# --------------
#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'].replace('-','Agender', inplace=True)

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

gender_count.plot(kind='bar')

#Code starts here 




# --------------
#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['Combat'].std()
sc_pearson = sc_covariance/(sc_strength*sc_combat)
print("Correlation between Strength & Combat : ",sc_pearson)

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("Correlation between Intelligence & Combat : ",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("Top superheroes or villains :\n",super_best_names)


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

fig, (ax_1,ax_2,ax_3) = plt.subplots(1,3, figsize=(20,10))

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

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

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


