Suppose I have a dataframe that contains columns with lots and lots of nan values - in fact most values are none, except one (or a few that are identical), but are distributed along different lines. For example:

df = pd.DataFrame({'A':[np.nan, 2, np.nan], 'B':[3.5, np.nan, 3.5], 'C':[np.nan, np.nan, 0.1]})

So how can I achieve a dataframe that looks like this?

  A    B    C
0  2  3.5  0.1
1  2  3.5  0.1
2  2  3.5  0.1

'bfill' would only work for column 'C', 'ffill' only for column 'B'...

So how can I replace all the nan values in the column with the notna value present anywhere and in any number of instances in that column?

CodePudding user response：

Forwardfill, backfill the dataframe.

df =df.ffill().bfill()