How to make pandas replace work like python default replace

Question

I have a csv that has a column called 'ra'.

This is the first 'ra' value the csv has: 8570.0 - I will use it as an example.

I need to remove '.0'.

So I've tried:

dtypes = {

'ra': 'str',

}

df['ra_csv'] = pd.DataFrame({'ra_csv':df['ra']}).replace('.0', '', regex=true).astype(str)

This code returns me '85' instead of '8570'. It's replacing all the 0s, and somehow removed the number '7' aswell.

How can I make it return '8750'? Thanks.

@Ch3steR, regex=False returns '8570.0', it doesnt replace '.0'. — fsimoes
– fsimoes, Commented Feb 28, 2021 at 18:01
check dtype? its a float or a string? too many question marks without a proper reproducible example. — anky
– anky, Commented Feb 28, 2021 at 18:02
@fsimoes Please add your dataframe to the question. You can edit your question from here. And what's the output of print(df.dtypes)? — Ch3steR
– Ch3steR, Commented Feb 28, 2021 at 18:05
Not sure if all the rows have valid data but based on sample df, using to_numeric might be more appropriate here, pd.to_numeric(df['ra_csv']).astype(int) — Vaishali
– Vaishali, Commented Feb 28, 2021 at 18:06

Vaishali · Accepted Answer · 2021-02-28 18:09:11Z

2

Option 1: use to_numeric to first convert the data to numeric type and convert to int,

df['ra_csv'] = pd.to_numeric(df['ra_csv']).astype(int)

Option 2: using str.replace

df['ra_csv'] = df['ra_csv'].str.replace('\..*', '')

You get

    ra_csv
0   8570

answered Feb 28, 2021 at 18:09

Vaishali

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Comments

Ch3steR · Accepted Answer · 2021-02-28 18:13:14Z

1

The regex pattern .0 has two matches in your string '8570.0'. . matches any character.

Since you are using df.replace setting regex=False wouldn't because it checks for exact matches only. From docs df.replace:

str: string exactly matching to_replace will be replaced with value

Possible fixes are either fix your regex or use pd.Series.str.replace

Using str.replace

df['ra'].str.replace('.0', '', regex=False)

answered Feb 28, 2021 at 18:13

Ch3steR

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