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 correspondingaxis, or by directly specifying index or column names. When using amulti-index, labels on different levels can be removed by specifyingthe level. See theuser 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 singlelabel 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’) orcolumns (1 or ‘columns’).

indexsingle label or list-like

Alternative to specifying axis (labels,axis=0is equivalent toindex=labels).

columnssingle label or list-like

Alternative to specifying axis (labels,axis=1is 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 operationin place and return None.

errors{‘ignore’, ‘raise’}, default ‘raise’

If ‘ignore’, suppress error and only existing labels aredropped.

Returns:

DataFrame or None

Returns DataFrame or None DataFrame with the specifiedindex 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   D0  0  1   2   31  4  5   6   72  8  9  10  11

Drop columns

>>>df.drop(['B','C'],axis=1)   A   D0  0   31  4   72  8  11

>>>df.drop(columns=['B','C'])   A   D0  0   31  4   72  8  11

Drop a row by index

>>>df.drop([0,1])   A  B   C   D2  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     smallllama   speed   45.0    30.0        weight  200.0   100.0        length  1.5     1.0cow     speed   30.0    20.0        weight  250.0   150.0        length  1.5     0.8falcon  speed   320.0   250.0        weight  1.0     0.8        length  0.3     0.2

Drop a specific index combination from the MultiIndexDataFrame, i.e., drop the combination'falcon' and'weight', which deletes only the corresponding row

>>>df.drop(index=('falcon','weight'))                big     smallllama   speed   45.0    30.0        weight  200.0   100.0        length  1.5     1.0cow     speed   30.0    20.0        weight  250.0   150.0        length  1.5     0.8falcon  speed   320.0   250.0        length  0.3     0.2

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

>>>df.drop(index='length',level=1)                big     smallllama   speed   45.0    30.0        weight  200.0   100.0cow     speed   30.0    20.0        weight  250.0   150.0falcon  speed   320.0   250.0        weight  1.0     0.8