# --------------
#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)
#Code starts here 

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

gender_count = data['Gender'].value_counts()

gender_count.plot(kind = 'bar')



# --------------
#Code starts here

alignment = data['Alignment'].value_counts()

alignment.plot(kind = 'pie')

plt.title('Character Alignment')


# --------------
#Code starts here

'''sc_df = data[['Strength','Combat']]
sc_covariance = sc_df.cov().iloc[0,1]
sc_strength = sc_df['Strength'].std()
sc_combat = sc_df['Strength'].std()
sc_pearson = sc_covariance / (sc_strength * sc_combat)

ic_df = data[['Intelligence','Combat']]
ic_covariance = ic_df.cov().iloc[0,1]
ic_intelligence = ic_df['Intelligence'].std()
ic_combat = ic_df['Combat'].std()
ic_pearson = ic_covariance / (ic_intelligence * ic_combat)

print(sc_pearson)
print(ic_pearson)'''

sc_df = data[['Strength','Combat']].copy()
sc_covariance=sc_df.cov().iloc[0,1]
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.cov().iloc[0,1]
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 = [super_best['Name']]

print(super_best_names)


# --------------
#Code starts here

fig, (ax_1, ax_2, ax_3) = plt.subplots(1,3)

data['Intelligence'].plot(kind = 'box', ax = ax_1)
data['Speed'].plot(kind = 'box', ax = ax_2)
data['Power'].plot(kind = 'box', ax = ax_3)
ax_1.set_title('Intelligence')
ax_2.set_title('Speed')
ax_3.set_title('Power')



