <td>5.00</td>\n      <td>USFS</td>\n      <td>CA</td>\n      <td>5</td>\n      <td>005</td>\n      <td>Amador</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00L\\xcc@\\x86\\xca\\x06^\\...</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>13</td>\n      <td>13</td>\n      <td>FS-1418865</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USCAENF</td>\n      <td>Eldorado National Forest</td>\n      <td>0503</td>\n      <td>Eldorado National Forest</td>\n      <td>91</td>\n      <td>26</td>\n      <td>None</td>\n      <td>WRIGHTS</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>2004</td>\n      <td>2453251.50</td>\n      <td>247</td>\n      <td>1600</td>\n      <td>9.00</td>\n      <td>Miscellaneous</td>\n      <td>2453251.50</td>\n      <td>247</td>\n      <td>1630</td>\n      <td>0.10</td>\n      <td>A</td>\n      <td>38.79</td>\n      <td>-120.19</td>\n      <td>5.00</td>\n      <td>USFS</td>\n      <td>CA</td>\n      <td>17</td>\n      <td>017</td>\n      <td>El Dorado</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00\\xe8d\\xc2\\x92_\\x0c^\\...</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>14</td>\n      <td>14</td>\n      <td>FS-1418872</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USCAENF</td>\n      <td>Eldorado National Forest</td>\n      <td>0503</td>\n      <td>Eldorado National Forest</td>\n      <td>99</td>\n      <td>21</td>\n      <td>None</td>\n      <td>TIGER</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>2004</td>\n      <td>2453276.50</td>\n      <td>272</td>\n      <td>1200</td>\n      <td>4.00</td>\n      <td>Campfire</td>\n      <td>2453276.50</td>\n      <td>272</td>\n      <td>1800</td>\n      <td>6.00</td>\n      <td>B</td>\n      <td>38.43</td>\n      <td>-120.51</td>\n      <td>14.00</td>\n      <td>MISSING/NOT SPECIFIED</td>\n      <td>CA</td>\n      <td>5</td>\n      <td>005</td>\n      <td>Amador</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00p=\\n\\xd7\\xa3 ^\\xc0\\x...</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>15</td>\n      <td>15</td>\n      <td>FS-1418874</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USCAENF</td>\n      <td>Eldorado National Forest</td>\n      <td>0503</td>\n      <td>Eldorado National Forest</td>\n      <td>102</td>\n      <td>28</td>\n      <td>None</td>\n      <td>BROWN</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>2004</td>\n      <td>2453281.50</td>\n      <td>277</td>\n      <td>0630</td>\n      <td>1.00</td>\n      <td>Lightning</td>\n      <td>2453281.50</td>\n      <td>277</td>\n      <td>1500</td>\n      <td>0.20</td>\n      <td>A</td>\n      <td>38.68</td>\n      <td>-120.28</td>\n      <td>5.00</td>\n      <td>USFS</td>\n      <td>CA</td>\n      <td>17</td>\n      <td>017</td>\n      <td>El Dorado</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00P\\xb7\\x06\\xf8\\xe6\\x1...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"<div style=\"color:white;display:fill;border-radius:8px;\n            background-color:#8F00FF;font-size:150%;\n            font-family:Nexa;letter-spacing:0.5px\">\n    <p style=\"padding: 5px;color:white;\"><b></b></p>\n</div>\n<a id='Introduction'></a>\n\n#  <b><span style='color:#8F00FF'>| 3. </span> Grouping the fires based on the forest locations </b>\n\n[Table of contents](#table) \n\nFor instance **Eldorado National Forest** has the highest number of incedients. Seeing the details based on the **site location** moreover looking at the number of accidents provide better overview of the data. The query down below serves this.\n\n","metadata":{}},{"cell_type":"code","source":"query_2 = pd.read_sql(\"\"\"SELECT *\n                         FROM fires\n                         ORDER BY SOURCE_REPORTING_UNIT\n                         LIMIT 35;\"\"\", conn)\nquery_2","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:47:42.383485Z","iopub.execute_input":"2023-10-05T11:47:42.383820Z","iopub.status.idle":"2023-10-05T11:47:51.447718Z","shell.execute_reply.started":"2023-10-05T11:47:42.383793Z","shell.execute_reply":"2023-10-05T11:47:51.446457Z"},"trusted":true},"execution_count":9,"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"    OBJECTID    FOD_ID       FPA_ID SOURCE_SYSTEM_TYPE SOURCE_SYSTEM  \\\n0    1453043  20008650  MOSFM-10105             NONFED      ST-MOMOS   \n1    1451352  20001021   MOSFM-1188             NONFED      ST-MOMOS   \n2    1451401  20001281   MOSFM-1473             NONFED      ST-MOMOS   \n3    1452418  20004055   MOSFM-4684             NONFED      ST-MOMOS   \n4    1452432  20004090   MOSFM-4726             NONFED      ST-MOMOS   \n5    1452476  20004242   MOSFM-4894             NONFED      ST-MOMOS   \n6    1453440  20009047  MOSFM-10554             NONFED      ST-MOMOS   \n7    1453441  20009048  MOSFM-10555             NONFED      ST-MOMOS   \n8    1453464  20009071  MOSFM-10578             NONFED      ST-MOMOS   \n9    1453467  20009074  MOSFM-10581             NONFED      ST-MOMOS   \n10   1453502  20009109  MOSFM-10621             NONFED      ST-MOMOS   \n11   1453503  20009110  MOSFM-10622             NONFED      ST-MOMOS   \n12   1453641  20009248  MOSFM-10785             NONFED      ST-MOMOS   \n13   1453715  20009322  MOSFM-10869             NONFED      ST-MOMOS   \n14   1453748  20009355  MOSFM-10903             NONFED      ST-MOMOS   \n15   1453749  20009356  MOSFM-10904             NONFED      ST-MOMOS   \n16   1453779  20009386  MOSFM-10939             NONFED      ST-MOMOS   \n17   1453247  20008854  MOSFM-10325             NONFED      ST-MOMOS   \n18   1453266  20008873  MOSFM-10355             NONFED      ST-MOMOS   \n19   1453336  20008943  MOSFM-10437             NONFED      ST-MOMOS   \n20   1453348  20008955  MOSFM-10452             NONFED      ST-MOMOS   \n21   1453367  20008974  MOSFM-10472             NONFED      ST-MOMOS   \n22   1453377  20008984  MOSFM-10483             NONFED      ST-MOMOS   \n23   1453378  20008985  MOSFM-10484             NONFED      ST-MOMOS   \n24   1453613  20009220  MOSFM-10750             NONFED      ST-MOMOS   \n25   1453614  20009221  MOSFM-10751             NONFED      ST-MOMOS   \n26   1453630  20009237  MOSFM-10770             NONFED      ST-MOMOS   \n27   1453642  20009249  MOSFM-10786             NONFED      ST-MOMOS   \n28   1453701  20009308  MOSFM-10853             NONFED      ST-MOMOS   \n29   1453716  20009323  MOSFM-10870             NONFED      ST-MOMOS   \n30   1453763  20009370  MOSFM-10920             NONFED      ST-MOMOS   \n31   1453919  20009526  MOSFM-11108             NONFED      ST-MOMOS   \n32   1453379  20008986  MOSFM-10485             NONFED      ST-MOMOS   \n33   1453392  20008999  MOSFM-10503             NONFED      ST-MOMOS   \n34   1453400  20009007  MOSFM-10511             NONFED      ST-MOMOS   \n\n   NWCG_REPORTING_AGENCY NWCG_REPORTING_UNIT_ID  \\\n0                 ST/C&L                USMOMOS   \n1                 ST/C&L                USMOMOS   \n2                 ST/C&L                USMOMOS   \n3                 ST/C&L                USMOMOS   \n4                 ST/C&L                USMOMOS   \n5                 ST/C&L                USMOMOS   \n6                 ST/C&L                USMOMOS   \n7                 ST/C&L                USMOMOS   \n8                 ST/C&L                USMOMOS   \n9                 ST/C&L                USMOMOS   \n10                ST/C&L                USMOMOS   \n11                ST/C&L                USMOMOS   \n12                ST/C&L                USMOMOS   \n13                ST/C&L                USMOMOS   \n14                ST/C&L                USMOMOS   \n15                ST/C&L                USMOMOS   \n16                ST/C&L                USMOMOS   \n17                ST/C&L                USMOMOS   \n18                ST/C&L                USMOMOS   \n19                ST/C&L                USMOMOS   \n20                ST/C&L                USMOMOS   \n21                ST/C&L                USMOMOS   \n22                ST/C&L                USMOMOS   \n23                ST/C&L                USMOMOS   \n24                ST/C&L                USMOMOS   \n25                ST/C&L                USMOMOS   \n26                ST/C&L                USMOMOS   \n27                ST/C&L                USMOMOS   \n28                ST/C&L                USMOMOS   \n29                ST/C&L                USMOMOS   \n30                ST/C&L                USMOMOS   \n31                ST/C&L                USMOMOS   \n32                ST/C&L                USMOMOS   \n33                ST/C&L                USMOMOS   \n34                ST/C&L                USMOMOS   \n\n               NWCG_REPORTING_UNIT_NAME SOURCE_REPORTING_UNIT  \\\n0   Missouri Department of Conservation                 00102   \n1   Missouri Department of Conservation                 00203   \n2   Missouri Department of Conservation                 00203   \n3   Missouri Department of Conservation                 00203   \n4   Missouri Department of Conservation                 00203   \n5   Missouri Department of Conservation                 00203   \n6   Missouri Department of Conservation                 00206   \n7   Missouri Department of Conservation                 00206   \n8   Missouri Department of Conservation                 00206   \n9   Missouri Department of Conservation                 00206   \n10  Missouri Department of Conservation                 00206   \n11  Missouri Department of Conservation                 00206   \n12  Missouri Department of Conservation                 00206   \n13  Missouri Department of Conservation                 00206   \n14  Missouri Department of Conservation                 00206   \n15  Missouri Department of Conservation                 00206   \n16  Missouri Department of Conservation                 00206   \n17  Missouri Department of Conservation                 00307   \n18  Missouri Department of Conservation                 00307   \n19  Missouri Department of Conservation                 00307   \n20  Missouri Department of Conservation                 00307   \n21  Missouri Department of Conservation                 00307   \n22  Missouri Department of Conservation                 00307   \n23  Missouri Department of Conservation                 00307   \n24  Missouri Department of Conservation                 00307   \n25  Missouri Department of Conservation                 00307   \n26  Missouri Department of Conservation                 00307   \n27  Missouri Department of Conservation                 00307   \n28  Missouri Department of Conservation                 00307   \n29  Missouri Department of Conservation                 00307   \n30  Missouri Department of Conservation                 00307   \n31  Missouri Department of Conservation                 00307   \n32  Missouri Department of Conservation                 00308   \n33  Missouri Department of Conservation                 00308   \n34  Missouri Department of Conservation                 00308   \n\n   SOURCE_REPORTING_UNIT_NAME LOCAL_FIRE_REPORT_ID LOCAL_INCIDENT_ID  \\\n0             ADAIR CO RFD #3                 None              None   \n1          FILLMORE FIRE DEPT                 None              None   \n2          FILLMORE FIRE DEPT                 None              None   \n3          FILLMORE FIRE DEPT                 None              None   \n4          FILLMORE FIRE DEPT                 None              None   \n5          FILLMORE FIRE DEPT                 None              None   \n6                SAVANNAH RFD                 None              None   \n7                SAVANNAH RFD                 None              None   \n8                SAVANNAH RFD                 None              None   \n9                SAVANNAH RFD                 None              None   \n10               SAVANNAH RFD                 None              None   \n11               SAVANNAH RFD                 None              None   \n12               SAVANNAH RFD                 None              None   \n13               SAVANNAH RFD                 None              None   \n14               SAVANNAH RFD                 None              None   \n15               SAVANNAH RFD                 None              None   \n16               SAVANNAH RFD                 None              None   \n17               FAIRFAX RFPD                 None              None   \n18               FAIRFAX RFPD                 None              None   \n19               FAIRFAX RFPD                 None              None   \n20               FAIRFAX RFPD                 None              None   \n21               FAIRFAX RFPD                 None              None   \n22               FAIRFAX RFPD                 None              None   \n23               FAIRFAX RFPD                 None              None   \n24               FAIRFAX RFPD                 None              None   \n25               FAIRFAX RFPD                 None              None   \n26               FAIRFAX RFPD                 None              None   \n27               FAIRFAX RFPD                 None              None   \n28               FAIRFAX RFPD                 None              None   \n29               FAIRFAX RFPD                 None              None   \n30               FAIRFAX RFPD                 None              None   \n31               FAIRFAX RFPD                 None              None   \n32        ROCK PORT FIRE DEPT                 None              None   \n33        ROCK PORT FIRE DEPT                 None              None   \n34        ROCK PORT FIRE DEPT                 None              None   \n\n   FIRE_CODE FIRE_NAME ICS_209_INCIDENT_NUMBER ICS_209_NAME MTBS_ID  \\\n0       None      None                    None         None    None   \n1       None      None                    None         None    None   \n2       None      None                    None         None    None   \n3       None      None                    None         None    None   \n4       None      None                    None         None    None   \n5       None      None                    None         None    None   \n6       None      None                    None         None    None   \n7       None      None                    None         None    None   \n8       None      None                    None         None    None   \n9       None      None                    None         None    None   \n10      None      None                    None         None    None   \n11      None      None                    None         None    None   \n12      None      None                    None         None    None   \n13      None      None                    None         None    None   \n14      None      None                    None         None    None   \n15      None      None                    None         None    None   \n16      None      None                    None         None    None   \n17      None      None                    None         None    None   \n18      None      None                    None         None    None   \n19      None      None                    None         None    None   \n20      None      None                    None         None    None   \n21      None      None                    None         None    None   \n22      None      None                    None         None    None   \n23      None      None                    None         None    None   \n24      None      None                    None         None    None   \n25      None      None                    None         None    None   \n26      None      None                    None         None    None   \n27      None      None                    None         None    None   \n28      None      None                    None         None    None   \n29      None      None                    None         None    None   \n30      None      None                    None         None    None   \n31      None      None                    None         None    None   \n32      None      None                    None         None    None   \n33      None      None                    None         None    None   \n34      None      None                    None         None    None   \n\n   MTBS_FIRE_NAME COMPLEX_NAME  FIRE_YEAR  DISCOVERY_DATE  DISCOVERY_DOY  \\\n0            None         None       1999      2451372.50            194   \n1            None         None       2000      2451801.50            258   \n2            None         None       2000      2451815.50            272   \n3            None         None       2002      2452347.50             73   \n4            None         None       2002      2452350.50             76   \n5            None         None       2002      2452360.50             86   \n6            None         None       1999      2451478.50            300   \n7            None         None       1999      2451478.50            300   \n8            None         None       1999      2451480.50            302   \n9            None         None       1999      2451480.50            302   \n10           None         None       1999      2451483.50            305   \n11           None         None       1999      2451483.50            305   \n12           None         None       1999      2451491.50            313   \n13           None         None       1999      2451496.50            318   \n14           None         None       1999      2451497.50            319   \n15           None         None       1999      2451497.50            319   \n16           None         None       1999      2451499.50            321   \n17           None         None       1999      2451433.50            255   \n18           None         None       1999      2451443.50            265   \n19           None         None       1999      2451462.50            284   \n20           None         None       1999      2451467.50            289   \n21           None         None       1999      2451472.50            294   \n22           None         None       1999      2451473.50            295   \n23           None         None       1999      2451473.50            295   \n24           None         None       1999      2451489.50            311   \n25           None         None       1999      2451489.50            311   \n26           None         None       1999      2451490.50            312   \n27           None         None       1999      2451491.50            313   \n28           None         None       1999      2451495.50            317   \n29           None         None       1999      2451496.50            318   \n30           None         None       1999      2451498.50            320   \n31           None         None       1999      2451518.50            340   \n32           None         None       1999      2451473.50            295   \n33           None         None       1999      2451474.50            296   \n34           None         None       1999      2451475.50            297   \n\n   DISCOVERY_TIME  STAT_CAUSE_CODE   STAT_CAUSE_DESCR CONT_DATE CONT_DOY  \\\n0            0600            13.00  Missing/Undefined      None     None   \n1            1630             5.00     Debris Burning      None     None   \n2            1400             5.00     Debris Burning      None     None   \n3            1158             5.00     Debris Burning      None     None   \n4            1145             5.00     Debris Burning      None     None   \n5            1700             5.00     Debris Burning      None     None   \n6            1240             9.00      Miscellaneous      None     None   \n7            1405             5.00     Debris Burning      None     None   \n8            1645             7.00              Arson      None     None   \n9            1600             7.00              Arson      None     None   \n10           1400             2.00      Equipment Use      None     None   \n11           0700             7.00              Arson      None     None   \n12           1600             4.00           Campfire      None     None   \n13           1155             7.00              Arson      None     None   \n14           1200             7.00              Arson      None     None   \n15           0900             7.00              Arson      None     None   \n16           1200             7.00              Arson      None     None   \n17           1340            13.00  Missing/Undefined      None     None   \n18           1430             5.00     Debris Burning      None     None   \n19           1430             7.00              Arson      None     None   \n20           1430             7.00              Arson      None     None   \n21           1215             5.00     Debris Burning      None     None   \n22           1440             7.00              Arson      None     None   \n23           1245             5.00     Debris Burning      None     None   \n24           1540             7.00              Arson      None     None   \n25           1620             5.00     Debris Burning      None     None   \n26           0830             7.00              Arson      None     None   \n27           0900             7.00              Arson      None     None   \n28           1110             5.00     Debris Burning      None     None   \n29           1305             5.00     Debris Burning      None     None   \n30           1245             7.00              Arson      None     None   \n31           1510             5.00     Debris Burning      None     None   \n32           1300             7.00              Arson      None     None   \n33           1301             5.00     Debris Burning      None     None   \n34           1301             5.00     Debris Burning      None     None   \n\n   CONT_TIME  FIRE_SIZE FIRE_SIZE_CLASS  LATITUDE  LONGITUDE  OWNER_CODE  \\\n0       None       1.00               B     40.28     -92.60        8.00   \n1       None      15.00               C     40.03     -95.09        8.00   \n2       None       2.00               B     40.03     -95.07        8.00   \n3       None      50.00               C     40.06     -95.05        8.00   \n4       None       1.00               B     40.06     -94.89        8.00   \n5       None       3.00               B     39.98     -95.10        8.00   \n6       None       5.00               B     37.62     -92.75        8.00   \n7       None       6.00               B     37.67     -92.57        8.00   \n8       None       2.00               B     37.53     -92.68        8.00   \n9       None       2.00               B     37.55     -92.71        8.00   \n10      None      53.00               C     38.05     -92.58        8.00   \n11      None       2.00               B     38.07     -92.91        8.00   \n12      None      33.00               C     38.20     -93.18        8.00   \n13      None      45.00               C     38.23     -92.92        8.00   \n14      None      20.00               C     37.70     -92.51        8.00   \n15      None      30.00               C     38.14     -92.91        8.00   \n16      None      32.00               C     37.92     -92.77        8.00   \n17      None       3.00               B     37.81     -92.74        8.00   \n18      None     537.00               E     38.27     -93.18        8.00   \n19      None       7.00               B     38.26     -93.18        8.00   \n20      None     133.00               D     38.17     -93.14        8.00   \n21      None      15.00               C     37.71     -93.13        8.00   \n22      None      25.00               C     37.96     -92.95        8.00   \n23      None       1.00               B     38.09     -92.78        8.00   \n24      None      15.00               C     38.10     -92.98        8.00   \n25      None      25.00               C     37.80     -92.92        8.00   \n26      None     150.00               D     38.16     -92.93        8.00   \n27      None      40.00               C     37.80     -92.74        8.00   \n28      None     251.00               D     38.18     -92.49        8.00   \n29      None       1.00               B     37.73     -92.81        8.00   \n30      None      76.00               C     38.09     -92.91        8.00   \n31      None       2.00               B     37.63     -92.71        8.00   \n32      None      59.00               C     37.74     -92.42        8.00   \n33      None       1.00               B     38.06     -92.76        8.00   \n34      None       2.00               B     38.06     -92.76        8.00   \n\n   OWNER_DESCR STATE    COUNTY FIPS_CODE FIPS_NAME  \\\n0      PRIVATE    MO     ADAIR       001     Adair   \n1      PRIVATE    MO    ANDREW       003    Andrew   \n2      PRIVATE    MO    ANDREW       003    Andrew   \n3      PRIVATE    MO    ANDREW       003    Andrew   \n4      PRIVATE    MO    ANDREW       003    Andrew   \n5      PRIVATE    MO    ANDREW       003    Andrew   \n6      PRIVATE    MO    ANDREW       003    Andrew   \n7      PRIVATE    MO    ANDREW       003    Andrew   \n8      PRIVATE    MO    ANDREW       003    Andrew   \n9      PRIVATE    MO    ANDREW       003    Andrew   \n10     PRIVATE    MO    ANDREW       003    Andrew   \n11     PRIVATE    MO    ANDREW       003    Andrew   \n12     PRIVATE    MO    ANDREW       003    Andrew   \n13     PRIVATE    MO    ANDREW       003    Andrew   \n14     PRIVATE    MO    ANDREW       003    Andrew   \n15     PRIVATE    MO    ANDREW       003    Andrew   \n16     PRIVATE    MO    ANDREW       003    Andrew   \n17     PRIVATE    MO  ATCHISON       005  Atchison   \n18     PRIVATE    MO  ATCHISON       005  Atchison   \n19     PRIVATE    MO      HOLT       087      Holt   \n20     PRIVATE    MO  ATCHISON       005  Atchison   \n21     PRIVATE    MO  ATCHISON       005  Atchison   \n22     PRIVATE    MO  ATCHISON       005  Atchison   \n23     PRIVATE    MO  ATCHISON       005  Atchison   \n24     PRIVATE    MO  ATCHISON       005  Atchison   \n25     PRIVATE    MO  ATCHISON       005  Atchison   \n26     PRIVATE    MO  ATCHISON       005  Atchison   \n27     PRIVATE    MO  ATCHISON       005  Atchison   \n28     PRIVATE    MO  ATCHISON       005  Atchison   \n29     PRIVATE    MO  ATCHISON       005  Atchison   \n30     PRIVATE    MO  ATCHISON       005  Atchison   \n31     PRIVATE    MO  ATCHISON       005  Atchison   \n32     PRIVATE    MO  ATCHISON       005  Atchison   \n33     PRIVATE    MO  ATCHISON       005  Atchison   \n34     PRIVATE    MO  ATCHISON       005  Atchison   \n\n                                                Shape  \n0   b'\\x00\\x01\\xad\\x10\\x00\\x00\\xd4G\\xa7\\xae|&W\\xc0...  \n1   b'\\x00\\x01\\xad\\x10\\x00\\x00\\xd8F\\x03x\\x0b\\xc6W\\...  \n2   b'\\x00\\x01\\xad\\x10\\x00\\x00\\xd0\\xf1\\x98\\x81\\xca...  \n3   b'\\x00\\x01\\xad\\x10\\x00\\x00\\xb4\\x974F\\xeb\\xc2W\\...  \n4   b'\\x00\\x01\\xad\\x10\\x00\\x00D\\xf0\\xbf\\x95\\xec\\xb...  \n5   b'\\x00\\x01\\xad\\x10\\x00\\x004{\\xa0\\x15\\x18\\xc6W\\...  \n6   b'\\x00\\x01\\xad\\x10\\x00\\x00\\xe8\\xe7ME*0W\\xc00\\x...  \n7   b'\\x00\\x01\\xad\\x10\\x00\\x00\\x106<\\xbdR$W\\xc0p\\x...  \n8   b'\\x00\\x01\\xad\\x10\\x00\\x00\\xa4\\x14t{I+W\\xc0\\xf...  \n9   b'\\x00\\x01\\xad\\x10\\x00\\x00\\xe4\\x87\\x11\\xc2\\xa3...  \n10  b'\\x00\\x01\\xad\\x10\\x00\\x00\\xe8i\\x87\\xbf&%W\\xc0...  \n11  b'\\x00\\x01\\xad\\x10\\x00\\x00p\\xcd\\xe4\\x9bm:W\\xc0...  \n12  b'\\x00\\x01\\xad\\x10\\x00\\x00\\xdc\\x9c\\x11\\xa5\\xbd...  \n13  b'\\x00\\x01\\xad\\x10\\x00\\x00 \\x8euq\\x1b;W\\xc0hq8...  \n14  b'\\x00\\x01\\xad\\x10\\x00\\x00`->\\x05\\xc0 W\\xc0 \\x...  \n15  b'\\x00\\x01\\xad\\x10\\x00\\x00\\xe8\\xcagy\\x1e:W\\xc0...  \n16  b'\\x00\\x01\\xad\\x10\\x00\\x00\\xa4\\x18\\xe7oB1W\\xc0...  \n17  b'\\x00\\x01\\xad\\x10\\x00\\x00H\\x85\\xb1\\x85 /W\\xc0...  \n18  b'\\x00\\x01\\xad\\x10\\x00\\x00\\xc0Z|\\n\\x80KW\\xc0\\x...  \n19  b'\\x00\\x01\\xad\\x10\\x00\\x00t\\xa6\\xd0y\\x8dKW\\xc0...  \n20  b'\\x00\\x01\\xad\\x10\\x00\\x00\\xc4rK\\xab!IW\\xc0\\xa...  \n21  b'\\x00\\x01\\xad\\x10\\x00\\x00DZ*oGHW\\xc0\\xe8q\\x8a...  \n22  b'\\x00\\x01\\xad\\x10\\x00\\x00h\\xe7\\xfb\\xa9\\xf1<W\\...  \n23  b'\\x00\\x01\\xad\\x10\\x00\\x00\\xf0{\\x9b\\xfe\\xec1W\\...  \n24  b'\\x00\\x01\\xad\\x10\\x00\\x00H\\xd7L\\xbe\\xd9>W\\xc0...  \n25  b'\\x00\\x01\\xad\\x10\\x00\\x00\\x98\\x03\\x04s\\xf4:W\\...  \n26  b'\\x00\\x01\\xad\\x10\\x00\\x00\\x1c\\xc4\\xce\\x14:;W\\...  \n27  b'\\x00\\x01\\xad\\x10\\x00\\x00\\xc4\\xb0\\x8a72/W\\xc0...  \n28  b'\\x00\\x01\\xad\\x10\\x00\\x00\\x88\\xd2\\xde\\xe0\\x0b...  \n29  b\"\\x00\\x01\\xad\\x10\\x00\\x00\\x0c\\x84d\\x01\\x134W\\...  \n30  b'\\x00\\x01\\xad\\x10\\x00\\x00\\xb8\"\\xf8\\xdfJ:W\\xc0...  \n31  b'\\x00\\x01\\xad\\x10\\x00\\x00p\\xdc)\\x1d\\xac-W\\xc0...  \n32  b'\\x00\\x01\\xad\\x10\\x00\\x00\\xe4\\xed\\x08\\xa7\\x05...  \n33  b'\\x00\\x01\\xad\\x10\\x00\\x00\\xcc\\xe9\\xb2\\x98\\xd8...  \n34  b'\\x00\\x01\\xad\\x10\\x00\\x00\\xcc\\xe9\\xb2\\x98\\xd8...  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>OBJECTID</th>\n      <th>FOD_ID</th>\n      <th>FPA_ID</th>\n      <th>SOURCE_SYSTEM_TYPE</th>\n      <th>SOURCE_SYSTEM</th>\n      <th>NWCG_REPORTING_AGENCY</th>\n      <th>NWCG_REPORTING_UNIT_ID</th>\n      <th>NWCG_REPORTING_UNIT_NAME</th>\n      <th>SOURCE_REPORTING_UNIT</th>\n      <th>SOURCE_REPORTING_UNIT_NAME</th>\n      <th>LOCAL_FIRE_REPORT_ID</th>\n      <th>LOCAL_INCIDENT_ID</th>\n      <th>FIRE_CODE</th>\n      <th>FIRE_NAME</th>\n      <th>ICS_209_INCIDENT_NUMBER</th>\n      <th>ICS_209_NAME</th>\n      <th>MTBS_ID</th>\n      <th>MTBS_FIRE_NAME</th>\n      <th>COMPLEX_NAME</th>\n      <th>FIRE_YEAR</th>\n      <th>DISCOVERY_DATE</th>\n      <th>DISCOVERY_DOY</th>\n      <th>DISCOVERY_TIME</th>\n      <th>STAT_CAUSE_CODE</th>\n      <th>STAT_CAUSE_DESCR</th>\n      <th>CONT_DATE</th>\n      <th>CONT_DOY</th>\n      <th>CONT_TIME</th>\n      <th>FIRE_SIZE</th>\n      <th>FIRE_SIZE_CLASS</th>\n      <th>LATITUDE</th>\n      <th>LONGITUDE</th>\n      <th>OWNER_CODE</th>\n      <th>OWNER_DESCR</th>\n      <th>STATE</th>\n      <th>COUNTY</th>\n      <th>FIPS_CODE</th>\n      <th>FIPS_NAME</th>\n      <th>Shape</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1453043</td>\n      <td>20008650</td>\n      <td>MOSFM-10105</td>\n      <td>NONFED</td>\n      <td>ST-MOMOS</td>\n      <td>ST/C&amp;L</td>\n      <td>USMOMOS</td>\n      <td>Missouri Department of Conservation</td>\n      <td>00102</td>\n      <td>ADAIR CO RFD #3</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>1999</td>\n      <td>2451372.50</td>\n      <td>194</td>\n      <td>0600</td>\n      <td>13.00</td>\n      <td>Missing/Undefined</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>1.00</td>\n      <td>B</td>\n      <td>40.28</td>\n      <td>-92.60</td>\n      <td>8.00</td>\n      <td>PRIVATE</td>\n      <td>MO</td>\n      <td>ADAIR</td>\n      <td>001</td>\n      <td>Adair</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00\\xd4G\\xa7\\xae|&amp;W\\xc0...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1451352</td>\n      <td>20001021</td>\n      <td>MOSFM-1188</td>\n      <td>NONFED</td>\n      <td>ST-MOMOS</td>\n      <td>ST/C&amp;L</td>\n      <td>USMOMOS</td>\n      <td>Missouri Department of Conservation</td>\n      <td>00203</td>\n      <td>FILLMORE FIRE DEPT</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>2000</td>\n      <td>2451801.50</td>\n      <td>258</td>\n      <td>1630</td>\n      <td>5.00</td>\n      <td>Debris Burning</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>15.00</td>\n      <td>C</td>\n      <td>40.03</td>\n      <td>-95.09</td>\n      <td>8.00</td>\n      <td>PRIVATE</td>\n      <td>MO</td>\n      <td>ANDREW</td>\n      <td>003</td>\n      <td>Andrew</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00\\xd8F\\x03x\\x0b\\xc6W\\...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1451401</td>\n      <td>20001281</td>\n      <td>MOSFM-1473</td>\n      <td>NONFED</td>\n      <td>ST-MOMOS</td>\n      <td>ST/C&amp;L</td>\n      <td>USMOMOS</td>\n      <td>Missouri Department of Conservation</td>\n      <td>00203</td>\n      <td>FILLMORE FIRE DEPT</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>2000</td>\n      <td>2451815.50</td>\n      <td>272</td>\n      <td>1400</td>\n      <td>5.00</td>\n      <td>Debris Burning</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>2.00</td>\n      <td>B</td>\n      <td>40.03</td>\n      <td>-95.07</td>\n      <td>8.00</td>\n      <td>PRIVATE</td>\n      <td>MO</td>\n      <td>ANDREW</td>\n      <td>003</td>\n      <td>Andrew</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00\\xd0\\xf1\\x98\\x81\\xca...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1452418</td>\n      <td>20004055</td>\n      <td>MOSFM-4684</td>\n      <td>NONFED</td>\n      <td>ST-MOMOS</td>\n      <td>ST/C&amp;L</td>\n      <td>USMOMOS</td>\n      <td>Missouri Department of Conservation</td>\n      <td>00203</td>\n      <td>FILLMORE FIRE DEPT</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>2002</td>\n      <td>2452347.50</td>\n      <td>73</td>\n      <td>1158</td>\n      <td>5.00</td>\n      <td>Debris Burning</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>50.00</td>\n      <td>C</td>\n      <td>40.06</td>\n      <td>-95.05</td>\n      <td>8.00</td>\n      <td>PRIVATE</td>\n      <td>MO</td>\n      <td>ANDREW</td>\n      <td>003</td>\n      <td>Andrew</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00\\xb4\\x974F\\xeb\\xc2W\\...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1452432</td>\n      <td>20004090</td>\n      <td>MOSFM-4726</td>\n      <td>NONFED</td>\n      <td>ST-MOMOS</td>\n      <td>ST/C&amp;L</td>\n      <td>USMOMOS</td>\n      <td>Missouri Department of Conservation</td>\n      <td>00203</td>\n      <td>FILLMORE FIRE DEPT</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>2002</td>\n      <td>2452350.50</td>\n      <td>76</td>\n      <td>1145</td>\n      <td>5.00</td>\n      <td>Debris Burning</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>1.00</td>\n      <td>B</td>\n      <td>40.06</td>\n      <td>-94.89</td>\n      <td>8.00</td>\n      <td>PRIVATE</td>\n      <td>MO</td>\n      <td>ANDREW</td>\n      <td>003</td>\n      <td>Andrew</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00D\\xf0\\xbf\\x95\\xec\\xb...</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>1452476</td>\n      <td>20004242</td>\n      <td>MOSFM-4894</td>\n      <td>NONFED</td>\n      <td>ST-MOMOS</td>\n      <td>ST/C&amp;L</td>\n      <td>USMOMOS</td>\n      <td>Missouri Department of Conservation</td>\n      <td>00203</td>\n      <td>FILLMORE FIRE DEPT</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>2002</td>\n      <td>2452360.50</td>\n      <td>86</td>\n      <td>1700</td>\n      <td>5.00</td>\n      <td>Debris Burning</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>3.00</td>\n      <td>B</td>\n      <td>39.98</td>\n      <td>-95.10</td>\n      <td>8.00</td>\n      <td>PRIVATE</td>\n      <td>MO</td>\n      <td>ANDREW</td>\n      <td>003</td>\n      <td>Andrew</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x004{\\xa0\\x15\\x18\\xc6W\\...</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>1453440</td>\n      <td>20009047</td>\n      <td>MOSFM-10554</td>\n      <td>NONFED</td>\n      <td>ST-MOMOS</td>\n      <td>ST/C&amp;L</td>\n      <td>USMOMOS</td>\n      <td>Missouri Department of Conservation</td>\n      <td>00206</td>\n      <td>SAVANNAH RFD</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>1999</td>\n      <td>2451478.50</td>\n      <td>300</td>\n      <td>1240</td>\n      <td>9.00</td>\n      <td>Miscellaneous</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>5.00</td>\n      <td>B</td>\n      <td>37.62</td>\n      <td>-92.75</td>\n      <td>8.00</td>\n      <td>PRIVATE</td>\n      <td>MO</td>\n      <td>ANDREW</td>\n      <td>003</td>\n      <td>Andrew</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00\\xe8\\xe7ME*0W\\xc00\\x...</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>1453441</td>\n      <td>20009048</td>\n      <td>MOSFM-10555</td>\n      <td>NONFED</td>\n      <td>ST-MOMOS</td>\n      <td>ST/C&amp;L</td>\n      <td>USMOMOS</td>\n      <td>Missouri Department of Conservation</td>\n      <td>00206</td>\n      <td>SAVANNAH RFD</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>1999</td>\n      <td>2451478.50</td>\n      <td>300</td>\n      <td>1405</td>\n      <td>5.00</td>\n      <td>Debris Burning</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>6.00</td>\n      <td>B</td>\n      <td>37.67</td>\n      <td>-92.57</td>\n      <td>8.00</td>\n      <td>PRIVATE</td>\n      <td>MO</td>\n      <td>ANDREW</td>\n      <td>003</td>\n      <td>Andrew</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00\\x106&lt;\\xbdR$W\\xc0p\\x...</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>1453464</td>\n      <td>20009071</td>\n      <td>MOSFM-10578</td>\n      <td>NONFED</td>\n      <td>ST-MOMOS</td>\n      <td>ST/C&amp;L</td>\n      <td>USMOMOS</td>\n      <td>Missouri Department of Conservation</td>\n      <td>00206</td>\n      <td>SAVANNAH RFD</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>1999</td>\n      <td>2451480.50</td>\n      <td>302</td>\n      <td>1645</td>\n      <td>7.00</td>\n      <td>Arson</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>2.00</td>\n      <td>B</td>\n      <td>37.53</td>\n      <td>-92.68</td>\n      <td>8.00</td>\n      <td>PRIVATE</td>\n      <td>MO</td>\n      <td>ANDREW</td>\n      <td>003</td>\n      <td>Andrew</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00\\xa4\\x14t{I+W\\xc0\\xf...</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>1453467</td>\n      <td>20009074</td>\n      <td>MOSFM-10581</td>\n      <td>NONFED</td>\n      <td>ST-MOMOS</td>\n      <td>ST/C&amp;L</td>\n      <td>USMOMOS</td>\n      <td>Missouri Department of Conservation</td>\n      <td>00206</td>\n      <td>SAVANNAH RFD</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>1999</td>\n      <td>2451480.50</td>\n      <td>302</td>\n      <td>1600</td>\n      <td>7.00</td>\n      <td>Arson</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>2.00</td>\n      <td>B</td>\n      <td>37.55</td>\n      <td>-92.71</td>\n      <td>8.00</td>\n      <td>PRIVATE</td>\n      <td>MO</td>\n      <td>ANDREW</td>\n      <td>003</td>\n      <td>Andrew</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00\\xe4\\x87\\x11\\xc2\\xa3...</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>1453502</td>\n      <td>20009109</td>\n      <td>MOSFM-10621</td>\n      <td>NONFED</td>\n      <td>ST-MOMOS</td>\n      <td>ST/C&amp;L</td>\n      <td>USMOMOS</td>\n      <td>Missouri Department of Conservation</td>\n      <td>00206</td>\n      <td>SAVANNAH RFD</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>1999</td>\n      <td>2451483.50</td>\n      <td>305</td>\n      <td>1400</td>\n      <td>2.00</td>\n      <td>Equipment Use</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>53.00</td>\n      <td>C</td>\n      <td>38.05</td>\n      <td>-92.58</td>\n      <td>8.00</td>\n      <td>PRIVATE</td>\n      <td>MO</td>\n      <td>ANDREW</td>\n      <td>003</td>\n      <td>Andrew</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00\\xe8i\\x87\\xbf&amp;%W\\xc0...</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>1453503</td>\n      <td>20009110</td>\n      <td>MOSFM-10622</td>\n      <td>NONFED</td>\n      <td>ST-MOMOS</td>\n      <td>ST/C&amp;L</td>\n      <td>USMOMOS</td>\n      <td>Missouri Department of Conservation</td>\n      <td>00206</td>\n      <td>SAVANNAH RFD</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>1999</td>\n      <td>2451483.50</td>\n      <td>305</td>\n      <td>0700</td>\n      <td>7.00</td>\n      <td>Arson</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>2.00</td>\n      <td>B</td>\n      <td>38.07</td>\n      <td>-92.91</td>\n      <td>8.00</td>\n      <td>PRIVATE</td>\n      <td>MO</td>\n      <td>ANDREW</td>\n      <td>003</td>\n      <td>Andrew</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00p\\xcd\\xe4\\x9bm:W\\xc0...</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>1453641</td>\n      <td>20009248</td>\n      <td>MOSFM-10785</td>\n      <td>NONFED</td>\n      <td>ST-MOMOS</td>\n      <td>ST/C&amp;L</td>\n      <td>USMOMOS</td>\n      <td>Missouri Department of Conservation</td>\n      <td>00206</td>\n      <td>SAVANNAH RFD</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>1999</td>\n      <td>2451491.50</td>\n      <td>313</td>\n      <td>1600</td>\n      <td>4.00</td>\n      <td>Campfire</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>33.00</td>\n      <td>C</td>\n      <td>38.20</td>\n      <td>-93.18</td>\n      <td>8.00</td>\n      <td>PRIVATE</td>\n      <td>MO</td>\n      <td>ANDREW</td>\n      <td>003</td>\n      <td>Andrew</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00\\xdc\\x9c\\x11\\xa5\\xbd...</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>1453715</td>\n      <td>20009322</td>\n      <td>MOSFM-10869</td>\n      <td>NONFED</td>\n      <td>ST-MOMOS</td>\n      <td>ST/C&amp;L</td>\n      <td>USMOMOS</td>\n      <td>Missouri Department of Conservation</td>\n      <td>00206</td>\n      <td>SAVANNAH RFD</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>1999</td>\n      <td>2451496.50</td>\n      <td>318</td>\n      <td>1155</td>\n      <td>7.00</td>\n      <td>Arson</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>45.00</td>\n      <td>C</td>\n      <td>38.23</td>\n      <td>-92.92</td>\n      <td>8.00</td>\n      <td>PRIVATE</td>\n      <td>MO</td>\n      <td>ANDREW</td>\n      <td>003</td>\n      <td>Andrew</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00 \\x8euq\\x1b;W\\xc0hq8...</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>1453748</td>\n      <td>20009355</td>\n      <td>MOSFM-10903</td>\n      <td>NONFED</td>\n      <td>ST-MOMOS</td>\n      <td>ST/C&amp;L</td>\n      <td>USMOMOS</td>\n      <td>Missouri Department of Conservation</td>\n      <td>00206</td>\n      <td>SAVANNAH RFD</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>1999</td>\n      <td>2451497.50</td>\n      <td>319</td>\n      <td>1200</td>\n      <td>7.00</td>\n      <td>Arson</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>20.00</td>\n      <td>C</td>\n      <td>37.70</td>\n      <td>-92.51</td>\n      <td>8.00</td>\n      <td>PRIVATE</td>\n      <td>MO</td>\n      <td>ANDREW</td>\n      <td>003</td>\n      <td>Andrew</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00`-&gt;\\x05\\xc0 W\\xc0 \\x...</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>1453749</td>\n      <td>20009356</td>\n      <td>MOSFM-10904</td>\n      <td>NONFED</td>\n      <td>ST-MOMOS</td>\n      <td>ST/C&amp;L</td>\n      <td>USMOMOS</td>\n      <td>Missouri Department of Conservation</td>\n      <td>00206</td>\n      <td>SAVANNAH RFD</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>1999</td>\n      <td>2451497.50</td>\n      <td>319</td>\n      <td>0900</td>\n      <td>7.00</td>\n      <td>Arson</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>30.00</td>\n      <td>C</td>\n      <td>38.14</td>\n      <td>-92.91</td>\n      <td>8.00</td>\n      <td>PRIVATE</td>\n      <td>MO</td>\n      <td>ANDREW</td>\n      <td>003</td>\n      <td>Andrew</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00\\xe8\\xcagy\\x1e:W\\xc0...</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>1453779</td>\n      <td>20009386</td>\n      <td>MOSFM-10939</td>\n      <td>NONFED</td>\n      <td>ST-MOMOS</td>\n      <td>ST/C&amp;L</td>\n      <td>USMOMOS</td>\n      <td>Missouri Department of Conservation</td>\n      <td>00206</td>\n      <td>SAVANNAH RFD</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>1999</td>\n      <td>2451499.50</td>\n      <td>321</td>\n      <td>1200</td>\n      <td>7.00</td>\n      <td>Arson</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>32.00</td>\n      <td>C</td>\n      <td>37.92</td>\n      <td>-92.77</td>\n      <td>8.00</td>\n      <td>PRIVATE</td>\n      <td>MO</td>\n      <td>ANDREW</td>\n      <td>003</td>\n      <td>Andrew</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00\\xa4\\x18\\xe7oB1W\\xc0...</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>1453247</td>\n      <td>20008854</td>\n      <td>MOSFM-10325</td>\n      <td>NONFED</td>\n      <td>ST-MOMOS</td>\n      <td>ST/C&amp;L</td>\n      <td>USMOMOS</td>\n      <td>Missouri Department of Conservation</td>\n      <td>00307</td>\n      <td>FAIRFAX RFPD</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>1999</td>\n      <td>2451433.50</td>\n      <td>255</td>\n      <td>1340</td>\n      <td>13.00</td>\n      <td>Missing/Undefined</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>3.00</td>\n      <td>B</td>\n      <td>37.81</td>\n      <td>-92.74</td>\n      <td>8.00</td>\n      <td>PRIVATE</td>\n      <td>MO</td>\n      <td>ATCHISON</td>\n      <td>005</td>\n      <td>Atchison</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00H\\x85\\xb1\\x85 /W\\xc0...</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>1453266</td>\n      <td>20008873</td>\n      <td>MOSFM-10355</td>\n      <td>NONFED</td>\n      <td>ST-MOMOS</td>\n      <td>ST/C&amp;L</td>\n      <td>USMOMOS</td>\n      <td>Missouri Department of Conservation</td>\n      <td>00307</td>\n      <td>FAIRFAX RFPD</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>1999</td>\n      <td>2451443.50</td>\n      <td>265</td>\n      <td>1430</td>\n      <td>5.00</td>\n      <td>Debris Burning</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>537.00</td>\n      <td>E</td>\n      <td>38.27</td>\n      <td>-93.18</td>\n      <td>8.00</td>\n      <td>PRIVATE</td>\n      <td>MO</td>\n      <td>ATCHISON</td>\n      <td>005</td>\n      <td>Atchison</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00\\xc0Z|\\n\\x80KW\\xc0\\x...</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>1453336</td>\n      <td>20008943</td>\n      <td>MOSFM-10437</td>\n      <td>NONFED</td>\n      <td>ST-MOMOS</td>\n      <td>ST/C&amp;L</td>\n      <td>USMOMOS</td>\n      <td>Missouri Department of Conservation</td>\n      <td>00307</td>\n      <td>FAIRFAX RFPD</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>1999</td>\n      <td>2451462.50</td>\n      <td>284</td>\n      <td>1430</td>\n      <td>7.00</td>\n      <td>Arson</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>7.00</td>\n      <td>B</td>\n      <td>38.26</td>\n      <td>-93.18</td>\n      <td>8.00</td>\n      <td>PRIVATE</td>\n      <td>MO</td>\n      <td>HOLT</td>\n      <td>087</td>\n      <td>Holt</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00t\\xa6\\xd0y\\x8dKW\\xc0...</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>1453348</td>\n      <td>20008955</td>\n      <td>MOSFM-10452</td>\n      <td>NONFED</td>\n      <td>ST-MOMOS</td>\n      <td>ST/C&amp;L</td>\n      <td>USMOMOS</td>\n      <td>Missouri Department of Conservation</td>\n      <td>00307</td>\n      <td>FAIRFAX RFPD</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>1999</td>\n      <td>2451467.50</td>\n      <td>289</td>\n      <td>1430</td>\n      <td>7.00</td>\n      <td>Arson</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>133.00</td>\n      <td>D</td>\n      <td>38.17</td>\n      <td>-93.14</td>\n      <td>8.00</td>\n      <td>PRIVATE</td>\n      <td>MO</td>\n      <td>ATCHISON</td>\n      <td>005</td>\n      <td>Atchison</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00\\xc4rK\\xab!IW\\xc0\\xa...</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>1453367</td>\n      <td>20008974</td>\n      <td>MOSFM-10472</td>\n      <td>NONFED</td>\n      <td>ST-MOMOS</td>\n      <td>ST/C&amp;L</td>\n      <td>USMOMOS</td>\n      <td>Missouri Department of Conservation</td>\n      <td>00307</td>\n      <td>FAIRFAX RFPD</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>1999</td>\n      <td>2451472.50</td>\n      <td>294</td>\n      <td>1215</td>\n      <td>5.00</td>\n      <td>Debris Burning</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>15.00</td>\n      <td>C</td>\n      <td>37.71</td>\n      <td>-93.13</td>\n      <td>8.00</td>\n      <td>PRIVATE</td>\n      <td>MO</td>\n      <td>ATCHISON</td>\n      <td>005</td>\n      <td>Atchison</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00DZ*oGHW\\xc0\\xe8q\\x8a...</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>1453377</td>\n      <td>20008984</td>\n      <td>MOSFM-10483</td>\n      <td>NONFED</td>\n      <td>ST-MOMOS</td>\n      <td>ST/C&amp;L</td>\n      <td>USMOMOS</td>\n      <td>Missouri Department of Conservation</td>\n      <td>00307</td>\n      <td>FAIRFAX RFPD</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>1999</td>\n      <td>2451473.50</td>\n      <td>295</td>\n      <td>1440</td>\n      <td>7.00</td>\n      <td>Arson</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>25.00</td>\n      <td>C</td>\n      <td>37.96</td>\n      <td>-92.95</td>\n      <td>8.00</td>\n      <td>PRIVATE</td>\n      <td>MO</td>\n      <td>ATCHISON</td>\n      <td>005</td>\n      <td>Atchison</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00h\\xe7\\xfb\\xa9\\xf1&lt;W\\...</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>1453378</td>\n      <td>20008985</td>\n      <td>MOSFM-10484</td>\n      <td>NONFED</td>\n      <td>ST-MOMOS</td>\n      <td>ST/C&amp;L</td>\n      <td>USMOMOS</td>\n      <td>Missouri Department of Conservation</td>\n      <td>00307</td>\n      <td>FAIRFAX RFPD</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>1999</td>\n      <td>2451473.50</td>\n      <td>295</td>\n      <td>1245</td>\n      <td>5.00</td>\n      <td>Debris Burning</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>1.00</td>\n      <td>B</td>\n      <td>38.09</td>\n      <td>-92.78</td>\n      <td>8.00</td>\n      <td>PRIVATE</td>\n      <td>MO</td>\n      <td>ATCHISON</td>\n      <td>005</td>\n      <td>Atchison</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00\\xf0{\\x9b\\xfe\\xec1W\\...</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>1453613</td>\n      <td>20009220</td>\n      <td>MOSFM-10750</td>\n      <td>NONFED</td>\n      <td>ST-MOMOS</td>\n      <td>ST/C&amp;L</td>\n      <td>USMOMOS</td>\n      <td>Missouri Department of Conservation</td>\n      <td>00307</td>\n      <td>FAIRFAX RFPD</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>1999</td>\n      <td>2451489.50</td>\n      <td>311</td>\n      <td>1540</td>\n      <td>7.00</td>\n      <td>Arson</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>15.00</td>\n      <td>C</td>\n      <td>38.10</td>\n      <td>-92.98</td>\n      <td>8.00</td>\n      <td>PRIVATE</td>\n      <td>MO</td>\n      <td>ATCHISON</td>\n      <td>005</td>\n      <td>Atchison</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00H\\xd7L\\xbe\\xd9&gt;W\\xc0...</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>1453614</td>\n      <td>20009221</td>\n      <td>MOSFM-10751</td>\n      <td>NONFED</td>\n      <td>ST-MOMOS</td>\n      <td>ST/C&amp;L</td>\n      <td>USMOMOS</td>\n      <td>Missouri Department of Conservation</td>\n      <td>00307</td>\n      <td>FAIRFAX RFPD</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>1999</td>\n      <td>2451489.50</td>\n      <td>311</td>\n      <td>1620</td>\n      <td>5.00</td>\n      <td>Debris Burning</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>25.00</td>\n      <td>C</td>\n      <td>37.80</td>\n      <td>-92.92</td>\n      <td>8.00</td>\n      <td>PRIVATE</td>\n      <td>MO</td>\n      <td>ATCHISON</td>\n      <td>005</td>\n      <td>Atchison</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00\\x98\\x03\\x04s\\xf4:W\\...</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>1453630</td>\n      <td>20009237</td>\n      <td>MOSFM-10770</td>\n      <td>NONFED</td>\n      <td>ST-MOMOS</td>\n      <td>ST/C&amp;L</td>\n      <td>USMOMOS</td>\n      <td>Missouri Department of Conservation</td>\n      <td>00307</td>\n      <td>FAIRFAX RFPD</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>1999</td>\n      <td>2451490.50</td>\n      <td>312</td>\n      <td>0830</td>\n      <td>7.00</td>\n      <td>Arson</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>150.00</td>\n      <td>D</td>\n      <td>38.16</td>\n      <td>-92.93</td>\n      <td>8.00</td>\n      <td>PRIVATE</td>\n      <td>MO</td>\n      <td>ATCHISON</td>\n      <td>005</td>\n      <td>Atchison</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00\\x1c\\xc4\\xce\\x14:;W\\...</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>1453642</td>\n      <td>20009249</td>\n      <td>MOSFM-10786</td>\n      <td>NONFED</td>\n      <td>ST-MOMOS</td>\n      <td>ST/C&amp;L</td>\n      <td>USMOMOS</td>\n      <td>Missouri Department of Conservation</td>\n      <td>00307</td>\n      <td>FAIRFAX RFPD</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>1999</td>\n      <td>2451491.50</td>\n      <td>313</td>\n      <td>0900</td>\n      <td>7.00</td>\n      <td>Arson</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>40.00</td>\n      <td>C</td>\n      <td>37.80</td>\n      <td>-92.74</td>\n      <td>8.00</td>\n      <td>PRIVATE</td>\n      <td>MO</td>\n      <td>ATCHISON</td>\n      <td>005</td>\n      <td>Atchison</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00\\xc4\\xb0\\x8a72/W\\xc0...</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>1453701</td>\n      <td>20009308</td>\n      <td>MOSFM-10853</td>\n      <td>NONFED</td>\n      <td>ST-MOMOS</td>\n      <td>ST/C&amp;L</td>\n      <td>USMOMOS</td>\n      <td>Missouri Department of Conservation</td>\n      <td>00307</td>\n      <td>FAIRFAX RFPD</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>1999</td>\n      <td>2451495.50</td>\n      <td>317</td>\n      <td>1110</td>\n      <td>5.00</td>\n      <td>Debris Burning</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>251.00</td>\n      <td>D</td>\n      <td>38.18</td>\n      <td>-92.49</td>\n      <td>8.00</td>\n      <td>PRIVATE</td>\n      <td>MO</td>\n      <td>ATCHISON</td>\n      <td>005</td>\n      <td>Atchison</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00\\x88\\xd2\\xde\\xe0\\x0b...</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>1453716</td>\n      <td>20009323</td>\n      <td>MOSFM-10870</td>\n      <td>NONFED</td>\n      <td>ST-MOMOS</td>\n      <td>ST/C&amp;L</td>\n      <td>USMOMOS</td>\n      <td>Missouri Department of Conservation</td>\n      <td>00307</td>\n      <td>FAIRFAX RFPD</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>1999</td>\n      <td>2451496.50</td>\n      <td>318</td>\n      <td>1305</td>\n      <td>5.00</td>\n      <td>Debris Burning</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>1.00</td>\n      <td>B</td>\n      <td>37.73</td>\n      <td>-92.81</td>\n      <td>8.00</td>\n      <td>PRIVATE</td>\n      <td>MO</td>\n      <td>ATCHISON</td>\n      <td>005</td>\n      <td>Atchison</td>\n      <td>b\"\\x00\\x01\\xad\\x10\\x00\\x00\\x0c\\x84d\\x01\\x134W\\...</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>1453763</td>\n      <td>20009370</td>\n      <td>MOSFM-10920</td>\n      <td>NONFED</td>\n      <td>ST-MOMOS</td>\n      <td>ST/C&amp;L</td>\n      <td>USMOMOS</td>\n      <td>Missouri Department of Conservation</td>\n      <td>00307</td>\n      <td>FAIRFAX RFPD</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>1999</td>\n      <td>2451498.50</td>\n      <td>320</td>\n      <td>1245</td>\n      <td>7.00</td>\n      <td>Arson</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>76.00</td>\n      <td>C</td>\n      <td>38.09</td>\n      <td>-92.91</td>\n      <td>8.00</td>\n      <td>PRIVATE</td>\n      <td>MO</td>\n      <td>ATCHISON</td>\n      <td>005</td>\n      <td>Atchison</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00\\xb8\"\\xf8\\xdfJ:W\\xc0...</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>1453919</td>\n      <td>20009526</td>\n      <td>MOSFM-11108</td>\n      <td>NONFED</td>\n      <td>ST-MOMOS</td>\n      <td>ST/C&amp;L</td>\n      <td>USMOMOS</td>\n      <td>Missouri Department of Conservation</td>\n      <td>00307</td>\n      <td>FAIRFAX RFPD</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>1999</td>\n      <td>2451518.50</td>\n      <td>340</td>\n      <td>1510</td>\n      <td>5.00</td>\n      <td>Debris Burning</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>2.00</td>\n      <td>B</td>\n      <td>37.63</td>\n      <td>-92.71</td>\n      <td>8.00</td>\n      <td>PRIVATE</td>\n      <td>MO</td>\n      <td>ATCHISON</td>\n      <td>005</td>\n      <td>Atchison</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00p\\xdc)\\x1d\\xac-W\\xc0...</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>1453379</td>\n      <td>20008986</td>\n      <td>MOSFM-10485</td>\n      <td>NONFED</td>\n      <td>ST-MOMOS</td>\n      <td>ST/C&amp;L</td>\n      <td>USMOMOS</td>\n      <td>Missouri Department of Conservation</td>\n      <td>00308</td>\n      <td>ROCK PORT FIRE DEPT</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>1999</td>\n      <td>2451473.50</td>\n      <td>295</td>\n      <td>1300</td>\n      <td>7.00</td>\n      <td>Arson</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>59.00</td>\n      <td>C</td>\n      <td>37.74</td>\n      <td>-92.42</td>\n      <td>8.00</td>\n      <td>PRIVATE</td>\n      <td>MO</td>\n      <td>ATCHISON</td>\n      <td>005</td>\n      <td>Atchison</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00\\xe4\\xed\\x08\\xa7\\x05...</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>1453392</td>\n      <td>20008999</td>\n      <td>MOSFM-10503</td>\n      <td>NONFED</td>\n      <td>ST-MOMOS</td>\n      <td>ST/C&amp;L</td>\n      <td>USMOMOS</td>\n      <td>Missouri Department of Conservation</td>\n      <td>00308</td>\n      <td>ROCK PORT FIRE DEPT</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>1999</td>\n      <td>2451474.50</td>\n      <td>296</td>\n      <td>1301</td>\n      <td>5.00</td>\n      <td>Debris Burning</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>1.00</td>\n      <td>B</td>\n      <td>38.06</td>\n      <td>-92.76</td>\n      <td>8.00</td>\n      <td>PRIVATE</td>\n      <td>MO</td>\n      <td>ATCHISON</td>\n      <td>005</td>\n      <td>Atchison</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00\\xcc\\xe9\\xb2\\x98\\xd8...</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>1453400</td>\n      <td>20009007</td>\n      <td>MOSFM-10511</td>\n      <td>NONFED</td>\n      <td>ST-MOMOS</td>\n      <td>ST/C&amp;L</td>\n      <td>USMOMOS</td>\n      <td>Missouri Department of Conservation</td>\n      <td>00308</td>\n      <td>ROCK PORT FIRE DEPT</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>1999</td>\n      <td>2451475.50</td>\n      <td>297</td>\n      <td>1301</td>\n      <td>5.00</td>\n      <td>Debris Burning</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>2.00</td>\n      <td>B</td>\n      <td>38.06</td>\n      <td>-92.76</td>\n      <td>8.00</td>\n      <td>PRIVATE</td>\n      <td>MO</td>\n      <td>ATCHISON</td>\n      <td>005</td>\n      <td>Atchison</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00\\xcc\\xe9\\xb2\\x98\\xd8...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"<div style=\"color:white;display:fill;border-radius:8px;\n            background-color:#8F00FF;font-size:150%;\n            font-family:Nexa;letter-spacing:0.5px\">\n    <p style=\"padding: 5px;color:white;\"><b></b></p>\n</div>\n<a id='Introduction'></a>\n\n#  <b><span style='color:#8F00FF'>| 4. </span> The most severe Fire cases </b>\n[Table of contents](#table)\n\nWith this query the most recent and the most severe fire cases are selected. They are ordered by the fire sizes. \n\n","metadata":{}},{"cell_type":"code","source":"# Identifying the beggiest fire sizes \n\n\nquery_2 = pd.read_sql(\"\"\" \n                        SELECT \n                        LOCAL_FIRE_REPORT_ID, \n                        DISCOVERY_DATE AS Date, \n                        STAT_CAUSE_DESCR AS MainCause, \n                        FIRE_YEAR AS Year, \n                        FIRE_SIZE \n                        FROM fires \n                        WHERE FIRE_SIZE  > 1000.00 AND year > 2010\n                        ORDER BY FIRE_SIZE \n                        LIMIT 20;\n                      \"\"\" , conn)\n\nquery_2\n","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:47:51.448984Z","iopub.execute_input":"2023-10-05T11:47:51.449389Z","iopub.status.idle":"2023-10-05T11:48:00.343966Z","shell.execute_reply.started":"2023-10-05T11:47:51.449360Z","shell.execute_reply":"2023-10-05T11:48:00.342785Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"   LOCAL_FIRE_REPORT_ID       Date          MainCause  Year  FIRE_SIZE\n0                  None 2457173.50          Lightning  2015    1000.40\n1                  None 2456085.50      Miscellaneous  2012    1001.00\n2                  None 2456175.50          Lightning  2012    1001.50\n3                  None 2455799.50          Lightning  2011    1002.00\n4                    48 2456028.50     Debris Burning  2012    1003.00\n5                     4 2456856.50          Lightning  2014    1003.00\n6                  None 2457194.50          Lightning  2015    1003.50\n7                  None 2456095.50          Powerline  2012    1003.60\n8                  None 2456012.50          Lightning  2012    1005.00\n9                   110 2457248.50          Lightning  2015    1005.00\n10                 None 2456551.50  Missing/Undefined  2013    1007.00\n11                  816 2455804.50      Equipment Use  2011    1008.00\n12                 None 2456141.50          Lightning  2012    1008.00\n13                    3 2456148.50          Lightning  2012    1008.70\n14                 None 2457202.50          Lightning  2015    1009.00\n15                 None 2456521.50  Missing/Undefined  2013    1010.00\n16                 None 2457060.50  Missing/Undefined  2015    1010.00\n17                 None 2457216.50          Lightning  2015    1011.90\n18                   19 2455776.50          Lightning  2011    1012.00\n19                 None 2457272.50  Missing/Undefined  2015    1012.00","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>LOCAL_FIRE_REPORT_ID</th>\n      <th>Date</th>\n      <th>MainCause</th>\n      <th>Year</th>\n      <th>FIRE_SIZE</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>None</td>\n      <td>2457173.50</td>\n      <td>Lightning</td>\n      <td>2015</td>\n      <td>1000.40</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>None</td>\n      <td>2456085.50</td>\n      <td>Miscellaneous</td>\n      <td>2012</td>\n      <td>1001.00</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>None</td>\n      <td>2456175.50</td>\n      <td>Lightning</td>\n      <td>2012</td>\n      <td>1001.50</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>None</td>\n      <td>2455799.50</td>\n      <td>Lightning</td>\n      <td>2011</td>\n      <td>1002.00</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>48</td>\n      <td>2456028.50</td>\n      <td>Debris Burning</td>\n      <td>2012</td>\n      <td>1003.00</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>4</td>\n      <td>2456856.50</td>\n      <td>Lightning</td>\n      <td>2014</td>\n      <td>1003.00</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>None</td>\n      <td>2457194.50</td>\n      <td>Lightning</td>\n      <td>2015</td>\n      <td>1003.50</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>None</td>\n      <td>2456095.50</td>\n      <td>Powerline</td>\n      <td>2012</td>\n      <td>1003.60</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>None</td>\n      <td>2456012.50</td>\n      <td>Lightning</td>\n      <td>2012</td>\n      <td>1005.00</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>110</td>\n      <td>2457248.50</td>\n      <td>Lightning</td>\n      <td>2015</td>\n      <td>1005.00</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>None</td>\n      <td>2456551.50</td>\n      <td>Missing/Undefined</td>\n      <td>2013</td>\n      <td>1007.00</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>816</td>\n      <td>2455804.50</td>\n      <td>Equipment Use</td>\n      <td>2011</td>\n      <td>1008.00</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>None</td>\n      <td>2456141.50</td>\n      <td>Lightning</td>\n      <td>2012</td>\n      <td>1008.00</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>3</td>\n      <td>2456148.50</td>\n      <td>Lightning</td>\n      <td>2012</td>\n      <td>1008.70</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>None</td>\n      <td>2457202.50</td>\n      <td>Lightning</td>\n      <td>2015</td>\n      <td>1009.00</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>None</td>\n      <td>2456521.50</td>\n      <td>Missing/Undefined</td>\n      <td>2013</td>\n      <td>1010.00</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>None</td>\n      <td>2457060.50</td>\n      <td>Missing/Undefined</td>\n      <td>2015</td>\n      <td>1010.00</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>None</td>\n      <td>2457216.50</td>\n      <td>Lightning</td>\n      <td>2015</td>\n      <td>1011.90</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>19</td>\n      <td>2455776.50</td>\n      <td>Lightning</td>\n      <td>2011</td>\n      <td>1012.00</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>None</td>\n      <td>2457272.50</td>\n      <td>Missing/Undefined</td>\n      <td>2015</td>\n      <td>1012.00</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"data.head()","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:48:00.347342Z","iopub.execute_input":"2023-10-05T11:48:00.347662Z","iopub.status.idle":"2023-10-05T11:48:00.372798Z","shell.execute_reply.started":"2023-10-05T11:48:00.347635Z","shell.execute_reply":"2023-10-05T11:48:00.371608Z"},"trusted":true},"execution_count":11,"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"   OBJECTID  FOD_ID      FPA_ID SOURCE_SYSTEM_TYPE SOURCE_SYSTEM  \\\n0         1       1  FS-1418826                FED   FS-FIRESTAT   \n1         2       2  FS-1418827                FED   FS-FIRESTAT   \n2         3       3  FS-1418835                FED   FS-FIRESTAT   \n3         4       4  FS-1418845                FED   FS-FIRESTAT   \n4         5       5  FS-1418847                FED   FS-FIRESTAT   \n\n  NWCG_REPORTING_AGENCY NWCG_REPORTING_UNIT_ID  NWCG_REPORTING_UNIT_NAME  \\\n0                    FS                USCAPNF    Plumas National Forest   \n1                    FS                USCAENF  Eldorado National Forest   \n2                    FS                USCAENF  Eldorado National Forest   \n3                    FS                USCAENF  Eldorado National Forest   \n4                    FS                USCAENF  Eldorado National Forest   \n\n  SOURCE_REPORTING_UNIT SOURCE_REPORTING_UNIT_NAME LOCAL_FIRE_REPORT_ID  \\\n0                  0511     Plumas National Forest                    1   \n1                  0503   Eldorado National Forest                   13   \n2                  0503   Eldorado National Forest                   27   \n3                  0503   Eldorado National Forest                   43   \n4                  0503   Eldorado National Forest                   44   \n\n  LOCAL_INCIDENT_ID FIRE_CODE FIRE_NAME ICS_209_INCIDENT_NUMBER ICS_209_NAME  \\\n0            PNF-47      BJ8K  FOUNTAIN                    None         None   \n1                13      AAC0    PIGEON                    None         None   \n2               021      A32W     SLACK                    None         None   \n3                 6      None      DEER                    None         None   \n4                 7      None  STEVENOT                    None         None   \n\n  MTBS_ID MTBS_FIRE_NAME COMPLEX_NAME  FIRE_YEAR  DISCOVERY_DATE  \\\n0    None           None         None       2005      2453403.50   \n1    None           None         None       2004      2453137.50   \n2    None           None         None       2004      2453156.50   \n3    None           None         None       2004      2453184.50   \n4    None           None         None       2004      2453184.50   \n\n   DISCOVERY_DOY DISCOVERY_TIME  STAT_CAUSE_CODE STAT_CAUSE_DESCR  CONT_DATE  \\\n0             33           1300             9.00    Miscellaneous 2453403.50   \n1            133           0845             1.00        Lightning 2453137.50   \n2            152           1921             5.00   Debris Burning 2453156.50   \n3            180           1600             1.00        Lightning 2453189.50   \n4            180           1600             1.00        Lightning 2453189.50   \n\n   CONT_DOY CONT_TIME  FIRE_SIZE FIRE_SIZE_CLASS  LATITUDE  LONGITUDE  \\\n0     33.00      1730       0.10               A     40.04    -121.01   \n1    133.00      1530       0.25               A     38.93    -120.40   \n2    152.00      2024       0.10               A     38.98    -120.74   \n3    185.00      1400       0.10               A     38.56    -119.91   \n4    185.00      1200       0.10               A     38.56    -119.93   \n\n   OWNER_CODE       OWNER_DESCR STATE COUNTY FIPS_CODE  FIPS_NAME  \\\n0        5.00              USFS    CA     63       063     Plumas   \n1        5.00              USFS    CA     61       061     Placer   \n2       13.00  STATE OR PRIVATE    CA     17       017  El Dorado   \n3        5.00              USFS    CA      3       003     Alpine   \n4        5.00              USFS    CA      3       003     Alpine   \n\n                                               Shape  \n0  b'\\x00\\x01\\xad\\x10\\x00\\x00\\xe8d\\xc2\\x92_@^\\xc0...  \n1  b'\\x00\\x01\\xad\\x10\\x00\\x00T\\xb6\\xeej\\xe2\\x19^\\...  \n2  b'\\x00\\x01\\xad\\x10\\x00\\x00\\xd0\\xa5\\xa0W\\x13/^\\...  \n3  b'\\x00\\x01\\xad\\x10\\x00\\x00\\x94\\xac\\xa3\\rt\\xfa]...  \n4  b'\\x00\\x01\\xad\\x10\\x00\\x00@\\xe3\\xaa.\\xb7\\xfb]\\...  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>OBJECTID</th>\n      <th>FOD_ID</th>\n      <th>FPA_ID</th>\n      <th>SOURCE_SYSTEM_TYPE</th>\n      <th>SOURCE_SYSTEM</th>\n      <th>NWCG_REPORTING_AGENCY</th>\n      <th>NWCG_REPORTING_UNIT_ID</th>\n      <th>NWCG_REPORTING_UNIT_NAME</th>\n      <th>SOURCE_REPORTING_UNIT</th>\n      <th>SOURCE_REPORTING_UNIT_NAME</th>\n      <th>LOCAL_FIRE_REPORT_ID</th>\n      <th>LOCAL_INCIDENT_ID</th>\n      <th>FIRE_CODE</th>\n      <th>FIRE_NAME</th>\n      <th>ICS_209_INCIDENT_NUMBER</th>\n      <th>ICS_209_NAME</th>\n      <th>MTBS_ID</th>\n      <th>MTBS_FIRE_NAME</th>\n      <th>COMPLEX_NAME</th>\n      <th>FIRE_YEAR</th>\n      <th>DISCOVERY_DATE</th>\n      <th>DISCOVERY_DOY</th>\n      <th>DISCOVERY_TIME</th>\n      <th>STAT_CAUSE_CODE</th>\n      <th>STAT_CAUSE_DESCR</th>\n      <th>CONT_DATE</th>\n      <th>CONT_DOY</th>\n      <th>CONT_TIME</th>\n      <th>FIRE_SIZE</th>\n      <th>FIRE_SIZE_CLASS</th>\n      <th>LATITUDE</th>\n      <th>LONGITUDE</th>\n      <th>OWNER_CODE</th>\n      <th>OWNER_DESCR</th>\n      <th>STATE</th>\n      <th>COUNTY</th>\n      <th>FIPS_CODE</th>\n      <th>FIPS_NAME</th>\n      <th>Shape</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>FS-1418826</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USCAPNF</td>\n      <td>Plumas National Forest</td>\n      <td>0511</td>\n      <td>Plumas National Forest</td>\n      <td>1</td>\n      <td>PNF-47</td>\n      <td>BJ8K</td>\n      <td>FOUNTAIN</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>2005</td>\n      <td>2453403.50</td>\n      <td>33</td>\n      <td>1300</td>\n      <td>9.00</td>\n      <td>Miscellaneous</td>\n      <td>2453403.50</td>\n      <td>33.00</td>\n      <td>1730</td>\n      <td>0.10</td>\n      <td>A</td>\n      <td>40.04</td>\n      <td>-121.01</td>\n      <td>5.00</td>\n      <td>USFS</td>\n      <td>CA</td>\n      <td>63</td>\n      <td>063</td>\n      <td>Plumas</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00\\xe8d\\xc2\\x92_@^\\xc0...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2</td>\n      <td>FS-1418827</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USCAENF</td>\n      <td>Eldorado National Forest</td>\n      <td>0503</td>\n      <td>Eldorado National Forest</td>\n      <td>13</td>\n      <td>13</td>\n      <td>AAC0</td>\n      <td>PIGEON</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>2004</td>\n      <td>2453137.50</td>\n      <td>133</td>\n      <td>0845</td>\n      <td>1.00</td>\n      <td>Lightning</td>\n      <td>2453137.50</td>\n      <td>133.00</td>\n      <td>1530</td>\n      <td>0.25</td>\n      <td>A</td>\n      <td>38.93</td>\n      <td>-120.40</td>\n      <td>5.00</td>\n      <td>USFS</td>\n      <td>CA</td>\n      <td>61</td>\n      <td>061</td>\n      <td>Placer</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00T\\xb6\\xeej\\xe2\\x19^\\...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>3</td>\n      <td>FS-1418835</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USCAENF</td>\n      <td>Eldorado National Forest</td>\n      <td>0503</td>\n      <td>Eldorado National Forest</td>\n      <td>27</td>\n      <td>021</td>\n      <td>A32W</td>\n      <td>SLACK</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>2004</td>\n      <td>2453156.50</td>\n      <td>152</td>\n      <td>1921</td>\n      <td>5.00</td>\n      <td>Debris Burning</td>\n      <td>2453156.50</td>\n      <td>152.00</td>\n      <td>2024</td>\n      <td>0.10</td>\n      <td>A</td>\n      <td>38.98</td>\n      <td>-120.74</td>\n      <td>13.00</td>\n      <td>STATE OR PRIVATE</td>\n      <td>CA</td>\n      <td>17</td>\n      <td>017</td>\n      <td>El Dorado</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00\\xd0\\xa5\\xa0W\\x13/^\\...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>4</td>\n      <td>FS-1418845</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USCAENF</td>\n      <td>Eldorado National Forest</td>\n      <td>0503</td>\n      <td>Eldorado National Forest</td>\n      <td>43</td>\n      <td>6</td>\n      <td>None</td>\n      <td>DEER</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>2004</td>\n      <td>2453184.50</td>\n      <td>180</td>\n      <td>1600</td>\n      <td>1.00</td>\n      <td>Lightning</td>\n      <td>2453189.50</td>\n      <td>185.00</td>\n      <td>1400</td>\n      <td>0.10</td>\n      <td>A</td>\n      <td>38.56</td>\n      <td>-119.91</td>\n      <td>5.00</td>\n      <td>USFS</td>\n      <td>CA</td>\n      <td>3</td>\n      <td>003</td>\n      <td>Alpine</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00\\x94\\xac\\xa3\\rt\\xfa]...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>5</td>\n      <td>FS-1418847</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USCAENF</td>\n      <td>Eldorado National Forest</td>\n      <td>0503</td>\n      <td>Eldorado National Forest</td>\n      <td>44</td>\n      <td>7</td>\n      <td>None</td>\n      <td>STEVENOT</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>2004</td>\n      <td>2453184.50</td>\n      <td>180</td>\n      <td>1600</td>\n      <td>1.00</td>\n      <td>Lightning</td>\n      <td>2453189.50</td>\n      <td>185.00</td>\n      <td>1200</td>\n      <td>0.10</td>\n      <td>A</td>\n      <td>38.56</td>\n      <td>-119.93</td>\n      <td>5.00</td>\n      <td>USFS</td>\n      <td>CA</td>\n      <td>3</td>\n      <td>003</td>\n      <td>Alpine</td>\n      <td>b'\\x00\\x01\\xad\\x10\\x00\\x00@\\xe3\\xaa.\\xb7\\xfb]\\...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"<div style=\"color:white;display:fill;border-radius:8px;\n            background-color:#8F00FF;font-size:150%;\n            font-family:Nexa;letter-spacing:0.5px\">\n    <p style=\"padding: 5px;color:white;\"><b></b></p>\n</div>\n<a id='Introduction'></a>\n\n#  <b><span style='color:#8F00FF'>| 5. </span> Further Expanatory data analysis for GeoSpatial Representation </b>\n\n[Table of contents](#table)\n\nWhen we want to implement any kind of deep learning and recommener system or plot our finding espacially on Geospatial data, often we should label_encode the needed columns with string type. \n\nHere four important columns of **STATE, FIRE_SIZE_CLASS, STAT_CAUSE_DESCR, FIPS_NAME, STAT_CAUSE_DESCR** are label encoded. \n\n\n\n","metadata":{}},{"cell_type":"code","source":"# label encoding certain values \nfrom sklearn.preprocessing import LabelEncoder \nimport numpy as np \n\nData_toEncode = np.array(['STATE', 'FIRE_SIZE_CLASS','STAT_CAUSE_DESCR', 'FIPS_NAME', 'STAT_CAUSE_DESCR'])\n\nlabelEncoder = LabelEncoder() \n\ndata.STATE = labelEncoder.fit_transform(data.STATE) \ndata.FIPS_NAME = labelEncoder.fit_transform(data.FIPS_NAME)\ndata.FIRE_SIZE_CLASS = labelEncoder.fit_transform(data.FIRE_SIZE_CLASS)\ndata.STAT_CAUSE_DESCR = labelEncoder.fit_transform(data.STAT_CAUSE_DESCR)\ndata.STAT_CAUSE_DESCR = labelEncoder.fit_transform(data.STAT_CAUSE_DESCR)\ndata.Shape = labelEncoder.fit_transform(data.Shape)\n\n# Label encoded data\ndata.head()","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:48:00.374046Z","iopub.execute_input":"2023-10-05T11:48:00.374387Z","iopub.status.idle":"2023-10-05T11:48:00.867288Z","shell.execute_reply.started":"2023-10-05T11:48:00.374357Z","shell.execute_reply":"2023-10-05T11:48:00.866098Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"   OBJECTID  FOD_ID      FPA_ID SOURCE_SYSTEM_TYPE SOURCE_SYSTEM  \\\n0         1       1  FS-1418826                FED   FS-FIRESTAT   \n1         2       2  FS-1418827                FED   FS-FIRESTAT   \n2         3       3  FS-1418835                FED   FS-FIRESTAT   \n3         4       4  FS-1418845                FED   FS-FIRESTAT   \n4         5       5  FS-1418847                FED   FS-FIRESTAT   \n\n  NWCG_REPORTING_AGENCY NWCG_REPORTING_UNIT_ID  NWCG_REPORTING_UNIT_NAME  \\\n0                    FS                USCAPNF    Plumas National Forest   \n1                    FS                USCAENF  Eldorado National Forest   \n2                    FS                USCAENF  Eldorado National Forest   \n3                    FS                USCAENF  Eldorado National Forest   \n4                    FS                USCAENF  Eldorado National Forest   \n\n  SOURCE_REPORTING_UNIT SOURCE_REPORTING_UNIT_NAME LOCAL_FIRE_REPORT_ID  \\\n0                  0511     Plumas National Forest                    1   \n1                  0503   Eldorado National Forest                   13   \n2                  0503   Eldorado National Forest                   27   \n3                  0503   Eldorado National Forest                   43   \n4                  0503   Eldorado National Forest                   44   \n\n  LOCAL_INCIDENT_ID FIRE_CODE FIRE_NAME ICS_209_INCIDENT_NUMBER ICS_209_NAME  \\\n0            PNF-47      BJ8K  FOUNTAIN                    None         None   \n1                13      AAC0    PIGEON                    None         None   \n2               021      A32W     SLACK                    None         None   \n3                 6      None      DEER                    None         None   \n4                 7      None  STEVENOT                    None         None   \n\n  MTBS_ID MTBS_FIRE_NAME COMPLEX_NAME  FIRE_YEAR  DISCOVERY_DATE  \\\n0    None           None         None       2005      2453403.50   \n1    None           None         None       2004      2453137.50   \n2    None           None         None       2004      2453156.50   \n3    None           None         None       2004      2453184.50   \n4    None           None         None       2004      2453184.50   \n\n   DISCOVERY_DOY DISCOVERY_TIME  STAT_CAUSE_CODE  STAT_CAUSE_DESCR  CONT_DATE  \\\n0             33           1300             9.00                 6 2453403.50   \n1            133           0845             1.00                 5 2453137.50   \n2            152           1921             5.00                 3 2453156.50   \n3            180           1600             1.00                 5 2453189.50   \n4            180           1600             1.00                 5 2453189.50   \n\n   CONT_DOY CONT_TIME  FIRE_SIZE  FIRE_SIZE_CLASS  LATITUDE  LONGITUDE  \\\n0     33.00      1730       0.10                0     40.04    -121.01   \n1    133.00      1530       0.25                0     38.93    -120.40   \n2    152.00      2024       0.10                0     38.98    -120.74   \n3    185.00      1400       0.10                0     38.56    -119.91   \n4    185.00      1200       0.10                0     38.56    -119.93   \n\n   OWNER_CODE       OWNER_DESCR  STATE COUNTY FIPS_CODE  FIPS_NAME  Shape  \n0        5.00              USFS      1     63       063         20     91  \n1        5.00              USFS      1     61       061         19     35  \n2       13.00  STATE OR PRIVATE      1     17       017          9     82  \n3        5.00              USFS      1      3       003          0     60  \n4        5.00              USFS      1      3       003          0     25  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>OBJECTID</th>\n      <th>FOD_ID</th>\n      <th>FPA_ID</th>\n      <th>SOURCE_SYSTEM_TYPE</th>\n      <th>SOURCE_SYSTEM</th>\n      <th>NWCG_REPORTING_AGENCY</th>\n      <th>NWCG_REPORTING_UNIT_ID</th>\n      <th>NWCG_REPORTING_UNIT_NAME</th>\n      <th>SOURCE_REPORTING_UNIT</th>\n      <th>SOURCE_REPORTING_UNIT_NAME</th>\n      <th>LOCAL_FIRE_REPORT_ID</th>\n      <th>LOCAL_INCIDENT_ID</th>\n      <th>FIRE_CODE</th>\n      <th>FIRE_NAME</th>\n      <th>ICS_209_INCIDENT_NUMBER</th>\n      <th>ICS_209_NAME</th>\n      <th>MTBS_ID</th>\n      <th>MTBS_FIRE_NAME</th>\n      <th>COMPLEX_NAME</th>\n      <th>FIRE_YEAR</th>\n      <th>DISCOVERY_DATE</th>\n      <th>DISCOVERY_DOY</th>\n      <th>DISCOVERY_TIME</th>\n      <th>STAT_CAUSE_CODE</th>\n      <th>STAT_CAUSE_DESCR</th>\n      <th>CONT_DATE</th>\n      <th>CONT_DOY</th>\n      <th>CONT_TIME</th>\n      <th>FIRE_SIZE</th>\n      <th>FIRE_SIZE_CLASS</th>\n      <th>LATITUDE</th>\n      <th>LONGITUDE</th>\n      <th>OWNER_CODE</th>\n      <th>OWNER_DESCR</th>\n      <th>STATE</th>\n      <th>COUNTY</th>\n      <th>FIPS_CODE</th>\n      <th>FIPS_NAME</th>\n      <th>Shape</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>FS-1418826</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USCAPNF</td>\n      <td>Plumas National Forest</td>\n      <td>0511</td>\n      <td>Plumas National Forest</td>\n      <td>1</td>\n      <td>PNF-47</td>\n      <td>BJ8K</td>\n      <td>FOUNTAIN</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>2005</td>\n      <td>2453403.50</td>\n      <td>33</td>\n      <td>1300</td>\n      <td>9.00</td>\n      <td>6</td>\n      <td>2453403.50</td>\n      <td>33.00</td>\n      <td>1730</td>\n      <td>0.10</td>\n      <td>0</td>\n      <td>40.04</td>\n      <td>-121.01</td>\n      <td>5.00</td>\n      <td>USFS</td>\n      <td>1</td>\n      <td>63</td>\n      <td>063</td>\n      <td>20</td>\n      <td>91</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2</td>\n      <td>FS-1418827</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USCAENF</td>\n      <td>Eldorado National Forest</td>\n      <td>0503</td>\n      <td>Eldorado National Forest</td>\n      <td>13</td>\n      <td>13</td>\n      <td>AAC0</td>\n      <td>PIGEON</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>2004</td>\n      <td>2453137.50</td>\n      <td>133</td>\n      <td>0845</td>\n      <td>1.00</td>\n      <td>5</td>\n      <td>2453137.50</td>\n      <td>133.00</td>\n      <td>1530</td>\n      <td>0.25</td>\n      <td>0</td>\n      <td>38.93</td>\n      <td>-120.40</td>\n      <td>5.00</td>\n      <td>USFS</td>\n      <td>1</td>\n      <td>61</td>\n      <td>061</td>\n      <td>19</td>\n      <td>35</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>3</td>\n      <td>FS-1418835</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USCAENF</td>\n      <td>Eldorado National Forest</td>\n      <td>0503</td>\n      <td>Eldorado National Forest</td>\n      <td>27</td>\n      <td>021</td>\n      <td>A32W</td>\n      <td>SLACK</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>2004</td>\n      <td>2453156.50</td>\n      <td>152</td>\n      <td>1921</td>\n      <td>5.00</td>\n      <td>3</td>\n      <td>2453156.50</td>\n      <td>152.00</td>\n      <td>2024</td>\n      <td>0.10</td>\n      <td>0</td>\n      <td>38.98</td>\n      <td>-120.74</td>\n      <td>13.00</td>\n      <td>STATE OR PRIVATE</td>\n      <td>1</td>\n      <td>17</td>\n      <td>017</td>\n      <td>9</td>\n      <td>82</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>4</td>\n      <td>FS-1418845</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USCAENF</td>\n      <td>Eldorado National Forest</td>\n      <td>0503</td>\n      <td>Eldorado National Forest</td>\n      <td>43</td>\n      <td>6</td>\n      <td>None</td>\n      <td>DEER</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>2004</td>\n      <td>2453184.50</td>\n      <td>180</td>\n      <td>1600</td>\n      <td>1.00</td>\n      <td>5</td>\n      <td>2453189.50</td>\n      <td>185.00</td>\n      <td>1400</td>\n      <td>0.10</td>\n      <td>0</td>\n      <td>38.56</td>\n      <td>-119.91</td>\n      <td>5.00</td>\n      <td>USFS</td>\n      <td>1</td>\n      <td>3</td>\n      <td>003</td>\n      <td>0</td>\n      <td>60</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>5</td>\n      <td>FS-1418847</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USCAENF</td>\n      <td>Eldorado National Forest</td>\n      <td>0503</td>\n      <td>Eldorado National Forest</td>\n      <td>44</td>\n      <td>7</td>\n      <td>None</td>\n      <td>STEVENOT</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>2004</td>\n      <td>2453184.50</td>\n      <td>180</td>\n      <td>1600</td>\n      <td>1.00</td>\n      <td>5</td>\n      <td>2453189.50</td>\n      <td>185.00</td>\n      <td>1200</td>\n      <td>0.10</td>\n      <td>0</td>\n      <td>38.56</td>\n      <td>-119.93</td>\n      <td>5.00</td>\n      <td>USFS</td>\n      <td>1</td>\n      <td>3</td>\n      <td>003</td>\n      <td>0</td>\n      <td>25</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# Fill the Nan values with avg of them  \n\ndata = data.replace({None: np.nan})\ndata['ICS_209_INCIDENT_NUMBER'] = data['ICS_209_INCIDENT_NUMBER'].fillna(0)\ndata['ICS_209_NAME'] = data['ICS_209_NAME'].fillna(0)\ndata['MTBS_ID'] = data['MTBS_ID'].fillna(0)\ndata['COMPLEX_NAME'] = data['COMPLEX_NAME'].fillna(0)\ndata['MTBS_FIRE_NAME'] = data['MTBS_FIRE_NAME'].fillna(0)\ndata['FIRE_CODE'] = data['FIRE_CODE'].fillna(0)\n\n# replaced data and NAN values \ndata","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:48:00.868913Z","iopub.execute_input":"2023-10-05T11:48:00.870125Z","iopub.status.idle":"2023-10-05T11:48:00.919262Z","shell.execute_reply.started":"2023-10-05T11:48:00.870078Z","shell.execute_reply":"2023-10-05T11:48:00.918101Z"},"trusted":true},"execution_count":13,"outputs":[{"execution_count":13,"output_type":"execute_result","data":{"text/plain":"    OBJECTID  FOD_ID      FPA_ID SOURCE_SYSTEM_TYPE SOURCE_SYSTEM  \\\n0          1       1  FS-1418826                FED   FS-FIRESTAT   \n1          2       2  FS-1418827                FED   FS-FIRESTAT   \n2          3       3  FS-1418835                FED   FS-FIRESTAT   \n3          4       4  FS-1418845                FED   FS-FIRESTAT   \n4          5       5  FS-1418847                FED   FS-FIRESTAT   \n..       ...     ...         ...                ...           ...   \n95        96      97  FS-1419089                FED   FS-FIRESTAT   \n96        97      98  FS-1419090                FED   FS-FIRESTAT   \n97        98      99  FS-1419093                FED   FS-FIRESTAT   \n98        99     100  FS-1419095                FED   FS-FIRESTAT   \n99       100     101  FS-1419096                FED   FS-FIRESTAT   \n\n   NWCG_REPORTING_AGENCY NWCG_REPORTING_UNIT_ID  \\\n0                     FS                USCAPNF   \n1                     FS                USCAENF   \n2                     FS                USCAENF   \n3                     FS                USCAENF   \n4                     FS                USCAENF   \n..                   ...                    ...   \n95                    FS                USNVHTF   \n96                    FS                USNVHTF   \n97                    FS                USNMCAF   \n98                    FS                USNMCAF   \n99                    FS                USMTKNF   \n\n            NWCG_REPORTING_UNIT_NAME SOURCE_REPORTING_UNIT  \\\n0             Plumas National Forest                  0511   \n1           Eldorado National Forest                  0503   \n2           Eldorado National Forest                  0503   \n3           Eldorado National Forest                  0503   \n4           Eldorado National Forest                  0503   \n..                               ...                   ...   \n95  Humboldt-Toiyabe National Forest                  0417   \n96  Humboldt-Toiyabe National Forest                  0417   \n97            Carson National Forest                  0302   \n98            Carson National Forest                  0302   \n99          Kootenai National Forest                  0114   \n\n          SOURCE_REPORTING_UNIT_NAME LOCAL_FIRE_REPORT_ID LOCAL_INCIDENT_ID  \\\n0             Plumas National Forest                    1            PNF-47   \n1           Eldorado National Forest                   13                13   \n2           Eldorado National Forest                   27               021   \n3           Eldorado National Forest                   43                 6   \n4           Eldorado National Forest                   44                 7   \n..                               ...                  ...               ...   \n95  Humboldt-Toiyabe National Forest                    1               M01   \n96  Humboldt-Toiyabe National Forest                    2               006   \n97            Carson National Forest                   64               064   \n98            Carson National Forest                   84               084   \n99          Kootenai National Forest                   34                10   \n\n   FIRE_CODE      FIRE_NAME ICS_209_INCIDENT_NUMBER ICS_209_NAME MTBS_ID  \\\n0       BJ8K       FOUNTAIN                       0            0       0   \n1       AAC0         PIGEON                       0            0       0   \n2       A32W          SLACK                       0            0       0   \n3          0           DEER                       0            0       0   \n4          0       STEVENOT                       0            0       0   \n..       ...            ...                     ...          ...     ...   \n95      BEZ8    HOT SPRINGS                       0            0       0   \n96      BZB0        SHINGLE                       0            0       0   \n97      F064  CEDAR SPRINGS                       0            0       0   \n98      F084        BUZZARD                       0            0       0   \n99      BXR6     STRAWBERRY                       0            0       0   \n\n   MTBS_FIRE_NAME  COMPLEX_NAME  FIRE_YEAR  DISCOVERY_DATE  DISCOVERY_DOY  \\\n0               0          0.00       2005      2453403.50             33   \n1               0          0.00       2004      2453137.50            133   \n2               0          0.00       2004      2453156.50            152   \n3               0          0.00       2004      2453184.50            180   \n4               0          0.00       2004      2453184.50            180   \n..            ...           ...        ...             ...            ...   \n95              0          0.00       2005      2453496.50            126   \n96              0          0.00       2005      2453559.50            189   \n97              0          0.00       2005      2453532.50            162   \n98              0          0.00       2005      2453543.50            173   \n99              0          0.00       2005      2453553.50            183   \n\n   DISCOVERY_TIME  STAT_CAUSE_CODE  STAT_CAUSE_DESCR  CONT_DATE  CONT_DOY  \\\n0            1300             9.00                 6 2453403.50     33.00   \n1            0845             1.00                 5 2453137.50    133.00   \n2            1921             5.00                 3 2453156.50    152.00   \n3            1600             1.00                 5 2453189.50    185.00   \n4            1600             1.00                 5 2453189.50    185.00   \n..            ...              ...               ...        ...       ...   \n95           1145             5.00                 3 2453499.50    129.00   \n96           0015             7.00                 0 2453559.50    189.00   \n97           1901             1.00                 5 2453532.50    162.00   \n98           1820             1.00                 5 2453543.50    173.00   \n99           1300             6.00                 7 2453553.50    183.00   \n\n   CONT_TIME  FIRE_SIZE  FIRE_SIZE_CLASS  LATITUDE  LONGITUDE  OWNER_CODE  \\\n0       1730       0.10                0     40.04    -121.01        5.00   \n1       1530       0.25                0     38.93    -120.40        5.00   \n2       2024       0.10                0     38.98    -120.74       13.00   \n3       1400       0.10                0     38.56    -119.91        5.00   \n4       1200       0.10                0     38.56    -119.93        5.00   \n..       ...        ...              ...       ...        ...         ...   \n95      1330       0.10                0     38.70    -119.84       13.00   \n96      0045       0.01                0     38.45    -119.46        5.00   \n97      2000       0.10                0     36.67    -107.25        5.00   \n98      2315       0.25                0     36.88    -107.22        5.00   \n99      1435       0.10                0     48.46    -115.67        5.00   \n\n         OWNER_DESCR  STATE COUNTY FIPS_CODE  FIPS_NAME  Shape  \n0               USFS      1     63       063         20     91  \n1               USFS      1     61       061         19     35  \n2   STATE OR PRIVATE      1     17       017          9     82  \n3               USFS      1      3       003          0     60  \n4               USFS      1      3       003          0     25  \n..               ...    ...    ...       ...        ...    ...  \n95  STATE OR PRIVATE      1      3       003          0     94  \n96              USFS      1     51       051         17      4  \n97              USFS      5     39       039         21     54  \n98              USFS      5     39       039         21     48  \n99              USFS      3     53       053         15     92  \n\n[100 rows x 39 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>OBJECTID</th>\n      <th>FOD_ID</th>\n      <th>FPA_ID</th>\n      <th>SOURCE_SYSTEM_TYPE</th>\n      <th>SOURCE_SYSTEM</th>\n      <th>NWCG_REPORTING_AGENCY</th>\n      <th>NWCG_REPORTING_UNIT_ID</th>\n      <th>NWCG_REPORTING_UNIT_NAME</th>\n      <th>SOURCE_REPORTING_UNIT</th>\n      <th>SOURCE_REPORTING_UNIT_NAME</th>\n      <th>LOCAL_FIRE_REPORT_ID</th>\n      <th>LOCAL_INCIDENT_ID</th>\n      <th>FIRE_CODE</th>\n      <th>FIRE_NAME</th>\n      <th>ICS_209_INCIDENT_NUMBER</th>\n      <th>ICS_209_NAME</th>\n      <th>MTBS_ID</th>\n      <th>MTBS_FIRE_NAME</th>\n      <th>COMPLEX_NAME</th>\n      <th>FIRE_YEAR</th>\n      <th>DISCOVERY_DATE</th>\n      <th>DISCOVERY_DOY</th>\n      <th>DISCOVERY_TIME</th>\n      <th>STAT_CAUSE_CODE</th>\n      <th>STAT_CAUSE_DESCR</th>\n      <th>CONT_DATE</th>\n      <th>CONT_DOY</th>\n      <th>CONT_TIME</th>\n      <th>FIRE_SIZE</th>\n      <th>FIRE_SIZE_CLASS</th>\n      <th>LATITUDE</th>\n      <th>LONGITUDE</th>\n      <th>OWNER_CODE</th>\n      <th>OWNER_DESCR</th>\n      <th>STATE</th>\n      <th>COUNTY</th>\n      <th>FIPS_CODE</th>\n      <th>FIPS_NAME</th>\n      <th>Shape</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>FS-1418826</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USCAPNF</td>\n      <td>Plumas National Forest</td>\n      <td>0511</td>\n      <td>Plumas National Forest</td>\n      <td>1</td>\n      <td>PNF-47</td>\n      <td>BJ8K</td>\n      <td>FOUNTAIN</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.00</td>\n      <td>2005</td>\n      <td>2453403.50</td>\n      <td>33</td>\n      <td>1300</td>\n      <td>9.00</td>\n      <td>6</td>\n      <td>2453403.50</td>\n      <td>33.00</td>\n      <td>1730</td>\n      <td>0.10</td>\n      <td>0</td>\n      <td>40.04</td>\n      <td>-121.01</td>\n      <td>5.00</td>\n      <td>USFS</td>\n      <td>1</td>\n      <td>63</td>\n      <td>063</td>\n      <td>20</td>\n      <td>91</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2</td>\n      <td>FS-1418827</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USCAENF</td>\n      <td>Eldorado National Forest</td>\n      <td>0503</td>\n      <td>Eldorado National Forest</td>\n      <td>13</td>\n      <td>13</td>\n      <td>AAC0</td>\n      <td>PIGEON</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.00</td>\n      <td>2004</td>\n      <td>2453137.50</td>\n      <td>133</td>\n      <td>0845</td>\n      <td>1.00</td>\n      <td>5</td>\n      <td>2453137.50</td>\n      <td>133.00</td>\n      <td>1530</td>\n      <td>0.25</td>\n      <td>0</td>\n      <td>38.93</td>\n      <td>-120.40</td>\n      <td>5.00</td>\n      <td>USFS</td>\n      <td>1</td>\n      <td>61</td>\n      <td>061</td>\n      <td>19</td>\n      <td>35</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>3</td>\n      <td>FS-1418835</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USCAENF</td>\n      <td>Eldorado National Forest</td>\n      <td>0503</td>\n      <td>Eldorado National Forest</td>\n      <td>27</td>\n      <td>021</td>\n      <td>A32W</td>\n      <td>SLACK</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.00</td>\n      <td>2004</td>\n      <td>2453156.50</td>\n      <td>152</td>\n      <td>1921</td>\n      <td>5.00</td>\n      <td>3</td>\n      <td>2453156.50</td>\n      <td>152.00</td>\n      <td>2024</td>\n      <td>0.10</td>\n      <td>0</td>\n      <td>38.98</td>\n      <td>-120.74</td>\n      <td>13.00</td>\n      <td>STATE OR PRIVATE</td>\n      <td>1</td>\n      <td>17</td>\n      <td>017</td>\n      <td>9</td>\n      <td>82</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>4</td>\n      <td>FS-1418845</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USCAENF</td>\n      <td>Eldorado National Forest</td>\n      <td>0503</td>\n      <td>Eldorado National Forest</td>\n      <td>43</td>\n      <td>6</td>\n      <td>0</td>\n      <td>DEER</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.00</td>\n      <td>2004</td>\n      <td>2453184.50</td>\n      <td>180</td>\n      <td>1600</td>\n      <td>1.00</td>\n      <td>5</td>\n      <td>2453189.50</td>\n      <td>185.00</td>\n      <td>1400</td>\n      <td>0.10</td>\n      <td>0</td>\n      <td>38.56</td>\n      <td>-119.91</td>\n      <td>5.00</td>\n      <td>USFS</td>\n      <td>1</td>\n      <td>3</td>\n      <td>003</td>\n      <td>0</td>\n      <td>60</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>5</td>\n      <td>FS-1418847</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USCAENF</td>\n      <td>Eldorado National Forest</td>\n      <td>0503</td>\n      <td>Eldorado National Forest</td>\n      <td>44</td>\n      <td>7</td>\n      <td>0</td>\n      <td>STEVENOT</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.00</td>\n      <td>2004</td>\n      <td>2453184.50</td>\n      <td>180</td>\n      <td>1600</td>\n      <td>1.00</td>\n      <td>5</td>\n      <td>2453189.50</td>\n      <td>185.00</td>\n      <td>1200</td>\n      <td>0.10</td>\n      <td>0</td>\n      <td>38.56</td>\n      <td>-119.93</td>\n      <td>5.00</td>\n      <td>USFS</td>\n      <td>1</td>\n      <td>3</td>\n      <td>003</td>\n      <td>0</td>\n      <td>25</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>95</th>\n      <td>96</td>\n      <td>97</td>\n      <td>FS-1419089</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USNVHTF</td>\n      <td>Humboldt-Toiyabe National Forest</td>\n      <td>0417</td>\n      <td>Humboldt-Toiyabe National Forest</td>\n      <td>1</td>\n      <td>M01</td>\n      <td>BEZ8</td>\n      <td>HOT SPRINGS</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.00</td>\n      <td>2005</td>\n      <td>2453496.50</td>\n      <td>126</td>\n      <td>1145</td>\n      <td>5.00</td>\n      <td>3</td>\n      <td>2453499.50</td>\n      <td>129.00</td>\n      <td>1330</td>\n      <td>0.10</td>\n      <td>0</td>\n      <td>38.70</td>\n      <td>-119.84</td>\n      <td>13.00</td>\n      <td>STATE OR PRIVATE</td>\n      <td>1</td>\n      <td>3</td>\n      <td>003</td>\n      <td>0</td>\n      <td>94</td>\n    </tr>\n    <tr>\n      <th>96</th>\n      <td>97</td>\n      <td>98</td>\n      <td>FS-1419090</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USNVHTF</td>\n      <td>Humboldt-Toiyabe National Forest</td>\n      <td>0417</td>\n      <td>Humboldt-Toiyabe National Forest</td>\n      <td>2</td>\n      <td>006</td>\n      <td>BZB0</td>\n      <td>SHINGLE</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.00</td>\n      <td>2005</td>\n      <td>2453559.50</td>\n      <td>189</td>\n      <td>0015</td>\n      <td>7.00</td>\n      <td>0</td>\n      <td>2453559.50</td>\n      <td>189.00</td>\n      <td>0045</td>\n      <td>0.01</td>\n      <td>0</td>\n      <td>38.45</td>\n      <td>-119.46</td>\n      <td>5.00</td>\n      <td>USFS</td>\n      <td>1</td>\n      <td>51</td>\n      <td>051</td>\n      <td>17</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>97</th>\n      <td>98</td>\n      <td>99</td>\n      <td>FS-1419093</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USNMCAF</td>\n      <td>Carson National Forest</td>\n      <td>0302</td>\n      <td>Carson National Forest</td>\n      <td>64</td>\n      <td>064</td>\n      <td>F064</td>\n      <td>CEDAR SPRINGS</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.00</td>\n      <td>2005</td>\n      <td>2453532.50</td>\n      <td>162</td>\n      <td>1901</td>\n      <td>1.00</td>\n      <td>5</td>\n      <td>2453532.50</td>\n      <td>162.00</td>\n      <td>2000</td>\n      <td>0.10</td>\n      <td>0</td>\n      <td>36.67</td>\n      <td>-107.25</td>\n      <td>5.00</td>\n      <td>USFS</td>\n      <td>5</td>\n      <td>39</td>\n      <td>039</td>\n      <td>21</td>\n      <td>54</td>\n    </tr>\n    <tr>\n      <th>98</th>\n      <td>99</td>\n      <td>100</td>\n      <td>FS-1419095</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USNMCAF</td>\n      <td>Carson National Forest</td>\n      <td>0302</td>\n      <td>Carson National Forest</td>\n      <td>84</td>\n      <td>084</td>\n      <td>F084</td>\n      <td>BUZZARD</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.00</td>\n      <td>2005</td>\n      <td>2453543.50</td>\n      <td>173</td>\n      <td>1820</td>\n      <td>1.00</td>\n      <td>5</td>\n      <td>2453543.50</td>\n      <td>173.00</td>\n      <td>2315</td>\n      <td>0.25</td>\n      <td>0</td>\n      <td>36.88</td>\n      <td>-107.22</td>\n      <td>5.00</td>\n      <td>USFS</td>\n      <td>5</td>\n      <td>39</td>\n      <td>039</td>\n      <td>21</td>\n      <td>48</td>\n    </tr>\n    <tr>\n      <th>99</th>\n      <td>100</td>\n      <td>101</td>\n      <td>FS-1419096</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USMTKNF</td>\n      <td>Kootenai National Forest</td>\n      <td>0114</td>\n      <td>Kootenai National Forest</td>\n      <td>34</td>\n      <td>10</td>\n      <td>BXR6</td>\n      <td>STRAWBERRY</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.00</td>\n      <td>2005</td>\n      <td>2453553.50</td>\n      <td>183</td>\n      <td>1300</td>\n      <td>6.00</td>\n      <td>7</td>\n      <td>2453553.50</td>\n      <td>183.00</td>\n      <td>1435</td>\n      <td>0.10</td>\n      <td>0</td>\n      <td>48.46</td>\n      <td>-115.67</td>\n      <td>5.00</td>\n      <td>USFS</td>\n      <td>3</td>\n      <td>53</td>\n      <td>053</td>\n      <td>15</td>\n      <td>92</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 39 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# identifying date, year, cause and fire size after 2010\n\nquery_3 = pd.read_sql(\"\"\"SELECT FIRE_SIZE, FIRE_YEAR\n                         FROM fires \n                         WHERE  FIRE_SIZE > 2000.84\n                         UNION \n                         SELECT STAT_CAUSE_CODE, FIRE_YEAR\n                         from fires\n                         WHERE FIRE_YEAR > 2010 \n                         LIMIT 40;\n                         \"\"\", conn)\n\nquery_3","metadata":{"execution":{"iopub.status.busy":"2023-10-05T14:08:37.396257Z","iopub.execute_input":"2023-10-05T14:08:37.396764Z","iopub.status.idle":"2023-10-05T14:08:49.419445Z","shell.execute_reply.started":"2023-10-05T14:08:37.396726Z","shell.execute_reply":"2023-10-05T14:08:49.418385Z"},"trusted":true},"execution_count":88,"outputs":[{"execution_count":88,"output_type":"execute_result","data":{"text/plain":"    FIRE_SIZE  FIRE_YEAR\n0        1.00       2011\n1        1.00       2012\n2        1.00       2013\n3        1.00       2014\n4        1.00       2015\n5        2.00       2011\n6        2.00       2012\n7        2.00       2013\n8        2.00       2014\n9        2.00       2015\n10       3.00       2011\n11       3.00       2012\n12       3.00       2013\n13       3.00       2014\n14       3.00       2015\n15       4.00       2011\n16       4.00       2012\n17       4.00       2013\n18       4.00       2014\n19       4.00       2015\n20       5.00       2011\n21       5.00       2012\n22       5.00       2013\n23       5.00       2014\n24       5.00       2015\n25       6.00       2011\n26       6.00       2012\n27       6.00       2013\n28       6.00       2014\n29       6.00       2015\n30       7.00       2011\n31       7.00       2012\n32       7.00       2013\n33       7.00       2014\n34       7.00       2015\n35       8.00       2011\n36       8.00       2012\n37       8.00       2013\n38       8.00       2014\n39       8.00       2015","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FIRE_SIZE</th>\n      <th>FIRE_YEAR</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.00</td>\n      <td>2011</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1.00</td>\n      <td>2012</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1.00</td>\n      <td>2013</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1.00</td>\n      <td>2014</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1.00</td>\n      <td>2015</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>2.00</td>\n      <td>2011</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>2.00</td>\n      <td>2012</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>2.00</td>\n      <td>2013</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>2.00</td>\n      <td>2014</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>2.00</td>\n      <td>2015</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>3.00</td>\n      <td>2011</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>3.00</td>\n      <td>2012</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>3.00</td>\n      <td>2013</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>3.00</td>\n      <td>2014</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>3.00</td>\n      <td>2015</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>4.00</td>\n      <td>2011</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>4.00</td>\n      <td>2012</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>4.00</td>\n      <td>2013</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>4.00</td>\n      <td>2014</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>4.00</td>\n      <td>2015</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>5.00</td>\n      <td>2011</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>5.00</td>\n      <td>2012</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>5.00</td>\n      <td>2013</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>5.00</td>\n      <td>2014</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>5.00</td>\n      <td>2015</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>6.00</td>\n      <td>2011</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>6.00</td>\n      <td>2012</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>6.00</td>\n      <td>2013</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>6.00</td>\n      <td>2014</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>6.00</td>\n      <td>2015</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>7.00</td>\n      <td>2011</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>7.00</td>\n      <td>2012</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>7.00</td>\n      <td>2013</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>7.00</td>\n      <td>2014</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>7.00</td>\n      <td>2015</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>8.00</td>\n      <td>2011</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>8.00</td>\n      <td>2012</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>8.00</td>\n      <td>2013</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>8.00</td>\n      <td>2014</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>8.00</td>\n      <td>2015</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# using lead \nquery_4 = pd.read_sql(\"\"\"SELECT FIRE_SIZE,\n                         DISCOVERY_TIME,\n                         STAT_CAUSE_CODE, \n                         SOURCE_REPORTING_UNIT, \n                         NWCG_REPORTING_UNIT_ID, \n                         NWCG_REPORTING_UNIT_NAME, \n                         LEAD(DISCOVERY_TIME, 1, 0) OVER (ORDER BY DISCOVERY_TIME) AS DISCOVERY_TIME,\n                         LEAD(DISCOVERY_TIME, 1, 0) OVER (ORDER BY DISCOVERY_TIME) - DISCOVERY_TIME AS DISCOVERY_TIME_Diff\n                         FROM fires\n                         LIMIT 5;\n                         \"\"\", conn)\n\nquery_4","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:48:11.624417Z","iopub.execute_input":"2023-10-05T11:48:11.624957Z","iopub.status.idle":"2023-10-05T11:48:25.702261Z","shell.execute_reply.started":"2023-10-05T11:48:11.624930Z","shell.execute_reply":"2023-10-05T11:48:25.701153Z"},"trusted":true},"execution_count":15,"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"   FIRE_SIZE DISCOVERY_TIME  STAT_CAUSE_CODE SOURCE_REPORTING_UNIT  \\\n0       0.30           None             1.00                 AKAKA   \n1       0.10           None             1.00                 AKAKA   \n2       1.00           None             9.00                 SDCCA   \n3       0.10           None            10.00                 ORWSA   \n4       0.10           None             9.00                 MTFBA   \n\n  NWCG_REPORTING_UNIT_ID NWCG_REPORTING_UNIT_NAME DISCOVERY_TIME  \\\n0                USAKAKA   Alaska Regional Office           None   \n1                USAKAKA   Alaska Regional Office           None   \n2                USSDCCA        Crow Creek Agency           None   \n3                USORWSA      Warm Springs Agency           None   \n4                USMTFBA      Fort Belknap Agency           None   \n\n  DISCOVERY_TIME_Diff  \n0                None  \n1                None  \n2                None  \n3                None  \n4                None  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FIRE_SIZE</th>\n      <th>DISCOVERY_TIME</th>\n      <th>STAT_CAUSE_CODE</th>\n      <th>SOURCE_REPORTING_UNIT</th>\n      <th>NWCG_REPORTING_UNIT_ID</th>\n      <th>NWCG_REPORTING_UNIT_NAME</th>\n      <th>DISCOVERY_TIME</th>\n      <th>DISCOVERY_TIME_Diff</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.30</td>\n      <td>None</td>\n      <td>1.00</td>\n      <td>AKAKA</td>\n      <td>USAKAKA</td>\n      <td>Alaska Regional Office</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.10</td>\n      <td>None</td>\n      <td>1.00</td>\n      <td>AKAKA</td>\n      <td>USAKAKA</td>\n      <td>Alaska Regional Office</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1.00</td>\n      <td>None</td>\n      <td>9.00</td>\n      <td>SDCCA</td>\n      <td>USSDCCA</td>\n      <td>Crow Creek Agency</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.10</td>\n      <td>None</td>\n      <td>10.00</td>\n      <td>ORWSA</td>\n      <td>USORWSA</td>\n      <td>Warm Springs Agency</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.10</td>\n      <td>None</td>\n      <td>9.00</td>\n      <td>MTFBA</td>\n      <td>USMTFBA</td>\n      <td>Fort Belknap Agency</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"query_5 = pd.read_sql(\"\"\"SELECT \n                            fires.DISCOVERY_TIME AS Time, \n                            fires.STAT_CAUSE_CODE AS Cause_Code, \n                            count(distinct FIRE_NAME ) AS FireName,\n                            count(distinct SOURCE_REPORTING_UNIT_NAME ) AS UnitName,\n                            AVG(FIRE_SIZE) AS Fire_Size,  \n                            AVG(Shape) AS Fire_Shape, \n                            ROUND(AVG(DISCOVERY_TIME)) AS Initial_Date, \n                            sum(CONT_TIME) AS Time,\n                            sum(COUNTY) AS SubState\n                            FROM fires;\"\"\", conn)\n\n\nquery_5","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:48:25.703715Z","iopub.execute_input":"2023-10-05T11:48:25.704739Z","iopub.status.idle":"2023-10-05T11:48:36.165379Z","shell.execute_reply.started":"2023-10-05T11:48:25.704702Z","shell.execute_reply":"2023-10-05T11:48:36.164262Z"},"trusted":true},"execution_count":16,"outputs":[{"execution_count":16,"output_type":"execute_result","data":{"text/plain":"   Time  Cause_Code  FireName  UnitName  Fire_Size  Fire_Shape  Initial_Date  \\\n0  1300        9.00    493633      4441      74.52        0.00       1453.00   \n\n           Time   SubState  \n0 1393492463.00 6588340.00  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Time</th>\n      <th>Cause_Code</th>\n      <th>FireName</th>\n      <th>UnitName</th>\n      <th>Fire_Size</th>\n      <th>Fire_Shape</th>\n      <th>Initial_Date</th>\n      <th>Time</th>\n      <th>SubState</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1300</td>\n      <td>9.00</td>\n      <td>493633</td>\n      <td>4441</td>\n      <td>74.52</td>\n      <td>0.00</td>\n      <td>1453.00</td>\n      <td>1393492463.00</td>\n      <td>6588340.00</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"query_6 = pd.read_sql(\"\"\"SELECT \n                                SUM(CASE \n                                    WHEN FIRE_YEAR IN (2005)\n                                    THEN FIRE_SIZE\n                                    ELSE 0 END) AS Fire_Id,\n                                SUM(CASE\n                                    WHEN DISCOVERY_TIME IN (1600)\n                                    THEN FIRE_SIZE\n                                    ELSE 0 END) AS  Fire_Size\n                                FROM fires;\"\"\", conn)\n\nquery_6","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:48:36.166946Z","iopub.execute_input":"2023-10-05T11:48:36.167305Z","iopub.status.idle":"2023-10-05T11:48:45.076902Z","shell.execute_reply.started":"2023-10-05T11:48:36.167276Z","shell.execute_reply":"2023-10-05T11:48:45.075790Z"},"trusted":true},"execution_count":17,"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"     Fire_Id  Fire_Size\n0 9665105.13 2193614.86","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Fire_Id</th>\n      <th>Fire_Size</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>9665105.13</td>\n      <td>2193614.86</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"query_6 = pd.read_sql(\"\"\"\n                        SELECT \n                            CASE  \n                            WHEN FIRE_YEAR > 2010 THEN  'New_Fire_Incedient'\n                            WHEN FIRE_SIZE > 0.25 THEN 'Biggest_Fire'\n                            WHEN Shape > 30 AND Shape < 90 THEN 'CirculatingFire'\n                            END AS Fire_Details, \n                            COUNT(DISTINCT FIRE_SIZE) AS Number_Of_Fires\n                            FROM  fires\n                            GROUP BY \n                             CASE\n                                WHEN FIRE_YEAR > 2010 THEN 'NewFireIncedient'\n                                WHEN FIRE_SIZE > 0.25 THEN 'VastFire'\n                                WHEN Shape > 25 AND Shape < 90 THEN 'CirculatingFire'\n                            END;\"\"\", conn)\n\nquery_6","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:48:45.079844Z","iopub.execute_input":"2023-10-05T11:48:45.080369Z","iopub.status.idle":"2023-10-05T11:48:57.824560Z","shell.execute_reply.started":"2023-10-05T11:48:45.080326Z","shell.execute_reply":"2023-10-05T11:48:57.823191Z"},"trusted":true},"execution_count":18,"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"         Fire_Details  Number_Of_Fires\n0                None              133\n1  New_Fire_Incedient             6288\n2        Biggest_Fire            11253","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Fire_Details</th>\n      <th>Number_Of_Fires</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>None</td>\n      <td>133</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>New_Fire_Incedient</td>\n      <td>6288</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Biggest_Fire</td>\n      <td>11253</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# fire size \n# avg time \n# avg overall rating \n# avg overll potential\n\n\nquery_7 = pd.read_sql(\"\"\"SELECT DISTINCT NWCG_REPORTING_UNIT_NAME, LOCAL_INCIDENT_ID \n                         FROM fires AS UnitName\n                         WHERE EXISTS \n                             (SELECT *\n                              FROM fires\n                              WHERE FIRE_SIZE > 0.90 \n                              AND fires.Fire_name LIKE '%EON%')\n                              LIMIT 10;\"\"\", conn)\n\n\nquery_7","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:48:57.826659Z","iopub.execute_input":"2023-10-05T11:48:57.827540Z","iopub.status.idle":"2023-10-05T11:48:57.960360Z","shell.execute_reply.started":"2023-10-05T11:48:57.827494Z","shell.execute_reply":"2023-10-05T11:48:57.959519Z"},"trusted":true},"execution_count":19,"outputs":[{"execution_count":19,"output_type":"execute_result","data":{"text/plain":"         NWCG_REPORTING_UNIT_NAME LOCAL_INCIDENT_ID\n0          Plumas National Forest            PNF-47\n1        Eldorado National Forest                13\n2        Eldorado National Forest               021\n3        Eldorado National Forest                 6\n4        Eldorado National Forest                 7\n5        Eldorado National Forest                 8\n6        Eldorado National Forest                 9\n7  Shasta-Trinity National Forest                02\n8  Shasta-Trinity National Forest                03\n9        Eldorado National Forest                10","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>NWCG_REPORTING_UNIT_NAME</th>\n      <th>LOCAL_INCIDENT_ID</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Plumas National Forest</td>\n      <td>PNF-47</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Eldorado National Forest</td>\n      <td>13</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Eldorado National Forest</td>\n      <td>021</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Eldorado National Forest</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Eldorado National Forest</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Eldorado National Forest</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Eldorado National Forest</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Shasta-Trinity National Forest</td>\n      <td>02</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Shasta-Trinity National Forest</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Eldorado National Forest</td>\n      <td>10</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"query_8 = pd.read_sql(\"\"\"SELECT OWNER_DESCR\n                         FROM fires \n                         WHERE NWCG_REPORTING_UNIT_ID = 'USCAENF'\n                         UNION ALL \n                         SELECT DISTINCT LOCAL_INCIDENT_ID\n                         FROM fires\n                         WHERE SOURCE_REPORTING_UNIT_NAME = 'Eldorado National Forest'\n                         LIMIT 20;\"\"\", conn)\n\nquery_8 ","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:48:57.962270Z","iopub.execute_input":"2023-10-05T11:48:57.963149Z","iopub.status.idle":"2023-10-05T11:48:58.348093Z","shell.execute_reply.started":"2023-10-05T11:48:57.963106Z","shell.execute_reply":"2023-10-05T11:48:58.346501Z"},"trusted":true},"execution_count":20,"outputs":[{"execution_count":20,"output_type":"execute_result","data":{"text/plain":"              OWNER_DESCR\n0                    USFS\n1        STATE OR PRIVATE\n2                    USFS\n3                    USFS\n4                    USFS\n5                    USFS\n6                    USFS\n7                    USFS\n8                    USFS\n9                    USFS\n10  MISSING/NOT SPECIFIED\n11                   USFS\n12                   USFS\n13                   USFS\n14                   USFS\n15       STATE OR PRIVATE\n16       STATE OR PRIVATE\n17       STATE OR PRIVATE\n18       STATE OR PRIVATE\n19                   USFS","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>OWNER_DESCR</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>USFS</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>STATE OR PRIVATE</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>USFS</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>USFS</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>USFS</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>USFS</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>USFS</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>USFS</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>USFS</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>USFS</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>MISSING/NOT SPECIFIED</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>USFS</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>USFS</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>USFS</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>USFS</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>STATE OR PRIVATE</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>STATE OR PRIVATE</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>STATE OR PRIVATE</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>STATE OR PRIVATE</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>USFS</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"\n# removing the isnan values \n# converting the types \n# dropping the columns we do not want  \n\n\ncols_to_drop = [\n                'COUNTY',\n                'OBJECTID', \n                'FOD_ID', \n                'LOCAL_FIRE_REPORT_ID', \n                'LOCAL_INCIDENT_ID', \n                'ICS_209_INCIDENT_NUMBER',    \n                'ICS_209_NAME',\n                'MTBS_ID',\n                'MTBS_FIRE_NAME',\n                'FIRE_CODE',\n                'COMPLEX_NAME',\n                'Shape',\n                'FPA_ID',\n                'SOURCE_REPORTING_UNIT',\n                'SOURCE_SYSTEM',\n                'FIRE_NAME',\n                'CONT_DOY',\n                'DISCOVERY_DOY',\n                'DISCOVERY_TIME',\n                'OWNER_DESCR'\n]\n\ndf = df.drop(columns = cols_to_drop, axis=1)","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:48:58.349644Z","iopub.execute_input":"2023-10-05T11:48:58.350107Z","iopub.status.idle":"2023-10-05T11:48:59.901671Z","shell.execute_reply.started":"2023-10-05T11:48:58.350067Z","shell.execute_reply":"2023-10-05T11:48:59.900763Z"},"trusted":true},"execution_count":21,"outputs":[]},{"cell_type":"code","source":"# dataframe with dropped columns\ndf.head()","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:48:59.902939Z","iopub.execute_input":"2023-10-05T11:48:59.903596Z","iopub.status.idle":"2023-10-05T11:48:59.923969Z","shell.execute_reply.started":"2023-10-05T11:48:59.903563Z","shell.execute_reply":"2023-10-05T11:48:59.922614Z"},"trusted":true},"execution_count":22,"outputs":[{"execution_count":22,"output_type":"execute_result","data":{"text/plain":"  SOURCE_SYSTEM_TYPE NWCG_REPORTING_AGENCY NWCG_REPORTING_UNIT_ID  \\\n0                FED                    FS                USCAPNF   \n1                FED                    FS                USCAENF   \n2                FED                    FS                USCAENF   \n3                FED                    FS                USCAENF   \n4                FED                    FS                USCAENF   \n\n   NWCG_REPORTING_UNIT_NAME SOURCE_REPORTING_UNIT_NAME  FIRE_YEAR  \\\n0    Plumas National Forest     Plumas National Forest       2005   \n1  Eldorado National Forest   Eldorado National Forest       2004   \n2  Eldorado National Forest   Eldorado National Forest       2004   \n3  Eldorado National Forest   Eldorado National Forest       2004   \n4  Eldorado National Forest   Eldorado National Forest       2004   \n\n   DISCOVERY_DATE  STAT_CAUSE_CODE STAT_CAUSE_DESCR  CONT_DATE CONT_TIME  \\\n0      2453403.50             9.00    Miscellaneous 2453403.50      1730   \n1      2453137.50             1.00        Lightning 2453137.50      1530   \n2      2453156.50             5.00   Debris Burning 2453156.50      2024   \n3      2453184.50             1.00        Lightning 2453189.50      1400   \n4      2453184.50             1.00        Lightning 2453189.50      1200   \n\n   FIRE_SIZE FIRE_SIZE_CLASS  LATITUDE  LONGITUDE  OWNER_CODE STATE FIPS_CODE  \\\n0       0.10               A     40.04    -121.01        5.00    CA       063   \n1       0.25               A     38.93    -120.40        5.00    CA       061   \n2       0.10               A     38.98    -120.74       13.00    CA       017   \n3       0.10               A     38.56    -119.91        5.00    CA       003   \n4       0.10               A     38.56    -119.93        5.00    CA       003   \n\n   FIPS_NAME  \n0     Plumas  \n1     Placer  \n2  El Dorado  \n3     Alpine  \n4     Alpine  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>SOURCE_SYSTEM_TYPE</th>\n      <th>NWCG_REPORTING_AGENCY</th>\n      <th>NWCG_REPORTING_UNIT_ID</th>\n      <th>NWCG_REPORTING_UNIT_NAME</th>\n      <th>SOURCE_REPORTING_UNIT_NAME</th>\n      <th>FIRE_YEAR</th>\n      <th>DISCOVERY_DATE</th>\n      <th>STAT_CAUSE_CODE</th>\n      <th>STAT_CAUSE_DESCR</th>\n      <th>CONT_DATE</th>\n      <th>CONT_TIME</th>\n      <th>FIRE_SIZE</th>\n      <th>FIRE_SIZE_CLASS</th>\n      <th>LATITUDE</th>\n      <th>LONGITUDE</th>\n      <th>OWNER_CODE</th>\n      <th>STATE</th>\n      <th>FIPS_CODE</th>\n      <th>FIPS_NAME</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>FED</td>\n      <td>FS</td>\n      <td>USCAPNF</td>\n      <td>Plumas National Forest</td>\n      <td>Plumas National Forest</td>\n      <td>2005</td>\n      <td>2453403.50</td>\n      <td>9.00</td>\n      <td>Miscellaneous</td>\n      <td>2453403.50</td>\n      <td>1730</td>\n      <td>0.10</td>\n      <td>A</td>\n      <td>40.04</td>\n      <td>-121.01</td>\n      <td>5.00</td>\n      <td>CA</td>\n      <td>063</td>\n      <td>Plumas</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>FED</td>\n      <td>FS</td>\n      <td>USCAENF</td>\n      <td>Eldorado National Forest</td>\n      <td>Eldorado National Forest</td>\n      <td>2004</td>\n      <td>2453137.50</td>\n      <td>1.00</td>\n      <td>Lightning</td>\n      <td>2453137.50</td>\n      <td>1530</td>\n      <td>0.25</td>\n      <td>A</td>\n      <td>38.93</td>\n      <td>-120.40</td>\n      <td>5.00</td>\n      <td>CA</td>\n      <td>061</td>\n      <td>Placer</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>FED</td>\n      <td>FS</td>\n      <td>USCAENF</td>\n      <td>Eldorado National Forest</td>\n      <td>Eldorado National Forest</td>\n      <td>2004</td>\n      <td>2453156.50</td>\n      <td>5.00</td>\n      <td>Debris Burning</td>\n      <td>2453156.50</td>\n      <td>2024</td>\n      <td>0.10</td>\n      <td>A</td>\n      <td>38.98</td>\n      <td>-120.74</td>\n      <td>13.00</td>\n      <td>CA</td>\n      <td>017</td>\n      <td>El Dorado</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>FED</td>\n      <td>FS</td>\n      <td>USCAENF</td>\n      <td>Eldorado National Forest</td>\n      <td>Eldorado National Forest</td>\n      <td>2004</td>\n      <td>2453184.50</td>\n      <td>1.00</td>\n      <td>Lightning</td>\n      <td>2453189.50</td>\n      <td>1400</td>\n      <td>0.10</td>\n      <td>A</td>\n      <td>38.56</td>\n      <td>-119.91</td>\n      <td>5.00</td>\n      <td>CA</td>\n      <td>003</td>\n      <td>Alpine</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>FED</td>\n      <td>FS</td>\n      <td>USCAENF</td>\n      <td>Eldorado National Forest</td>\n      <td>Eldorado National Forest</td>\n      <td>2004</td>\n      <td>2453184.50</td>\n      <td>1.00</td>\n      <td>Lightning</td>\n      <td>2453189.50</td>\n      <td>1200</td>\n      <td>0.10</td>\n      <td>A</td>\n      <td>38.56</td>\n      <td>-119.93</td>\n      <td>5.00</td>\n      <td>CA</td>\n      <td>003</td>\n      <td>Alpine</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"<div style=\"color:white;display:fill;border-radius:8px;\n            background-color:#8F00FF;font-size:150%;\n            font-family:Nexa;letter-spacing:0.5px\">\n    <p style=\"padding: 5px;color:white;\"><b></b></p>\n</div>\n<a id='Introduction'></a>\n\n#  <b><span style='color:#8F00FF'>| 6. </span> Visualization EDA for GeoSpatial analysis </b>\n[Table of contents](#table)\n","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport seaborn as sns\nfrom matplotlib import pyplot as plt\nfrom tqdm.notebook import tqdm\n\nf, ax = plt.subplots(figsize=(10, 5))\nax = sns.histplot(data=data, y='STAT_CAUSE_DESCR', color='orange') \nax.set_xlabel('The cause of the Fire')\nax.set_ylabel('The count of the cause of Fire')\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:48:59.926118Z","iopub.execute_input":"2023-10-05T11:48:59.927047Z","iopub.status.idle":"2023-10-05T11:49:00.710247Z","shell.execute_reply.started":"2023-10-05T11:48:59.926994Z","shell.execute_reply":"2023-10-05T11:49:00.709157Z"},"trusted":true},"execution_count":23,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x500 with 1 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"},"metadata":{}}]},{"cell_type":"code","source":"# Fire Groupe - hist plot stacking version \nf, ax = plt.subplots(figsize=(10, 5))\nax = sns.histplot(data=data, y='FIRE_SIZE_CLASS', color='orange', hue='STAT_CAUSE_DESCR', multiple='stack')\nax.set_xlabel('Fire Size')\nax.set_ylabel('State of Cause')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:49:00.711729Z","iopub.execute_input":"2023-10-05T11:49:00.712399Z","iopub.status.idle":"2023-10-05T11:49:01.249094Z","shell.execute_reply.started":"2023-10-05T11:49:00.712358Z","shell.execute_reply":"2023-10-05T11:49:01.247966Z"},"trusted":true},"execution_count":24,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x500 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"# sql analysis with python\n# group based on this - count based on the other column\n\npd.options.display.max_rows = None\n\n# here we group by STAT_CAUSE_CODE and FIRE_SIZE\n# as we can see the state 27 has the highest number of fires occurent \ndata.groupby(['STAT_CAUSE_CODE', 'FIRE_SIZE']).count()['STATE']","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:49:01.250989Z","iopub.execute_input":"2023-10-05T11:49:01.251816Z","iopub.status.idle":"2023-10-05T11:49:01.265466Z","shell.execute_reply.started":"2023-10-05T11:49:01.251770Z","shell.execute_reply":"2023-10-05T11:49:01.263947Z"},"trusted":true},"execution_count":25,"outputs":[{"execution_count":25,"output_type":"execute_result","data":{"text/plain":"STAT_CAUSE_CODE  FIRE_SIZE\n1.00             0.10         27\n                 0.20          2\n                 0.25          4\n                 0.30          1\n                 0.50          3\n                 2.00          1\n                 2.50          1\n                 10.00         1\n                 45.60         1\n2.00             0.10          2\n                 0.60          1\n                 7700.00       1\n                 16823.00      1\n4.00             0.10         12\n                 0.50          1\n                 5.00          1\n                 6.00          1\n                 9.00          1\n5.00             0.10          6\n                 0.25          3\n                 0.50          1\n                 0.80          1\n                 1.00          1\n                 2.00          1\n                 3.00          1\n                 10.00         1\n                 17.00         1\n                 25.00         1\n                 125.00        1\n6.00             0.10          1\n7.00             0.01          1\n                 0.10          3\n                 50.30         1\n8.00             0.50          1\n9.00             0.10          7\n                 0.25          1\n                 0.30          1\n                 3.00          1\n                 5.00          1\n                 8.20          1\n                 10.00         1\nName: STATE, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"data_an = data.groupby('NWCG_REPORTING_UNIT_NAME', as_index=False).count().sort_values('FIRE_SIZE', ascending=False) \ndata_an.head()","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:49:01.267389Z","iopub.execute_input":"2023-10-05T11:49:01.268229Z","iopub.status.idle":"2023-10-05T11:49:01.302130Z","shell.execute_reply.started":"2023-10-05T11:49:01.268185Z","shell.execute_reply":"2023-10-05T11:49:01.301290Z"},"trusted":true},"execution_count":26,"outputs":[{"execution_count":26,"output_type":"execute_result","data":{"text/plain":"              NWCG_REPORTING_UNIT_NAME  OBJECTID  FOD_ID  FPA_ID  \\\n10            Kootenai National Forest        18      18      18   \n6             Eldorado National Forest        17      17      17   \n11             Lincoln National Forest        16      16      16   \n7       Fremont-Winema National Forest         9       9       9   \n13  National Forests in North Carolina         8       8       8   \n\n    SOURCE_SYSTEM_TYPE  SOURCE_SYSTEM  NWCG_REPORTING_AGENCY  \\\n10                  18             18                     18   \n6                   17             17                     17   \n11                  16             16                     16   \n7                    9              9                      9   \n13                   8              8                      8   \n\n    NWCG_REPORTING_UNIT_ID  SOURCE_REPORTING_UNIT  SOURCE_REPORTING_UNIT_NAME  \\\n10                      18                     18                          18   \n6                       17                     17                          17   \n11                      16                     16                          16   \n7                        9                      9                           9   \n13                       8                      8                           8   \n\n    LOCAL_FIRE_REPORT_ID  LOCAL_INCIDENT_ID  FIRE_CODE  FIRE_NAME  \\\n10                    18                 18         18         18   \n6                     17                 17         17         17   \n11                    16                 16         16         16   \n7                      9                  9          9          9   \n13                     8                  8          8          8   \n\n    ICS_209_INCIDENT_NUMBER  ICS_209_NAME  MTBS_ID  MTBS_FIRE_NAME  \\\n10                       18            18       18              18   \n6                        17            17       17              17   \n11                       16            16       16              16   \n7                         9             9        9               9   \n13                        8             8        8               8   \n\n    COMPLEX_NAME  FIRE_YEAR  DISCOVERY_DATE  DISCOVERY_DOY  DISCOVERY_TIME  \\\n10            18         18              18             18              18   \n6             17         17              17             17              17   \n11            16         16              16             16              16   \n7              9          9               9              9               9   \n13             8          8               8              8               8   \n\n    STAT_CAUSE_CODE  STAT_CAUSE_DESCR  CONT_DATE  CONT_DOY  CONT_TIME  \\\n10               18                18         17        17         17   \n6                17                17         17        17         17   \n11               16                16         16        16         16   \n7                 9                 9          9         9          9   \n13                8                 8          8         8          8   \n\n    FIRE_SIZE  FIRE_SIZE_CLASS  LATITUDE  LONGITUDE  OWNER_CODE  OWNER_DESCR  \\\n10         18               18        18         18          18           18   \n6          17               17        17         17          17           17   \n11         16               16        16         16          16           16   \n7           9                9         9          9           9            9   \n13          8                8         8          8           8            8   \n\n    STATE  COUNTY  FIPS_CODE  FIPS_NAME  Shape  \n10     18      18         18         18     18  \n6      17      16         16         17     17  \n11     16      16         16         16     16  \n7       9       9          9          9      9  \n13      8       8          8          8      8  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>NWCG_REPORTING_UNIT_NAME</th>\n      <th>OBJECTID</th>\n      <th>FOD_ID</th>\n      <th>FPA_ID</th>\n      <th>SOURCE_SYSTEM_TYPE</th>\n      <th>SOURCE_SYSTEM</th>\n      <th>NWCG_REPORTING_AGENCY</th>\n      <th>NWCG_REPORTING_UNIT_ID</th>\n      <th>SOURCE_REPORTING_UNIT</th>\n      <th>SOURCE_REPORTING_UNIT_NAME</th>\n      <th>LOCAL_FIRE_REPORT_ID</th>\n      <th>LOCAL_INCIDENT_ID</th>\n      <th>FIRE_CODE</th>\n      <th>FIRE_NAME</th>\n      <th>ICS_209_INCIDENT_NUMBER</th>\n      <th>ICS_209_NAME</th>\n      <th>MTBS_ID</th>\n      <th>MTBS_FIRE_NAME</th>\n      <th>COMPLEX_NAME</th>\n      <th>FIRE_YEAR</th>\n      <th>DISCOVERY_DATE</th>\n      <th>DISCOVERY_DOY</th>\n      <th>DISCOVERY_TIME</th>\n      <th>STAT_CAUSE_CODE</th>\n      <th>STAT_CAUSE_DESCR</th>\n      <th>CONT_DATE</th>\n      <th>CONT_DOY</th>\n      <th>CONT_TIME</th>\n      <th>FIRE_SIZE</th>\n      <th>FIRE_SIZE_CLASS</th>\n      <th>LATITUDE</th>\n      <th>LONGITUDE</th>\n      <th>OWNER_CODE</th>\n      <th>OWNER_DESCR</th>\n      <th>STATE</th>\n      <th>COUNTY</th>\n      <th>FIPS_CODE</th>\n      <th>FIPS_NAME</th>\n      <th>Shape</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>10</th>\n      <td>Kootenai National Forest</td>\n      <td>18</td>\n      <td>18</td>\n      <td>18</td>\n      <td>18</td>\n      <td>18</td>\n      <td>18</td>\n      <td>18</td>\n      <td>18</td>\n      <td>18</td>\n      <td>18</td>\n      <td>18</td>\n      <td>18</td>\n      <td>18</td>\n      <td>18</td>\n      <td>18</td>\n      <td>18</td>\n      <td>18</td>\n      <td>18</td>\n      <td>18</td>\n      <td>18</td>\n      <td>18</td>\n      <td>18</td>\n      <td>18</td>\n      <td>18</td>\n      <td>17</td>\n      <td>17</td>\n      <td>17</td>\n      <td>18</td>\n      <td>18</td>\n      <td>18</td>\n      <td>18</td>\n      <td>18</td>\n      <td>18</td>\n      <td>18</td>\n      <td>18</td>\n      <td>18</td>\n      <td>18</td>\n      <td>18</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Eldorado National Forest</td>\n      <td>17</td>\n      <td>17</td>\n      <td>17</td>\n      <td>17</td>\n      <td>17</td>\n      <td>17</td>\n      <td>17</td>\n      <td>17</td>\n      <td>17</td>\n      <td>17</td>\n      <td>17</td>\n      <td>17</td>\n      <td>17</td>\n      <td>17</td>\n      <td>17</td>\n      <td>17</td>\n      <td>17</td>\n      <td>17</td>\n      <td>17</td>\n      <td>17</td>\n      <td>17</td>\n      <td>17</td>\n      <td>17</td>\n      <td>17</td>\n      <td>17</td>\n      <td>17</td>\n      <td>17</td>\n      <td>17</td>\n      <td>17</td>\n      <td>17</td>\n      <td>17</td>\n      <td>17</td>\n      <td>17</td>\n      <td>17</td>\n      <td>16</td>\n      <td>16</td>\n      <td>17</td>\n      <td>17</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Lincoln National Forest</td>\n      <td>16</td>\n      <td>16</td>\n      <td>16</td>\n      <td>16</td>\n      <td>16</td>\n      <td>16</td>\n      <td>16</td>\n      <td>16</td>\n      <td>16</td>\n      <td>16</td>\n      <td>16</td>\n      <td>16</td>\n      <td>16</td>\n      <td>16</td>\n      <td>16</td>\n      <td>16</td>\n      <td>16</td>\n      <td>16</td>\n      <td>16</td>\n      <td>16</td>\n      <td>16</td>\n      <td>16</td>\n      <td>16</td>\n      <td>16</td>\n      <td>16</td>\n      <td>16</td>\n      <td>16</td>\n      <td>16</td>\n      <td>16</td>\n      <td>16</td>\n      <td>16</td>\n      <td>16</td>\n      <td>16</td>\n      <td>16</td>\n      <td>16</td>\n      <td>16</td>\n      <td>16</td>\n      <td>16</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Fremont-Winema National Forest</td>\n      <td>9</td>\n      <td>9</td>\n      <td>9</td>\n      <td>9</td>\n      <td>9</td>\n      <td>9</td>\n      <td>9</td>\n      <td>9</td>\n      <td>9</td>\n      <td>9</td>\n      <td>9</td>\n      <td>9</td>\n      <td>9</td>\n      <td>9</td>\n      <td>9</td>\n      <td>9</td>\n      <td>9</td>\n      <td>9</td>\n      <td>9</td>\n      <td>9</td>\n      <td>9</td>\n      <td>9</td>\n      <td>9</td>\n      <td>9</td>\n      <td>9</td>\n      <td>9</td>\n      <td>9</td>\n      <td>9</td>\n      <td>9</td>\n      <td>9</td>\n      <td>9</td>\n      <td>9</td>\n      <td>9</td>\n      <td>9</td>\n      <td>9</td>\n      <td>9</td>\n      <td>9</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>National Forests in North Carolina</td>\n      <td>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>8</td>\n      <td>8</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"\nacres_by_year = pd.DataFrame(fires.groupby(['FIRE_YEAR']).FIRE_SIZE.sum().sort_values(ascending=False)).reset_index() \n\nacres_by_year.rename(columns={'FIRE_SIZE': 'Acres'}, inplace=True) \n\nalt.Chart(acres_by_year).mark_line().encode( \n    alt.X('FIRE_YEAR:N', title=None), \n    alt.Y('Acres'), \n).properties(\n    title=f'Acres Burned by Year', \n    width = 600, \n    height =300, \n).configure_axis(\n    labelFontSize=14, \n    titleFontSize=14\n)\n","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:49:01.303507Z","iopub.execute_input":"2023-10-05T11:49:01.304082Z","iopub.status.idle":"2023-10-05T11:49:01.421791Z","shell.execute_reply.started":"2023-10-05T11:49:01.304051Z","shell.execute_reply":"2023-10-05T11:49:01.421053Z"},"trusted":true},"execution_count":27,"outputs":[{"execution_count":27,"output_type":"execute_result","data":{"text/html":"\n<style>\n  #altair-viz-cf356d6296d7494aad2f1cbf8f5bf70f.vega-embed {\n    width: 100%;\n    display: flex;\n  }\n\n  #altair-viz-cf356d6296d7494aad2f1cbf8f5bf70f.vega-embed details,\n  #altair-viz-cf356d6296d7494aad2f1cbf8f5bf70f.vega-embed details summary {\n    position: relative;\n  }\n</style>\n<div id=\"altair-viz-cf356d6296d7494aad2f1cbf8f5bf70f\"></div>\n<script type=\"text/javascript\">\n  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n  (function(spec, embedOpt){\n    let outputDiv = 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Caused By Lightning Induced Fires\", \"width\": 600, \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.14.1.json\", \"datasets\": {\"data-2b15e215c13d15a666082aa615ecaa02\": [{\"FIRE_YEAR\": 1992, \"frac_acres_lightning\": 0.45779059852152143}, {\"FIRE_YEAR\": 1993, \"frac_acres_lightning\": 0.5075343151958105}, {\"FIRE_YEAR\": 1994, \"frac_acres_lightning\": 0.5814679231184071}, {\"FIRE_YEAR\": 1995, \"frac_acres_lightning\": 0.4056171890769122}, {\"FIRE_YEAR\": 1996, \"frac_acres_lightning\": 0.5410995929930849}, {\"FIRE_YEAR\": 1997, \"frac_acres_lightning\": 0.69006125612426}, {\"FIRE_YEAR\": 1998, \"frac_acres_lightning\": 0.41573486760089523}, {\"FIRE_YEAR\": 1999, \"frac_acres_lightning\": 0.6022525922613295}, {\"FIRE_YEAR\": 2000, \"frac_acres_lightning\": 0.6407824230513731}, {\"FIRE_YEAR\": 2001, \"frac_acres_lightning\": 0.5248223585510707}, {\"FIRE_YEAR\": 2002, \"frac_acres_lightning\": 0.588085163159009}, {\"FIRE_YEAR\": 2003, \"frac_acres_lightning\": 0.5418844913603091}, {\"FIRE_YEAR\": 2004, \"frac_acres_lightning\": 0.8720439267895918}, {\"FIRE_YEAR\": 2005, \"frac_acres_lightning\": 0.7868361300047169}, {\"FIRE_YEAR\": 2006, \"frac_acres_lightning\": 0.5532367089577542}, {\"FIRE_YEAR\": 2007, \"frac_acres_lightning\": 0.6447267564522882}, {\"FIRE_YEAR\": 2008, \"frac_acres_lightning\": 0.4135742091011213}, {\"FIRE_YEAR\": 2009, \"frac_acres_lightning\": 0.6501403696449661}, {\"FIRE_YEAR\": 2010, \"frac_acres_lightning\": 0.581295480583084}, {\"FIRE_YEAR\": 2011, \"frac_acres_lightning\": 0.3912244171823675}, {\"FIRE_YEAR\": 2012, \"frac_acres_lightning\": 0.7292254268684234}, {\"FIRE_YEAR\": 2013, \"frac_acres_lightning\": 0.668253474953367}, {\"FIRE_YEAR\": 2014, \"frac_acres_lightning\": 0.5644367262709098}, {\"FIRE_YEAR\": 2015, \"frac_acres_lightning\": 0.8023380005939935}]}}, {\"mode\": \"vega-lite\"});\n</script>","text/plain":"alt.Chart(...)"},"metadata":{}}]},{"cell_type":"code","source":"# Most common Fire Type  \nimport seaborn as sns \nfrom matplotlib import pyplot as plt \n\nsns.set_style(\"darkgrid\")\nf, ax = plt.subplots(figsize=(20, 10))\nax = sns.histplot(data=data, x='DISCOVERY_DATE', bins=50, color='orange')\nax.set_xlabel('The Date of Fire Discovery')\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:51:49.499894Z","iopub.execute_input":"2023-10-05T11:51:49.500295Z","iopub.status.idle":"2023-10-05T11:51:49.964432Z","shell.execute_reply.started":"2023-10-05T11:51:49.500264Z","shell.execute_reply":"2023-10-05T11:51:49.963048Z"},"trusted":true},"execution_count":52,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 2000x1000 with 1 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style=\"color:white;display:fill;border-radius:8px;\n            background-color:#8F00FF;font-size:150%;\n            font-family:Nexa;letter-spacing:0.5px\">\n    <p style=\"padding: 5px;color:white;\"><b></b></p>\n</div>\n<a id='Introduction'></a>\n\n#  <b><span style='color:#8F00FF'>| 8. </span> Queries with Python GeoSpatial </b>\n[Table of contents](#table)\n\n","metadata":{}},{"cell_type":"code","source":"grouped_data = data[['STAT_CAUSE_DESCR', 'Shape']].groupby('STAT_CAUSE_DESCR').count()\ngrouped_data","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:49:02.613319Z","iopub.execute_input":"2023-10-05T11:49:02.614043Z","iopub.status.idle":"2023-10-05T11:49:02.626996Z","shell.execute_reply.started":"2023-10-05T11:49:02.614000Z","shell.execute_reply":"2023-10-05T11:49:02.625782Z"},"trusted":true},"execution_count":30,"outputs":[{"execution_count":30,"output_type":"execute_result","data":{"text/plain":"                  Shape\nSTAT_CAUSE_DESCR       \n0                     5\n1                    16\n2                     1\n3                    18\n4                     5\n5                    41\n6                    13\n7                     1","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Shape</th>\n    </tr>\n    <tr>\n      <th>STAT_CAUSE_DESCR</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>16</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>18</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>41</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>13</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"<div style=\"color:white;display:fill;border-radius:8px;\n            background-color:#8F00FF;font-size:150%;\n            font-family:Nexa;letter-spacing:0.5px\">\n    <p style=\"padding: 5px;color:white;\"><b></b></p>\n</div>\n<a id='Introduction'></a>\n\n#  <b><span style='color:#8F00FF'>| 9. </span> Fire Incedent Distribution </b>\n[Table of contents](#table)\n\n\n","metadata":{}},{"cell_type":"code","source":"# pie chart version 1\n\nsns.set_style(\"darkgrid\") \nf, ax = plt.subplots(figsize=(20, 10))\n\nexplode = (0, 0.1, 0, 0)\ncolors = ['#ff9999','#66b3ff','#99ff99','#ffcc99']  \n\nax.pie(grouped_data.Shape, labels=grouped_data.index, colors=colors, autopct='%1.1f%%',\n        shadow=False, startangle=90)\nax.set_facecolor('lightgrey')\nax.set_xlabel('Distribution of fire shapes') \nplt.tight_layout()\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2023-10-05T12:35:22.476008Z","iopub.execute_input":"2023-10-05T12:35:22.476369Z","iopub.status.idle":"2023-10-05T12:35:22.849142Z","shell.execute_reply.started":"2023-10-05T12:35:22.476341Z","shell.execute_reply":"2023-10-05T12:35:22.847826Z"},"trusted":true},"execution_count":65,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 2000x1000 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"pip install basemap","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:49:02.870775Z","iopub.execute_input":"2023-10-05T11:49:02.871346Z","iopub.status.idle":"2023-10-05T11:49:16.449915Z","shell.execute_reply.started":"2023-10-05T11:49:02.871306Z","shell.execute_reply":"2023-10-05T11:49:16.448885Z"},"trusted":true},"execution_count":32,"outputs":[{"name":"stdout","text":"Collecting basemap\n  Downloading basemap-1.3.8-cp310-cp310-manylinux1_x86_64.whl (860 kB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m860.7/860.7 kB\u001b[0m \u001b[31m12.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n\u001b[?25hCollecting basemap-data<1.4,>=1.3.2 (from basemap)\n  Downloading basemap_data-1.3.2-py2.py3-none-any.whl (30.5 MB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m30.5/30.5 MB\u001b[0m \u001b[31m38.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n\u001b[?25hRequirement already satisfied: pyshp<2.4,>=1.2 in /opt/conda/lib/python3.10/site-packages (from basemap) (2.3.1)\nRequirement already satisfied: matplotlib<3.8,>=1.5 in /opt/conda/lib/python3.10/site-packages (from basemap) (3.7.2)\nRequirement already satisfied: pyproj<3.7.0,>=1.9.3 in /opt/conda/lib/python3.10/site-packages (from basemap) (3.6.0)\nRequirement already satisfied: numpy<1.26,>=1.21 in /opt/conda/lib/python3.10/site-packages (from basemap) (1.23.5)\nRequirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib<3.8,>=1.5->basemap) (1.1.0)\nRequirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.10/site-packages (from matplotlib<3.8,>=1.5->basemap) (0.11.0)\nRequirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib<3.8,>=1.5->basemap) (4.40.0)\nRequirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib<3.8,>=1.5->basemap) (1.4.4)\nRequirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib<3.8,>=1.5->basemap) (21.3)\nRequirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib<3.8,>=1.5->basemap) (9.5.0)\nRequirement already satisfied: pyparsing<3.1,>=2.3.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib<3.8,>=1.5->basemap) (3.0.9)\nRequirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.10/site-packages (from matplotlib<3.8,>=1.5->basemap) (2.8.2)\nRequirement already satisfied: certifi in /opt/conda/lib/python3.10/site-packages (from pyproj<3.7.0,>=1.9.3->basemap) (2023.7.22)\nRequirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib<3.8,>=1.5->basemap) (1.16.0)\nInstalling collected packages: basemap-data, basemap\nSuccessfully installed basemap-1.3.8 basemap-data-1.3.2\nNote: you may need to restart the kernel to use updated packages.\n","output_type":"stream"}]},{"cell_type":"code","source":"# continue \nfrom mpl_toolkits.basemap import Basemap\nimport matplotlib.pyplot as plt\n\n# creating the map \nm = Basemap(projection = 'mill', llcrnrlat = -90, urcrnrlat = 90, llcrnrlon = -180, urcrnrlon = 180, resolution = 'c')\n\nm.drawcoastlines() \nm.drawcountries(color='gray')\nm.drawstates(color='gray') \n\n\n# creating variable for latitude and longituse to list \n\nlatitude = data['LATITUDE'].tolist()\nlongitude = data['LONGITUDE'].tolist() \n\nm.scatter(longitude, latitude, latlon = True, s=10, c='red', marker  ='o', alpha = 1 ) \n\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:49:16.451164Z","iopub.execute_input":"2023-10-05T11:49:16.451518Z","iopub.status.idle":"2023-10-05T11:49:17.428748Z","shell.execute_reply.started":"2023-10-05T11:49:16.451489Z","shell.execute_reply":"2023-10-05T11:49:17.427347Z"},"trusted":true},"execution_count":33,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"<div style=\"color:white;display:fill;border-radius:8px;\n            background-color:#8F00FF;font-size:150%;\n            font-family:Nexa;letter-spacing:0.5px\">\n    <p style=\"padding: 5px;color:white;\"><b></b></p>\n</div>\n<a id='Introduction'></a>\n\n#  <b><span style='color:#8F00FF'>| 10. </span> Folium GeoSpatial Representation </b>\n[Table of contents](#table)\n\n","metadata":{}},{"cell_type":"code","source":"# the folium map \n\nimport pandas as pd\nimport folium \n\nlatitude = data['LATITUDE'].fillna(0).tolist()\nlongitude = data['LONGITUDE'].fillna(0).tolist()\n\ncenter = [20.593684,78.96288] \n\nmap = folium.Map(location = center, zoom_start =2)\n","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:49:17.430268Z","iopub.execute_input":"2023-10-05T11:49:17.430784Z","iopub.status.idle":"2023-10-05T11:49:17.689208Z","shell.execute_reply.started":"2023-10-05T11:49:17.430740Z","shell.execute_reply":"2023-10-05T11:49:17.688167Z"},"trusted":true},"execution_count":34,"outputs":[]},{"cell_type":"code","source":"for i, j in data.iterrows(): \n    location = [j['LATITUDE'], j['LONGITUDE']] \n    folium.Marker(location, popup = f'Country:{j[\"STATE\"]}').add_to(map)","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:49:17.690645Z","iopub.execute_input":"2023-10-05T11:49:17.691339Z","iopub.status.idle":"2023-10-05T11:49:17.721060Z","shell.execute_reply.started":"2023-10-05T11:49:17.691296Z","shell.execute_reply":"2023-10-05T11:49:17.720072Z"},"trusted":true},"execution_count":35,"outputs":[]},{"cell_type":"code","source":"map","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:49:17.722869Z","iopub.execute_input":"2023-10-05T11:49:17.723298Z","iopub.status.idle":"2023-10-05T11:49:17.857473Z","shell.execute_reply.started":"2023-10-05T11:49:17.723257Z","shell.execute_reply":"2023-10-05T11:49:17.856053Z"},"trusted":true},"execution_count":36,"outputs":[{"execution_count":36,"output_type":"execute_result","data":{"text/plain":"<folium.folium.Map at 0x7a6dc31acb50>","text/html":"<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span 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L.marker(\n                [38.45361111, -119.45861111],\n                {}\n            ).addTo(map_790463855dbaefc9037d943e43f0e330);\n        \n    \n        var popup_838ac05b6857bf4e19c584d91a4161ad = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n\n        \n            \n                var html_2f903aeab248b243fcbc0389e8620cb9 = $(`&lt;div id=&quot;html_2f903aeab248b243fcbc0389e8620cb9&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Country:1&lt;/div&gt;`)[0];\n                popup_838ac05b6857bf4e19c584d91a4161ad.setContent(html_2f903aeab248b243fcbc0389e8620cb9);\n            \n        \n\n        marker_8c3dbd1940946eff5a56f96694697161.bindPopup(popup_838ac05b6857bf4e19c584d91a4161ad)\n        ;\n\n        \n    \n    \n            var marker_44087b05b767c5baed3d4b24329366d4 = L.marker(\n                [36.66944444, -107.25361111],\n                {}\n            ).addTo(map_790463855dbaefc9037d943e43f0e330);\n        \n    \n        var popup_542642ad0bbdf10936645908f41e4ea0 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n\n        \n            \n                var html_d681ece30b0337608d7f2be57ab19a1f = $(`&lt;div id=&quot;html_d681ece30b0337608d7f2be57ab19a1f&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Country:5&lt;/div&gt;`)[0];\n                popup_542642ad0bbdf10936645908f41e4ea0.setContent(html_d681ece30b0337608d7f2be57ab19a1f);\n            \n        \n\n        marker_44087b05b767c5baed3d4b24329366d4.bindPopup(popup_542642ad0bbdf10936645908f41e4ea0)\n        ;\n\n        \n    \n    \n            var marker_8325cc6e56b85bba773f044f99a2eaf3 = L.marker(\n                [36.87638889, -107.21916667],\n                {}\n            ).addTo(map_790463855dbaefc9037d943e43f0e330);\n        \n    \n        var popup_09b652f6ae3a063baf08fda516d41424 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n\n        \n            \n                var html_b81906e17524b8ebb92a264dd2c52f78 = $(`&lt;div id=&quot;html_b81906e17524b8ebb92a264dd2c52f78&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Country:5&lt;/div&gt;`)[0];\n                popup_09b652f6ae3a063baf08fda516d41424.setContent(html_b81906e17524b8ebb92a264dd2c52f78);\n            \n        \n\n        marker_8325cc6e56b85bba773f044f99a2eaf3.bindPopup(popup_09b652f6ae3a063baf08fda516d41424)\n        ;\n\n        \n    \n    \n            var marker_0e7063fa23f36e31e681e70071c529b4 = L.marker(\n                [48.45833333, -115.66666667],\n                {}\n            ).addTo(map_790463855dbaefc9037d943e43f0e330);\n        \n    \n        var popup_8c49a6a599a3d59b749db7de6baa4123 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n\n        \n            \n                var html_a94cfe77bf01672831df946b77f8aa25 = $(`&lt;div id=&quot;html_a94cfe77bf01672831df946b77f8aa25&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Country:3&lt;/div&gt;`)[0];\n                popup_8c49a6a599a3d59b749db7de6baa4123.setContent(html_a94cfe77bf01672831df946b77f8aa25);\n            \n        \n\n        marker_0e7063fa23f36e31e681e70071c529b4.bindPopup(popup_8c49a6a599a3d59b749db7de6baa4123)\n        ;\n\n        \n    \n&lt;/script&gt;\n&lt;/html&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"import folium\nfrom folium import Marker\nfrom folium.plugins import MarkerCluster\nimport math\n\n# add a circle maker \nv_map = folium.Map(location=[42.32,-71.0589], tiles='cartodbpositron', zoom_start=4) \n\n# Add points to the map\nmc = MarkerCluster()\nfor idx, row in data.iterrows(): \n    if not math.isnan(row['LONGITUDE']) and not math.isnan(row['LATITUDE']):\n        mc.add_child(Marker(location=[row['LATITUDE'], row['LONGITUDE']],\n                            tooltip=str(round(row['LATITUDE']*100, 2))+\"%\"))\nv_map.add_child(mc)\n\n# Display the map\nv_map","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:49:17.858775Z","iopub.execute_input":"2023-10-05T11:49:17.859082Z","iopub.status.idle":"2023-10-05T11:49:18.155981Z","shell.execute_reply.started":"2023-10-05T11:49:17.859058Z","shell.execute_reply":"2023-10-05T11:49:18.154761Z"},"trusted":true},"execution_count":37,"outputs":[{"execution_count":37,"output_type":"execute_result","data":{"text/plain":"<folium.folium.Map at 0x7a6e678f2bf0>","text/html":"<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe srcdoc=\"&lt;!DOCTYPE html&gt;\n&lt;html&gt;\n&lt;head&gt;\n    \n    &lt;meta http-equiv=&quot;content-type&quot; content=&quot;text/html; charset=UTF-8&quot; /&gt;\n    \n        &lt;script&gt;\n            L_NO_TOUCH = false;\n            L_DISABLE_3D = false;\n        &lt;/script&gt;\n    \n    &lt;style&gt;html, body {width: 100%;height: 100%;margin: 0;padding: 0;}&lt;/style&gt;\n    &lt;style&gt;#map {position:absolute;top:0;bottom:0;right:0;left:0;}&lt;/style&gt;\n    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.js&quot;&gt;&lt;/script&gt;\n    &lt;script src=&quot;https://code.jquery.com/jquery-1.12.4.min.js&quot;&gt;&lt;/script&gt;\n    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/js/bootstrap.bundle.min.js&quot;&gt;&lt;/script&gt;\n    &lt;script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js&quot;&gt;&lt;/script&gt;\n    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.css&quot;/&gt;\n    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/css/bootstrap.min.css&quot;/&gt;\n    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://netdna.bootstrapcdn.com/bootstrap/3.0.0/css/bootstrap.min.css&quot;/&gt;\n    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/@fortawesome/fontawesome-free@6.2.0/css/all.min.css&quot;/&gt;\n    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css&quot;/&gt;\n    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/gh/python-visualization/folium/folium/templates/leaflet.awesome.rotate.min.css&quot;/&gt;\n    \n            &lt;meta name=&quot;viewport&quot; content=&quot;width=device-width,\n                initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; /&gt;\n            &lt;style&gt;\n                #map_76e0a1dc2e05396cf1073ffab27ff8b8 {\n                    position: relative;\n                    width: 100.0%;\n                    height: 100.0%;\n                    left: 0.0%;\n                    top: 0.0%;\n                }\n                .leaflet-container { font-size: 1rem; }\n            &lt;/style&gt;\n        \n    &lt;script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/leaflet.markercluster/1.1.0/leaflet.markercluster.js&quot;&gt;&lt;/script&gt;\n    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/leaflet.markercluster/1.1.0/MarkerCluster.css&quot;/&gt;\n    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/leaflet.markercluster/1.1.0/MarkerCluster.Default.css&quot;/&gt;\n&lt;/head&gt;\n&lt;body&gt;\n    \n    \n            &lt;div class=&quot;folium-map&quot; id=&quot;map_76e0a1dc2e05396cf1073ffab27ff8b8&quot; &gt;&lt;/div&gt;\n        \n&lt;/body&gt;\n&lt;script&gt;\n    \n    \n            var map_76e0a1dc2e05396cf1073ffab27ff8b8 = L.map(\n                &quot;map_76e0a1dc2e05396cf1073ffab27ff8b8&quot;,\n                {\n                    center: [42.32, -71.0589],\n                    crs: L.CRS.EPSG3857,\n                    zoom: 4,\n                    zoomControl: true,\n                    preferCanvas: false,\n                }\n            );\n\n            \n\n        \n    \n            var tile_layer_ea2bb289f4c101ff92a7683b4f65c9f4 = L.tileLayer(\n                &quot;https://cartodb-basemaps-{s}.global.ssl.fastly.net/light_all/{z}/{x}/{y}.png&quot;,\n                {&quot;attribution&quot;: &quot;\\u0026copy; \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://www.openstreetmap.org/copyright\\&quot;\\u003eOpenStreetMap\\u003c/a\\u003e contributors \\u0026copy; \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://cartodb.com/attributions\\&quot;\\u003eCartoDB\\u003c/a\\u003e, CartoDB \\u003ca target=\\&quot;_blank\\&quot; href =\\&quot;http://cartodb.com/attributions\\&quot;\\u003eattributions\\u003c/a\\u003e&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 18, &quot;maxZoom&quot;: 18, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n            ).addTo(map_76e0a1dc2e05396cf1073ffab27ff8b8);\n        \n    \n            var marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73 = L.markerClusterGroup(\n                {}\n            );\n            map_76e0a1dc2e05396cf1073ffab27ff8b8.addLayer(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            var marker_e2c23a7bb946c8d9a09820939b9d9bd4 = L.marker(\n                [40.03694444, -121.00583333],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_e2c23a7bb946c8d9a09820939b9d9bd4.bindTooltip(\n                `&lt;div&gt;\n                     4003.69%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_9581a670297db99df3aeabc09af51630 = L.marker(\n                [38.93305556, -120.40444444],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_9581a670297db99df3aeabc09af51630.bindTooltip(\n                `&lt;div&gt;\n                     3893.31%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_38534cdc4c39c01417fbdec503f051e0 = L.marker(\n                [38.98416667, -120.73555556],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_38534cdc4c39c01417fbdec503f051e0.bindTooltip(\n                `&lt;div&gt;\n                     3898.42%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_a2dd4ed0674f77cfd550066c931a101f = L.marker(\n                [38.55916667, -119.91333333],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_a2dd4ed0674f77cfd550066c931a101f.bindTooltip(\n                `&lt;div&gt;\n                     3855.92%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_bff82ba5fda9baec0bdcdb361fe8afe9 = L.marker(\n                [38.55916667, -119.93305556],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_bff82ba5fda9baec0bdcdb361fe8afe9.bindTooltip(\n                `&lt;div&gt;\n                     3855.92%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_41b1877e97ea28b46d99ef21a92f6aca = L.marker(\n                [38.63527778, -120.10361111],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_41b1877e97ea28b46d99ef21a92f6aca.bindTooltip(\n                `&lt;div&gt;\n                     3863.53%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_1192f62213023b4f8c40a9145993555c = L.marker(\n                [38.68833333, -120.15333333],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_1192f62213023b4f8c40a9145993555c.bindTooltip(\n                `&lt;div&gt;\n                     3868.83%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_9fe1e8d5f9089ecc6308bc6808217661 = L.marker(\n                [40.96805556, -122.43388889],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_9fe1e8d5f9089ecc6308bc6808217661.bindTooltip(\n                `&lt;div&gt;\n                     4096.81%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_a022e6a0bf30a65545d75fd23c19b45a = L.marker(\n                [41.23361111, -122.28333333],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_a022e6a0bf30a65545d75fd23c19b45a.bindTooltip(\n                `&lt;div&gt;\n                     4123.36%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_83f0f7b48fc59bbd1ae1fafc78044aab = L.marker(\n                [38.54833333, -120.14916667],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_83f0f7b48fc59bbd1ae1fafc78044aab.bindTooltip(\n                `&lt;div&gt;\n                     3854.83%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_d5dc93b45c8f19c6a5a86df59764f211 = L.marker(\n                [38.69166667, -120.15972222],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_d5dc93b45c8f19c6a5a86df59764f211.bindTooltip(\n                `&lt;div&gt;\n                     3869.17%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_a78d08965bcb1d5b9ecd0d6ff7149a9a = L.marker(\n                [38.5275, -120.10611111],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_a78d08965bcb1d5b9ecd0d6ff7149a9a.bindTooltip(\n                `&lt;div&gt;\n                     3852.75%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_0feecc55470d32f176a58a055a2dc5b1 = L.marker(\n                [38.78666667, -120.19333333],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_0feecc55470d32f176a58a055a2dc5b1.bindTooltip(\n                `&lt;div&gt;\n                     3878.67%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_08a363d45613bb3b8ac57e893b45ea67 = L.marker(\n                [38.43333333, -120.51],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_08a363d45613bb3b8ac57e893b45ea67.bindTooltip(\n                `&lt;div&gt;\n                     3843.33%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_baa8ff671102ec252da39209d78f6d1a = L.marker(\n                [38.67583333, -120.27972222],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_baa8ff671102ec252da39209d78f6d1a.bindTooltip(\n                `&lt;div&gt;\n                     3867.58%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_9d5c596eb1e64a4da9ae904ec8695adf = L.marker(\n                [38.56416667, -120.54222222],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_9d5c596eb1e64a4da9ae904ec8695adf.bindTooltip(\n                `&lt;div&gt;\n                     3856.42%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_88e1d1a846a309c1026f823e2a2d9eca = L.marker(\n                [38.52333333, -120.21166667],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_88e1d1a846a309c1026f823e2a2d9eca.bindTooltip(\n                `&lt;div&gt;\n                     3852.33%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_548580b4166571b7de2edcbdbd190233 = L.marker(\n                [38.78, -120.26],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_548580b4166571b7de2edcbdbd190233.bindTooltip(\n                `&lt;div&gt;\n                     3878.0%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_2a24b3ce90bbaaaf545fec45de3d355c = L.marker(\n                [38.945, -120.41166667],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_2a24b3ce90bbaaaf545fec45de3d355c.bindTooltip(\n                `&lt;div&gt;\n                     3894.5%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_112f43f39df5590c135369607c699d82 = L.marker(\n                [33.44083333, -105.72055556],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_112f43f39df5590c135369607c699d82.bindTooltip(\n                `&lt;div&gt;\n                     3344.08%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_67b44c274433eb2001250d150e9807e3 = L.marker(\n                [33.30722222, -105.62861111],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_67b44c274433eb2001250d150e9807e3.bindTooltip(\n                `&lt;div&gt;\n                     3330.72%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_ea27fa0d80e65b76b1cbe6a8913ba667 = L.marker(\n                [33.44444444, -105.76805556],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_ea27fa0d80e65b76b1cbe6a8913ba667.bindTooltip(\n                `&lt;div&gt;\n                     3344.44%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_972c0220750d21a83c137fa6aee8fbc5 = L.marker(\n                [33.55944444, -105.76611111],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_972c0220750d21a83c137fa6aee8fbc5.bindTooltip(\n                `&lt;div&gt;\n                     3355.94%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_33d57e2f20c63b111388a852400f0044 = L.marker(\n                [33.30805556, -105.52611111],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_33d57e2f20c63b111388a852400f0044.bindTooltip(\n                `&lt;div&gt;\n                     3330.81%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_f43180da4c7204debd552d7fdcfd4efd = L.marker(\n                [33.54527778, -105.22944444],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_f43180da4c7204debd552d7fdcfd4efd.bindTooltip(\n                `&lt;div&gt;\n                     3354.53%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_c813300da425df33a1717d3dfd892a35 = L.marker(\n                [33.31583333, -105.51222222],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_c813300da425df33a1717d3dfd892a35.bindTooltip(\n                `&lt;div&gt;\n                     3331.58%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_75041f69d2efbbec28c4944fec5cc829 = L.marker(\n                [33.44444444, -105.63111111],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_75041f69d2efbbec28c4944fec5cc829.bindTooltip(\n                `&lt;div&gt;\n                     3344.44%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_0a2fa366e3e65afe8e22287547ece17d = L.marker(\n                [33.36805556, -105.50722222],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_0a2fa366e3e65afe8e22287547ece17d.bindTooltip(\n                `&lt;div&gt;\n                     3336.81%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_de4777d56d4d2c277bf682a636cc36d9 = L.marker(\n                [33.38527778, -105.63861111],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_de4777d56d4d2c277bf682a636cc36d9.bindTooltip(\n                `&lt;div&gt;\n                     3338.53%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_c3de97511b71c76f97aadee0811eb563 = L.marker(\n                [33.38277778, -105.70027778],\n                {}\n            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    marker_8e64c128921188a7426cb977d4c7025d.bindTooltip(\n                `&lt;div&gt;\n                     4399.56%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_3858a0db7a2b0a43c74fd1db6325c825 = L.marker(\n                [33.49666667, -105.73972222],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_3858a0db7a2b0a43c74fd1db6325c825.bindTooltip(\n                `&lt;div&gt;\n                     3349.67%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_8b48ec789067f341a39301b0dc827231 = L.marker(\n                [33.41777778, -105.7075],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_8b48ec789067f341a39301b0dc827231.bindTooltip(\n                `&lt;div&gt;\n                     3341.78%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_fc40f1c77098b1730f036720a6ae6805 = L.marker(\n                [33.25527778, -105.62611111],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_fc40f1c77098b1730f036720a6ae6805.bindTooltip(\n                `&lt;div&gt;\n                     3325.53%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_5af7f33c9c0addc628eabe2ede7b4d57 = L.marker(\n                [44.04333333, -121.38611111],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_5af7f33c9c0addc628eabe2ede7b4d57.bindTooltip(\n                `&lt;div&gt;\n                     4404.33%\n                 &lt;/div&gt;`,\n     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var marker_3c43740941b1c2eb20c19145ed6dc399 = L.marker(\n                [35.93166667, -81.71666667],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_3c43740941b1c2eb20c19145ed6dc399.bindTooltip(\n                `&lt;div&gt;\n                     3593.17%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_5690be6855f52b2831d8ee512581297a = L.marker(\n                [36.00166667, -81.59],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_5690be6855f52b2831d8ee512581297a.bindTooltip(\n                `&lt;div&gt;\n                     3600.17%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_45fa09bb3f6ca1c4804761a01695961c = L.marker(\n                [35.985, -81.85166667],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_45fa09bb3f6ca1c4804761a01695961c.bindTooltip(\n                `&lt;div&gt;\n                     3598.5%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_a5ceb057f0de76dd1e1bfd91a72fa2c7 = L.marker(\n                [35.9, -81.68333333],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_a5ceb057f0de76dd1e1bfd91a72fa2c7.bindTooltip(\n                `&lt;div&gt;\n                     3590.0%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_323cc03479761b5290f575eec55ace80 = L.marker(\n                [36.035, -81.585],\n                {}\n            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   marker_e0f11086547da422381fdcb083a8f5ee.bindTooltip(\n                `&lt;div&gt;\n                     4235.47%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_f28056f0bb0ff6ca950e627c4d49560d = L.marker(\n                [35.00138889, -83.38416667],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_f28056f0bb0ff6ca950e627c4d49560d.bindTooltip(\n                `&lt;div&gt;\n                     3500.14%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_aeb90d077602a87dda4eac37709dc14c = L.marker(\n                [44.91111111, -119.69611111],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_aeb90d077602a87dda4eac37709dc14c.bindTooltip(\n                `&lt;div&gt;\n                     4491.11%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_c07bd808ff067f5dc82f9f10bdeb0edb = L.marker(\n                [39.29222222, -105.18305556],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_c07bd808ff067f5dc82f9f10bdeb0edb.bindTooltip(\n                `&lt;div&gt;\n                     3929.22%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_bda45454673ff1050344b395b9748ee6 = L.marker(\n                [43.72527778, -121.57416667],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_bda45454673ff1050344b395b9748ee6.bindTooltip(\n                `&lt;div&gt;\n                     4372.53%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_4e8aa9f1be380dc29b49a300eef4d309 = L.marker(\n                [44.41, -121.31555556],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_4e8aa9f1be380dc29b49a300eef4d309.bindTooltip(\n                `&lt;div&gt;\n                     4441.0%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_a58ff19c7d68384f550f94a7aa4c5e49 = L.marker(\n                [42.13388889, -121.23416667],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_a58ff19c7d68384f550f94a7aa4c5e49.bindTooltip(\n                `&lt;div&gt;\n                     4213.39%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_f73bc6c8965091b4b4f82a77e74a328d = L.marker(\n                [42.31138889, -120.90277778],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_f73bc6c8965091b4b4f82a77e74a328d.bindTooltip(\n                `&lt;div&gt;\n                     4231.14%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_181cd80fcb1a192a7ba577de86ea57e9 = L.marker(\n                [42.95083333, -120.83611111],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_181cd80fcb1a192a7ba577de86ea57e9.bindTooltip(\n                `&lt;div&gt;\n                     4295.08%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_c8ab3279639bf0b476bd5edf660b0f89 = L.marker(\n                [44.81611111, -107.33111111],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_c8ab3279639bf0b476bd5edf660b0f89.bindTooltip(\n                `&lt;div&gt;\n                     4481.61%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_98bde78a1c42e16982f55fbdee1f53e6 = L.marker(\n                [46.22083333, -117.785],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_98bde78a1c42e16982f55fbdee1f53e6.bindTooltip(\n                `&lt;div&gt;\n                     4622.08%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_7aef45c6f8e7e27df83440a71bdcdc57 = L.marker(\n                [42.38694444, -120.80861111],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_7aef45c6f8e7e27df83440a71bdcdc57.bindTooltip(\n                `&lt;div&gt;\n                     4238.69%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_fada7e3949658f6e8dbe58e8ac489916 = L.marker(\n                [42.51472222, -121.12],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_fada7e3949658f6e8dbe58e8ac489916.bindTooltip(\n                `&lt;div&gt;\n                     4251.47%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_478459557bfb7206e0a7b6da756f6911 = L.marker(\n                [48.92194444, -115.0925],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_478459557bfb7206e0a7b6da756f6911.bindTooltip(\n                `&lt;div&gt;\n                     4892.19%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_ceb81c33194c1671068637a8be10e9ba = L.marker(\n                [48.99833333, -115.16555556],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_ceb81c33194c1671068637a8be10e9ba.bindTooltip(\n                `&lt;div&gt;\n                     4899.83%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_dc4a6ac1243903b0432da1c747293092 = L.marker(\n                [42.92138889, -121.03083333],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_dc4a6ac1243903b0432da1c747293092.bindTooltip(\n                `&lt;div&gt;\n                     4292.14%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_f4aeeec32c9997cca243051bc8f4b58b = L.marker(\n                [47.66555556, -115.56916667],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_f4aeeec32c9997cca243051bc8f4b58b.bindTooltip(\n                `&lt;div&gt;\n                     4766.56%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_f1801887097fe3ef9bbd032adeb9f9c5 = L.marker(\n                [38.91333333, -105.98361111],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_f1801887097fe3ef9bbd032adeb9f9c5.bindTooltip(\n                `&lt;div&gt;\n                     3891.33%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_283269421c3c5d70b449ed17e3af44b9 = L.marker(\n                [39.10027778, -106.3675],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_283269421c3c5d70b449ed17e3af44b9.bindTooltip(\n                `&lt;div&gt;\n                     3910.03%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_427664be67425b89497c4049e4d4c3af = L.marker(\n                [40.08888889, -111.37833333],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_427664be67425b89497c4049e4d4c3af.bindTooltip(\n                `&lt;div&gt;\n                     4008.89%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_1436d6fd4c0e054acf40889d6bc48b18 = L.marker(\n                [48.89944444, -115.10694444],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_1436d6fd4c0e054acf40889d6bc48b18.bindTooltip(\n                `&lt;div&gt;\n                     4889.94%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_1522594d113347ed336e05d48674da3d = L.marker(\n                [48.90694444, -115.07666667],\n                {}\n            ).addTo(marker_cluster_8b395ceb6f1b2c97fc023834d61e8b73);\n        \n    \n            marker_1522594d113347ed336e05d48674da3d.bindTooltip(\n                `&lt;div&gt;\n                     4890.69%\n                 &lt;/div&gt;`,\n                {&quot;sticky&quot;: true}\n            );\n        \n    \n            var marker_0dc2bc3b6cafc61f6eb5c7085de06936 = L.marker(\n                [48.40777778, 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MiniMap\n\ndf['LATITUDE']=df['LATITUDE'].astype(float)\ndf['LONGITUDE'] = df['LONGITUDE'].astype(float)\n\n","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:49:18.157445Z","iopub.execute_input":"2023-10-05T11:49:18.157846Z","iopub.status.idle":"2023-10-05T11:49:18.189659Z","shell.execute_reply.started":"2023-10-05T11:49:18.157802Z","shell.execute_reply":"2023-10-05T11:49:18.188422Z"},"trusted":true},"execution_count":38,"outputs":[]},{"cell_type":"code","source":"\nimport folium\n\nfrom folium.plugins import HeatMap\n\n\nmap_obj = folium.Map(location = [38.27312, -98.5821872], zoom_start = 4)\n\nheat_data = [[row['LATITUDE'],row['LONGITUDE']] for index, row in data.iterrows()]\n\n# Plot it on the map\nHeatMap(heat_data).add_to(map_obj)\n\n\nmap_obj\n","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:49:18.191106Z","iopub.execute_input":"2023-10-05T11:49:18.191468Z","iopub.status.idle":"2023-10-05T11:49:18.217025Z","shell.execute_reply.started":"2023-10-05T11:49:18.191436Z","shell.execute_reply":"2023-10-05T11:49:18.215954Z"},"trusted":true},"execution_count":39,"outputs":[{"execution_count":39,"output_type":"execute_result","data":{"text/plain":"<folium.folium.Map at 0x7a6dac0ec130>","text/html":"<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe srcdoc=\"&lt;!DOCTYPE html&gt;\n&lt;html&gt;\n&lt;head&gt;\n    \n    &lt;meta http-equiv=&quot;content-type&quot; content=&quot;text/html; charset=UTF-8&quot; /&gt;\n    \n        &lt;script&gt;\n            L_NO_TOUCH = false;\n            L_DISABLE_3D = false;\n        &lt;/script&gt;\n    \n    &lt;style&gt;html, body {width: 100%;height: 100%;margin: 0;padding: 0;}&lt;/style&gt;\n    &lt;style&gt;#map {position:absolute;top:0;bottom:0;right:0;left:0;}&lt;/style&gt;\n    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.js&quot;&gt;&lt;/script&gt;\n    &lt;script src=&quot;https://code.jquery.com/jquery-1.12.4.min.js&quot;&gt;&lt;/script&gt;\n    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/js/bootstrap.bundle.min.js&quot;&gt;&lt;/script&gt;\n    &lt;script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js&quot;&gt;&lt;/script&gt;\n    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.css&quot;/&gt;\n    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/css/bootstrap.min.css&quot;/&gt;\n    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://netdna.bootstrapcdn.com/bootstrap/3.0.0/css/bootstrap.min.css&quot;/&gt;\n    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/@fortawesome/fontawesome-free@6.2.0/css/all.min.css&quot;/&gt;\n    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css&quot;/&gt;\n    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/gh/python-visualization/folium/folium/templates/leaflet.awesome.rotate.min.css&quot;/&gt;\n    \n            &lt;meta name=&quot;viewport&quot; content=&quot;width=device-width,\n                initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; /&gt;\n            &lt;style&gt;\n                #map_2750c843cb6bb53fc82024144d82293a {\n                    position: relative;\n                    width: 100.0%;\n                    height: 100.0%;\n                    left: 0.0%;\n                    top: 0.0%;\n                }\n                .leaflet-container { font-size: 1rem; }\n            &lt;/style&gt;\n        \n    &lt;script src=&quot;https://cdn.jsdelivr.net/gh/python-visualization/folium@main/folium/templates/leaflet_heat.min.js&quot;&gt;&lt;/script&gt;\n&lt;/head&gt;\n&lt;body&gt;\n    \n    \n            &lt;div class=&quot;folium-map&quot; id=&quot;map_2750c843cb6bb53fc82024144d82293a&quot; &gt;&lt;/div&gt;\n        \n&lt;/body&gt;\n&lt;script&gt;\n    \n    \n            var map_2750c843cb6bb53fc82024144d82293a = L.map(\n                &quot;map_2750c843cb6bb53fc82024144d82293a&quot;,\n                {\n                    center: [38.27312, -98.5821872],\n                    crs: L.CRS.EPSG3857,\n                    zoom: 4,\n                    zoomControl: true,\n                    preferCanvas: false,\n                }\n            );\n\n            \n\n        \n    \n            var tile_layer_94e01b12b9964081d19f9f7ad556e5f3 = L.tileLayer(\n                &quot;https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png&quot;,\n                {&quot;attribution&quot;: &quot;Data by \\u0026copy; \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://openstreetmap.org\\&quot;\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://www.openstreetmap.org/copyright\\&quot;\\u003eODbL\\u003c/a\\u003e.&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 18, &quot;maxZoom&quot;: 18, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n            ).addTo(map_2750c843cb6bb53fc82024144d82293a);\n        \n    \n            var heat_map_dd45b25c2f42059467c19587086ff13f = L.heatLayer(\n                [[40.03694444, -121.00583333], [38.93305556, -120.40444444], [38.98416667, -120.73555556], [38.55916667, -119.91333333], [38.55916667, -119.93305556], [38.63527778, -120.10361111], [38.68833333, -120.15333333], [40.96805556, -122.43388889], [41.23361111, -122.28333333], [38.54833333, -120.14916667], [38.69166667, -120.15972222], [38.5275, -120.10611111], [38.78666667, -120.19333333], [38.43333333, -120.51], [38.67583333, -120.27972222], [38.56416667, -120.54222222], [38.52333333, -120.21166667], [38.78, -120.26], [38.945, -120.41166667], [33.44083333, -105.72055556], [33.30722222, -105.62861111], [33.44444444, -105.76805556], [33.55944444, -105.76611111], [33.30805556, -105.52611111], [33.54527778, -105.22944444], [33.31583333, -105.51222222], [33.44444444, -105.63111111], [33.36805556, -105.50722222], [33.38527778, -105.63861111], [33.38277778, -105.70027778], [33.37972222, -105.68027778], [43.99555556, -121.41416667], [33.49666667, -105.73972222], [33.41777778, -105.7075], [33.25527778, -105.62611111], [44.04333333, -121.38611111], [35.22833333, -82.88444444], [35.00027778, -83.35111111], [35.93166667, -81.71666667], [36.00166667, -81.59], [35.985, -81.85166667], [35.9, 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!important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"# heatmap with minimap and time \nfrom folium import plugins\nfrom folium.plugins import MiniMap\n\n\n\nheat_data = [[row['LATITUDE'],row['LONGITUDE']] for index, row in data.iterrows()]\n\n\nmap_hooray = folium.Map(location=[38.27312, -98.5821872],tiles=\"stamentoner\", zoom_start = 13) \n\n# Plot it on the map\nhm = plugins.HeatMapWithTime(heat_data,auto_play=True,max_opacity=0.8)\n\n\nMiniMap().add_to(map_hooray)\n\n\nhm.add_to(map_hooray)\n# Display the map\nmap_hooray\n","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:49:18.218505Z","iopub.execute_input":"2023-10-05T11:49:18.219008Z","iopub.status.idle":"2023-10-05T11:49:18.256143Z","shell.execute_reply.started":"2023-10-05T11:49:18.218977Z","shell.execute_reply":"2023-10-05T11:49:18.255317Z"},"trusted":true},"execution_count":40,"outputs":[{"execution_count":40,"output_type":"execute_result","data":{"text/plain":"<folium.folium.Map at 0x7a6dac0ed780>","text/html":"<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe srcdoc=\"&lt;!DOCTYPE html&gt;\n&lt;html&gt;\n&lt;head&gt;\n    \n    &lt;meta http-equiv=&quot;content-type&quot; content=&quot;text/html; charset=UTF-8&quot; /&gt;\n    \n        &lt;script&gt;\n            L_NO_TOUCH = false;\n            L_DISABLE_3D = false;\n        &lt;/script&gt;\n    \n    &lt;style&gt;html, body {width: 100%;height: 100%;margin: 0;padding: 0;}&lt;/style&gt;\n    &lt;style&gt;#map {position:absolute;top:0;bottom:0;right:0;left:0;}&lt;/style&gt;\n    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.js&quot;&gt;&lt;/script&gt;\n    &lt;script src=&quot;https://code.jquery.com/jquery-1.12.4.min.js&quot;&gt;&lt;/script&gt;\n    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/js/bootstrap.bundle.min.js&quot;&gt;&lt;/script&gt;\n    &lt;script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js&quot;&gt;&lt;/script&gt;\n    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.css&quot;/&gt;\n    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/css/bootstrap.min.css&quot;/&gt;\n    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://netdna.bootstrapcdn.com/bootstrap/3.0.0/css/bootstrap.min.css&quot;/&gt;\n    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/@fortawesome/fontawesome-free@6.2.0/css/all.min.css&quot;/&gt;\n    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css&quot;/&gt;\n    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/gh/python-visualization/folium/folium/templates/leaflet.awesome.rotate.min.css&quot;/&gt;\n    \n            &lt;meta name=&quot;viewport&quot; content=&quot;width=device-width,\n                initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; /&gt;\n            &lt;style&gt;\n                #map_67a6ee262d1b8478237ea2a1d9afffd5 {\n                    position: relative;\n                    width: 100.0%;\n                    height: 100.0%;\n                    left: 0.0%;\n                    top: 0.0%;\n                }\n                .leaflet-container { font-size: 1rem; }\n            &lt;/style&gt;\n        \n    &lt;script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/leaflet-minimap/3.6.1/Control.MiniMap.js&quot;&gt;&lt;/script&gt;\n    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/leaflet-minimap/3.6.1/Control.MiniMap.css&quot;/&gt;\n    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/iso8601-js-period@0.2.1/iso8601.min.js&quot;&gt;&lt;/script&gt;\n    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/leaflet-timedimension@1.1.1/dist/leaflet.timedimension.min.js&quot;&gt;&lt;/script&gt;\n    &lt;script src=&quot;https://cdn.jsdelivr.net/gh/python-visualization/folium/folium/templates/pa7_hm.min.js&quot;&gt;&lt;/script&gt;\n    &lt;script src=&quot;https://cdn.jsdelivr.net/gh/python-visualization/folium/folium/templates/pa7_leaflet_hm.min.js&quot;&gt;&lt;/script&gt;\n    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/leaflet-timedimension@1.1.1/dist/leaflet.timedimension.control.css&quot;/&gt;\n    \n            &lt;script&gt;\n                var TDHeatmap = L.TimeDimension.Layer.extend({\n\n            initialize: function(data, options) {\n                var heatmapCfg = {\n                    radius: 15,\n                    blur: 0.8,\n                    maxOpacity: 1.,\n                    scaleRadius: false,\n                    useLocalExtrema: false,\n                    latField: &#x27;lat&#x27;,\n                    lngField: &#x27;lng&#x27;,\n                    valueField: &#x27;count&#x27;,\n                    defaultWeight : 1,\n                };\n                heatmapCfg = $.extend({}, heatmapCfg, options.heatmapOptions || {});\n                var layer = new HeatmapOverlay(heatmapCfg);\n                L.TimeDimension.Layer.prototype.initialize.call(this, layer, options);\n                this._currentLoadedTime = 0;\n                this._currentTimeData = {\n                    data: []\n                    };\n                this.data= data;\n                this.defaultWeight = heatmapCfg.defaultWeight || 1;\n            },\n            onAdd: function(map) {\n                L.TimeDimension.Layer.prototype.onAdd.call(this, map);\n                map.addLayer(this._baseLayer);\n                if (this._timeDimension) {\n                    this._getDataForTime(this._timeDimension.getCurrentTime());\n                }\n            },\n            _onNewTimeLoading: function(ev) {\n                this._getDataForTime(ev.time);\n                return;\n            },\n            isReady: function(time) {\n                return (this._currentLoadedTime == time);\n            },\n            _update: function() {\n                this._baseLayer.setData(this._currentTimeData);\n                return true;\n            },\n            _getDataForTime: function(time) {\n                    delete this._currentTimeData.data;\n                    this._currentTimeData.data = [];\n                    var data = this.data[time-1];\n                    for (var i = 0; i &lt; data.length; i++) {\n                        this._currentTimeData.data.push({\n                                lat: data[i][0],\n                                lng: data[i][1],\n                                count: data[i].length&gt;2 ? data[i][2] : this.defaultWeight\n                            });\n                        }\n                    this._currentLoadedTime = time;\n                    if (this._timeDimension &amp;&amp; time == this._timeDimension.getCurrentTime() &amp;&amp; !this._timeDimension.isLoading()) {\n                        this._update();\n                    }\n                    this.fire(&#x27;timeload&#x27;, {\n                        time: time\n                    });\n                }\n        });\n\n        L.Control.TimeDimensionCustom = L.Control.TimeDimension.extend({\n            initialize: function(index, options) {\n                var playerOptions = {\n                    buffer: 1,\n                    minBufferReady: -1\n                    };\n                options.playerOptions = $.extend({}, playerOptions, options.playerOptions || {});\n                L.Control.TimeDimension.prototype.initialize.call(this, options);\n                this.index = index;\n                },\n            _getDisplayDateFormat: function(date){\n                return this.index[date.getTime()-1];\n                }\n            });\n            &lt;/script&gt;\n                \n&lt;/head&gt;\n&lt;body&gt;\n    \n    \n            &lt;div class=&quot;folium-map&quot; id=&quot;map_67a6ee262d1b8478237ea2a1d9afffd5&quot; &gt;&lt;/div&gt;\n        \n&lt;/body&gt;\n&lt;script&gt;\n    \n    \n            var map_67a6ee262d1b8478237ea2a1d9afffd5 = L.map(\n                &quot;map_67a6ee262d1b8478237ea2a1d9afffd5&quot;,\n                {\n                    center: [38.27312, -98.5821872],\n                    crs: L.CRS.EPSG3857,\n                    zoom: 13,\n                    zoomControl: true,\n                    preferCanvas: false,\n                }\n            );\n\n            \n\n        \n    \n            var tile_layer_5548a8eb9a814e9c0dcab261efb79bbf = L.tileLayer(\n                &quot;https://stamen-tiles-{s}.a.ssl.fastly.net/toner/{z}/{x}/{y}.png&quot;,\n                {&quot;attribution&quot;: &quot;Map tiles by \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://stamen.com\\&quot;\\u003eStamen Design\\u003c/a\\u003e, under \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://creativecommons.org/licenses/by/3.0\\&quot;\\u003eCC BY 3.0\\u003c/a\\u003e. Data by \\u0026copy; \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://openstreetmap.org\\&quot;\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://www.openstreetmap.org/copyright\\&quot;\\u003eODbL\\u003c/a\\u003e.&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 18, &quot;maxZoom&quot;: 18, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n            ).addTo(map_67a6ee262d1b8478237ea2a1d9afffd5);\n        \n    \n            var tile_layer_e1a37b7afa42075339a4f7f553f91d4b = L.tileLayer(\n                &quot;https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png&quot;,\n                {&quot;attribution&quot;: &quot;Data by \\u0026copy; \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://openstreetmap.org\\&quot;\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://www.openstreetmap.org/copyright\\&quot;\\u003eODbL\\u003c/a\\u003e.&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 18, &quot;maxZoom&quot;: 18, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n            );\n            var mini_map_40aed81d5bce8beb2f4dc043e192a91d = new L.Control.MiniMap(\n                tile_layer_e1a37b7afa42075339a4f7f553f91d4b,\n                {&quot;autoToggleDisplay&quot;: false, &quot;centerFixed&quot;: false, &quot;collapsedHeight&quot;: 25, &quot;collapsedWidth&quot;: 25, &quot;height&quot;: 150, &quot;minimized&quot;: false, &quot;position&quot;: &quot;bottomright&quot;, &quot;toggleDisplay&quot;: false, &quot;width&quot;: 150, &quot;zoomAnimation&quot;: false, &quot;zoomLevelOffset&quot;: -5}\n            );\n            map_67a6ee262d1b8478237ea2a1d9afffd5.addControl(mini_map_40aed81d5bce8beb2f4dc043e192a91d);\n        \n    \n\n            var times = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100];\n\n            map_67a6ee262d1b8478237ea2a1d9afffd5.timeDimension = L.timeDimension(\n                {times : times, currentTime: new Date(1)}\n            );\n\n            var heat_map_f5a3d2f85b16a76f8c4982dbd5bf21d9Control = new L.Control.TimeDimensionCustom([&#x27;1&#x27;, &#x27;2&#x27;, &#x27;3&#x27;, &#x27;4&#x27;, &#x27;5&#x27;, &#x27;6&#x27;, &#x27;7&#x27;, &#x27;8&#x27;, &#x27;9&#x27;, &#x27;10&#x27;, &#x27;11&#x27;, &#x27;12&#x27;, &#x27;13&#x27;, &#x27;14&#x27;, &#x27;15&#x27;, &#x27;16&#x27;, &#x27;17&#x27;, &#x27;18&#x27;, &#x27;19&#x27;, &#x27;20&#x27;, 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geopandas","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:49:18.257273Z","iopub.execute_input":"2023-10-05T11:49:18.257901Z","iopub.status.idle":"2023-10-05T11:49:28.720985Z","shell.execute_reply.started":"2023-10-05T11:49:18.257865Z","shell.execute_reply":"2023-10-05T11:49:28.719213Z"},"trusted":true},"execution_count":41,"outputs":[{"name":"stdout","text":"Requirement already satisfied: geopandas in /opt/conda/lib/python3.10/site-packages (0.13.2)\nRequirement already satisfied: fiona>=1.8.19 in /opt/conda/lib/python3.10/site-packages (from geopandas) (1.9.4.post1)\nRequirement already satisfied: packaging in /opt/conda/lib/python3.10/site-packages (from geopandas) (21.3)\nRequirement already satisfied: pandas>=1.1.0 in /opt/conda/lib/python3.10/site-packages (from geopandas) (2.0.3)\nRequirement already satisfied: pyproj>=3.0.1 in /opt/conda/lib/python3.10/site-packages (from geopandas) (3.6.0)\nRequirement already satisfied: shapely>=1.7.1 in /opt/conda/lib/python3.10/site-packages (from geopandas) (1.8.5.post1)\nRequirement already satisfied: attrs>=19.2.0 in /opt/conda/lib/python3.10/site-packages (from fiona>=1.8.19->geopandas) (23.1.0)\nRequirement already satisfied: certifi in /opt/conda/lib/python3.10/site-packages (from fiona>=1.8.19->geopandas) (2023.7.22)\nRequirement already satisfied: click~=8.0 in /opt/conda/lib/python3.10/site-packages (from fiona>=1.8.19->geopandas) (8.1.7)\nRequirement already satisfied: click-plugins>=1.0 in /opt/conda/lib/python3.10/site-packages (from fiona>=1.8.19->geopandas) (1.1.1)\nRequirement already satisfied: cligj>=0.5 in /opt/conda/lib/python3.10/site-packages (from fiona>=1.8.19->geopandas) (0.7.2)\nRequirement already satisfied: six in /opt/conda/lib/python3.10/site-packages (from fiona>=1.8.19->geopandas) (1.16.0)\nRequirement already satisfied: python-dateutil>=2.8.2 in /opt/conda/lib/python3.10/site-packages (from pandas>=1.1.0->geopandas) (2.8.2)\nRequirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.10/site-packages (from pandas>=1.1.0->geopandas) (2023.3)\nRequirement already satisfied: tzdata>=2022.1 in /opt/conda/lib/python3.10/site-packages (from pandas>=1.1.0->geopandas) (2023.3)\nRequirement already satisfied: numpy>=1.21.0 in /opt/conda/lib/python3.10/site-packages (from pandas>=1.1.0->geopandas) (1.23.5)\nRequirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging->geopandas) (3.0.9)\n","output_type":"stream"}]},{"cell_type":"code","source":"%matplotlib inline\n\nimport os\nimport geopandas as gpd\nimport pandas as pd\n\nimport matplotlib.pyplot as plt\nimport geopandas\nfrom shapely.geometry import box\n\n# sqlite3 to geopandas \n# onverting to geopandas dataframe \n\nDataPath = \"../input/188-million-us-wildfires/FPA_FOD_20170508.sqlite\"\nconnection = sqlite3.connect(DataPath)\ncursor = connection.cursor()\ngdf = gpd.GeoDataFrame(data, geometry=geopandas.points_from_xy(data.LATITUDE, data.LONGITUDE), crs=\"EPSG:4326\")\ngdf.info() \n","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:49:28.723159Z","iopub.execute_input":"2023-10-05T11:49:28.724150Z","iopub.status.idle":"2023-10-05T11:49:29.104031Z","shell.execute_reply.started":"2023-10-05T11:49:28.724108Z","shell.execute_reply":"2023-10-05T11:49:29.102790Z"},"trusted":true},"execution_count":42,"outputs":[{"name":"stdout","text":"<class 'geopandas.geodataframe.GeoDataFrame'>\nRangeIndex: 100 entries, 0 to 99\nData columns (total 40 columns):\n #   Column                      Non-Null Count  Dtype   \n---  ------                      --------------  -----   \n 0   OBJECTID                    100 non-null    int64   \n 1   FOD_ID                      100 non-null    int64   \n 2   FPA_ID                      100 non-null    object  \n 3   SOURCE_SYSTEM_TYPE          100 non-null    object  \n 4   SOURCE_SYSTEM               100 non-null    object  \n 5   NWCG_REPORTING_AGENCY       100 non-null    object  \n 6   NWCG_REPORTING_UNIT_ID      100 non-null    object  \n 7   NWCG_REPORTING_UNIT_NAME    100 non-null    object  \n 8   SOURCE_REPORTING_UNIT       100 non-null    object  \n 9   SOURCE_REPORTING_UNIT_NAME  100 non-null    object  \n 10  LOCAL_FIRE_REPORT_ID        100 non-null    object  \n 11  LOCAL_INCIDENT_ID           99 non-null     object  \n 12  FIRE_CODE                   100 non-null    object  \n 13  FIRE_NAME                   100 non-null    object  \n 14  ICS_209_INCIDENT_NUMBER     100 non-null    object  \n 15  ICS_209_NAME                100 non-null    object  \n 16  MTBS_ID                     100 non-null    object  \n 17  MTBS_FIRE_NAME              100 non-null    object  \n 18  COMPLEX_NAME                100 non-null    float64 \n 19  FIRE_YEAR                   100 non-null    int64   \n 20  DISCOVERY_DATE              100 non-null    float64 \n 21  DISCOVERY_DOY               100 non-null    int64   \n 22  DISCOVERY_TIME              100 non-null    object  \n 23  STAT_CAUSE_CODE             100 non-null    float64 \n 24  STAT_CAUSE_DESCR            100 non-null    int64   \n 25  CONT_DATE                   99 non-null     float64 \n 26  CONT_DOY                    99 non-null     float64 \n 27  CONT_TIME                   99 non-null     object  \n 28  FIRE_SIZE                   100 non-null    float64 \n 29  FIRE_SIZE_CLASS             100 non-null    int64   \n 30  LATITUDE                    100 non-null    float64 \n 31  LONGITUDE                   100 non-null    float64 \n 32  OWNER_CODE                  100 non-null    float64 \n 33  OWNER_DESCR                 100 non-null    object  \n 34  STATE                       100 non-null    int64   \n 35  COUNTY                      91 non-null     object  \n 36  FIPS_CODE                   91 non-null     object  \n 37  FIPS_NAME                   100 non-null    int64   \n 38  Shape                       100 non-null    int64   \n 39  geometry                    100 non-null    geometry\ndtypes: float64(9), geometry(1), int64(9), object(21)\nmemory usage: 31.4+ KB\n","output_type":"stream"}]},{"cell_type":"code","source":"# finding the geometry - This is the most important feature in Geospatial Analysis \ngdf.head()","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:49:29.105176Z","iopub.execute_input":"2023-10-05T11:49:29.105512Z","iopub.status.idle":"2023-10-05T11:49:29.141876Z","shell.execute_reply.started":"2023-10-05T11:49:29.105483Z","shell.execute_reply":"2023-10-05T11:49:29.140833Z"},"trusted":true},"execution_count":43,"outputs":[{"execution_count":43,"output_type":"execute_result","data":{"text/plain":"   OBJECTID  FOD_ID      FPA_ID SOURCE_SYSTEM_TYPE SOURCE_SYSTEM  \\\n0         1       1  FS-1418826                FED   FS-FIRESTAT   \n1         2       2  FS-1418827                FED   FS-FIRESTAT   \n2         3       3  FS-1418835                FED   FS-FIRESTAT   \n3         4       4  FS-1418845                FED   FS-FIRESTAT   \n4         5       5  FS-1418847                FED   FS-FIRESTAT   \n\n  NWCG_REPORTING_AGENCY NWCG_REPORTING_UNIT_ID  NWCG_REPORTING_UNIT_NAME  \\\n0                    FS                USCAPNF    Plumas National Forest   \n1                    FS                USCAENF  Eldorado National Forest   \n2                    FS                USCAENF  Eldorado National Forest   \n3                    FS                USCAENF  Eldorado National Forest   \n4                    FS                USCAENF  Eldorado National Forest   \n\n  SOURCE_REPORTING_UNIT SOURCE_REPORTING_UNIT_NAME LOCAL_FIRE_REPORT_ID  \\\n0                  0511     Plumas National Forest                    1   \n1                  0503   Eldorado National Forest                   13   \n2                  0503   Eldorado National Forest                   27   \n3                  0503   Eldorado National Forest                   43   \n4                  0503   Eldorado National Forest                   44   \n\n  LOCAL_INCIDENT_ID FIRE_CODE FIRE_NAME ICS_209_INCIDENT_NUMBER ICS_209_NAME  \\\n0            PNF-47      BJ8K  FOUNTAIN                       0            0   \n1                13      AAC0    PIGEON                       0            0   \n2               021      A32W     SLACK                       0            0   \n3                 6         0      DEER                       0            0   \n4                 7         0  STEVENOT                       0            0   \n\n  MTBS_ID MTBS_FIRE_NAME  COMPLEX_NAME  FIRE_YEAR  DISCOVERY_DATE  \\\n0       0              0          0.00       2005      2453403.50   \n1       0              0          0.00       2004      2453137.50   \n2       0              0          0.00       2004      2453156.50   \n3       0              0          0.00       2004      2453184.50   \n4       0              0          0.00       2004      2453184.50   \n\n   DISCOVERY_DOY DISCOVERY_TIME  STAT_CAUSE_CODE  STAT_CAUSE_DESCR  CONT_DATE  \\\n0             33           1300             9.00                 6 2453403.50   \n1            133           0845             1.00                 5 2453137.50   \n2            152           1921             5.00                 3 2453156.50   \n3            180           1600             1.00                 5 2453189.50   \n4            180           1600             1.00                 5 2453189.50   \n\n   CONT_DOY CONT_TIME  FIRE_SIZE  FIRE_SIZE_CLASS  LATITUDE  LONGITUDE  \\\n0     33.00      1730       0.10                0     40.04    -121.01   \n1    133.00      1530       0.25                0     38.93    -120.40   \n2    152.00      2024       0.10                0     38.98    -120.74   \n3    185.00      1400       0.10                0     38.56    -119.91   \n4    185.00      1200       0.10                0     38.56    -119.93   \n\n   OWNER_CODE       OWNER_DESCR  STATE COUNTY FIPS_CODE  FIPS_NAME  Shape  \\\n0        5.00              USFS      1     63       063         20     91   \n1        5.00              USFS      1     61       061         19     35   \n2       13.00  STATE OR PRIVATE      1     17       017          9     82   \n3        5.00              USFS      1      3       003          0     60   \n4        5.00              USFS      1      3       003          0     25   \n\n                      geometry  \n0  POINT (40.03694 -121.00583)  \n1  POINT (38.93306 -120.40444)  \n2  POINT (38.98417 -120.73556)  \n3  POINT (38.55917 -119.91333)  \n4  POINT (38.55917 -119.93306)  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>OBJECTID</th>\n      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<td>1</td>\n      <td>63</td>\n      <td>063</td>\n      <td>20</td>\n      <td>91</td>\n      <td>POINT (40.03694 -121.00583)</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2</td>\n      <td>FS-1418827</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USCAENF</td>\n      <td>Eldorado National Forest</td>\n      <td>0503</td>\n      <td>Eldorado National Forest</td>\n      <td>13</td>\n      <td>13</td>\n      <td>AAC0</td>\n      <td>PIGEON</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.00</td>\n      <td>2004</td>\n      <td>2453137.50</td>\n      <td>133</td>\n      <td>0845</td>\n      <td>1.00</td>\n      <td>5</td>\n      <td>2453137.50</td>\n      <td>133.00</td>\n      <td>1530</td>\n      <td>0.25</td>\n      <td>0</td>\n      <td>38.93</td>\n      <td>-120.40</td>\n      <td>5.00</td>\n      <td>USFS</td>\n      <td>1</td>\n      <td>61</td>\n      <td>061</td>\n      <td>19</td>\n      <td>35</td>\n      <td>POINT (38.93306 -120.40444)</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>3</td>\n      <td>FS-1418835</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USCAENF</td>\n      <td>Eldorado National Forest</td>\n      <td>0503</td>\n      <td>Eldorado National Forest</td>\n      <td>27</td>\n      <td>021</td>\n      <td>A32W</td>\n      <td>SLACK</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.00</td>\n      <td>2004</td>\n      <td>2453156.50</td>\n      <td>152</td>\n      <td>1921</td>\n      <td>5.00</td>\n      <td>3</td>\n      <td>2453156.50</td>\n      <td>152.00</td>\n      <td>2024</td>\n      <td>0.10</td>\n      <td>0</td>\n      <td>38.98</td>\n      <td>-120.74</td>\n      <td>13.00</td>\n      <td>STATE OR PRIVATE</td>\n      <td>1</td>\n      <td>17</td>\n      <td>017</td>\n      <td>9</td>\n      <td>82</td>\n      <td>POINT (38.98417 -120.73556)</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>4</td>\n      <td>FS-1418845</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USCAENF</td>\n      <td>Eldorado National Forest</td>\n      <td>0503</td>\n      <td>Eldorado National Forest</td>\n      <td>43</td>\n      <td>6</td>\n      <td>0</td>\n      <td>DEER</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.00</td>\n      <td>2004</td>\n      <td>2453184.50</td>\n      <td>180</td>\n      <td>1600</td>\n      <td>1.00</td>\n      <td>5</td>\n      <td>2453189.50</td>\n      <td>185.00</td>\n      <td>1400</td>\n      <td>0.10</td>\n      <td>0</td>\n      <td>38.56</td>\n      <td>-119.91</td>\n      <td>5.00</td>\n      <td>USFS</td>\n      <td>1</td>\n      <td>3</td>\n      <td>003</td>\n      <td>0</td>\n      <td>60</td>\n      <td>POINT (38.55917 -119.91333)</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>5</td>\n      <td>FS-1418847</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USCAENF</td>\n      <td>Eldorado National Forest</td>\n      <td>0503</td>\n      <td>Eldorado National Forest</td>\n      <td>44</td>\n      <td>7</td>\n      <td>0</td>\n      <td>STEVENOT</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.00</td>\n      <td>2004</td>\n      <td>2453184.50</td>\n      <td>180</td>\n      <td>1600</td>\n      <td>1.00</td>\n      <td>5</td>\n      <td>2453189.50</td>\n      <td>185.00</td>\n      <td>1200</td>\n      <td>0.10</td>\n      <td>0</td>\n      <td>38.56</td>\n      <td>-119.93</td>\n      <td>5.00</td>\n      <td>USFS</td>\n      <td>1</td>\n      <td>3</td>\n      <td>003</td>\n      <td>0</td>\n      <td>25</td>\n      <td>POINT (38.55917 -119.93306)</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"print(gdf.crs)\n","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:49:29.143357Z","iopub.execute_input":"2023-10-05T11:49:29.143666Z","iopub.status.idle":"2023-10-05T11:49:29.150108Z","shell.execute_reply.started":"2023-10-05T11:49:29.143641Z","shell.execute_reply":"2023-10-05T11:49:29.148817Z"},"trusted":true},"execution_count":44,"outputs":[{"name":"stdout","text":"EPSG:4326\n","output_type":"stream"}]},{"cell_type":"code","source":"# measuring the distance \n# Select one release incident in particular\n# creating a buffer \n\ntwo_mile_buffer = gdf.geometry.buffer(2*5280)\ntwo_mile_buffer.head()\n","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:49:29.152193Z","iopub.execute_input":"2023-10-05T11:49:29.152664Z","iopub.status.idle":"2023-10-05T11:49:29.304350Z","shell.execute_reply.started":"2023-10-05T11:49:29.152593Z","shell.execute_reply":"2023-10-05T11:49:29.303219Z"},"trusted":true},"execution_count":45,"outputs":[{"name":"stderr","text":"/tmp/ipykernel_32/1643940722.py:5: UserWarning: Geometry is in a geographic CRS. Results from 'buffer' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n\n  two_mile_buffer = gdf.geometry.buffer(2*5280)\n","output_type":"stream"},{"execution_count":45,"output_type":"execute_result","data":{"text/plain":"0    POLYGON ((10600.03694 -121.00583, 10549.18766 ...\n1    POLYGON ((10598.93306 -120.40444, 10548.08377 ...\n2    POLYGON ((10598.98417 -120.73556, 10548.13488 ...\n3    POLYGON ((10598.55917 -119.91333, 10547.70988 ...\n4    POLYGON ((10598.55917 -119.93306, 10547.70988 ...\ndtype: geometry"},"metadata":{}}]},{"cell_type":"code","source":"\nfor lat, lon in zip(data['LATITUDE'][:5], data['LONGITUDE'][:5]): \n    geometry = lat, lon \n    print(geometry)","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:49:29.305862Z","iopub.execute_input":"2023-10-05T11:49:29.306157Z","iopub.status.idle":"2023-10-05T11:49:29.312134Z","shell.execute_reply.started":"2023-10-05T11:49:29.306132Z","shell.execute_reply":"2023-10-05T11:49:29.311020Z"},"trusted":true},"execution_count":46,"outputs":[{"name":"stdout","text":"(40.03694444, -121.00583333)\n(38.93305556, -120.40444444)\n(38.98416667, -120.73555556)\n(38.55916667, -119.91333333)\n(38.55916667, -119.93305556)\n","output_type":"stream"}]},{"cell_type":"markdown","source":"<div style=\"color:white;display:fill;border-radius:8px;\n            background-color:#8F00FF;font-size:150%;\n            font-family:Nexa;letter-spacing:0.5px\">\n    <p style=\"padding: 5px;color:white;\"><b></b></p>\n</div>\n<a id='Introduction'></a>\n\n#  <b><span style='color:#8F00FF'>| 12. </span> Geometry points </b>\n[Table of contents](#table)\n\nFor each data set in the geospatial analysis it is necessary to Geometry points. A point in geometry is a location. It has no size i.e. no width, no length and no depth. \n\n\n","metadata":{}},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport geopandas as gpd\nfrom shapely.geometry import Point\nimport matplotlib\nimport matplotlib.pyplot as plt \nimport folium\nimport os\n\nfor lat, lon in zip(data['LATITUDE'][:5], data['LONGITUDE'][:5]): \n    geometry = Point(lat,lon)\n    print(geometry)","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:49:29.313482Z","iopub.execute_input":"2023-10-05T11:49:29.313823Z","iopub.status.idle":"2023-10-05T11:49:29.328312Z","shell.execute_reply.started":"2023-10-05T11:49:29.313792Z","shell.execute_reply":"2023-10-05T11:49:29.326983Z"},"trusted":true},"execution_count":47,"outputs":[{"name":"stdout","text":"POINT (40.03694444 -121.00583333)\nPOINT (38.93305556 -120.40444444)\nPOINT (38.98416667 -120.73555556)\nPOINT (38.55916667 -119.91333333)\nPOINT (38.55916667 -119.93305556)\n","output_type":"stream"}]},{"cell_type":"code","source":"fig, ax = plt.subplots(figsize=(20, 10)) \ngdf.plot(markersize=0.1, ax=ax, aspect=1, facecolor=\"none\", edgecolor=\"black\", linewidth=5); \nax.set_title(' Incedent Points') \nplt.show()\n\n\n","metadata":{"execution":{"iopub.status.busy":"2023-10-05T14:13:10.467590Z","iopub.execute_input":"2023-10-05T14:13:10.468021Z","iopub.status.idle":"2023-10-05T14:13:10.784855Z","shell.execute_reply.started":"2023-10-05T14:13:10.467988Z","shell.execute_reply":"2023-10-05T14:13:10.783750Z"},"trusted":true},"execution_count":89,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 2000x1000 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"# creating a projection \nprojected_gdf = gdf.to_crs({'init': 'epsg:32618'})\nprojected_gdf.head()","metadata":{"execution":{"iopub.status.busy":"2023-10-05T11:49:29.671551Z","iopub.execute_input":"2023-10-05T11:49:29.672478Z","iopub.status.idle":"2023-10-05T11:49:29.746977Z","shell.execute_reply.started":"2023-10-05T11:49:29.672447Z","shell.execute_reply":"2023-10-05T11:49:29.745845Z"},"trusted":true},"execution_count":49,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n  in_crs_string = _prepare_from_proj_string(in_crs_string)\n","output_type":"stream"},{"execution_count":49,"output_type":"execute_result","data":{"text/plain":"   OBJECTID  FOD_ID      FPA_ID SOURCE_SYSTEM_TYPE SOURCE_SYSTEM  \\\n0         1       1  FS-1418826                FED   FS-FIRESTAT   \n1         2       2  FS-1418827                FED   FS-FIRESTAT   \n2         3       3  FS-1418835                FED   FS-FIRESTAT   \n3         4       4  FS-1418845                FED   FS-FIRESTAT   \n4         5       5  FS-1418847                FED   FS-FIRESTAT   \n\n  NWCG_REPORTING_AGENCY NWCG_REPORTING_UNIT_ID  NWCG_REPORTING_UNIT_NAME  \\\n0                    FS                USCAPNF    Plumas National Forest   \n1                    FS                USCAENF  Eldorado National Forest   \n2                    FS                USCAENF  Eldorado National Forest   \n3                    FS                USCAENF  Eldorado National Forest   \n4                    FS                USCAENF  Eldorado National Forest   \n\n  SOURCE_REPORTING_UNIT SOURCE_REPORTING_UNIT_NAME LOCAL_FIRE_REPORT_ID  \\\n0                  0511     Plumas National Forest                    1   \n1                  0503   Eldorado National Forest                   13   \n2                  0503   Eldorado National Forest                   27   \n3                  0503   Eldorado National Forest                   43   \n4                  0503   Eldorado National Forest                   44   \n\n  LOCAL_INCIDENT_ID FIRE_CODE FIRE_NAME ICS_209_INCIDENT_NUMBER ICS_209_NAME  \\\n0            PNF-47      BJ8K  FOUNTAIN                       0            0   \n1                13      AAC0    PIGEON                       0            0   \n2               021      A32W     SLACK                       0            0   \n3                 6         0      DEER                       0            0   \n4                 7         0  STEVENOT                       0            0   \n\n  MTBS_ID MTBS_FIRE_NAME  COMPLEX_NAME  FIRE_YEAR  DISCOVERY_DATE  \\\n0       0              0          0.00       2005      2453403.50   \n1       0              0          0.00       2004      2453137.50   \n2       0              0          0.00       2004      2453156.50   \n3       0              0          0.00       2004      2453184.50   \n4       0              0          0.00       2004      2453184.50   \n\n   DISCOVERY_DOY DISCOVERY_TIME  STAT_CAUSE_CODE  STAT_CAUSE_DESCR  CONT_DATE  \\\n0             33           1300             9.00                 6 2453403.50   \n1            133           0845             1.00                 5 2453137.50   \n2            152           1921             5.00                 3 2453156.50   \n3            180           1600             1.00                 5 2453189.50   \n4            180           1600             1.00                 5 2453189.50   \n\n   CONT_DOY CONT_TIME  FIRE_SIZE  FIRE_SIZE_CLASS  LATITUDE  LONGITUDE  \\\n0     33.00      1730       0.10                0     40.04    -121.01   \n1    133.00      1530       0.25                0     38.93    -120.40   \n2    152.00      2024       0.10                0     38.98    -120.74   \n3    185.00      1400       0.10                0     38.56    -119.91   \n4    185.00      1200       0.10                0     38.56    -119.93   \n\n   OWNER_CODE       OWNER_DESCR  STATE COUNTY FIPS_CODE  FIPS_NAME  Shape  \\\n0        5.00              USFS      1     63       063         20     91   \n1        5.00              USFS      1     61       061         19     35   \n2       13.00  STATE OR PRIVATE      1     17       017          9     82   \n3        5.00              USFS      1      3       003          0     60   \n4        5.00              USFS      1      3       003          0     25   \n\n          geometry  \n0  POINT (inf inf)  \n1  POINT (inf inf)  \n2  POINT (inf inf)  \n3  POINT (inf inf)  \n4  POINT (inf inf)  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>OBJECTID</th>\n      <th>FOD_ID</th>\n      <th>FPA_ID</th>\n      <th>SOURCE_SYSTEM_TYPE</th>\n      <th>SOURCE_SYSTEM</th>\n      <th>NWCG_REPORTING_AGENCY</th>\n      <th>NWCG_REPORTING_UNIT_ID</th>\n      <th>NWCG_REPORTING_UNIT_NAME</th>\n      <th>SOURCE_REPORTING_UNIT</th>\n      <th>SOURCE_REPORTING_UNIT_NAME</th>\n      <th>LOCAL_FIRE_REPORT_ID</th>\n      <th>LOCAL_INCIDENT_ID</th>\n      <th>FIRE_CODE</th>\n      <th>FIRE_NAME</th>\n      <th>ICS_209_INCIDENT_NUMBER</th>\n      <th>ICS_209_NAME</th>\n      <th>MTBS_ID</th>\n      <th>MTBS_FIRE_NAME</th>\n      <th>COMPLEX_NAME</th>\n      <th>FIRE_YEAR</th>\n      <th>DISCOVERY_DATE</th>\n      <th>DISCOVERY_DOY</th>\n      <th>DISCOVERY_TIME</th>\n      <th>STAT_CAUSE_CODE</th>\n      <th>STAT_CAUSE_DESCR</th>\n      <th>CONT_DATE</th>\n      <th>CONT_DOY</th>\n      <th>CONT_TIME</th>\n      <th>FIRE_SIZE</th>\n      <th>FIRE_SIZE_CLASS</th>\n      <th>LATITUDE</th>\n      <th>LONGITUDE</th>\n      <th>OWNER_CODE</th>\n      <th>OWNER_DESCR</th>\n      <th>STATE</th>\n      <th>COUNTY</th>\n      <th>FIPS_CODE</th>\n      <th>FIPS_NAME</th>\n      <th>Shape</th>\n      <th>geometry</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>FS-1418826</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USCAPNF</td>\n      <td>Plumas National Forest</td>\n      <td>0511</td>\n      <td>Plumas National Forest</td>\n      <td>1</td>\n      <td>PNF-47</td>\n      <td>BJ8K</td>\n      <td>FOUNTAIN</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.00</td>\n      <td>2005</td>\n      <td>2453403.50</td>\n      <td>33</td>\n      <td>1300</td>\n      <td>9.00</td>\n      <td>6</td>\n      <td>2453403.50</td>\n      <td>33.00</td>\n      <td>1730</td>\n      <td>0.10</td>\n      <td>0</td>\n      <td>40.04</td>\n      <td>-121.01</td>\n      <td>5.00</td>\n      <td>USFS</td>\n      <td>1</td>\n      <td>63</td>\n      <td>063</td>\n      <td>20</td>\n      <td>91</td>\n      <td>POINT (inf inf)</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2</td>\n      <td>FS-1418827</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USCAENF</td>\n      <td>Eldorado National Forest</td>\n      <td>0503</td>\n      <td>Eldorado National Forest</td>\n      <td>13</td>\n      <td>13</td>\n      <td>AAC0</td>\n      <td>PIGEON</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.00</td>\n      <td>2004</td>\n      <td>2453137.50</td>\n      <td>133</td>\n      <td>0845</td>\n      <td>1.00</td>\n      <td>5</td>\n      <td>2453137.50</td>\n      <td>133.00</td>\n      <td>1530</td>\n      <td>0.25</td>\n      <td>0</td>\n      <td>38.93</td>\n      <td>-120.40</td>\n      <td>5.00</td>\n      <td>USFS</td>\n      <td>1</td>\n      <td>61</td>\n      <td>061</td>\n      <td>19</td>\n      <td>35</td>\n      <td>POINT (inf inf)</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>3</td>\n      <td>FS-1418835</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USCAENF</td>\n      <td>Eldorado National Forest</td>\n      <td>0503</td>\n      <td>Eldorado National Forest</td>\n      <td>27</td>\n      <td>021</td>\n      <td>A32W</td>\n      <td>SLACK</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.00</td>\n      <td>2004</td>\n      <td>2453156.50</td>\n      <td>152</td>\n      <td>1921</td>\n      <td>5.00</td>\n      <td>3</td>\n      <td>2453156.50</td>\n      <td>152.00</td>\n      <td>2024</td>\n      <td>0.10</td>\n      <td>0</td>\n      <td>38.98</td>\n      <td>-120.74</td>\n      <td>13.00</td>\n      <td>STATE OR PRIVATE</td>\n      <td>1</td>\n      <td>17</td>\n      <td>017</td>\n      <td>9</td>\n      <td>82</td>\n      <td>POINT (inf inf)</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>4</td>\n      <td>FS-1418845</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USCAENF</td>\n      <td>Eldorado National Forest</td>\n      <td>0503</td>\n      <td>Eldorado National Forest</td>\n      <td>43</td>\n      <td>6</td>\n      <td>0</td>\n      <td>DEER</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.00</td>\n      <td>2004</td>\n      <td>2453184.50</td>\n      <td>180</td>\n      <td>1600</td>\n      <td>1.00</td>\n      <td>5</td>\n      <td>2453189.50</td>\n      <td>185.00</td>\n      <td>1400</td>\n      <td>0.10</td>\n      <td>0</td>\n      <td>38.56</td>\n      <td>-119.91</td>\n      <td>5.00</td>\n      <td>USFS</td>\n      <td>1</td>\n      <td>3</td>\n      <td>003</td>\n      <td>0</td>\n      <td>60</td>\n      <td>POINT (inf inf)</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>5</td>\n      <td>FS-1418847</td>\n      <td>FED</td>\n      <td>FS-FIRESTAT</td>\n      <td>FS</td>\n      <td>USCAENF</td>\n      <td>Eldorado National Forest</td>\n      <td>0503</td>\n      <td>Eldorado National Forest</td>\n      <td>44</td>\n      <td>7</td>\n      <td>0</td>\n      <td>STEVENOT</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.00</td>\n      <td>2004</td>\n      <td>2453184.50</td>\n      <td>180</td>\n      <td>1600</td>\n      <td>1.00</td>\n      <td>5</td>\n      <td>2453189.50</td>\n      <td>185.00</td>\n      <td>1200</td>\n      <td>0.10</td>\n      <td>0</td>\n      <td>38.56</td>\n      <td>-119.93</td>\n      <td>5.00</td>\n      <td>USFS</td>\n      <td>1</td>\n      <td>3</td>\n      <td>003</td>\n      <td>0</td>\n      <td>25</td>\n      <td>POINT (inf inf)</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# plotting the projected values \nfig, ax = plt.subplots(figsize=(10, 10))\nprojected_gdf.plot(markersize=0.1,ax=ax, aspect=1, facecolor=\"none\", edgecolor=\"black\", linewidth=5);\nax.set_title('pojected points of the Incident')\nplt.tight_layout()\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2023-10-05T14:14:44.284309Z","iopub.execute_input":"2023-10-05T14:14:44.284751Z","iopub.status.idle":"2023-10-05T14:14:44.689345Z","shell.execute_reply.started":"2023-10-05T14:14:44.284713Z","shell.execute_reply":"2023-10-05T14:14:44.688048Z"},"trusted":true},"execution_count":90,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x1000 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"##  <b><span style='color:#8F00FF'>| 1.1. </span> Resources I used for this Notebook </b>\n\n- notebook by DAVE FISHER-HICKEY\n- notebook by THJAQUENOUD\n- notebook done by Dan \n- kaggle GeoSpatial Course \n- [Pygis Website](https://pygis.io/docs/e_buffer_neighbors.html) \n- [Introduction to GIS analysis](https://pmarchand1.github.io/atelier_rgeo/rgeo_workshop.html)\n- GeoPandas Documentation \n- Folium Documentation\n- [Math Planet](https://www.mathplanet.com/education/geometry/points-lines-planes-and-angles/an-introduction-to-geometry#:~:text=A%20point%20in%20geometry%20is,extends%20infinitely%20in%20two%20directions.)","metadata":{}}]}