Acces each row and check each column value in dataframe

Question

I want to iterate over each row and and want to check in each column if value is NaN if it is then i want to replace it with the previous value of the same row which is not null.

I believe the prefer way would be using lamba function. But still not figure out to code it Note: I have thousands of rows and 200 columns in each row

what do you mean by "previous value"? in row (on the left) or in column (above)? An what if previous value is Nan? — IoaTzimas, Sep 03 '20 at 17:54
Does this answer your question? [How to replace NaNs by preceding values in pandas DataFrame?](https://stackoverflow.com/questions/27905295/how-to-replace-nans-by-preceding-values-in-pandas-dataframe) — Stas Buzuluk, Sep 03 '20 at 17:55
@StasBuzuluk i want to go row by row and want to replace it with the previous value of the same row not by column. — robinHood013, Sep 03 '20 at 18:00

score 1 · Accepted Answer · answered Sep 03 '20 at 18:02

1

The following should do the work:

df.fillna(method='ffill', axis=1, inplace=True)

Can you please clarify what you want to be done with NaNs in first column(s)?

answered Sep 03 '20 at 18:02

IoaTzimas

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your code line basically replaces the NaN values with its previous value whichis not null. ANd it seems exaclty what i want – robinHood013 Sep 03 '20 at 18:08

score 1 · Answer 2 · answered Sep 03 '20 at 18:07

1

i think you can use this -

your_df.apply(lambda x : x.fillna(method='ffill'), axis=1)

answered Sep 03 '20 at 18:07

vamsi_s

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Acces each row and check each column value in dataframe

2 Answers2