# --------------
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)
df1 = df[df['purpose']=='debt_consolidation']
p_a_b = len(df1[df1['fico']>700])/len(df1) * p_b / p_a
result = len(df1[df1['fico']>700])/len(df1) == p_a
print(result)
# 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
df['purpose'].value_counts().plot(kind='bar')
df1 = df[df['paid.back.loan']=='No']
df1['purpose'].value_counts().plot(kind='bar')
# code ends here


# --------------
# code starts here
inst_median = df['installment'].median()
inst_mean = df['installment'].mean()
plt.hist(df['installment'])
plt.hist(df['log.annual.inc'])
# code ends here


