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

# code starts here

df = pd.read_csv(path)
p_a = len(df[df['fico']>700])/len(df)
p_b = len(df[df['purpose']=='debt_consolidation'])/len(df)
print(p_b)
df1 = df[df['purpose']=='debt_consolidation']
p_a_b = p_a+p_b
result = p_a_b==p_a

# code ends here


# --------------
# code starts here
prob_lp = len(df[df['paid.back.loan']=='Yes'])/len(df)

prob_cs = len(df[df['credit.policy']=='Yes'])/len(df)

new_df = df[df['paid.back.loan']=='Yes']

prob_pd_cs = len(new_df[new_df['credit.policy']=='Yes'])/len(new_df)

bayes = (prob_pd_cs*prob_lp)/prob_cs
print(bayes)
# code ends here



# --------------
# code starts here

print(df)
df['purpose'].value_counts(normalize=True).plot(kind ='bar')
df1 = df[df['paid.back.loan']=='No']
df1['purpose'].value_counts(normalize=True).plot(kind='bar')
# code ends here


# --------------
# code starts here
inst_median = df['installment'].median()
inst_mean = df['installment'].mean()
df['installment'].hist(bins=20)
print(df.columns)
df['log.annual.inc'].hist(bins=20)



# code ends here


