drop(self, labels=None, axis: 'Axis' = 0, index=None, columns=None, level: 'Level | None' = None, inplace: 'bool' = False, errors: 'str' = 'raise')
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 :None:None:`user guide <advanced.shown_levels>` for more information about the now unused levels.
Index or column labels to drop. A tuple will be used as a single label and not treated as a list-like.
Whether to drop labels from the index (0 or 'index') or columns (1 or 'columns').
Alternative to specifying axis ( labels, axis=0
is equivalent to index=labels
).
Alternative to specifying axis ( labels, axis=1
is equivalent to columns=labels
).
For MultiIndex, level from which the labels will be removed.
If False, return a copy. Otherwise, do operation inplace and return None.
If 'ignore', suppress error and only existing labels are dropped.
If any of the labels is not found in the selected axis.
DataFrame without the removed index or column labels or None if inplace=True
.
Drop specified labels from rows or columns.
DataFrame.drop_duplicates
Return DataFrame with duplicate rows removed, optionally only considering certain columns.
DataFrame.dropna
Return DataFrame with labels on given axis omitted where (all or any) data are missing.
DataFrame.loc
Label-location based indexer for selection by label.
Series.drop
Return Series with specified index labels removed.
>>> 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
This example is valid syntax, but we were not able to check execution>>> df.drop(['B', 'C'], axis=1) A D 0 0 3 1 4 7 2 8 11This example is valid syntax, but we were not able to check execution
>>> df.drop(columns=['B', 'C']) A D 0 0 3 1 4 7 2 8 11
Drop a row by index
This example is valid syntax, but we were not able to check execution>>> df.drop([0, 1]) A B C D 2 8 9 10 11
Drop columns and/or rows of MultiIndex DataFrame
This example is valid syntax, but we were not able to check execution>>> 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
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 0.2This example is valid syntax, but we were not able to check execution
>>> 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 0.3This example is valid syntax, but we were not able to check execution
>>> 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 0.8See :
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pandas.core.frame.DataFrame.reset_index
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