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CleaningPR.py
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CleaningPR.py
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import pandas as pd
import numpy as np
community_areas = {
1: "Rogers Park",
2: "West Ridge",
3: "Uptown",
4: "Lincoln Square",
5: "North Center",
6: "Lake View",
7: "Lincoln Park",
8: "Near North Side",
9: "Edison Park",
10: "Norwood Park",
11: "Jefferson Park",
12: "Forest Glen",
13: "North Park",
14: "Albany Park",
15: "Portage Park",
16: "Irving Park",
17: "Dunning",
18: "Montclare",
19: "Belmont Cragin",
20: "Hermosa",
21: "Avondale",
22: "Logan Square",
23: "Humboldt Park",
24: "West Town",
25: "Austin",
26: "West Garfield Park",
27: "East Garfield Park",
28: "Near West Side",
29: "North Lawndale",
30: "South Lawndale",
31: "Lower West Side",
32: "Loop",
33: "Near South Side",
34: "Armour Square",
35: "Douglas",
36: "Oakland",
37: "Fuller Park",
38: "Grand Boulevard",
39: "Kenwood",
40: "Washington Park",
41: "Hyde Park",
42: "Woodlawn",
43: "South Shore",
44: "Chatham",
45: "Avalon Park",
46: "South Chicago",
47: "Burnside",
48: "Calumet Heights",
49: "Roseland",
50: "Pullman",
51: "South Deering",
52: "East Side",
53: "West Pullman",
54: "Riverdale",
55: "Hegewisch",
56: "Garfield Ridge",
57: "Archer Heights",
58: "Brighton Park",
59: "McKinley Park",
60: "Bridgeport",
61: "New City",
62: "West Elsdon",
63: "Gage Park",
64: "Clearing",
65: "West Lawn",
66: "Chicago Lawn",
67: "West Englewood",
68: "Englewood",
69: "Greater Grand Crossing",
70: "Ashburn",
71: "Auburn Gresham",
72: "Beverly",
73: "Washington Heights",
74: "Mount Greenwood",
75: "Morgan Park",
76: "O'Hare",
77: "Edgewater"
}
severity_scores = {
'THEFT': 'Low',
'ROBBERY': 'High',
'SEX OFFENSE': 'High',
'OTHER OFFENSE': 'Medium',
'WEAPONS VIOLATION': 'Medium',
'OFFENSE INVOLVING CHILDREN': 'High',
'DECEPTIVE PRACTICE': 'Medium',
'STALKING': 'Medium',
'MOTOR VEHICLE THEFT': 'Medium',
'CRIMINAL DAMAGE': 'Medium',
'CRIMINAL TRESPASS': 'Low',
'BATTERY': 'Medium',
'ASSAULT': 'Medium',
'HOMICIDE': 'High',
'PROSTITUTION': 'Low',
'BURGLARY': 'Medium',
'NARCOTICS': 'Medium',
'KIDNAPPING': 'High',
'ARSON': 'High',
'CONCEALED CARRY LICENSE VIOLATION': 'Medium',
'CRIMINAL SEXUAL ASSAULT': 'High',
'INTERFERENCE WITH PUBLIC OFFICER': 'Medium',
'PUBLIC PEACE VIOLATION': 'Low',
'LIQUOR LAW VIOLATION': 'Low',
'INTIMIDATION': 'Medium',
'GAMBLING': 'Low',
'OBSCENITY': 'Medium',
'PUBLIC INDECENCY': 'Low',
'OTHER NARCOTIC VIOLATION': 'Medium',
'NON-CRIMINAL': 'Low',
'HUMAN TRAFFICKING': 'High'
}
def convertCrimeData(crime_data:pd.DataFrame):
crime_data_copy = crime_data.copy()
crime_data_copy['Arrest'] = crime_data_copy['Arrest'].replace({
True:1,
False:0
})
crime_data_copy['New_Date'] = pd.to_datetime(crime_data_copy['Date']) # Create new column
crime_data_copy.dropna(inplace=True) # Drop the NaN values
crime_data_copy = crime_data_copy[crime_data_copy['Community Area'] != 0] # Dropping the row which contain Community Area as 0
crime_data_copy['RegionName'] = crime_data_copy['Community Area'].apply(get_community) # change coordinates to neighborhood name
# crime_data.rename(columns={'Location':'RegionName'}, inplace = True) # Rename the column
return crime_data_copy
def dropCrimeDataColumns(col:list, crime_data:pd.DataFrame):
crime_data = crime_data[col]
return crime_data
def get_community(code):
return community_areas[code]
def decade_crime(crime_data:pd.DataFrame):
crime_data_decade = crime_data.copy()
crime_data_decade = crime_data_decade[(crime_data_decade['New_Date'].dt.year >= 2014)]
crime_data_decade['Severity_Score'] = crime_data_decade['Primary Type'].map(severity_scores)
return (crime_data_decade)
def pre_covid_post_covid(crime_data:pd.DataFrame):
crime_data_2017_2019 = crime_data.copy()
crime_data_2021_present = crime_data.copy()
# Pre Covid
crime_data_2017_2019 = crime_data_2017_2019[(crime_data_2017_2019['New_Date'].dt.year >= 2017) & (crime_data_2017_2019['New_Date'].dt.year <= 2019)]
# print("Before Droping NAN for pre covid")
# print("Number of NaN value for Location ",crime_data_2017_2019['Location'].isna().sum())
# print("Number of NaN value for Location Description",crime_data_2017_2019['Location Description'].isna().sum())
# print("Number of NaN value for Community Area ",crime_data_2017_2019['Community Area'].isna().sum())
# print("Number of NaN value for Primary Type ",crime_data_2017_2019['Primary Type'].isna().sum(), end="\n\n\n")
# crime_data_2017_2019.dropna(inplace=True)
# # checking purpose
# # print("Min new_date value: ", crime_data_2021_present['New_Date'].min()) # Earliest record of 2021
# # print(crime_data_2021_present['New_Date'].dt.year.unique()) # Making sure that the range (2021-2024)
# # checking purpose
# print("After Droping NAN for pre covid")
# print("Number of NaN value for Location ",crime_data_2017_2019['Location'].isna().sum())
# print("Number of NaN value for Location Description",crime_data_2017_2019['Location Description'].isna().sum())
# print("Number of NaN value for Community Area ",crime_data_2017_2019['Community Area'].isna().sum())
# print("Number of NaN value for Primary Type ",crime_data_2017_2019['Primary Type'].isna().sum(), end="\n\n\n")
# Post Covid
crime_data_2021_present = crime_data_2021_present[crime_data_2021_present['New_Date'].dt.year >= 2021]
crime_data_2021_present['Severity_Score'] = crime_data_2021_present['Primary Type'].map(severity_scores)
# print("Before Droping NAN for post covid")
# print("Number of NaN value for Location ",crime_data_2021_present['Location'].isna().sum())
# print("Number of NaN value for Location Description",crime_data_2021_present['Location Description'].isna().sum())
# print("Number of NaN value for Community Area ",crime_data_2021_present['Community Area'].isna().sum())
# print("Number of NaN value for Primary Type ",crime_data_2021_present['Primary Type'].isna().sum(), end="\n\n\n")
# crime_data_2021_present.dropna(inplace=True)
# # checking purpose
# # print("Min new_date value: ", crime_data_2021_present['New_Date'].min()) # Earliest record of 2021
# # print(crime_data_2021_present['New_Date'].dt.year.unique()) # Making sure that the range (2021-2024)
# # checking purpose
# print("After Droping NAN for post covid")
# print("Number of NaN value for Location ",crime_data_2021_present['Location'].isna().sum())
# print("Number of NaN value for Location Description",crime_data_2021_present['Location Description'].isna().sum())
# print("Number of NaN value for Community Area ",crime_data_2021_present['Community Area'].isna().sum())
# print("Number of NaN value for Primary Type ",crime_data_2021_present['Primary Type'].isna().sum(), end="\n\n\n")
# crime_data_2017_2019.loc[:, 'Location'] = crime_data_2017_2019['Community Area'].apply(get_community)
# crime_data_2021_present.loc[:, 'Location'] = crime_data_2021_present['Community Area'].apply(get_community)
# crime_data_2017_2019.rename(columns={'Location':'RegionName'}, inplace = True)
# crime_data_2021_present.rename(columns={'Location':'RegionName'}, inplace = True)
crime_data_2017_2019['Severity_Score'] = crime_data_2017_2019['Primary Type'].map(severity_scores)
return (crime_data_2017_2019, crime_data_2021_present)
def filterNeighborhood(neighborhood_data:pd.DataFrame):
neighborhood_data = neighborhood_data[(neighborhood_data['State'] == 'IL') & (neighborhood_data['City'] == 'Chicago')]
return neighborhood_data
def pre_covid_hd_post_covid_hd(neighborhood_data:pd.DataFrame):
# Pre Covid
first_half_column = neighborhood_data.loc[0:, ['RegionName']]
second_half_column = neighborhood_data.loc[0:, '2017-01-31':'2019-12-31']
# Post Covid
first_half_column_2 = neighborhood_data.loc[0:, ['RegionName']]
second_half_column_2 = neighborhood_data.loc[0:, '2021-01-31':]
neighborhood_data_2017_2019 = pd.concat([first_half_column, second_half_column], axis=1)
neighborhood_data_2021_present = pd.concat([first_half_column_2, second_half_column_2], axis=1)
return (neighborhood_data_2017_2019, neighborhood_data_2021_present)
def transpose_data(data:pd.DataFrame):
data.reset_index(inplace=True)
neighborhood_names_list = data["RegionName"].to_list()
data.drop(columns='index', inplace=True)
map = {}
for num in range(0,181):
map[num] = neighborhood_names_list[num]
data = data.transpose()
data.rename(columns=map, inplace= True)
data = data.iloc[1:]
data.reset_index(inplace=True)
data.rename(columns={'index':'date'}, inplace=True)
# droping the column which contain NA value (We can afford to drop a neighborhood)
data.dropna(axis=1, inplace = True)
# Converting the price to 2 decimal point
columns = data.columns.to_list()
columns = columns[1:]
for c in columns:
data[c] = data[c].apply(lambda x: round(x, 2))
data['date'] = pd.to_datetime(data['date'])
return data.copy()
def cleanCrimeData(crime_data:pd.DataFrame):
'''
Step 1) Converting the Date
Convert the crime data to a much suitable format and dropping the rows which contain NaN
'''
crime_data = convertCrimeData(crime_data)
'''
Step 2) Dropping the unecessary columns such as X & Y Coordinate,
Date, Block, IUCR, Description, Domestic, Beat, District, FBI code, Ward, Updated on, Latitude, Longitude
'''
col = ['ID', 'New_Date', 'Primary Type', 'Location Description', 'Arrest', 'Community Area', 'RegionName']
crime_data = dropCrimeDataColumns(col, crime_data)
print("Columns: ", crime_data.columns.to_list())
'''
Step 3) Filtering
Pre Covid, Post Covid and Decade Crime Data (Taking the data for the past decade to use for machine learning model)
'''
# The function called sepeates the pre covid (2017-2019) and post covid (2021-present) crimes into 2 different dataframes.
(crime_data_2017_2019, crime_data_2021_present) = pre_covid_post_covid(crime_data)
crime_data_2014 = decade_crime(crime_data)
# Pre Covid Range Verification
# print("Pre Covid Min new_date value: ", crime_data_2017_2019['New_Date'].min()) # Earliest record
# print("Pre Covid Max new_date value: ", crime_data_2017_2019['New_Date'].max()) # Latest record
# print()
# Post Covid Range Verification
# print("Post Covid Min new_date value: ", crime_data_2021_present['New_Date'].min()) # Earliest record
# print("Post Covid Max new_date value: ", crime_data_2021_present['New_Date'].max()) # Latest record
# print()
# Decade Crime Range Verification
# print("Decade Crime Min new_date value: ", crime_data_2014['New_Date'].min()) # Earliest record
# print("Decade Crime Max new_date value: ", crime_data_2014['New_Date'].max()) # Latest record
'''
Step 4) Saving the Dataframe to a CSV file
'''
crime_data_2021_present.to_csv('csv_files/Crimes_2021_to_Present.csv', index=False)
crime_data_2017_2019.to_csv('csv_files/Crimes_2017_to_2019.csv', index=False)
crime_data_2014.to_csv('csv_files/Crimes_2014.csv', index=False)
def cleanHousingData(neighborhood_data:pd.DataFrame):
'''
Step 1: Filtering
'''
neighborhood_data = filterNeighborhood(neighborhood_data)
(neighborhood_data_2017_2019, neighborhood_data_2021_present) = pre_covid_hd_post_covid_hd(neighborhood_data)
'''
Step 2: Transposing the Data
NOTE: Only run this once because otherwise it will produce an error due to excessive rotation
'''
neighborhood_data_2017_2019 = transpose_data(data=neighborhood_data_2017_2019)
neighborhood_data_2021_present = transpose_data(data=neighborhood_data_2021_present)
# print("Pre-Covid Housing Data in Chicago")
# display(neighborhood_data_2017_2019.head())
# print("Post-Covid Housing Data in Chicago")
# display(neighborhood_data_2021_present.head())
'''
Step 3: Saving the DataFrame
'''
neighborhood_data_2017_2019.to_csv('csv_files/neighborhood_data_2017_2019.csv', index = False)
neighborhood_data_2021_present.to_csv('csv_files/neighborhood_data_2021_present.csv', index = False)
def main():
print("Welcome to the Cleaning Process, BE PATIENT WITH ME")
crime_data = pd.read_csv('csv_files/Crimes_2001_to_Present.csv')
neighborhood_data = pd.read_csv('csv_files/Neighborhood_House_Price.csv')
crime_data = convertCrimeData(crime_data) # Convert the crime data to a much suitable format
col = ['ID', 'New_Date', 'Primary Type', 'Location Description', 'Arrest', 'Community Area', 'RegionName']
crime_data = dropCrimeDataColumns(col, crime_data) # Drop the unnecessary columns
(pre_covid_data, post_covid_data) = pre_covid_post_covid(crime_data)
decade_crime_data = decade_crime(crime_data)
print(pre_covid_data)
print(post_covid_data)
neighborhood_data = filterNeighborhood(neighborhood_data)
(neighborhood_data_2017_2019, neighborhood_data_2021_present) = pre_covid_hd_post_covid_hd(neighborhood_data)
neighborhood_data_2017_2019 = transpose_data(neighborhood_data_2017_2019)
neighborhood_data_2021_present = transpose_data(neighborhood_data_2021_present)
print(neighborhood_data_2017_2019)
print(neighborhood_data_2021_present)
post_covid_data.to_csv('csv_files/Crimes_2021_to_Present.csv', index=False)
pre_covid_data.to_csv('csv_files/Crimes_2017_to_2019.csv', index=False)
decade_crime_data.to_csv('csv_files/Crimes_2014.csv', index=False)
neighborhood_data_2017_2019.to_csv('csv_files/neighborhood_data_2017_2019.csv', index = False)
neighborhood_data_2021_present.to_csv('csv_files/neighborhood_data_2021_present.csv', index = False)
if __name__ == "__main__":
main()