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

df = pd.read_csv(path)
p_a = df[df['fico'].astype(float)> 700].shape[0]/ df.shape[0]
print(p_a)
p_b = df[df['purpose'] == 'debt_consolidation'].shape[0]/ df.shape[0]
print(p_b)
df1 = df[df['purpose'] == 'debt_consolidation']
print(df1.shape)
p_a_b = df1[df1['fico'].astype(float) >700].shape[0]/df1.shape[0]
result = p_a_b == p_a
print(result)


# --------------
# code starts here
prob_lp = df[df['paid.back.loan']=='Yes'].shape[0]/ df.shape[0]
prob_cs = df[df['credit.policy']=='Yes'].shape[0]/ df.shape[0]
new_df = df[df['paid.back.loan'] == 'Yes']
prob_pd_cs = new_df[new_df['credit.policy']=='Yes'].shape[0]/ new_df.shape[0]
bayes = prob_pd_cs * (prob_lp/ prob_cs)
print(bayes)
# code ends here


# --------------
# code starts here
df['purpose'].value_counts().plot(kind='bar');
plt.show()

df1 = df[df['paid.back.loan']=='No']
df1['purpose'].value_counts().plot(kind='bar');
plt.show()

# code ends here


# --------------
# code starts here
# print(df.columns.tolist())

inst_median = df['installment'].median()
inst_mean = df['installment'].median()
plt.hist(df['installment'], alpha=0.5);
plt.show()

plt.hist(df['log.annual.inc']);
plt.show()
# code ends here


