6

I have a CSV file that looks something like this -

    Location ID      Location Name
        3543459      A
         20541       B
          C320       C
           ...       ..

When I read the file using pd.read_csv, I get something like this - 

Location ID      Location Name
   03543459      A
   0020541       B
   000C320       C
       ...       ..

How to avoid leading zeros? I did some research, all the questions I could ifind were based on producing the leading zeros in the df.

smci
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Karvy1
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    `df["Location ID"].str.lstrip("0")`? – Rakesh Aug 03 '18 at 07:30
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    If you make `Location ID` an integer column, they'll disappear automatically. Is `Location ID` supposed to be integer or string (or Categorical)? – smci Aug 03 '18 at 07:35

4 Answers4

10

Use post processing by str.lstrip:

df['Location ID'] = df['Location ID'].str.lstrip('0')
jezrael
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2

I had mixed type below line worked ..

 df['col'] = df['col'].apply(lambda x:x.lstrip('0') if type(x) == str else x)
Hietsh Kumar
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1
df['Location ID'] = df['Location ID'].apply(lambda x: x.lstrip('0'))
U13-Forward
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0

For anyone with more complex strings (e.g., 'AB00003423'), you can use Series.str.extract() and a regular expression:

extractedNumbers = df.ID_col.str.extract('^[A-Z]+0+([0-9]+)$')

This will return a column of whatever is inside the parentheses (or "capture group(s)") of the regular expression.

Normally a dataframe is returned with 1 column per capture group, use expand=False to return a Series instead.

johnDanger
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