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?

Answer

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"])