<jupyter_start><jupyter_text>DATA SECTION Data Requirements:Geodata from Foursquare for all the following:HotelsMetroAmenitiesTrendy placesGovernment authoritiesCafes(Foursquare)San Francisco Crime Data which depicts all the total crime in the neighborhood(https://cocl.us/sanfran_crime_dataset)Geojson file for boundries(https://cocl.us/sanfran_geojson/sanfran_geo.json)Processing:Data processing will be done using Python 3. Foursquare API will be used to get all the required geographical information. Pandas will be used for manipulation and Folium will be used mapping.<jupyter_code>import pandas as pd
df_sfcrime = pd.read_csv("https://cocl.us/sanfran_crime_dataset")
df_tmp = df_sfcrime.groupby(['PdDistrict']).count().reset_index()
df_tmp.drop(['Category','Descript','DayOfWeek','Date','Time', 'Resolution','Address','X','Y','Location','PdId'], axis=1, inplace=True)
df_tmp.rename(columns={'PdDistrict':'Neighborhood', 'IncidntNum':'Count'}, inplace=True)
df_tmp<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.119, 'fraction_numerical': 0.002, 'mean_word_length': 9.214285714285714, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 3, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '79561', 'license': 'MIT', 'n_tokens_mistral': 328, 'n_tokens_neox': 309, 'n_words': 73, 'path': 'Data_Section.ipynb', 'repo_name': 'adityashnkr/Safe_Locations', 'size': '1000'}