CODEWITHSUNDEEP
pythonpandas
yogz123
yogz123

Reputation: 723

Recode all but one value in pandas dataframe column

I'm trying to recode data on loan statuses so that every observation is either Default or Fully Paid. Specifically, I'd like to recode anyone != 'Fully Paid' as 'Default'.

Here are my values:

df.loan_status.unique()

array(['Fully Paid', 'Charged Off', 'Default', 'Late (31-120 days)',
   'In Grace Period', 'Late (16-30 days)',
   'Does not meet the credit policy. Status:Fully Paid',
   'Does not meet the credit policy. Status:Charged Off', 'Issued'], dtype=object)

I tried the following code but all observations got recoded as 'Default':

statuses= df['loan_status'].unique()
for status in statuses:
    if status!='Fully Paid':
        df['loan_status']='Default'

Any advice on how to do this would be greatly appreciated!

Upvotes: 2

Views: 601

Answers (1)

piRSquared
piRSquared

Reputation: 294258

I like this approach.

Andras Deak / MaxU; option 1 

df.loc[df.loan_status.ne('Fully Paid'), 'loan_status'] = 'Default'

Option 2
pd.Series.where

ls = df.loan_status
df.update(ls.where(ls.eq('Fully Paid'), 'Default'))

Option 3
pd.Series.mask

ls = df.loan_status
df.update(ls.mask(ls.ne('Fully Paid')).fillna('Default'))

Option 4
numpy.where

ls = df.loan_status.values
paid, dflt = 'Fully Paid', 'Default'
df.loc[:, 'loan_status'] = np.where(ls == paid, paid, dflt)

Upvotes: 1

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