Python way to convert categorical values into binary in the same column

Question

Gurus, we are looking for a pythonic way (python 2.7) to convert categorical values in a column into binary values into a single new column. Example: In the "Loan_status" column,

 Loan_Status
 Charged Off
 Default
 Fully Paid
 Current
 Does not meet the credit policy. Status:1
 Does not meet the credit policy. Status:0

We are trying to make "Charged Off", "Default" into "0", "Fully Paid", "Current" into "1", and delete any row that contains "Does not meet the credit policy. Status:1" and "Does not meet the credit policy. Status:0".

Desired Output:

 Loan_Status
 0
 0
 1
 1

Is there any pythonic way to do it? Pandas get_dummies will generate multiple columns, so it doesn't seem to work. Thanks!

David Maust · Accepted Answer · 2016-01-19 04:25:30Z

2

Let's define a list of positive and negative class labels.

positive = ['Fully Paid', 'Current']
negative = ['Charged Off', 'Default']

First, filter the dataframe for rows that are invalid for your model. We can use isin to for filtering only values in either

filtered_df = df[df['Loan_Status'].isin(positive + negative)].copy()

Second, create a new column for positive labels. If it needs to be 0 or 1 we can cast the boolean result to type int.

filtered_df['Loan_Status'] = filtered_df['Loan_Status'].isin(positive).astype(int)

edited Jan 19, 2016 at 4:25

answered Jan 19, 2016 at 3:21

David Maust

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2 Comments

Chubaka Over a year ago

great answer! Thank you!

Chubaka Over a year ago

Hmmm encounter a new question: if I have filtered multiple columns like this, any easy way to combine them into a big and clean table?

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