Pandas: replace null value with the value of another column
How can I replace a null value in a column with a value from the same row of another column where the value of the previous row of the column is null, i.e. replace only where there was no zero yet? For example: a given data frame with columns a
, b
and c
:
+----+-----+-----+----+
| | a | b | c |
|----+-----+-----|----|
| 0 | 2 | 0 | 0 |
| 1 | 5 | 0 | 0 |
| 2 | 3 | 4 | 0 |
| 3 | 2 | 0 | 3 |
| 4 | 1 | 8 | 1 |
+----+-----+-----+----+
replace zero values in b
and c
with values a
where the previous value is zero
+----+-----+-----+----+
| | a | b | c |
|----+-----+-----|----|
| 0 | 2 | 2 | 2 |
| 1 | 5 | 5 | 5 |
| 2 | 3 | 4 | 3 |
| 3 | 2 | 0 | 3 | <-- zero in this row is not replaced because of
| 4 | 1 | 8 | 1 | non-zero value (4) in row before it.
+----+-----+-----+----+
source to share
In [90]: (df[~df.apply(lambda c: c.eq(0) & c.shift().fillna(0).eq(0))]
...: .fillna(pd.DataFrame(np.tile(df.a.values[:, None], df.shape[1]),
...: columns=df.columns, index=df.index))
...: .astype(int)
...: )
Out[90]:
a b c
0 2 2 2
1 5 5 5
2 3 4 3
3 2 0 3
4 1 8 1
Explanation:
In [91]: df[~df.apply(lambda c: c.eq(0) & c.shift().fillna(0).eq(0))]
Out[91]:
a b c
0 2 NaN NaN
1 5 NaN NaN
2 3 4.0 NaN
3 2 0.0 3.0
4 1 8.0 1.0
we can now fill the NaN with the corresponding values from the DF below (which is built as 3 concatenated columns a
):
In [92]: pd.DataFrame(np.tile(df.a.values[:, None], df.shape[1]), columns=df.columns, index=df.index)
Out[92]:
a b c
0 2 2 2
1 5 5 5
2 3 3 3
3 2 2 2
4 1 1 1
source to share