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t_test.py
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t_test.py
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import pandas as pd
import scipy.stats as stats
import matplotlib.pyplot as plt
import numpy as np
def ttest_uic_theft(data:pd.DataFrame, data2:pd.DataFrame):
'''This test is used when you have one group of participants measured at two
different time points or under two different conditions. For example, you might
use a paired samples t-test to compare the blood pressure of individuals before
and after a treatment. '''
selected_neighborhoods = [
"Near West Side",
"West Town",
"Loop",
"Near North Side",
"Near South Side",
"Lower West Side",
"East Garfield Park",
"North Lawndale",
"South Lawndale",
"Humboldt Park",
]
data = data[data['RegionName'].isin(selected_neighborhoods)]
data = data[data['Primary Type'] == 'THEFT']
#print(data)
data2 = data2[data2['RegionName'].isin(selected_neighborhoods)]
data2 = data2[data2['Primary Type'] == 'THEFT']
preCovid = (data.groupby(['RegionName']).count().reset_index())
postCovid = (data2.groupby(['RegionName']).count().reset_index())
preCovid.rename(columns={'Arrest':'NUM CRIME'}, inplace=True)
postCovid.rename(columns={'Arrest':'NUM CRIME'}, inplace=True)
preCovid = preCovid[['RegionName', 'NUM CRIME']]
postCovid = postCovid[['RegionName', 'NUM CRIME']]
# print(preCovid)
# print(postCovid)
t_stat, pval = stats.ttest_rel(np.log(preCovid['NUM CRIME']).to_list(), np.log(postCovid['NUM CRIME']).to_list())
print(t_stat, pval)
if pval < 0.05:
if t_stat > 0:
print("Reject the NULL Hypothesis: There is a significant decrease in theft post COVID compared to pre COVID")
else:
print("Reject the NULL Hypothesis: There is a significant increase in theft post COVID compared to pre COVID")
else:
print("Fail to reject the NULL hypothesis: There is no significant difference in theft between pre and post covid")
# plt.figure(figsize=(12, 6))
# plt.subplot(1, 2, 1)
# plt.hist(np.log(preCovid['NUM CRIME']), bins=30, color='blue', alpha=0.7)
# plt.title('Pre-COVID Crime Data')
# plt.xlabel('Number of Crimes')
# plt.ylabel('Frequency')
# plt.subplot(1, 2, 2)
# plt.hist(np.log(postCovid['NUM CRIME']), bins=30, color='red', alpha=0.7)
# plt.title('Post-COVID Crime Data')
# plt.xlabel('Number of Crimes')
# plt.ylabel('Frequency')
# plt.tight_layout()
# plt.show()
def ttest_uic_battery(data:pd.DataFrame, data2:pd.DataFrame):
'''This test is used when you have one group of participants measured at two
different time points or under two different conditions. For example, you might
use a paired samples t-test to compare the blood pressure of individuals before
and after a treatment. '''
selected_neighborhoods = [
"Near West Side",
"West Town",
"Loop",
"Near North Side",
"Near South Side",
"Lower West Side",
"East Garfield Park",
"North Lawndale",
"South Lawndale",
"Humboldt Park",
]
data = data[data['RegionName'].isin(selected_neighborhoods)]
data = data[data['Primary Type'] == 'BATTERY']
#print(data)
data2 = data2[data2['RegionName'].isin(selected_neighborhoods)]
data2 = data2[data2['Primary Type'] == 'BATTERY']
preCovid = (data.groupby(['RegionName']).count().reset_index())
postCovid = (data2.groupby(['RegionName']).count().reset_index())
preCovid.rename(columns={'Arrest':'NUM CRIME'}, inplace=True)
postCovid.rename(columns={'Arrest':'NUM CRIME'}, inplace=True)
preCovid = preCovid[['RegionName', 'NUM CRIME']]
postCovid = postCovid[['RegionName', 'NUM CRIME']]
# print(preCovid)
# print(postCovid)
t_stat, pval = stats.ttest_rel(np.log(preCovid['NUM CRIME']).to_list(), np.log(postCovid['NUM CRIME']).to_list())
print(t_stat, pval)
if pval < 0.05:
if t_stat > 0:
print("Reject the NULL Hypothesis: There is a significant decrease in battery post COVID compared to pre COVID")
else:
print("Reject the NULL Hypothesis: There is a significant increase in battery post COVID compared to pre COVID")
else:
print("Fail to reject the NULL hypothesis: There is no significant difference in battery between pre and post covid")
# plt.figure(figsize=(12, 6))
# plt.subplot(1, 2, 1)
# plt.hist(np.log(preCovid['NUM CRIME']), bins=30, color='blue', alpha=0.7)
# plt.title('Pre-COVID Crime Data')
# plt.xlabel('Number of Crimes')
# plt.ylabel('Frequency')
# plt.subplot(1, 2, 2)
# plt.hist(np.log(postCovid['NUM CRIME']), bins=30, color='red', alpha=0.7)
# plt.title('Post-COVID Crime Data')
# plt.xlabel('Number of Crimes')
# plt.ylabel('Frequency')
# plt.tight_layout()
# plt.show()
def main():
pre = pd.read_csv('csv_files/Crimes_2017_to_2019.csv')
post = pd.read_csv('csv_files/Crimes_2021_to_Present.csv')
ttest_uic_battery(pre, post)
def main2():
pre = pd.read_csv('csv_files/Crimes_2017_to_2019.csv')
post = pd.read_csv('csv_files/Crimes_2021_to_Present.csv')
ttest_uic_theft(pre, post)
# if __name__ == "__main__":
# main()
# if __name__ == "__main__":
# main2()