Remove a character from a pandas dataframe columns

user_8340881 0
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I have a dataframe as below:

import pandas as pd
import numpy as np
df = pd.DataFrame({'col1':['AA_L8_ZZ', 'AA_L08_YY', 'AA_L800_XX', 'AA_L0008_CC']})
df

    col1
0   AA_L8_ZZ
1   AA_L08_YY
2   AA_L800_XX
3   AA_L0008_CC

I want to remove all 0's after character 'L'. My expected output:

    col1
0   AA_L8_ZZ
1   AA_L8_YY
2   AA_L800_XX
3   AA_L8_CC
pandas python-3.x
2 Answers
In [114]: import pandas as pd
     ...: import numpy as np
     ...: df = pd.DataFrame({'col1':['AA_L8_ZZ', 'AA_L08_YY', 'AA_L800_XX', 'AA_L0008_CC']})
     ...: df
Out[114]:
          col1
0     AA_L8_ZZ
1    AA_L08_YY
2   AA_L800_XX
3  AA_L0008_CC

In [115]: df.col1.str.replace("L([0]*)","L")
Out[115]:
0      AA_L8_ZZ
1      AA_L8_YY
2    AA_L800_XX
3      AA_L8_CC
Name: col1, dtype: object
user_68496820

Pandas string replace suffices for this. The code below looks for any 0, preceded by L, and replaces the 0 with an empty string :

df.col1.str.replace(r"(?<=L)0+", "")

0      AA_L8_ZZ
1      AA_L8_YY
2    AA_L800_XX
3      AA_L8_CC

If you need more speed, you could go down into plain Python with list comprehension:

import re
df["cleaned"] = [re.sub(r"(?<=L)0+", "", entry) for entry in df.col1]
df
     col1       cleaned
0   AA_L8_ZZ    AA_L8_ZZ
1   AA_L08_YY   AA_L8_YY
2   AA_L800_XX  AA_L800_XX
3   AA_L0008_CC AA_L8_CC
user_71757130
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