pandas.DataFrame.drop — pandas 2.2.3 documentation (original) (raw)
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 guidefor 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=0is equivalent to index=labels).
columnssingle label or list-like
Alternative to specifying axis (labels, axis=1is 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
Label-location based indexer for selection by label.
Return DataFrame with labels on given axis omitted where (all or any) data are missing.
Return DataFrame with duplicate rows removed, optionally only considering certain columns.
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