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import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

import numpy as np
import pandas as pd
import seaborn as sns
Deliveries_Data = pd.read_csv('../input/deliveries_expanded.csv')
Matches_Data = pd.read_csv('../input/matches_expanded.csv')

Overall_Matches_State = pd.Series.to_frame(Matches_Data['State'].value_counts())
Overall_Matches_State['%Contribution'] = Overall_Matches_State['State'] / Overall_Matches_State['State'].sum() * 100

Overall_Matches_Zone = pd.Series.to_frame(Matches_Data['Zone'].value_counts())
Overall_Matches_Zone['%Contribution'] = Overall_Matches_Zone['Zone'] / Overall_Matches_Zone['Zone'].sum() * 100

import matplotlib.pyplot as plt
Matches_understood_by_year =  pd.Series.to_frame(Matches_Data.groupby('season')['Zone'].value_counts())
Plot_Zone = Matches_Data.groupby('season')['Zone'].value_counts().unstack().plot(kind="bar", figsize = (15,5))
print(Plot_Zone.get_legend().set_bbox_to_anchor((0.15,1)))

Matches_Data.groupby('city')['Winner_Zone_Type'].value_counts().unstack().dropna().plot(kind='bar', figsize=(15, 5))