How to change a single column of an existing csv with pandas

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I have a .csv file which I can read using pd.read_csv(). In this file i have SomeBoolean column that I need to update with new logic. However when I write the file again using pd.to_csv() some other column values are changed, e.g. the sometimesNaN column has some empty values which are interpreted as NaN while reading the csv, but then they are also written as NaN instead of an empty value.

Is there a way in which I can update the SomeBoolean column without accidentally affecting the others such as the sometimesNaN column?

Index   Date         SomeBoolean    Values        sometimesNaN
0       2021-05-18   False          216.351155    NaN
1       2021-05-18   False          876.222176    NaN
2       2021-05-18   False          767.214479    NaN
pandas dataframe python
1 Answers

You can consider to make Pandas not to interpret empty values in sometimesNaN column as NaN so that it won't overwritten original empty values when written back.

To achieve this, you can specify the parameter keep_default_na=False in reading in the csv during pd.read_csv() call.

keep_default_na : bool, default True

Whether or not to include the default NaN values when parsing the data. Depending on whether na_values is passed in, the behavior is as follows:

If keep_default_na is True, and na_values are specified, na_values is appended to the default NaN values used for parsing.

If keep_default_na is True, and na_values are not specified, only the default NaN values are used for parsing.

If keep_default_na is False, and na_values are specified, only the NaN values specified na_values are used for parsing.

If keep_default_na is False, and na_values are not specified, no strings will be parsed as NaN.

Note that this behavior applies to all columns. So if you have NaN values in other columns you want Pandas to interpret the empty values as NaN when reading in the csv, you may not be able to use this.

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