modin.pandas.DataFrame.drop¶

DataFrame.drop(labels: Union[Hashable, Sequence[Hashable]] = None, axis: Union[int, Literal['index', 'columns', 'rows']] = 0, index: Union[Hashable, Sequence[Hashable]] = None, columns: Union[Hashable, Sequence[Hashable]] = None, level: Hashable = None, inplace: bool = False, errors: Literal['ignore', 'raise'] = 'raise')[source]¶

Drop specified labels from rows or columns.

Remove rows or columns by specifying label names and corresponding axis, or by specifying directly index or column names. When using a multi-index, labels on different levels can be removed by specifying the level. See the user guide <advanced.shown_levels> for more information about the now unused levels.

Parameters:

  • labels (single label or list-like) – Index or column labels to drop. A tuple will be used as a single label and not treated as a list-like.

  • axis ({0 or 'index', 1 or 'columns'}, default 0) – Whether to drop labels from the index (0 or ‘index’) or columns (1 or ‘columns’).

  • index (single label or list-like) – Alternative to specifying axis (labels, axis=0 is equivalent to index=labels).

  • columns (single label or list-like) – Alternative to specifying axis (labels, axis=1 is equivalent to columns=labels).

  • level (int or level name, optional) – For MultiIndex, level from which the labels will be removed.

  • inplace (bool, default False) – If False, return a copy. Otherwise, do operation inplace and return None.

  • errors ({'ignore', 'raise'}, default 'raise') – If ‘ignore’, suppress error and only existing labels are dropped.

Returns:

DataFrame without the removed index or column labels or None if inplace=True.

Return type:

DataFrame or None

Raises:

KeyError – If any of the labels is not found in the selected axis.

Examples

>>> df = pd.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
Copy

Drop columns

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

Drop a row by index

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

Drop columns and/or rows of MultiIndex DataFrame

>>> midx = pd.MultiIndex(levels=[['lama', 'cow', 'falcon'],
...                              ['speed', 'weight', 'length']],
...                      codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2],
...                             [0, 1, 2, 0, 1, 2, 0, 1, 2]])
>>> df = pd.DataFrame(index=midx, columns=['big', 'small'],
...                   data=[[45, 30], [200, 100], [1.5, 1], [30, 20],
...                         [250, 150], [1.5, 0.8], [320, 250],
...                         [1, 0.8], [0.3, 0.2]])
>>> df
                 big  small
lama   speed    45.0   30.0
       weight  200.0  100.0
       length    1.5    1.0
cow    speed    30.0   20.0
       weight  250.0  150.0
       length    1.5    0.8
falcon speed   320.0  250.0
       weight    1.0    0.8
       length    0.3    0.2
Copy

Drop a specific index combination from the MultiIndex DataFrame, i.e., drop the combination 'falcon' and 'weight', which deletes only the corresponding row

>>> df.drop(index=('falcon', 'weight'))
                 big  small
lama   speed    45.0   30.0
       weight  200.0  100.0
       length    1.5    1.0
cow    speed    30.0   20.0
       weight  250.0  150.0
       length    1.5    0.8
falcon speed   320.0  250.0
       length    0.3    0.2
Copy
>>> df.drop(index='cow', columns='small')
                 big
lama   speed    45.0
       weight  200.0
       length    1.5
falcon speed   320.0
       weight    1.0
       length    0.3
Copy
>>> df.drop(index='length', level=1)
                 big  small
lama   speed    45.0   30.0
       weight  200.0  100.0
cow    speed    30.0   20.0
       weight  250.0  150.0
falcon speed   320.0  250.0
       weight    1.0    0.8
Copy
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