dask.dataframe.DataFrame.drop

DataFrame.drop(labels=None, axis=0, columns=None, errors='raise')[source]

Drop specified labels from rows or columns.

This docstring was copied from pandas.core.frame.DataFrame.drop.

Some inconsistencies with the Dask version may exist.

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
labelssingle label or list-like

Index or column labels to drop.

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 (Not supported in Dask)

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 (Not supported in Dask)

For MultiIndex, level from which the labels will be removed.

inplacebool, default False (Not supported in Dask)

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
>>> 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