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I have a Pandas dataframe with a column called Zip Code. The column is an object data type and some rows are not in proper zip code format. I would like to remove rows that do not contain ##### format zipcode. 

    Subscriber Type     Zip Code
0   Subscriber         94040
1   Customer           11231
2   Customer           11231
3   Customer           32
4   Customer           nil

What would be an easy way to do so? Is there a way to compare format and the records something like this? df.drop(df['Zip Code'] != #####)

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  • why don't you do df=df[df['Zip Code']!=#####). Commented Jul 26, 2016 at 17:49

1 Answer 1

Reset to default
5

try this:

In [23]: df = df[df['Zip Code'].str.contains(r'^\d{5}$')]

In [24]: df
Out[24]:
  Subscriber Type Zip Code
0      Subscriber    94040
1        Customer    11231
2        Customer    11231

Explanation:

In [22]: df['Zip Code'].str.contains(r'^\d{5}$')
Out[22]:
0     True
1     True
2     True
3    False
4    False
Name: Zip Code, dtype: bool

PS thanks to @Alberto Garcia-Raboso for the refined RegEx!

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3 Comments

Works perfectly, Thanks!
r'\d{5}' gives false positives (for example: 11231asdf, asdf11231, as11231df). You want a more stringent regex: r'^\d{5}$'
@AlbertoGarcia-Raboso, thank you! I've updated my answer

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