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 <advanced.shown_levels> for more information about the now unused levels.  Parameters 
 
labels:single 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’).  
index:single label or list-like


Alternative to specifying axis (labels, axis=0 is equivalent to index=labels).  
columns:single label or list-like


Alternative to specifying axis (labels, axis=1 is equivalent to columns=labels).  
level:int or level name, optional


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