pyspark.pandas.DataFrame.drop¶

DataFrame.drop(labels: Union[Any, Tuple[Any, …], List[Union[Any, Tuple[Any, …]]], None] = None, axis: Union[int, str, None] = 0, index: Union[Any, Tuple[Any, …], List[Union[Any, Tuple[Any, …]]]] = None, columns: Union[Any, Tuple[Any, …], List[Union[Any, Tuple[Any, …]]]] = None) → pyspark.pandas.frame.DataFrame[source]¶

Drop specified labels from columns.

Remove rows and/or columns by specifying label names and corresponding axis, or by specifying directly index and/or column names. Drop rows of a MultiIndex DataFrame is not supported yet.

Parameters

labelssingle label or list-like: Column labels to drop.
axis{0 or ‘index’, 1 or ‘columns’}, default 0: Changed in version 3.3: Set dropping by index is default.
indexsingle label or list-like: Alternative to specifying axis (labels, axis=0 is equivalent to index=columns).

Changed in version 3.3: Added dropping rows by ‘index’.
columnssingle label or list-like: Alternative to specifying axis (labels, axis=1 is equivalent to columns=labels).

Returns

droppedDataFrame

See also

Series.dropna

Notes

Currently, dropping rows of a MultiIndex DataFrame is not supported yet.

Examples

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

Drop columns

>>> df.drop(['B', 'C'], axis=1)
   A   D
0  0   3
1  4   7
2  8  11

>>> df.drop(columns=['B', 'C'])
   A   D
0  0   3
1  4   7
2  8  11

Drop a row by index

>>> df.drop([0, 1])
   A  B   C   D
2  8  9  10  11

>>> df.drop(index=[0, 1], columns='A')
   B   C   D
2  9  10  11

Also support dropping columns for MultiIndex

>>> df = ps.DataFrame({'x': [1, 2], 'y': [3, 4], 'z': [5, 6], 'w': [7, 8]},
...                   columns=['x', 'y', 'z', 'w'])
>>> columns = [('a', 'x'), ('a', 'y'), ('b', 'z'), ('b', 'w')]
>>> df.columns = pd.MultiIndex.from_tuples(columns)
>>> df  
   a     b
   x  y  z  w
0  1  3  5  7
1  2  4  6  8
>>> df.drop(labels='a', axis=1)  
   b
   z  w
0  5  7
1  6  8

pyspark.pandas.DataFrame.between_time pyspark.pandas.DataFrame.droplevel