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 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 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 toindex=labels
).- columnssingle label or list-like
Alternative to specifying axis (
labels, axis=1
is equivalent tocolumns=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 inplace and return None.
- errors{‘ignore’, ‘raise’}, default ‘raise’
If ‘ignore’, suppress error and only existing labels are dropped.
- Returns
- DataFrame or None
DataFrame without the removed index or column labels 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=[['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 0.2
>>> 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
>>> 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