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This is my dataframe:
date ids
0 2011-04-23 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...
1 2011-04-24 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...
2 2011-04-25 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...
3 2011-04-26 Nan
4 2011-04-27 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...
5 2011-04-28 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...
I want to replace Nan with []. How to do that? Fillna([]) did not work. I even tried replace(np.nan, []) but it gives error:
TypeError('Invalid "to_replace" type: \'float\'',)
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Pandas Replace Blank Values with NaN using mask() You can also replace blank values with NAN with DataFrame. mask() methods. The mask() method replaces the values of the rows where the condition evaluates to True.
Just use [[]]*s. isna(). sum() and you'll be back in business.
Use df. replace(np. nan,'',regex=True) method to replace all NaN values to an empty string in the Pandas DataFrame column.
My approach is similar to @hellpanderrr's, but instead tests for list-ness rather than using isnan:
df['ids'] = df['ids'].apply(lambda d: d if isinstance(d, list) else [])
I originally tried using pd.isnull (or pd.notnull) but, when given a list, that returns the null-ness of each element.
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