pyspark.pandas.Series.pad

Series.pad(axis: Union[int, str, None] = None, inplace: bool = False, limit: Optional[int] = None) → FrameLike

Synonym for DataFrame.fillna() or Series.fillna() with method=`ffill`.

Note

the current implementation of ‘ffill’ uses Spark’s Window without specifying partition specification. This leads to moveing all data into a single a partition in a single machine and could cause serious performance degradation. Avoid this method with very large datasets.

Parameters

axis: {0 or `index`}

1 and columns are not supported.

inplace: boolean, default False

Fill in place (do not create a new object)

limit: int, default None

If method is specified, this is the maximum number of consecutive NaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None

Returns

DataFrame or Series

DataFrame or Series with NA entries filled.

Examples

>>> psdf = ps.DataFrame({
...     'A': [None, 3, None, None],
...     'B': [2, 4, None, 3],
...     'C': [None, None, None, 1],
...     'D': [0, 1, 5, 4]
...     },
...     columns=['A', 'B', 'C', 'D'])
>>> psdf
     A    B    C  D
0  NaN  2.0  NaN  0
1  3.0  4.0  NaN  1
2  NaN  NaN  NaN  5
3  NaN  3.0  1.0  4

Propagate non-null values forward.

>>> psdf.ffill()
     A    B    C  D
0  NaN  2.0  NaN  0
1  3.0  4.0  NaN  1
2  3.0  4.0  NaN  5
3  3.0  3.0  1.0  4

For Series

>>> psser = ps.Series([2, 4, None, 3])
>>> psser
0    2.0
1    4.0
2    NaN
3    3.0
dtype: float64

>>> psser.ffill()
0    2.0
1    4.0
2    4.0
3    3.0
dtype: float64

pyspark.pandas.Series.notnull pyspark.pandas.Series.dropna