diff --git a/la_crime_analysis/hack4la-los-angleo-arrest-data.ipynb b/la_crime_analysis/hack4la-los-angleo-arrest-data.ipynb new file mode 100644 index 0000000..6b53343 --- /dev/null +++ b/la_crime_analysis/hack4la-los-angleo-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":[{"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 diff --git a/la_crime_analysis/text.txt b/la_crime_analysis/text.txt deleted file mode 100644 index e69de29..0000000