pandas.DataFrame.drop#

DataFrame.drop(labels=None, *, axis=0, index=None, columns=None, level=None, inplace=False, errors='raise')[source] #

Drop specified labels from rows or columns.

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

Parameters:

labelssingle 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’).
indexsingle label or list-like: Alternative to specifying axis (labels, axis=0 is equivalent to index=labels).
columnssingle label or list-like: Alternative to specifying axis (labels, axis=1 is equivalent to columns=labels).
levelint or level name, optional: For MultiIndex, level from which the labels will be removed.
inplacebool, default False: If False, return a copy. Otherwise, do operation in place and return None.
errors{‘ignore’, ‘raise’}, default ‘raise’: If ‘ignore’, suppress error and only existing labels are dropped.

Returns:

DataFrame or None: Returns DataFrame or None DataFrame with the specified index or column labels removed or None if inplace=True.

Raises:

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

See also

DataFrame.loc: Label-location based indexer for selection by label.
DataFrame.dropna: Return DataFrame with labels on given axis omitted where (all or any) data are missing.
DataFrame.drop_duplicates: Return DataFrame with duplicate rows removed, optionally only considering certain columns.
Series.drop: Return Series with specified index labels removed.

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

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

Drop columns and/or rows of MultiIndex DataFrame

>>> midx = pd.MultiIndex(levels=[['llama', '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
llama 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

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
llama 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

>>> df.drop(index='cow', columns='small')
 big
llama speed 45.0
 weight 200.0
 length 1.5
falcon speed 320.0
 weight 1.0
 length 0.3

>>> df.drop(index='length', level=1)
 big small
llama 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