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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 My interest is in the loan status. I want to know if I can predict if a loan will be charged off or not.
Thus there are multiple predictors that can be removed since they  reasonably have no bearing on the predictive power of the model I wish to create
Step1:  Let's drop all the ones that showed up with stdev of 0
acc_now_delinq
chargeoff_within_12_mths
delinq_amnt
policy_code
collections_12_mths_ex_med
tax_liens
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Python Code:
df.drop('id', axis=1, inplace=True)
df.drop('member_id', axis=1,  inplace=True)
df.drop('url', axis=1,  inplace=True)
df.describe()
Explanation: My interest is in the loan status. I want to know if I can predict if a loan will be charged off or not.
Thus there are multiple predictors that can be removed since they  reasonably have no bearing on the predictive power of the model I wish to create:
id
member_id
loan_amnt This is too highly correlated with funded_amnt # add code to prove this
url simply links to lending club website
policy_code they are all 1
Application Type
response - loan_status
predictors -
Categorical
term
home_ownership
grade
sub_grade there will be multicollinearity but we can try to find the better predictor (or remove one and try it again)
End of explanation
df.drop('acc_now_delinq', axis=1, inplace=True)
df.drop('chargeoff_within_12_mths', axis=1,  inplace=True)
df.drop('delinq_amnt', axis=1,  inplace=True)
df.drop('policy_code', axis=1,  inplace=True)
df.drop('collections_12_mths_ex_med', axis=1,  inplace=True)
df.drop('tax_liens', axis=1,  inplace=True)
df.describe()
df['loan_status_raw'] = df['loan_status'].astype("category")
pd.isnull(df)
Explanation: Let's drop all the ones that showed up with stdev of 0
acc_now_delinq
chargeoff_within_12_mths
delinq_amnt
policy_code
collections_12_mths_ex_med
tax_liens
End of explanation
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