Linear regression with 2015 temperatures final.py.txt

import statsmodels.formula.api as smf
#import numpy as np
import pandas as pd
incidents=pd.read_csv("Police_Incidents_2015_updatedwithTemp.csv",squeeze=True)
parse=lambda x:(pd.to_datetime(x, format='%H:%M:%S').hour)
incidents["crime_happened"]=1
incidents=incidents.dropna()
#Time column contains the time of the crime incident
incidents["hour"]=incidents["Time"].apply(parse)
#just temperature
grouped=incidents.groupby(["temperature"],as_index=False).agg({"crime_happened":"sum"})
model = smf.ols(formula="crime_happened ~ temperature", data=grouped).fit()
print(model.summary())
# r^2 11.5%