# --------------
#Header files
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt


#path of the data file- path
data=pd.read_csv(path)

data['Gender'] = data['Gender'].replace('-','Agender')
data['Gender']

gender_count = data['Gender'].value_counts()
gender_count.plot.bar()
#Code starts here 




# --------------
#Code starts here
alignment = data['Alignment'].value_counts()
alignment.plot.pie(labels='Character Alignment')


# --------------
#Code starts here
sc_df = data[['Strength','Combat']].copy()

sc_covariance = sc_df['Strength'].cov(sc_df['Combat'])
sc_strength = sc_df['Strength'].std()
sc_combat = sc_df['Combat'].std()
sc_pearson = sc_covariance / (sc_strength*sc_combat)
print(sc_pearson)

ic_df = data[['Intelligence','Combat']].copy()

ic_covariance = ic_df['Intelligence'].cov(ic_df['Combat'])
ic_intelligence = ic_df['Intelligence'].std()
ic_combat = ic_df['Combat'].std()
ic_pearson = ic_covariance / (ic_intelligence*ic_combat)

print(ic_pearson)


# --------------
#Code starts here
total_high = data['Total'].quantile(0.99)

super_best = data[data['Total']>total_high]

super_best_names = list(super_best['Name'])
print(super_best_names)


# --------------
#Code starts here
fig,(ax_1,ax_2,ax_3) = plt.subplots(1,3,figsize=(30,10))
data['Intelligence'].plot(kind='box',ax=ax_1,title = 'Intelligence')
data['Speed'].plot(kind='box',ax=ax_2,title = 'Speed')
data['Power'].plot(kind='box',ax=ax_3,title = 'Power')


