pyspark.pandas.DataFrame.where¶
-
DataFrame.where(cond: Union[DataFrame, Series], other: Union[DataFrame, Series, Any] = nan, axis: Union[int, str] = None) → DataFrame¶ Replace values where the condition is False.
- Parameters
- condboolean DataFrame
Where cond is True, keep the original value. Where False, replace with corresponding value from other.
- otherscalar, DataFrame
Entries where cond is False are replaced with corresponding value from other.
- axisint, default None
Can only be set to 0 at the moment for compatibility with pandas.
- Returns
- DataFrame
Examples
>>> from pyspark.pandas.config import set_option, reset_option >>> set_option("compute.ops_on_diff_frames", True) >>> df1 = ps.DataFrame({'A': [0, 1, 2, 3, 4], 'B':[100, 200, 300, 400, 500]}) >>> df2 = ps.DataFrame({'A': [0, -1, -2, -3, -4], 'B':[-100, -200, -300, -400, -500]}) >>> df1 A B 0 0 100 1 1 200 2 2 300 3 3 400 4 4 500 >>> df2 A B 0 0 -100 1 -1 -200 2 -2 -300 3 -3 -400 4 -4 -500
>>> df1.where(df1 > 0).sort_index() A B 0 NaN 100.0 1 1.0 200.0 2 2.0 300.0 3 3.0 400.0 4 4.0 500.0
>>> df1.where(df1 > 1, 10).sort_index() A B 0 10 100 1 10 200 2 2 300 3 3 400 4 4 500
>>> df1.where(df1 > 1, df1 + 100).sort_index() A B 0 100 100 1 101 200 2 2 300 3 3 400 4 4 500
>>> df1.where(df1 > 1, df2).sort_index() A B 0 0 100 1 -1 200 2 2 300 3 3 400 4 4 500
When the column name of cond is different from self, it treats all values are False
>>> cond = ps.DataFrame({'C': [0, -1, -2, -3, -4], 'D':[4, 3, 2, 1, 0]}) % 3 == 0 >>> cond C D 0 True False 1 False True 2 False False 3 True False 4 False True
>>> df1.where(cond).sort_index() A B 0 NaN NaN 1 NaN NaN 2 NaN NaN 3 NaN NaN 4 NaN NaN
When the type of cond is Series, it just check boolean regardless of column name
>>> cond = ps.Series([1, 2]) > 1 >>> cond 0 False 1 True dtype: bool
>>> df1.where(cond).sort_index() A B 0 NaN NaN 1 1.0 200.0 2 NaN NaN 3 NaN NaN 4 NaN NaN
>>> reset_option("compute.ops_on_diff_frames")