You can use the following methods to drop multiple columns from a pandas DataFrame:

Method 1: Drop Multiple Columns by Name

df.drop(columns=['col1', 'col2', 'col4'], inplace=True)

Method 2: Drop Columns in Range by Name

df.drop(columns=df.loc[:, 'col1':'col4'], inplace=True)

Method 3: Drop Multiple Columns by Index

df.drop(columns=df.columns[[0, 3, 4]], inplace=True)

Method 4: Drop Columns in Range by Index

df.drop(columns=df.columns[1:4], inplace=True)

Note: The argument inplace=True tells pandas to drop the columns in place without reassigning the DataFrame.

The following examples show how to use each method in practice with the following pandas DataFrame:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'team': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'],
                   'points': [18, 22, 19, 14, 14, 11, 20, 28],
                   'assists': [5, 7, 7, 9, 12, 9, 9, 4],
                   'rebounds': [11, 8, 10, 6, 6, 5, 9, 12],
                   'steals': [4, 5, 10, 12, 4, 8, 7, 2]})

#view DataFrame
print(df)

  team  points  assists  rebounds  steals
0    A      18        5        11       4
1    B      22        7         8       5
2    C      19        7        10      10
3    D      14        9         6      12
4    E      14       12         6       4
5    F      11        9         5       8
6    G      20        9         9       7
7    H      28        4        12       2

Example 1: Drop Multiple Columns by Name

The following code shows how to drop the points, rebounds, and steals columns by name:

#drop multiple columns by name
df.drop(columns=['points', 'rebounds', 'steals'], inplace=True)

#view updated Dataframe
print(df)

  team  assists
0    A        5
1    B        7
2    C        7
3    D        9
4    E       12
5    F        9
6    G        9
7    H        4

Example 2: Drop Columns in Range by Name

The following code shows how to drop each column between the points and rebounds columns by name:

#drop columns in range by name
df.drop(columns=df.loc[:, 'points':'rebounds'], inplace=True)

#view updated Dataframe
print(df)

  team  steals
0    A       4
1    B       5
2    C      10
3    D      12
4    E       4
5    F       8
6    G       7
7    H       2

Example 3: Drop Multiple Columns by Index

The following code shows how to drop the columns in index positions 0, 3 and 4 from the DataFrame:

#drop multiple columns by index
df.drop(columns=df.columns[[0, 3, 4]], inplace=True)

#view updated Dataframe
print(df)

   points  assists
0      18        5
1      22        7
2      19        7
3      14        9
4      14       12
5      11        9
6      20        9
7      28        4

Example 4: Drop Columns in Range by Index

The following code shows how to drop the columns in index positions 0, 3 and 4 from the DataFrame:

#drop columns by index range
df.drop(columns=df.columns[1:4], inplace=True)

#view updated Dataframe
print(df)

  team  steals
0    A       4
1    B       5
2    C      10
3    D      12
4    E       4
5    F       8
6    G       7
7    H       2

Note that the syntax df.columns[1:4] specifies columns in index positions 1 up to 4.

Thus, this syntax drops the columns in index positions 1, 2 and 3.

Note: You can find the complete documentation for the pandas drop() function .

The following tutorials explain how to perform other common tasks in pandas: