diff --git a/la_crime_analysis/hack4la-los-angeles-arrest-data.ipynb b/la_crime_analysis/hack4la-los-angeles-arrest-data.ipynb new file mode 100644 index 0000000..51f231d --- /dev/null +++ b/la_crime_analysis/hack4la-los-angeles-arrest-data.ipynb @@ -0,0 +1 @@ +{"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":[{"source":"\"Kaggle\"","metadata":{},"cell_type":"markdown"},{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom scipy.stats import norm\nfrom sklearn.preprocessing import StandardScaler\nfrom scipy import stats\nimport warnings\n\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-10-22T06:51:31.68824Z","iopub.execute_input":"2023-10-22T06:51:31.688605Z","iopub.status.idle":"2023-10-22T06:51:31.705983Z","shell.execute_reply.started":"2023-10-22T06:51:31.688576Z","shell.execute_reply":"2023-10-22T06:51:31.70487Z"},"trusted":true},"execution_count":38,"outputs":[{"name":"stdout","text":"/kaggle/input/los-angeles-crime-arrest-data/UCR-COMPSTAT062618.pdf\n/kaggle/input/los-angeles-crime-arrest-data/socrata_metadata_arrest-data-from-2010-to-present.json\n/kaggle/input/los-angeles-crime-arrest-data/crime-data-from-2010-to-present.csv\n/kaggle/input/los-angeles-crime-arrest-data/socrata_metadata_crime-data-from-2010-to-present.json\n/kaggle/input/los-angeles-crime-arrest-data/MO_CODES_Numerical_20180627.pdf\n/kaggle/input/los-angeles-crime-arrest-data/ucr_handbook_2013.pdf\n/kaggle/input/los-angeles-crime-arrest-data/arrest-data-from-2010-to-present.csv\n","output_type":"stream"}]},{"cell_type":"code","source":"df_arrests = pd.read_csv(\"/kaggle/input/los-angeles-crime-arrest-data/arrest-data-from-2010-to-present.csv\")","metadata":{"execution":{"iopub.status.busy":"2023-10-22T06:51:31.708079Z","iopub.execute_input":"2023-10-22T06:51:31.70906Z","iopub.status.idle":"2023-10-22T06:51:42.360338Z","shell.execute_reply.started":"2023-10-22T06:51:31.709024Z","shell.execute_reply":"2023-10-22T06:51:42.359338Z"},"trusted":true},"execution_count":39,"outputs":[]},{"cell_type":"code","source":"df_arrests.head()","metadata":{"execution":{"iopub.status.busy":"2023-10-22T06:51:42.361562Z","iopub.execute_input":"2023-10-22T06:51:42.362406Z","iopub.status.idle":"2023-10-22T06:51:42.391218Z","shell.execute_reply.started":"2023-10-22T06:51:42.362372Z","shell.execute_reply":"2023-10-22T06:51:42.390169Z"},"trusted":true},"execution_count":40,"outputs":[{"execution_count":40,"output_type":"execute_result","data":{"text/plain":" Report ID Arrest Date Time Area ID Area Name \\\n0 5666847 2019-06-22T00:00:00.000 1630.0 14 Pacific \n1 5666688 2019-06-22T00:00:00.000 1010.0 10 West Valley \n2 5666570 2019-06-22T00:00:00.000 400.0 15 N Hollywood \n3 5666529 2019-06-22T00:00:00.000 302.0 17 Devonshire \n4 5666742 2019-06-22T00:00:00.000 1240.0 14 Pacific \n\n Reporting District Age Sex Code Descent Code Charge Group Code ... \\\n0 1457 44 M W 24.0 ... \n1 1061 8 M O NaN ... \n2 1543 31 F O 22.0 ... \n3 1738 23 F W 22.0 ... \n4 1472 28 M W 8.0 ... \n\n Charge Description \\\n0 VANDALISM \n1 NaN \n2 DRUNK DRIVING ALCOHOL/DRUGS \n3 DRUNK DRIVING ALCOHOL/DRUGS \n4 OBSTRUCT/RESIST EXECUTIVE OFFICER \n\n Address \\\n0 12300 CULVER BL \n1 19000 VANOWEN ST \n2 MAGNOLIA AV \n3 HAYVENHURST ST \n4 6600 ESPLANADE ST \n\n Cross Street \\\n0 NaN \n1 NaN \n2 LAUREL CANYON BL \n3 N REGAN FY \n4 NaN \n\n Location Zip Codes Census Tracts \\\n0 {'latitude': '33.992', 'human_address': '{\"add... 24031.0 918.0 \n1 {'latitude': '34.1687', 'human_address': '{\"ad... 19339.0 321.0 \n2 {'latitude': '34.1649', 'human_address': '{\"ad... 8890.0 205.0 \n3 {'latitude': '34.2692', 'human_address': '{\"ad... 19329.0 69.0 \n4 {'latitude': '33.9609', 'human_address': '{\"ad... 25075.0 937.0 \n\n Precinct Boundaries LA Specific Plans Council Districts \\\n0 1137.0 10.0 10.0 \n1 1494.0 NaN 4.0 \n2 1332.0 17.0 5.0 \n3 388.0 NaN 2.0 \n4 241.0 10.0 10.0 \n\n Neighborhood Councils (Certified) \n0 85.0 \n1 10.0 \n2 39.0 \n3 78.0 \n4 16.0 \n\n[5 rows x 23 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Report IDArrest DateTimeArea IDArea NameReporting DistrictAgeSex CodeDescent CodeCharge Group Code...Charge DescriptionAddressCross StreetLocationZip CodesCensus TractsPrecinct BoundariesLA Specific PlansCouncil DistrictsNeighborhood Councils (Certified)
056668472019-06-22T00:00:00.0001630.014Pacific145744MW24.0...VANDALISM12300 CULVER BLNaN{'latitude': '33.992', 'human_address': '{\"add...24031.0918.01137.010.010.085.0
156666882019-06-22T00:00:00.0001010.010West Valley10618MONaN...NaN19000 VANOWEN STNaN{'latitude': '34.1687', 'human_address': '{\"ad...19339.0321.01494.0NaN4.010.0
256665702019-06-22T00:00:00.000400.015N Hollywood154331FO22.0...DRUNK DRIVING ALCOHOL/DRUGSMAGNOLIA AVLAUREL CANYON BL{'latitude': '34.1649', 'human_address': '{\"ad...8890.0205.01332.017.05.039.0
356665292019-06-22T00:00:00.000302.017Devonshire173823FW22.0...DRUNK DRIVING ALCOHOL/DRUGSHAYVENHURST STN REGAN FY{'latitude': '34.2692', 'human_address': '{\"ad...19329.069.0388.0NaN2.078.0
456667422019-06-22T00:00:00.0001240.014Pacific147228MW8.0...OBSTRUCT/RESIST EXECUTIVE OFFICER6600 ESPLANADE STNaN{'latitude': '33.9609', 'human_address': '{\"ad...25075.0937.0241.010.010.016.0
\n

5 rows × 23 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"df_arrests.shape","metadata":{"execution":{"iopub.status.busy":"2023-10-22T06:51:42.392404Z","iopub.execute_input":"2023-10-22T06:51:42.393013Z","iopub.status.idle":"2023-10-22T06:51:42.409024Z","shell.execute_reply.started":"2023-10-22T06:51:42.392981Z","shell.execute_reply":"2023-10-22T06:51:42.407683Z"},"trusted":true},"execution_count":41,"outputs":[{"execution_count":41,"output_type":"execute_result","data":{"text/plain":"(1276160, 23)"},"metadata":{}}]},{"cell_type":"code","source":"df_arrests.columns","metadata":{"execution":{"iopub.status.busy":"2023-10-22T06:51:42.412206Z","iopub.execute_input":"2023-10-22T06:51:42.412657Z","iopub.status.idle":"2023-10-22T06:51:42.427356Z","shell.execute_reply.started":"2023-10-22T06:51:42.412625Z","shell.execute_reply":"2023-10-22T06:51:42.425961Z"},"trusted":true},"execution_count":42,"outputs":[{"execution_count":42,"output_type":"execute_result","data":{"text/plain":"Index(['Report ID', 'Arrest Date', 'Time', 'Area ID', 'Area Name',\n 'Reporting District', 'Age', 'Sex Code', 'Descent Code',\n 'Charge Group Code', 'Charge Group Description', 'Arrest Type Code',\n 'Charge', 'Charge Description', 'Address', 'Cross Street', 'Location',\n 'Zip Codes', 'Census Tracts', 'Precinct Boundaries',\n 'LA Specific Plans', 'Council Districts',\n 'Neighborhood Councils (Certified)'],\n dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"df_arrests.columns = [c.replace(' ', '_') for c in df_arrests.columns]","metadata":{"execution":{"iopub.status.busy":"2023-10-22T06:51:42.4294Z","iopub.execute_input":"2023-10-22T06:51:42.430065Z","iopub.status.idle":"2023-10-22T06:51:42.440588Z","shell.execute_reply.started":"2023-10-22T06:51:42.430019Z","shell.execute_reply":"2023-10-22T06:51:42.439502Z"},"trusted":true},"execution_count":43,"outputs":[]},{"cell_type":"code","source":"df_arrests['Charge'].describe()","metadata":{"execution":{"iopub.status.busy":"2023-10-22T06:51:42.442215Z","iopub.execute_input":"2023-10-22T06:51:42.443887Z","iopub.status.idle":"2023-10-22T06:51:42.824919Z","shell.execute_reply.started":"2023-10-22T06:51:42.443844Z","shell.execute_reply":"2023-10-22T06:51:42.824043Z"},"trusted":true},"execution_count":44,"outputs":[{"execution_count":44,"output_type":"execute_result","data":{"text/plain":"count 1276160\nunique 8848\ntop 23152(A)VC\nfreq 95872\nName: Charge, dtype: object"},"metadata":{}}]},{"cell_type":"code","source":"df_arrests['Location'].head()","metadata":{"execution":{"iopub.status.busy":"2023-10-22T06:51:42.826283Z","iopub.execute_input":"2023-10-22T06:51:42.826799Z","iopub.status.idle":"2023-10-22T06:51:42.83466Z","shell.execute_reply.started":"2023-10-22T06:51:42.826769Z","shell.execute_reply":"2023-10-22T06:51:42.833545Z"},"trusted":true},"execution_count":45,"outputs":[{"execution_count":45,"output_type":"execute_result","data":{"text/plain":"0 {'latitude': '33.992', 'human_address': '{\"add...\n1 {'latitude': '34.1687', 'human_address': '{\"ad...\n2 {'latitude': '34.1649', 'human_address': '{\"ad...\n3 {'latitude': '34.2692', 'human_address': '{\"ad...\n4 {'latitude': '33.9609', 'human_address': '{\"ad...\nName: Location, dtype: object"},"metadata":{}}]},{"cell_type":"code","source":"df_arrests.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-10-22T06:51:42.836377Z","iopub.execute_input":"2023-10-22T06:51:42.836722Z","iopub.status.idle":"2023-10-22T06:51:42.852839Z","shell.execute_reply.started":"2023-10-22T06:51:42.836694Z","shell.execute_reply":"2023-10-22T06:51:42.851508Z"},"trusted":true},"execution_count":46,"outputs":[{"execution_count":46,"output_type":"execute_result","data":{"text/plain":"Report_ID int64\nArrest_Date object\nTime float64\nArea_ID int64\nArea_Name object\nReporting_District int64\nAge int64\nSex_Code object\nDescent_Code object\nCharge_Group_Code float64\nCharge_Group_Description object\nArrest_Type_Code object\nCharge object\nCharge_Description object\nAddress object\nCross_Street object\nLocation object\nZip_Codes float64\nCensus_Tracts float64\nPrecinct_Boundaries float64\nLA_Specific_Plans float64\nCouncil_Districts float64\nNeighborhood_Councils_(Certified) float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"df_arrests.describe()","metadata":{"execution":{"iopub.status.busy":"2023-10-22T06:51:42.85418Z","iopub.execute_input":"2023-10-22T06:51:42.854532Z","iopub.status.idle":"2023-10-22T06:51:43.664803Z","shell.execute_reply.started":"2023-10-22T06:51:42.854496Z","shell.execute_reply":"2023-10-22T06:51:43.663375Z"},"trusted":true},"execution_count":47,"outputs":[{"execution_count":47,"output_type":"execute_result","data":{"text/plain":" Report_ID Time Area_ID Reporting_District \\\ncount 1.276160e+06 1.275966e+06 1.276160e+06 1.276160e+06 \nmean 4.580398e+07 1.388382e+03 1.022075e+01 1.068619e+03 \nstd 6.333676e+07 6.632716e+02 6.197272e+00 6.182593e+02 \nmin 0.000000e+00 0.000000e+00 1.000000e+00 1.000000e+02 \n25% 3.421109e+06 9.450000e+02 5.000000e+00 5.250000e+02 \n50% 4.682964e+06 1.515000e+03 1.100000e+01 1.113000e+03 \n75% 1.114043e+08 1.930000e+03 1.500000e+01 1.546000e+03 \nmax 9.921099e+08 2.400000e+03 2.100000e+01 2.199000e+03 \n\n Age Charge_Group_Code Zip_Codes Census_Tracts \\\ncount 1.276160e+06 1.191571e+06 1.275360e+06 1.264591e+06 \nmean 3.417930e+01 1.614084e+01 2.056087e+04 5.655900e+02 \nstd 1.363792e+01 7.090700e+00 5.773735e+03 3.851383e+02 \nmin 0.000000e+00 1.000000e+00 2.436000e+03 1.000000e+00 \n25% 2.300000e+01 1.000000e+01 1.973000e+04 3.030000e+02 \n50% 3.200000e+01 1.700000e+01 2.307500e+04 5.370000e+02 \n75% 4.500000e+01 2.300000e+01 2.366800e+04 7.450000e+02 \nmax 9.700000e+01 9.900000e+01 2.732500e+04 2.343000e+03 \n\n Precinct_Boundaries LA_Specific_Plans Council_Districts \\\ncount 1.270577e+06 440834.000000 1.266583e+06 \nmean 8.630631e+02 10.086631 8.554810e+00 \nstd 3.646982e+02 7.652713 3.977106e+00 \nmin 1.000000e+00 1.000000 1.000000e+00 \n25% 5.270000e+02 7.000000 5.000000e+00 \n50% 8.970000e+02 7.000000 9.000000e+00 \n75% 1.172000e+03 10.000000 1.200000e+01 \nmax 1.560000e+03 54.000000 1.500000e+01 \n\n Neighborhood_Councils_(Certified) \ncount 1.226324e+06 \nmean 4.653097e+01 \nstd 2.613038e+01 \nmin 1.000000e+00 \n25% 2.600000e+01 \n50% 4.500000e+01 \n75% 7.300000e+01 \nmax 9.500000e+01 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Report_IDTimeArea_IDReporting_DistrictAgeCharge_Group_CodeZip_CodesCensus_TractsPrecinct_BoundariesLA_Specific_PlansCouncil_DistrictsNeighborhood_Councils_(Certified)
count1.276160e+061.275966e+061.276160e+061.276160e+061.276160e+061.191571e+061.275360e+061.264591e+061.270577e+06440834.0000001.266583e+061.226324e+06
mean4.580398e+071.388382e+031.022075e+011.068619e+033.417930e+011.614084e+012.056087e+045.655900e+028.630631e+0210.0866318.554810e+004.653097e+01
std6.333676e+076.632716e+026.197272e+006.182593e+021.363792e+017.090700e+005.773735e+033.851383e+023.646982e+027.6527133.977106e+002.613038e+01
min0.000000e+000.000000e+001.000000e+001.000000e+020.000000e+001.000000e+002.436000e+031.000000e+001.000000e+001.0000001.000000e+001.000000e+00
25%3.421109e+069.450000e+025.000000e+005.250000e+022.300000e+011.000000e+011.973000e+043.030000e+025.270000e+027.0000005.000000e+002.600000e+01
50%4.682964e+061.515000e+031.100000e+011.113000e+033.200000e+011.700000e+012.307500e+045.370000e+028.970000e+027.0000009.000000e+004.500000e+01
75%1.114043e+081.930000e+031.500000e+011.546000e+034.500000e+012.300000e+012.366800e+047.450000e+021.172000e+0310.0000001.200000e+017.300000e+01
max9.921099e+082.400000e+032.100000e+012.199000e+039.700000e+019.900000e+012.732500e+042.343000e+031.560000e+0354.0000001.500000e+019.500000e+01
\n
"},"metadata":{}}]},{"cell_type":"code","source":"df_arrests.Age.mean()","metadata":{"execution":{"iopub.status.busy":"2023-10-22T06:51:43.666733Z","iopub.execute_input":"2023-10-22T06:51:43.667224Z","iopub.status.idle":"2023-10-22T06:51:43.677243Z","shell.execute_reply.started":"2023-10-22T06:51:43.667178Z","shell.execute_reply":"2023-10-22T06:51:43.675925Z"},"trusted":true},"execution_count":48,"outputs":[{"execution_count":48,"output_type":"execute_result","data":{"text/plain":"34.17930040120361"},"metadata":{}}]},{"cell_type":"code","source":"df_arrests.Charge.count()","metadata":{"execution":{"iopub.status.busy":"2023-10-22T06:51:43.678741Z","iopub.execute_input":"2023-10-22T06:51:43.679289Z","iopub.status.idle":"2023-10-22T06:51:43.822481Z","shell.execute_reply.started":"2023-10-22T06:51:43.67922Z","shell.execute_reply":"2023-10-22T06:51:43.821112Z"},"trusted":true},"execution_count":49,"outputs":[{"execution_count":49,"output_type":"execute_result","data":{"text/plain":"1276160"},"metadata":{}}]},{"cell_type":"code","source":"df_arrests.Age.value_counts()","metadata":{"execution":{"iopub.status.busy":"2023-10-22T06:51:43.823594Z","iopub.execute_input":"2023-10-22T06:51:43.824159Z","iopub.status.idle":"2023-10-22T06:51:43.853358Z","shell.execute_reply.started":"2023-10-22T06:51:43.824109Z","shell.execute_reply":"2023-10-22T06:51:43.851887Z"},"trusted":true},"execution_count":50,"outputs":[{"execution_count":50,"output_type":"execute_result","data":{"text/plain":"Age\n21 45069\n22 45060\n23 44414\n24 43341\n25 41936\n ... \n94 6\n92 5\n93 5\n96 4\n97 1\nName: count, Length: 97, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"df_arrests.Location.value_counts()","metadata":{"execution":{"iopub.status.busy":"2023-10-22T06:51:43.858267Z","iopub.execute_input":"2023-10-22T06:51:43.858668Z","iopub.status.idle":"2023-10-22T06:51:44.340668Z","shell.execute_reply.started":"2023-10-22T06:51:43.858636Z","shell.execute_reply":"2023-10-22T06:51:44.339354Z"},"trusted":true},"execution_count":51,"outputs":[{"execution_count":51,"output_type":"execute_result","data":{"text/plain":"Location\n{'latitude': '34.1837', 'human_address': '{\"address\": \"\", \"city\": \"\", \"state\": \"\", \"zip\": \"\"}', 'longitude': '-118.4465'} 7989\n{'latitude': '34.0423', 'human_address': '{\"address\": \"\", \"city\": \"\", \"state\": \"\", \"zip\": \"\"}', 'longitude': '-118.2452'} 5382\n{'latitude': '34.1016', 'human_address': '{\"address\": \"\", \"city\": \"\", \"state\": \"\", \"zip\": \"\"}', 'longitude': '-118.3387'} 5175\n{'latitude': '34.0601', 'human_address': '{\"address\": \"\", \"city\": \"\", \"state\": \"\", \"zip\": \"\"}', 'longitude': '-118.2761'} 4656\n{'latitude': '33.9708', 'human_address': '{\"address\": \"\", \"city\": \"\", \"state\": \"\", \"zip\": \"\"}', 'longitude': '-118.2783'} 4555\n ... \n{'latitude': '34.0604', 'human_address': '{\"address\": \"\", \"city\": \"\", \"state\": \"\", \"zip\": \"\"}', 'longitude': '-118.5028'} 1\n{'latitude': '33.9126', 'human_address': '{\"address\": \"\", \"city\": \"\", \"state\": \"\", \"zip\": \"\"}', 'longitude': '-118.1796'} 1\n{'latitude': '34.25', 'human_address': '{\"address\": \"\", \"city\": \"\", \"state\": \"\", \"zip\": \"\"}', 'longitude': '-118.3574'} 1\n{'latitude': '33.9988', 'human_address': '{\"address\": \"\", \"city\": \"\", \"state\": \"\", \"zip\": \"\"}', 'longitude': '-118.2431'} 1\n{'latitude': '34.1592', 'human_address': '{\"address\": \"\", \"city\": \"\", \"state\": \"\", \"zip\": \"\"}', 'longitude': '-118.627'} 1\nName: count, Length: 53716, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"df_arrests.Area_Name.value_counts()","metadata":{"execution":{"iopub.status.busy":"2023-10-22T06:51:44.342476Z","iopub.execute_input":"2023-10-22T06:51:44.342839Z","iopub.status.idle":"2023-10-22T06:51:44.57578Z","shell.execute_reply.started":"2023-10-22T06:51:44.342809Z","shell.execute_reply":"2023-10-22T06:51:44.574476Z"},"trusted":true},"execution_count":52,"outputs":[{"execution_count":52,"output_type":"execute_result","data":{"text/plain":"Area_Name\nCentral 125985\nHollywood 119882\nPacific 100213\n77th Street 69949\nSouthwest 68498\nNewton 67609\nVan Nuys 66766\nRampart 65558\nMission 64306\nN Hollywood 62407\nOlympic 50095\nNortheast 49497\nSoutheast 48049\nHarbor 46982\nFoothill 46815\nTopanga 43399\nHollenbeck 41869\nWest Valley 39454\nDevonshire 39276\nWilshire 31368\nWest LA 28183\nName: count, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"df_arrests.Charge_Description.value_counts()","metadata":{"execution":{"iopub.status.busy":"2023-10-22T06:51:44.57749Z","iopub.execute_input":"2023-10-22T06:51:44.577863Z","iopub.status.idle":"2023-10-22T06:51:44.822032Z","shell.execute_reply.started":"2023-10-22T06:51:44.577832Z","shell.execute_reply":"2023-10-22T06:51:44.820778Z"},"trusted":true},"execution_count":53,"outputs":[{"execution_count":53,"output_type":"execute_result","data":{"text/plain":"Charge_Description\nDRUNK DRIVING ALCOHOL/DRUGS 95410\nDRINKING IN PUBLIC 92965\nCORPORAL INJURY ON SPOUSE/COHABITANT/ETC 44812\nPOSSESSION CONTROLLED SUBSTANCE 43697\nFTA AFTER WRITTEN PROMISE 31177\n ... \nBUY/SELL PROP W/ID REMOVED, > $400 1\nUNAUTHORIZED ALTERATION OF DRIVER'S LIC 1\nATTEMPTED TORTURE 1\nMATERIAL WITNESS/CIVIL CONTEMPT 1\nATTEMPT POSSESS CONTROLLED SUBSTANCE 1\nName: count, Length: 2334, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"df_arrests.Area_Name.unique()","metadata":{"execution":{"iopub.status.busy":"2023-10-22T06:51:44.823498Z","iopub.execute_input":"2023-10-22T06:51:44.823851Z","iopub.status.idle":"2023-10-22T06:51:44.959599Z","shell.execute_reply.started":"2023-10-22T06:51:44.823821Z","shell.execute_reply":"2023-10-22T06:51:44.958393Z"},"trusted":true},"execution_count":54,"outputs":[{"execution_count":54,"output_type":"execute_result","data":{"text/plain":"array(['Pacific', 'West Valley', 'N Hollywood', 'Devonshire', 'Mission',\n 'Central', 'Southeast', '77th Street', 'West LA', 'Southwest',\n 'Northeast', 'Hollywood', 'Harbor', 'Van Nuys', 'Foothill',\n 'Hollenbeck', 'Newton', 'Wilshire', 'Topanga', 'Olympic',\n 'Rampart'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Graphs for Arrest Data ","metadata":{"execution":{"iopub.status.busy":"2023-10-22T06:51:44.961009Z","iopub.execute_input":"2023-10-22T06:51:44.961354Z","iopub.status.idle":"2023-10-22T06:51:44.966087Z","shell.execute_reply.started":"2023-10-22T06:51:44.961324Z","shell.execute_reply":"2023-10-22T06:51:44.96489Z"},"trusted":true},"execution_count":55,"outputs":[]},{"cell_type":"code","source":"#Histogram\nsns.distplot(df_arrests['Age'])","metadata":{"execution":{"iopub.status.busy":"2023-10-22T06:51:44.967467Z","iopub.execute_input":"2023-10-22T06:51:44.967802Z","iopub.status.idle":"2023-10-22T06:51:51.115255Z","shell.execute_reply.started":"2023-10-22T06:51:44.967772Z","shell.execute_reply":"2023-10-22T06:51:51.114225Z"},"trusted":true},"execution_count":56,"outputs":[{"name":"stderr","text":"/tmp/ipykernel_32/4250040629.py:2: UserWarning: \n\n`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n\nPlease adapt your code to use either `displot` (a figure-level function with\nsimilar flexibility) or `histplot` (an axes-level function for histograms).\n\nFor a guide to updating your code to use the new functions, please see\nhttps://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n\n sns.distplot(df_arrests['Age'])\n","output_type":"stream"},{"execution_count":56,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"#Scatter Plot \nvar = \ndata\ndata.plot.scatter(x=var, y='SalePrice', ylim=())","metadata":{"execution":{"iopub.status.busy":"2023-10-22T06:51:51.116909Z","iopub.execute_input":"2023-10-22T06:51:51.11744Z","iopub.status.idle":"2023-10-22T06:51:51.12536Z","shell.execute_reply.started":"2023-10-22T06:51:51.117409Z","shell.execute_reply":"2023-10-22T06:51:51.123135Z"},"trusted":true},"execution_count":57,"outputs":[{"traceback":["\u001b[0;36m Cell \u001b[0;32mIn[57], line 2\u001b[0;36m\u001b[0m\n\u001b[0;31m var =\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"],"ename":"SyntaxError","evalue":"invalid syntax (595406866.py, line 2)","output_type":"error"}]},{"cell_type":"code","source":"#Scatter Plot","metadata":{"execution":{"iopub.status.busy":"2023-10-22T06:51:51.126477Z","iopub.status.idle":"2023-10-22T06:51:51.126881Z","shell.execute_reply.started":"2023-10-22T06:51:51.126684Z","shell.execute_reply":"2023-10-22T06:51:51.126703Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Skewness and Kurtosis","metadata":{"execution":{"iopub.status.busy":"2023-10-22T06:51:51.129004Z","iopub.status.idle":"2023-10-22T06:51:51.1298Z","shell.execute_reply.started":"2023-10-22T06:51:51.129459Z","shell.execute_reply":"2023-10-22T06:51:51.129523Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Box Plot","metadata":{"execution":{"iopub.status.busy":"2023-10-22T06:51:51.131027Z","iopub.status.idle":"2023-10-22T06:51:51.131605Z","shell.execute_reply.started":"2023-10-22T06:51:51.131323Z","shell.execute_reply":"2023-10-22T06:51:51.13135Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Correlation Matrix","metadata":{"execution":{"iopub.status.busy":"2023-10-22T06:51:51.133602Z","iopub.status.idle":"2023-10-22T06:51:51.134007Z","shell.execute_reply.started":"2023-10-22T06:51:51.133816Z","shell.execute_reply":"2023-10-22T06:51:51.133835Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Correlation Matrix","metadata":{"execution":{"iopub.status.busy":"2023-10-22T06:51:51.136092Z","iopub.status.idle":"2023-10-22T06:51:51.136535Z","shell.execute_reply.started":"2023-10-22T06:51:51.136342Z","shell.execute_reply":"2023-10-22T06:51:51.136361Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]} diff --git a/la_crime_analysis/hack4la-los-angeles-crime-data.ipynb b/la_crime_analysis/hack4la-los-angeles-crime-data.ipynb new file mode 100644 index 0000000..9301a1b --- /dev/null +++ b/la_crime_analysis/hack4la-los-angeles-crime-data.ipynb @@ -0,0 +1 @@ +{"cells":[{"source":"\"Kaggle\"","metadata":{},"cell_type":"markdown"},{"cell_type":"code","execution_count":1,"id":"d97cae5b","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.execute_input":"2023-10-22T06:46:06.502586Z","iopub.status.busy":"2023-10-22T06:46:06.502114Z","iopub.status.idle":"2023-10-22T06:46:08.565005Z","shell.execute_reply":"2023-10-22T06:46:08.563551Z"},"papermill":{"duration":2.07361,"end_time":"2023-10-22T06:46:08.56772","exception":false,"start_time":"2023-10-22T06:46:06.49411","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["/kaggle/input/los-angeles-crime-arrest-data/UCR-COMPSTAT062618.pdf\n","/kaggle/input/los-angeles-crime-arrest-data/socrata_metadata_arrest-data-from-2010-to-present.json\n","/kaggle/input/los-angeles-crime-arrest-data/crime-data-from-2010-to-present.csv\n","/kaggle/input/los-angeles-crime-arrest-data/socrata_metadata_crime-data-from-2010-to-present.json\n","/kaggle/input/los-angeles-crime-arrest-data/MO_CODES_Numerical_20180627.pdf\n","/kaggle/input/los-angeles-crime-arrest-data/ucr_handbook_2013.pdf\n","/kaggle/input/los-angeles-crime-arrest-data/arrest-data-from-2010-to-present.csv\n"]}],"source":["# This Python 3 environment comes with many helpful analytics libraries installed\n","# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n","# For example, here's several helpful packages to load\n","\n","import numpy as np # linear algebra\n","import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n","import seaborn as sns\n","import matplotlib.pyplot as plt\n","from scipy.stats import norm\n","from sklearn.preprocessing import StandardScaler\n","from scipy import stats\n","import warnings\n","\n","\n","# Input data files are available in the read-only \"../input/\" directory\n","# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n","\n","import os\n","for dirname, _, filenames in os.walk('/kaggle/input'):\n"," for filename in filenames:\n"," print(os.path.join(dirname, filename))\n","\n","# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n","# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"]},{"cell_type":"code","execution_count":2,"id":"3e737e77","metadata":{"execution":{"iopub.execute_input":"2023-10-22T06:46:08.580377Z","iopub.status.busy":"2023-10-22T06:46:08.579937Z","iopub.status.idle":"2023-10-22T06:46:24.096447Z","shell.execute_reply":"2023-10-22T06:46:24.094877Z"},"papermill":{"duration":15.525238,"end_time":"2023-10-22T06:46:24.098818","exception":false,"start_time":"2023-10-22T06:46:08.57358","status":"completed"},"tags":[]},"outputs":[],"source":["df_crime = pd.read_csv(\"/kaggle/input/los-angeles-crime-arrest-data/crime-data-from-2010-to-present.csv\")"]},{"cell_type":"code","execution_count":3,"id":"c903a2e2","metadata":{"execution":{"iopub.execute_input":"2023-10-22T06:46:24.111677Z","iopub.status.busy":"2023-10-22T06:46:24.111298Z","iopub.status.idle":"2023-10-22T06:46:24.151238Z","shell.execute_reply":"2023-10-22T06:46:24.149936Z"},"papermill":{"duration":0.048788,"end_time":"2023-10-22T06:46:24.153211","exception":false,"start_time":"2023-10-22T06:46:24.104423","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["
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DR NumberDate ReportedDate OccurredTime OccurredArea IDArea NameReporting DistrictCrime CodeCrime Code DescriptionMO Codes...Weapon DescriptionStatus CodeStatus DescriptionCrime Code 1Crime Code 2Crime Code 3Crime Code 4AddressCross StreetLocation
01020055562010-01-25T00:00:002010-01-22T00:00:00230020Olympic2071510VEHICLE - STOLENNaN...NaNICInvest Cont510.0NaNNaNNaNVAN NESS15TH{'latitude': '34.0454', 'needs_recoding': Fals...
11018222892010-11-11T00:00:002010-11-10T00:00:00180018Southeast1803510VEHICLE - STOLENNaN...NaNICInvest Cont510.0NaNNaNNaN88THWALL{'latitude': '33.9572', 'needs_recoding': Fals...
21011056092010-01-28T00:00:002010-01-27T00:00:00223011Northeast1125510VEHICLE - STOLENNaN...NaNICInvest Cont510.0NaNNaNNaNYORKAVENUE 51{'latitude': '34.1211', 'needs_recoding': Fals...
31016200512010-11-11T00:00:002010-11-07T00:00:00160016Foothill1641510VEHICLE - STOLENNaN...NaNICInvest Cont510.0NaNNaNNaNEL DORADOTRUESDALE{'latitude': '34.241', 'needs_recoding': False...
41019104982010-04-07T00:00:002010-04-07T00:00:00160019Mission1902510VEHICLE - STOLENNaN...NaNICInvest Cont510.0NaNNaNNaNGLENOAKSDRELL{'latitude': '34.3147', 'needs_recoding': Fals...
\n","

5 rows × 26 columns

\n","
"],"text/plain":[" DR Number Date Reported Date Occurred Time Occurred \\\n","0 102005556 2010-01-25T00:00:00 2010-01-22T00:00:00 2300 \n","1 101822289 2010-11-11T00:00:00 2010-11-10T00:00:00 1800 \n","2 101105609 2010-01-28T00:00:00 2010-01-27T00:00:00 2230 \n","3 101620051 2010-11-11T00:00:00 2010-11-07T00:00:00 1600 \n","4 101910498 2010-04-07T00:00:00 2010-04-07T00:00:00 1600 \n","\n"," Area ID Area Name Reporting District Crime Code Crime Code Description \\\n","0 20 Olympic 2071 510 VEHICLE - STOLEN \n","1 18 Southeast 1803 510 VEHICLE - STOLEN \n","2 11 Northeast 1125 510 VEHICLE - STOLEN \n","3 16 Foothill 1641 510 VEHICLE - STOLEN \n","4 19 Mission 1902 510 VEHICLE - STOLEN \n","\n"," MO Codes ... Weapon Description Status Code Status Description \\\n","0 NaN ... NaN IC Invest Cont \n","1 NaN ... NaN IC Invest Cont \n","2 NaN ... NaN IC Invest Cont \n","3 NaN ... NaN IC Invest Cont \n","4 NaN ... NaN IC Invest Cont \n","\n"," Crime Code 1 Crime Code 2 Crime Code 3 Crime Code 4 Address \\\n","0 510.0 NaN NaN NaN VAN NESS \n","1 510.0 NaN NaN NaN 88TH \n","2 510.0 NaN NaN NaN YORK \n","3 510.0 NaN NaN NaN EL DORADO \n","4 510.0 NaN NaN NaN GLENOAKS \n","\n"," Cross Street Location \n","0 15TH {'latitude': '34.0454', 'needs_recoding': Fals... \n","1 WALL {'latitude': '33.9572', 'needs_recoding': Fals... \n","2 AVENUE 51 {'latitude': '34.1211', 'needs_recoding': Fals... \n","3 TRUESDALE {'latitude': '34.241', 'needs_recoding': False... \n","4 DRELL {'latitude': '34.3147', 'needs_recoding': Fals... \n","\n","[5 rows x 26 columns]"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["df_crime.head()"]},{"cell_type":"code","execution_count":4,"id":"ae754e34","metadata":{"execution":{"iopub.execute_input":"2023-10-22T06:46:24.165906Z","iopub.status.busy":"2023-10-22T06:46:24.165553Z","iopub.status.idle":"2023-10-22T06:46:24.171541Z","shell.execute_reply":"2023-10-22T06:46:24.170535Z"},"papermill":{"duration":0.014571,"end_time":"2023-10-22T06:46:24.173461","exception":false,"start_time":"2023-10-22T06:46:24.15889","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["(1993259, 26)"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["df_crime.shape"]},{"cell_type":"code","execution_count":5,"id":"b938dc38","metadata":{"execution":{"iopub.execute_input":"2023-10-22T06:46:24.18726Z","iopub.status.busy":"2023-10-22T06:46:24.186952Z","iopub.status.idle":"2023-10-22T06:46:24.194027Z","shell.execute_reply":"2023-10-22T06:46:24.193085Z"},"papermill":{"duration":0.016113,"end_time":"2023-10-22T06:46:24.196101","exception":false,"start_time":"2023-10-22T06:46:24.179988","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["Index(['DR Number', 'Date Reported', 'Date Occurred', 'Time Occurred',\n"," 'Area ID', 'Area Name', 'Reporting District', 'Crime Code',\n"," 'Crime Code Description', 'MO Codes', 'Victim Age', 'Victim Sex',\n"," 'Victim Descent', 'Premise Code', 'Premise Description',\n"," 'Weapon Used Code', 'Weapon Description', 'Status Code',\n"," 'Status Description', 'Crime Code 1', 'Crime Code 2', 'Crime Code 3',\n"," 'Crime Code 4', 'Address', 'Cross Street', 'Location '],\n"," dtype='object')"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["df_crime.columns"]},{"cell_type":"code","execution_count":6,"id":"4991a2fe","metadata":{"execution":{"iopub.execute_input":"2023-10-22T06:46:24.209394Z","iopub.status.busy":"2023-10-22T06:46:24.209061Z","iopub.status.idle":"2023-10-22T06:46:24.214413Z","shell.execute_reply":"2023-10-22T06:46:24.213739Z"},"papermill":{"duration":0.014334,"end_time":"2023-10-22T06:46:24.216364","exception":false,"start_time":"2023-10-22T06:46:24.20203","status":"completed"},"tags":[]},"outputs":[],"source":["df_crime.columns = [c.replace(' ', '_') for c in df_crime.columns]"]},{"cell_type":"code","execution_count":7,"id":"fc5f0cc0","metadata":{"execution":{"iopub.execute_input":"2023-10-22T06:46:24.229503Z","iopub.status.busy":"2023-10-22T06:46:24.229187Z","iopub.status.idle":"2023-10-22T06:46:24.399697Z","shell.execute_reply":"2023-10-22T06:46:24.398931Z"},"papermill":{"duration":0.179357,"end_time":"2023-10-22T06:46:24.40172","exception":false,"start_time":"2023-10-22T06:46:24.222363","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["count 1993259\n","unique 21\n","top 77th Street\n","freq 137513\n","Name: Area_Name, dtype: object"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["df_crime['Area_Name'].describe()"]},{"cell_type":"code","execution_count":8,"id":"045c3735","metadata":{"execution":{"iopub.execute_input":"2023-10-22T06:46:24.415326Z","iopub.status.busy":"2023-10-22T06:46:24.414917Z","iopub.status.idle":"2023-10-22T06:46:24.475521Z","shell.execute_reply":"2023-10-22T06:46:24.474683Z"},"papermill":{"duration":0.069676,"end_time":"2023-10-22T06:46:24.477432","exception":false,"start_time":"2023-10-22T06:46:24.407756","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["count 1.993259e+06\n","mean 5.069209e+02\n","std 2.104709e+02\n","min 1.100000e+02\n","25% 3.300000e+02\n","50% 4.410000e+02\n","75% 6.260000e+02\n","max 9.560000e+02\n","Name: Crime_Code, dtype: float64"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["df_crime['Crime_Code'].describe()"]},{"cell_type":"code","execution_count":9,"id":"4bff3148","metadata":{"execution":{"iopub.execute_input":"2023-10-22T06:46:24.490174Z","iopub.status.busy":"2023-10-22T06:46:24.489887Z","iopub.status.idle":"2023-10-22T06:46:24.498115Z","shell.execute_reply":"2023-10-22T06:46:24.496826Z"},"papermill":{"duration":0.016772,"end_time":"2023-10-22T06:46:24.49994","exception":false,"start_time":"2023-10-22T06:46:24.483168","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["0 VEHICLE - STOLEN\n","1 VEHICLE - STOLEN\n","2 VEHICLE - STOLEN\n","3 VEHICLE - STOLEN\n","4 VEHICLE - STOLEN\n","Name: Crime_Code_Description, dtype: object"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["df_crime['Crime_Code_Description'].head()"]},{"cell_type":"code","execution_count":10,"id":"02422c84","metadata":{"execution":{"iopub.execute_input":"2023-10-22T06:46:24.5136Z","iopub.status.busy":"2023-10-22T06:46:24.513292Z","iopub.status.idle":"2023-10-22T06:46:25.163199Z","shell.execute_reply":"2023-10-22T06:46:25.161637Z"},"papermill":{"duration":0.659647,"end_time":"2023-10-22T06:46:25.165904","exception":false,"start_time":"2023-10-22T06:46:24.506257","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["
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DR_NumberTime_OccurredArea_IDReporting_DistrictCrime_CodeVictim_AgePremise_CodeWeapon_Used_CodeCrime_Code_1Crime_Code_2Crime_Code_3Crime_Code_4
count1.993259e+061.993259e+061.993259e+061.993259e+061.993259e+061.993259e+061.993209e+06667618.0000001.993250e+06130161.0000003235.00000096.000000
mean1.452681e+081.361522e+031.110423e+011.156819e+035.069209e+023.178082e+013.111638e+02371.1451265.067552e+02950.934166971.498300974.218750
std2.759142e+076.467373e+026.007329e+006.007339e+022.104709e+022.060810e+012.110329e+02113.6431542.103421e+02125.44224587.08415984.043623
min2.100000e+021.000000e+001.000000e+001.000000e+021.100000e+02-9.000000e+001.010000e+02101.0000001.100000e+02210.00000093.000000421.000000
25%1.212041e+089.300000e+026.000000e+006.440000e+023.300000e+022.000000e+011.020000e+02400.0000003.300000e+02998.000000998.000000998.000000
50%1.502066e+081.430000e+031.200000e+011.203000e+034.410000e+023.200000e+012.100000e+02400.0000004.410000e+02998.000000998.000000998.000000
75%1.707102e+081.900000e+031.600000e+011.672000e+036.260000e+024.600000e+015.010000e+02400.0000006.260000e+02998.000000998.000000998.000000
max9.102204e+082.359000e+032.100000e+012.199000e+039.560000e+021.180000e+029.710000e+02516.0000009.990000e+02999.000000999.000000999.000000
\n","
"],"text/plain":[" DR_Number Time_Occurred Area_ID Reporting_District \\\n","count 1.993259e+06 1.993259e+06 1.993259e+06 1.993259e+06 \n","mean 1.452681e+08 1.361522e+03 1.110423e+01 1.156819e+03 \n","std 2.759142e+07 6.467373e+02 6.007329e+00 6.007339e+02 \n","min 2.100000e+02 1.000000e+00 1.000000e+00 1.000000e+02 \n","25% 1.212041e+08 9.300000e+02 6.000000e+00 6.440000e+02 \n","50% 1.502066e+08 1.430000e+03 1.200000e+01 1.203000e+03 \n","75% 1.707102e+08 1.900000e+03 1.600000e+01 1.672000e+03 \n","max 9.102204e+08 2.359000e+03 2.100000e+01 2.199000e+03 \n","\n"," Crime_Code Victim_Age Premise_Code Weapon_Used_Code \\\n","count 1.993259e+06 1.993259e+06 1.993209e+06 667618.000000 \n","mean 5.069209e+02 3.178082e+01 3.111638e+02 371.145126 \n","std 2.104709e+02 2.060810e+01 2.110329e+02 113.643154 \n","min 1.100000e+02 -9.000000e+00 1.010000e+02 101.000000 \n","25% 3.300000e+02 2.000000e+01 1.020000e+02 400.000000 \n","50% 4.410000e+02 3.200000e+01 2.100000e+02 400.000000 \n","75% 6.260000e+02 4.600000e+01 5.010000e+02 400.000000 \n","max 9.560000e+02 1.180000e+02 9.710000e+02 516.000000 \n","\n"," Crime_Code_1 Crime_Code_2 Crime_Code_3 Crime_Code_4 \n","count 1.993250e+06 130161.000000 3235.000000 96.000000 \n","mean 5.067552e+02 950.934166 971.498300 974.218750 \n","std 2.103421e+02 125.442245 87.084159 84.043623 \n","min 1.100000e+02 210.000000 93.000000 421.000000 \n","25% 3.300000e+02 998.000000 998.000000 998.000000 \n","50% 4.410000e+02 998.000000 998.000000 998.000000 \n","75% 6.260000e+02 998.000000 998.000000 998.000000 \n","max 9.990000e+02 999.000000 999.000000 999.000000 "]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["df_crime.describe()"]},{"cell_type":"code","execution_count":11,"id":"e751f067","metadata":{"execution":{"iopub.execute_input":"2023-10-22T06:46:25.180019Z","iopub.status.busy":"2023-10-22T06:46:25.179703Z","iopub.status.idle":"2023-10-22T06:46:25.189178Z","shell.execute_reply":"2023-10-22T06:46:25.187834Z"},"papermill":{"duration":0.018759,"end_time":"2023-10-22T06:46:25.191102","exception":false,"start_time":"2023-10-22T06:46:25.172343","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["31.780824268195953"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["df_crime.Victim_Age.mean()"]},{"cell_type":"code","execution_count":12,"id":"f2556dbf","metadata":{"execution":{"iopub.execute_input":"2023-10-22T06:46:25.205509Z","iopub.status.busy":"2023-10-22T06:46:25.205145Z","iopub.status.idle":"2023-10-22T06:46:25.212346Z","shell.execute_reply":"2023-10-22T06:46:25.210956Z"},"papermill":{"duration":0.016666,"end_time":"2023-10-22T06:46:25.214357","exception":false,"start_time":"2023-10-22T06:46:25.197691","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["1993259"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["df_crime.DR_Number.count()"]},{"cell_type":"code","execution_count":13,"id":"6eb8518c","metadata":{"execution":{"iopub.execute_input":"2023-10-22T06:46:25.229423Z","iopub.status.busy":"2023-10-22T06:46:25.229048Z","iopub.status.idle":"2023-10-22T06:46:25.335393Z","shell.execute_reply":"2023-10-22T06:46:25.334506Z"},"papermill":{"duration":0.11606,"end_time":"2023-10-22T06:46:25.337477","exception":false,"start_time":"2023-10-22T06:46:25.221417","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["Area_Name\n","77th Street 137513\n","Southwest 128111\n","N Hollywood 107707\n","Pacific 105655\n","Southeast 105104\n","Mission 98395\n","Northeast 94912\n","Van Nuys 94358\n","Newton 94123\n","Hollywood 92742\n","Topanga 92262\n","Devonshire 91347\n","Central 90489\n","Olympic 89634\n","Harbor 86972\n","West Valley 84521\n","Rampart 84241\n","West LA 83736\n","Wilshire 82575\n","Foothill 75348\n","Hollenbeck 73514\n","Name: count, dtype: int64"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["df_crime.Area_Name.value_counts()"]},{"cell_type":"code","execution_count":14,"id":"3ad13ebb","metadata":{"execution":{"iopub.execute_input":"2023-10-22T06:46:25.353742Z","iopub.status.busy":"2023-10-22T06:46:25.35271Z","iopub.status.idle":"2023-10-22T06:46:25.653656Z","shell.execute_reply":"2023-10-22T06:46:25.652573Z"},"papermill":{"duration":0.310401,"end_time":"2023-10-22T06:46:25.655474","exception":false,"start_time":"2023-10-22T06:46:25.345073","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["Address\n","6TH ST 4524\n","7TH ST 3567\n","9300 TAMPA AV 3396\n","6TH 3025\n","5TH ST 2874\n"," ... \n","FLEET AV 1\n","PURDUE ST 1\n","100 SPINNAKER MA 1\n","6500 SUNSET AV 1\n","CORINGA 1\n","Name: count, Length: 74330, dtype: int64"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["df_crime.Address.value_counts()"]},{"cell_type":"code","execution_count":15,"id":"efb645f3","metadata":{"execution":{"iopub.execute_input":"2023-10-22T06:46:25.670002Z","iopub.status.busy":"2023-10-22T06:46:25.669618Z","iopub.status.idle":"2023-10-22T06:46:25.796254Z","shell.execute_reply":"2023-10-22T06:46:25.794743Z"},"papermill":{"duration":0.136765,"end_time":"2023-10-22T06:46:25.798777","exception":false,"start_time":"2023-10-22T06:46:25.662012","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["Crime_Code_Description\n","BATTERY - SIMPLE ASSAULT 180434\n","BURGLARY FROM VEHICLE 153451\n","VEHICLE - STOLEN 151622\n","THEFT PLAIN - PETTY ($950 & UNDER) 141489\n","BURGLARY 140926\n"," ... \n","BLOCKING DOOR INDUCTION CENTER 3\n","TRAIN WRECKING 2\n","FIREARMS RESTRAINING ORDER (FIREARMS RO) 2\n","DRUNK ROLL - ATTEMPT 1\n","FIREARMS TEMPORARY RESTRAINING ORDER (TEMP FIREARMS RO) 1\n","Name: count, Length: 140, dtype: int64"]},"execution_count":15,"metadata":{},"output_type":"execute_result"}],"source":["df_crime.Crime_Code_Description.value_counts()"]},{"cell_type":"code","execution_count":16,"id":"4098746b","metadata":{"execution":{"iopub.execute_input":"2023-10-22T06:46:25.814459Z","iopub.status.busy":"2023-10-22T06:46:25.814128Z","iopub.status.idle":"2023-10-22T06:46:25.929511Z","shell.execute_reply":"2023-10-22T06:46:25.928102Z"},"papermill":{"duration":0.125574,"end_time":"2023-10-22T06:46:25.931988","exception":false,"start_time":"2023-10-22T06:46:25.806414","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["array(['Olympic', 'Southeast', 'Northeast', 'Foothill', 'Mission',\n"," 'Newton', 'West Valley', '77th Street', 'Pacific', 'N Hollywood',\n"," 'Topanga', 'Devonshire', 'Rampart', 'Central', 'Southwest',\n"," 'Hollenbeck', 'Hollywood', 'Harbor', 'West LA', 'Wilshire',\n"," 'Van Nuys'], dtype=object)"]},"execution_count":16,"metadata":{},"output_type":"execute_result"}],"source":["df_crime.Area_Name.unique()"]},{"cell_type":"code","execution_count":null,"id":"78647279","metadata":{"papermill":{"duration":0.006525,"end_time":"2023-10-22T06:46:25.945495","exception":false,"start_time":"2023-10-22T06:46:25.93897","status":"completed"},"tags":[]},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"id":"633962c9","metadata":{"papermill":{"duration":0.006805,"end_time":"2023-10-22T06:46:25.958924","exception":false,"start_time":"2023-10-22T06:46:25.952119","status":"completed"},"tags":[]},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"id":"634d28ce","metadata":{"papermill":{"duration":0.006498,"end_time":"2023-10-22T06:46:25.972296","exception":false,"start_time":"2023-10-22T06:46:25.965798","status":"completed"},"tags":[]},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"id":"263937fe","metadata":{"papermill":{"duration":0.006707,"end_time":"2023-10-22T06:46:25.985693","exception":false,"start_time":"2023-10-22T06:46:25.978986","status":"completed"},"tags":[]},"outputs":[],"source":[]},{"cell_type":"code","execution_count":17,"id":"260acf7a","metadata":{"execution":{"iopub.execute_input":"2023-10-22T06:46:26.00108Z","iopub.status.busy":"2023-10-22T06:46:26.000766Z","iopub.status.idle":"2023-10-22T06:46:26.005208Z","shell.execute_reply":"2023-10-22T06:46:26.003966Z"},"papermill":{"duration":0.015167,"end_time":"2023-10-22T06:46:26.007651","exception":false,"start_time":"2023-10-22T06:46:25.992484","status":"completed"},"tags":[]},"outputs":[],"source":["#Graphs for Crime Data "]},{"cell_type":"code","execution_count":18,"id":"2873cbc9","metadata":{"execution":{"iopub.execute_input":"2023-10-22T06:46:26.0233Z","iopub.status.busy":"2023-10-22T06:46:26.022986Z","iopub.status.idle":"2023-10-22T06:46:34.53808Z","shell.execute_reply":"2023-10-22T06:46:34.536917Z"},"papermill":{"duration":8.525552,"end_time":"2023-10-22T06:46:34.540484","exception":false,"start_time":"2023-10-22T06:46:26.014932","status":"completed"},"tags":[]},"outputs":[{"name":"stderr","output_type":"stream","text":["/tmp/ipykernel_20/3006690556.py:2: UserWarning: \n","\n","`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n","\n","Please adapt your code to use either `displot` (a figure-level function with\n","similar flexibility) or `histplot` (an axes-level function for histograms).\n","\n","For a guide to updating your code to use the new functions, please see\n","https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n","\n"," sns.distplot(df_crime['Victim_Age'])\n"]},{"data":{"text/plain":[""]},"execution_count":18,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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"]},"metadata":{},"output_type":"display_data"}],"source":["#Histogram \n","sns.distplot(df_crime['Victim_Age'])"]},{"cell_type":"code","execution_count":19,"id":"65006ccc","metadata":{"execution":{"iopub.execute_input":"2023-10-22T06:46:34.558283Z","iopub.status.busy":"2023-10-22T06:46:34.557194Z","iopub.status.idle":"2023-10-22T06:46:34.563165Z","shell.execute_reply":"2023-10-22T06:46:34.56208Z"},"papermill":{"duration":0.016887,"end_time":"2023-10-22T06:46:34.565129","exception":false,"start_time":"2023-10-22T06:46:34.548242","status":"completed"},"tags":[]},"outputs":[],"source":["#Scatter Plot"]},{"cell_type":"code","execution_count":20,"id":"970e1aa8","metadata":{"execution":{"iopub.execute_input":"2023-10-22T06:46:34.582136Z","iopub.status.busy":"2023-10-22T06:46:34.581771Z","iopub.status.idle":"2023-10-22T06:46:34.585605Z","shell.execute_reply":"2023-10-22T06:46:34.584679Z"},"papermill":{"duration":0.015459,"end_time":"2023-10-22T06:46:34.588318","exception":false,"start_time":"2023-10-22T06:46:34.572859","status":"completed"},"tags":[]},"outputs":[],"source":["#Skewness and Kurtosis"]},{"cell_type":"code","execution_count":21,"id":"baef42fa","metadata":{"execution":{"iopub.execute_input":"2023-10-22T06:46:34.605755Z","iopub.status.busy":"2023-10-22T06:46:34.605066Z","iopub.status.idle":"2023-10-22T06:46:34.609261Z","shell.execute_reply":"2023-10-22T06:46:34.608029Z"},"papermill":{"duration":0.015453,"end_time":"2023-10-22T06:46:34.611557","exception":false,"start_time":"2023-10-22T06:46:34.596104","status":"completed"},"tags":[]},"outputs":[],"source":["#Box Plot"]},{"cell_type":"code","execution_count":22,"id":"c95499be","metadata":{"execution":{"iopub.execute_input":"2023-10-22T06:46:34.628509Z","iopub.status.busy":"2023-10-22T06:46:34.628146Z","iopub.status.idle":"2023-10-22T06:46:34.632327Z","shell.execute_reply":"2023-10-22T06:46:34.6314Z"},"papermill":{"duration":0.014851,"end_time":"2023-10-22T06:46:34.634165","exception":false,"start_time":"2023-10-22T06:46:34.619314","status":"completed"},"tags":[]},"outputs":[],"source":["# Correlation Matrix"]},{"cell_type":"code","execution_count":23,"id":"293effef","metadata":{"execution":{"iopub.execute_input":"2023-10-22T06:46:34.651015Z","iopub.status.busy":"2023-10-22T06:46:34.650599Z","iopub.status.idle":"2023-10-22T06:46:34.655433Z","shell.execute_reply":"2023-10-22T06:46:34.654058Z"},"papermill":{"duration":0.015736,"end_time":"2023-10-22T06:46:34.657557","exception":false,"start_time":"2023-10-22T06:46:34.641821","status":"completed"},"tags":[]},"outputs":[],"source":["# Correlation Matrix"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.12"},"papermill":{"default_parameters":{},"duration":31.797952,"end_time":"2023-10-22T06:46:35.390083","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2023-10-22T06:46:03.592131","version":"2.4.0"}},"nbformat":4,"nbformat_minor":5} diff --git a/la_crime_analysis/hack4la-los-angleo-arrest-data.ipynb b/la_crime_analysis/hack4la-los-angleo-arrest-data.ipynb deleted file mode 100644 index 6b53343..0000000 --- a/la_crime_analysis/hack4la-los-angleo-arrest-data.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"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":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom scipy.stats import norm\nfrom sklearn.preprocessing import StandardScaler\nfrom scipy import stats\nimport warnings\n\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-10-17T02:15:51.220396Z","iopub.execute_input":"2023-10-17T02:15:51.220934Z","iopub.status.idle":"2023-10-17T02:15:51.229657Z","shell.execute_reply.started":"2023-10-17T02:15:51.220903Z","shell.execute_reply":"2023-10-17T02:15:51.228513Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"/kaggle/input/los-angeles-crime-arrest-data/UCR-COMPSTAT062618.pdf\n/kaggle/input/los-angeles-crime-arrest-data/socrata_metadata_arrest-data-from-2010-to-present.json\n/kaggle/input/los-angeles-crime-arrest-data/crime-data-from-2010-to-present.csv\n/kaggle/input/los-angeles-crime-arrest-data/socrata_metadata_crime-data-from-2010-to-present.json\n/kaggle/input/los-angeles-crime-arrest-data/MO_CODES_Numerical_20180627.pdf\n/kaggle/input/los-angeles-crime-arrest-data/ucr_handbook_2013.pdf\n/kaggle/input/los-angeles-crime-arrest-data/arrest-data-from-2010-to-present.csv\n","output_type":"stream"}]},{"cell_type":"code","source":"df_arrests = pd.read_csv(\"/kaggle/input/los-angeles-crime-arrest-data/arrest-data-from-2010-to-present.csv\")","metadata":{"execution":{"iopub.status.busy":"2023-10-17T02:19:10.085959Z","iopub.execute_input":"2023-10-17T02:19:10.086354Z","iopub.status.idle":"2023-10-17T02:19:20.454848Z","shell.execute_reply.started":"2023-10-17T02:19:10.086326Z","shell.execute_reply":"2023-10-17T02:19:20.453744Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"df_arrests.head()","metadata":{"execution":{"iopub.status.busy":"2023-10-15T20:00:09.248277Z","iopub.execute_input":"2023-10-15T20:00:09.248652Z","iopub.status.idle":"2023-10-15T20:00:09.293760Z","shell.execute_reply.started":"2023-10-15T20:00:09.248624Z","shell.execute_reply":"2023-10-15T20:00:09.292622Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":" Report ID Arrest Date Time Area ID Area Name \\\n0 5666847 2019-06-22T00:00:00.000 1630.0 14 Pacific \n1 5666688 2019-06-22T00:00:00.000 1010.0 10 West Valley \n2 5666570 2019-06-22T00:00:00.000 400.0 15 N Hollywood \n3 5666529 2019-06-22T00:00:00.000 302.0 17 Devonshire \n4 5666742 2019-06-22T00:00:00.000 1240.0 14 Pacific \n\n Reporting District Age Sex Code Descent Code Charge Group Code ... \\\n0 1457 44 M W 24.0 ... \n1 1061 8 M O NaN ... \n2 1543 31 F O 22.0 ... \n3 1738 23 F W 22.0 ... \n4 1472 28 M W 8.0 ... \n\n Charge Description \\\n0 VANDALISM \n1 NaN \n2 DRUNK DRIVING ALCOHOL/DRUGS \n3 DRUNK DRIVING ALCOHOL/DRUGS \n4 OBSTRUCT/RESIST EXECUTIVE OFFICER \n\n Address \\\n0 12300 CULVER BL \n1 19000 VANOWEN ST \n2 MAGNOLIA AV \n3 HAYVENHURST ST \n4 6600 ESPLANADE ST \n\n Cross Street \\\n0 NaN \n1 NaN \n2 LAUREL CANYON BL \n3 N REGAN FY \n4 NaN \n\n Location Zip Codes Census Tracts \\\n0 {'latitude': '33.992', 'human_address': '{\"add... 24031.0 918.0 \n1 {'latitude': '34.1687', 'human_address': '{\"ad... 19339.0 321.0 \n2 {'latitude': '34.1649', 'human_address': '{\"ad... 8890.0 205.0 \n3 {'latitude': '34.2692', 'human_address': '{\"ad... 19329.0 69.0 \n4 {'latitude': '33.9609', 'human_address': '{\"ad... 25075.0 937.0 \n\n Precinct Boundaries LA Specific Plans Council Districts \\\n0 1137.0 10.0 10.0 \n1 1494.0 NaN 4.0 \n2 1332.0 17.0 5.0 \n3 388.0 NaN 2.0 \n4 241.0 10.0 10.0 \n\n Neighborhood Councils (Certified) \n0 85.0 \n1 10.0 \n2 39.0 \n3 78.0 \n4 16.0 \n\n[5 rows x 23 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Report IDArrest DateTimeArea IDArea NameReporting DistrictAgeSex CodeDescent CodeCharge Group Code...Charge DescriptionAddressCross StreetLocationZip CodesCensus TractsPrecinct BoundariesLA Specific PlansCouncil DistrictsNeighborhood Councils (Certified)
056668472019-06-22T00:00:00.0001630.014Pacific145744MW24.0...VANDALISM12300 CULVER BLNaN{'latitude': '33.992', 'human_address': '{\"add...24031.0918.01137.010.010.085.0
156666882019-06-22T00:00:00.0001010.010West Valley10618MONaN...NaN19000 VANOWEN STNaN{'latitude': '34.1687', 'human_address': '{\"ad...19339.0321.01494.0NaN4.010.0
256665702019-06-22T00:00:00.000400.015N Hollywood154331FO22.0...DRUNK DRIVING ALCOHOL/DRUGSMAGNOLIA AVLAUREL CANYON BL{'latitude': '34.1649', 'human_address': '{\"ad...8890.0205.01332.017.05.039.0
356665292019-06-22T00:00:00.000302.017Devonshire173823FW22.0...DRUNK DRIVING ALCOHOL/DRUGSHAYVENHURST STN REGAN FY{'latitude': '34.2692', 'human_address': '{\"ad...19329.069.0388.0NaN2.078.0
456667422019-06-22T00:00:00.0001240.014Pacific147228MW8.0...OBSTRUCT/RESIST EXECUTIVE OFFICER6600 ESPLANADE STNaN{'latitude': '33.9609', 'human_address': '{\"ad...25075.0937.0241.010.010.016.0
\n

5 rows × 23 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"df_arrests.rows","metadata":{"execution":{"iopub.status.busy":"2023-10-17T02:22:26.086890Z","iopub.execute_input":"2023-10-17T02:22:26.087291Z","iopub.status.idle":"2023-10-17T02:22:26.117091Z","shell.execute_reply.started":"2023-10-17T02:22:26.087260Z","shell.execute_reply":"2023-10-17T02:22:26.115548Z"},"trusted":true},"execution_count":9,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_32/3971610920.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf_arrests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrows\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 5985\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5986\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5987\u001b[0m ):\n\u001b[1;32m 5988\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5989\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'rows'"],"ename":"AttributeError","evalue":"'DataFrame' object has no attribute 'rows'","output_type":"error"}]},{"cell_type":"code","source":"df_arrests.columns\n","metadata":{"execution":{"iopub.status.busy":"2023-10-15T20:00:18.084934Z","iopub.execute_input":"2023-10-15T20:00:18.085334Z","iopub.status.idle":"2023-10-15T20:00:18.091884Z","shell.execute_reply.started":"2023-10-15T20:00:18.085302Z","shell.execute_reply":"2023-10-15T20:00:18.090847Z"},"trusted":true},"execution_count":11,"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"Index(['Report ID', 'Arrest Date', 'Time', 'Area ID', 'Area Name',\n 'Reporting District', 'Age', 'Sex Code', 'Descent Code',\n 'Charge Group Code', 'Charge Group Description', 'Arrest Type Code',\n 'Charge', 'Charge Description', 'Address', 'Cross Street', 'Location',\n 'Zip Codes', 'Census Tracts', 'Precinct Boundaries',\n 'LA Specific Plans', 'Council Districts',\n 'Neighborhood Councils (Certified)'],\n dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"df_arrests['Charge'].describe()","metadata":{"execution":{"iopub.status.busy":"2023-10-17T02:19:36.342619Z","iopub.execute_input":"2023-10-17T02:19:36.342961Z","iopub.status.idle":"2023-10-17T02:19:36.543255Z","shell.execute_reply.started":"2023-10-17T02:19:36.342934Z","shell.execute_reply":"2023-10-17T02:19:36.542270Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"count 1276160\nunique 8848\ntop 23152(A)VC\nfreq 95872\nName: Charge, dtype: object"},"metadata":{}}]},{"cell_type":"code","source":"df_arrests['Location'].head()","metadata":{"execution":{"iopub.status.busy":"2023-10-17T02:19:49.343058Z","iopub.execute_input":"2023-10-17T02:19:49.343440Z","iopub.status.idle":"2023-10-17T02:19:49.351801Z","shell.execute_reply.started":"2023-10-17T02:19:49.343414Z","shell.execute_reply":"2023-10-17T02:19:49.350756Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"0 {'latitude': '33.992', 'human_address': '{\"add...\n1 {'latitude': '34.1687', 'human_address': '{\"ad...\n2 {'latitude': '34.1649', 'human_address': '{\"ad...\n3 {'latitude': '34.2692', 'human_address': '{\"ad...\n4 {'latitude': '33.9609', 'human_address': '{\"ad...\nName: Location, dtype: object"},"metadata":{}}]},{"cell_type":"code","source":"#histogram of ages for arrests\nsns.distplot(df_arrests['Age'])","metadata":{"execution":{"iopub.status.busy":"2023-10-17T02:20:09.768099Z","iopub.execute_input":"2023-10-17T02:20:09.768490Z","iopub.status.idle":"2023-10-17T02:20:15.618967Z","shell.execute_reply.started":"2023-10-17T02:20:09.768463Z","shell.execute_reply":"2023-10-17T02:20:15.618111Z"},"trusted":true},"execution_count":8,"outputs":[{"name":"stderr","text":"/tmp/ipykernel_32/2257843134.py:2: UserWarning: \n\n`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n\nPlease adapt your code to use either `displot` (a figure-level function with\nsimilar flexibility) or `histplot` (an axes-level function for histograms).\n\nFor a guide to updating your code to use the new functions, please see\nhttps://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n\n sns.distplot(df_arrests['Age'])\n","output_type":"stream"},{"execution_count":8,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"#scatter plot \nvar = \ndata\ndata.plot.scatter(x=var, y='SalePrice', ylim=())","metadata":{"execution":{"iopub.status.busy":"2023-10-15T19:52:57.174824Z","iopub.status.idle":"2023-10-15T19:52:57.175572Z","shell.execute_reply.started":"2023-10-15T19:52:57.175367Z","shell.execute_reply":"2023-10-15T19:52:57.175396Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#scatter plot","metadata":{"execution":{"iopub.status.busy":"2023-10-15T19:52:57.177613Z","iopub.status.idle":"2023-10-15T19:52:57.178082Z","shell.execute_reply.started":"2023-10-15T19:52:57.177820Z","shell.execute_reply":"2023-10-15T19:52:57.177837Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#skewness and kurtosis","metadata":{"execution":{"iopub.status.busy":"2023-10-15T19:52:57.179167Z","iopub.status.idle":"2023-10-15T19:52:57.179512Z","shell.execute_reply.started":"2023-10-15T19:52:57.179358Z","shell.execute_reply":"2023-10-15T19:52:57.179374Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Box plot","metadata":{"execution":{"iopub.status.busy":"2023-10-15T19:52:57.180188Z","iopub.status.idle":"2023-10-15T19:52:57.180493Z","shell.execute_reply.started":"2023-10-15T19:52:57.180340Z","shell.execute_reply":"2023-10-15T19:52:57.180355Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#correlation matrix","metadata":{"execution":{"iopub.status.busy":"2023-10-15T19:52:57.182675Z","iopub.status.idle":"2023-10-15T19:52:57.183016Z","shell.execute_reply.started":"2023-10-15T19:52:57.182859Z","shell.execute_reply":"2023-10-15T19:52:57.182876Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#saleprice correlation matrix","metadata":{"execution":{"iopub.status.busy":"2023-10-15T19:52:57.184477Z","iopub.status.idle":"2023-10-15T19:52:57.184995Z","shell.execute_reply.started":"2023-10-15T19:52:57.184726Z","shell.execute_reply":"2023-10-15T19:52:57.184751Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]} \ No newline at end of file