# --------------
#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 = pd.DataFrame(data)
#Code starts here 
data['Gender'].replace('-','Agender',inplace =True)
gender_count = data['Gender'].value_counts()
print(gender_count)
gender_count.plot(kind="bar")



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



# --------------
#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_combat*sc_strength)

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



# --------------
#Code starts here
total_high = data.Total.quantile(0.99)
super_best= data[data['Total']>total_high]
super_best_names =super_best['Name'].tolist()
print(super_best_names)


# --------------
#Code starts here
fig, (ax_1, ax_2, ax_3) = plt.subplots(1, 3)
ax_1.boxplot(data['Intelligence'])
ax_1.set_title('Intelligence')

ax_2.boxplot(data['Speed'])
ax_2.set_title('Speed')

ax_3.boxplot(data['Power'])
ax_3.set_title('Power')


