bigframes.pandas.DataFrame.fillna

DataFrame.fillna(value=None) → DataFrame

Fill NA (NULL in BigQuery) values using the specified method.

Note that empty strings '', numpy.inf, and numpy.nan are *not* considered NA values. This NA/NULL logic differs from numpy, but it is the same as BigQuery and the pandas.ArrowDtype.

Examples:

>>> df = bpd.DataFrame(
...     [
...         pa.array([np.nan, 2, None, 0], type=pa.float64()),
...         pa.array([3, np.nan, None, 1], type=pa.float64()),
...         pa.array([None, None, np.nan, None], type=pa.float64()),
...         pa.array([4, 5, None, np.nan], type=pa.float64()),
...     ], columns=list("ABCD"), dtype=pd.ArrowDtype(pa.float64()))
>>> df
      A     B     C     D
0   NaN   2.0  <NA>   0.0
1   3.0   NaN  <NA>   1.0
2  <NA>  <NA>   NaN  <NA>
3   4.0   5.0  <NA>   NaN

[4 rows x 4 columns]

Replace all NA (NULL) elements with 0s.

>>> df.fillna(0)
     A    B    C    D
0  NaN  2.0  0.0  0.0
1  3.0  NaN  0.0  1.0
2  0.0  0.0  NaN  0.0
3  4.0  5.0  0.0  NaN

[4 rows x 4 columns]

You can use fill values from another DataFrame:

>>> df_fill = bpd.DataFrame(np.arange(12).reshape(3, 4),
...                         columns=['A', 'B', 'C', 'D'])
>>> df_fill
   A  B   C   D
0  0  1   2   3
1  4  5   6   7
2  8  9  10  11

[3 rows x 4 columns]
>>> df.fillna(df_fill)
     A    B     C     D
0  NaN  2.0   2.0   0.0
1  3.0  NaN   6.0   1.0
2  8.0  9.0   NaN  11.0
3  4.0  5.0  <NA>   NaN

[4 rows x 4 columns]

Parameters:

value (scalar, Series) – Value to use to fill holes (e.g. 0), alternately a Series of values specifying which value to use for each index (for a Series) or column (for a DataFrame). Values not in the Series will not be filled. This value cannot be a list.

Returns:

Object with missing values filled

Return type:

bigframes.pandas.DataFrame