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By using dropna() method you can drop rows with NaN (Not a Number) and None values from pandas DataFrame. Note that by default it returns the copy of the DataFrame after removing rows. If you wanted to remove from the existing DataFrame, you should use inplace=True .
Merge Python Pandas dataframe with a common column and set NaN for unmatched values. To merge two Pandas DataFrame with common column, use the merge() function and set the ON parameter as the column name. To set NaN for unmatched values, use the “how” parameter and set it left or right.
Pandas DataFrame dropna() function is used to remove rows and columns with Null/NaN values. By default, this function returns a new DataFrame and the source DataFrame remains unchanged. We can create null values using None, pandas.
You can use combine_first or fillna:
print df['feedback_id'].combine_first(df['_id'])
0 568a8c25cac4991645c287ac
1 568df45b177e30c6487d3603
2 568df434832b090048f34974
3 568cd22e9e82dfc166d7dff1
4 568df3f0832b090048f34711
5 568e5a38b4a797c664143dda
Name: feedback_id, dtype: object
print df['feedback_id'].fillna(df['_id'])
0 568a8c25cac4991645c287ac
1 568df45b177e30c6487d3603
2 568df434832b090048f34974
3 568cd22e9e82dfc166d7dff1
4 568df3f0832b090048f34711
5 568e5a38b4a797c664143dda
Name: feedback_id, dtype: object
If you want a solution that doesn't require referencing df twice or any of its columns explicitly:
df.bfill(axis=1).iloc[:, 0]
With two columns, this will copy non-null values from the right column into the left, then select the left column.
For an in-place solution, you can use pd.Series.update with pd.DataFrame.pop:
df['feedback_id'].update(df.pop('_id'))
print(df)
feedback_id
0 568a8c25cac4991645c287ac
1 568df45b177e30c6487d3603
2 568df434832b090048f34974
3 568cd22e9e82dfc166d7dff1
4 568df3f0832b090048f34711
5 568e5a38b4a797c664143dda
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