Say I have two columns, A and B, in my dataframe:
A B 1 NaN 2 5 3 NaN 4 6 I want to get a new column, C, which fills in NaN cells in column B using values from column A:
A B C 1 NaN 1 2 5 5 3 NaN 3 4 6 6 How do I do this?
I'm sure this is a very basic question, but as I am new to Pandas, any help will be appreciated!
You can use combine_first:
df['c'] = df['b'].combine_first(df['a']) Docs: http://pandas.pydata.org/pandas-docs/version/0.17.0/generated/pandas.Series.combine_first.html
You can use where which is a vectorized if/else:
df['C'] = df['A'].where(df['B'].isnull(), df['B']) A B C 0 1 NaN 1 1 2 5 5 2 3 NaN 3 3 4 6 6 
df['C'] = df.A.where(df.B.isnull(), df.B) as isnull is available for df and series, also I wouldn't encourage the practice of accessing columns as attributes as it can lead to strange behaviour, better to do this df['C'] = df['A'].where(df['B'].isnull(), df['B'])df['c'] = df['b'].fillna(df['a']) So what .fillna will do is it will fill all the Nan values in the data frame We can pass any value to it Here we pass the value df['a'] So this method will put the corresponding values of 'a' into the Nan values of 'b' And the final answer will be in 'c'
.fillna() does, and perhaps a documentation link.