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'''
Specific modules to answer business questions
'''
import numpy as np
import pandas as pd
from matplotlib import cm
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
def time_data_analysis(analysis_df):
'''
aggregates and plots data of crime over years
'''
# Extracting year from date column
analysis_df['Year'] = analysis_df['Date'].map(
lambda x: x.split()[0].split('/')[2])
analysis_series = analysis_df.groupby('Primary Type')['Year'].value_counts()
# Aggregating data on the basis of year
time_data = dict()
for index, count in analysis_series.items():
p_type = index[0]
year = index[1]
if p_type not in time_data:
time_data[p_type] = [[],[]]
# Append
time_data[p_type][0].append(year)
time_data[p_type][1].append(count)
# Plot a line graph
for p_type, value in time_data.items():
years = value[0]
counts = value[1]
plt.plot(years, counts, label=p_type)
# Add labels and title
plt.title("Time Data Analysis")
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.legend()
plt.show()
def arrest_analysis(analysis_df):
'''
displays no of arrests and non-arrests for each crime type
'''
color = cm.inferno_r(np.linspace(.4,.8, 30))
analysis_df.groupby(['Primary Type', 'Arrest']).size().unstack().plot(
kind='barh', stacked=True, figsize=(10,10))
plt.show()
def count_per_year_analysis(analysis_df):
'''
number of crime cases reported by year
'''
color = cm.magma_r(np.linspace(.2,1.0, 20))
analysis_df.groupby(['Year']).count()['ID'].plot(kind='barh',
figsize=(10,10),color=color)
plt.show()
def max_arrests(analysis_df):
'''
crime type with maximum arrests
'''
data = analysis_df.groupby(['Primary Type']).count()['ID'].idxmax()
print ('Crime type with Maximum arrests: ', data)
def crime_distribution_by_year(analysis_df):
'''
crime type with maximum arrests
'''
crime_types = analysis_df['Primary Type'].unique()
analysis_df.groupby(['Primary Type']).count()['ID'].idxmax();
ax = plt.gca()
year_group = analysis_df.groupby(['Year',
'Primary Type'])['Year'].count().unstack('Primary Type').fillna(0)
year_group[crime_types].plot(kind = 'bar', stacked = True,figsize=(10,10),
ax=ax);
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.show();
def unsafe_neighbourhood(analysis_df):
'''
returns the most unsafe neighbourhood
'''
data = analysis_df.groupby(['Neighbourhood']).count()['Primary Type'].idxmax()
print ('Most unsafe Neighbourhood in Chicago: ', data)
def crime_type_prediction(analysis_df, predict_loc):
'''
Trains and predicts crime type, based on location given as one-hot encoding
'''
df_crime = analysis_df['Primary Type']
y = analysis_df['Location Description']
df1 = pd.get_dummies(df_crime).head(200)
X = df1.as_matrix()
clf = LogisticRegression(random_state=0, solver='lbfgs',
multi_class='multinomial').fit(X, y.head(200))
y_test=clf.predict(predict_loc)
print ('Predicted Location: ', y_test)