CODEWITHSUNDEEP
pythonpandasdataframenumpy
Ze0ruso
Ze0ruso

Reputation: 516

how to drop rows with 'nan' in a column in a pandas dataframe?

I have a dataframe (denoted as 'df') where some values are missing in a column (denoted as 'col1').

I applied a set function to find unique values in the column:

print(set(df['col1']))

Output:
{0.0, 1.0, 2.0, 3.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan}

I am trying to drop these 'nan' rows from the dataframe where I have tried this:

df['col1'] = df['col1'].dropna()

However, the column rows remain unchanged.

I'm thinking that the above repeated 'nan' values in the above set may not be normal behaviour.

Any suggestions on how to remove these values?

Upvotes: 2

Views: 12683

Answers (2)

Ynjxsjmh
Ynjxsjmh

Reputation: 29982

Marko Knöbl explains it well, problem is that you assign the dropped Series back, you can also try

df = df[df['col1'].notna()]

Upvotes: 3

Marko Knöbl
Marko Knöbl

Reputation: 502

I think what you're doing is taking one column from a DataFrame, removing all the NaNs from it, but then adding that column to the same DataFrame again - where any missing values from the index will be filled by NaNs again.

Do you want to remove that row from the entire DataFrame? If yes, try df.dropna(subset=["col1"])

Upvotes: 5

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