.drop()

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.

Syntax

# 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:

Example

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