The .drop() method returns a new DataFrame object with rows or columns removed based on column or index names. The original DataFrame object, used to call the method, remains unchanged.


# Drop names from specified axis.
df = dataframevalue.drop(names, axis)

# Drop names from columns.
df = dataframevalue.drop(columns=names)

# Drop names from rows. (axis defaults to "0")
df = dataframevalue.drop(names)
  • dataframevalue is the DataFrame with the source data.
  • names is a single label or a list of the items to drop.
  • axis is equal to 0 for dropping rows and 1 for dropping columns, it defaults to 0.
  • columns or index can be used to specify the labels to drop without using axis.

DataFrame.drop() has the following parameters:

Parameter Name Data Type Usage
labels single label or list The labels to drop from columns or index as specified by axis.
axis 0/1 or ‘index’/‘columns’ Specifies if labels refers to columns or indices. Defaults to 0.
index single label or list Specifies index labels to drop. Ignores axis.
columns single label or list Specifies column labels to drop. Ignores axis.
level index of level name For MultiIndex dataframes, the level from which the label(s) will be removed.
inplace bool If True, alters the existing DataFrame rather than returning a new one. Defaults to False.
errors ‘ignore’/‘raise’ Specifies if exception is raised for non-existent labels (‘raise’) or if those labels are ignored (‘ignore’). Default is ‘raise’.


In the following example, the .drop() method is used in two separate instances:

import pandas as pd
d = {'col 1' : [1,2,3,4], 'col 2' : ['A','B','C','D'], 'col 3' : [5,6,7,8], 'col 4' : ['E','F','G','H']}
df = pd.DataFrame(data = d)
print(f"Original df:\n {df}\n")
first_drop = df.drop(columns='col 3')
print(f"First drop():\n {first_drop}\n")
second_drop = df.drop(2)
print(f"Second drop():\n {second_drop}")

For the first .drop(), the entire third column ('col 3') is removed. With the next .drop(), the second row is removed altogether. These instances are reflected in the output below:

Original df:
col 1 col 2 col 3 col 4
0 1 A 5 E
1 2 B 6 F
2 3 C 7 G
3 4 D 8 H
After first drop:
col 1 col 2 col 4
0 1 A E
1 2 B F
2 3 C G
3 4 D H
After second drop:
col 1 col 2 col 3 col 4
0 1 A 5 E
1 2 B 6 F
3 4 D 8 H


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