python pandas modify column [duplicate]

Question

I have what has to be considered a syntax question. Doing some data scrubbing where there are numeric fields, that need to be tidied up.

Like this:

Sometimes there are commas, sometime multiple commas and sometime none.

So my simple mind says, find and replace will fix this.

This works:

def fixComma(strIn):

    return  strIn.replace(","  , ""   )  


dfAssetMeter_a['lastReadingflt']      =  dfAssetMeter_a['LASTREADING'].apply(fixComma).astype(float)

Questions: Is this “Python” way to do this?

Would somebody be kind enough to provide the correct syntax for removing the commas in line.

I thought something like this would work.

     =  dfAssetMeter_a['LASTREADING']. .replace(","  , ""   ) .astype(float)

Your last code snippet doesn't contain logical syntax. Please spend some time formatting your Q(s). Also, please put in some research effort. Just googling, say, "pandas string comma integer" easily gets you +10 instructive SO posts. Like here, here, or here. — ouroboros1
– ouroboros1, Commented Jun 27, 2024 at 16:31
Looking at the docs is always a good idea. Pandas .replace is different from Python .replace which is why the Pandas string accessor .str.replace has to be used. — user19077881
– user19077881, Commented Jun 27, 2024 at 17:02

e-motta · Accepted Answer · 2024-06-27 15:59:36Z

1

You can use the str accessor along with replace, and pandas.to_numeric:

import pandas as pd

df = pd.DataFrame(
    {
        "LASTREADING": [
            "20,956",
            "1,006,486",
            "0",
            "0",
            "85,186",
            "77,573",
        ]
    }
)

df["lastReadingflt"] = pd.to_numeric(df["LASTREADING"].str.replace(",", ""))

  LASTREADING  lastReadingflt
0      20,956           20956
1   1,006,486         1006486
2           0               0
3           0               0
4      85,186           85186
5      77,573           77573

answered Jun 27, 2024 at 15:59

e-motta

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1 Comment

KBD Over a year ago

I was close but no cigar. Thank for your quick reply. Best Regards

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