CODEWITHSUNDEEP
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Reputation: 53

Adding value to rows of a specific column when a specific conditions is met

I have a pandas dataframe with the column ZIP which contains lots of ZIP Codes.

However, I get the source data in an .xlsx-file. In Excel this column is a number field. That's why Excel ignores the 0 as the first character for ZIP codes which typically start with a zero.

Normally a ZIP code in my country has five digits.

Now, I want to refill the zero in my dataframe column "ZIP" again. The rows with the missing zero's are easy to spot because they have 4 characters instead of 5.

2314
1234
1234
4322
1234
1234
4323
97862
43242
43212
43213

How can I add a '0' to all rows in the column "ZIP" where there are only for characters instead of the normal five?

I know that I could get a leading zero like this:

missing_zero = "0"
df['ZIP'] = missing_zero + df['ZIP'].map(str)

But this would add a zero to all the rows instead of just the rows with the missing zero.

I'd appreciate any insights on how to add the where character length = 4 logic

Thanks in advance!

Upvotes: 0

Views: 46

Answers (2)

rhug123
rhug123

Reputation: 8768

Try this if your column is formatted as a string.

df['zip'].str.zfill(5)

Upvotes: 2

KJDII
KJDII

Reputation: 861

Does something like this work for you?


df['ZIP'] = df['ZIP'].apply(lambda x: '0' + str(x) if len(str(x)) == 4 else str(x))
print(df)

     ZIP
0  02315
1  01234
2  01234
3  12345
4  12345

Upvotes: 1

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