Boston Crime Report- Data Cleaning and Visualisation.py

#!/usr/bin/env python
# coding: utf-8

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import pandas as pd


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crime = pd.read_csv("crime_boston.csv")


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crime


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crime.isnull()


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crime.isnull().any(axis=1)


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rows_with_missing_values = crime.isnull().any(axis=1)


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crime[rows_with_missing_values]


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cleaned_crime = crime.drop(columns=['YEAR', 'MONTH', 'HOUR'])


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cleaned_crime


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cleaned_crime['OFFENSE_CODE_GROUP'].unique()


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cleaned_crime['OFFENSE_DESCRIPTION'].unique()


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cleaned_crime = crime.drop(columns='Location')


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cleaned_crime


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cleaned_crime


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import numpy as np
import seaborn as sn


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import matplotlib.pyplot as plt


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sn.countplot(x='OFFENSE_CODE_GROUP', y = '', data = cleaned_crime)


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cleaned_crime['YEAR']


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cleaned_crime['YEAR'] ==2019 


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cleaned_crime1= cleaned_crime.loc[cleaned_crime['YEAR'] ==2019,: ]


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cleaned_crime1


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cleaned_crime['OFFENSE_CODE_GROUP'] =="Warrant Arrests"


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cleaned_crime2= cleaned_crime.loc[cleaned_crime['OFFENSE_CODE_GROUP'] =="Warrant Arrests",: ]


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cleaned_crime2


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sn.countplot(x="DAY_OF_WEEK", data=cleaned_crime2); plt.ylabel("Count of Warrant Arrests")


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sn.countplot(x="DISTRICT", data=cleaned_crime2); plt.ylabel("Count of Warrant Arrests")


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