Pandas: How to Drop All Columns Except Specific Ones


You can use the following methods to drop all columns except specific ones from a pandas DataFrame:

Method 1: Use Double Brackets

df = df[['col2', 'col6']]

Method 2: Use .loc

df = df.loc[:, ['col2', 'col6']]

Both methods drop all columns in the DataFrame except the columns called col2 and col6.

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

import pandas as pd

#create DataFrame with six columns
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, 3, 3, 2, 5, 4, 3, 8],
                   'blocks': [1, 0, 0, 3, 2, 2, 1, 5]})

#view DataFrame
print(df)

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

Example 1: Drop All Columns Except Specific Ones Using Double Brackets

We can use the following syntax to drop all columns in the DataFrame except the ones called points and blocks:

#drop all columns except points and blocks
df = df[['points', 'blocks']]

#view updated DataFrame
print(df)

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

Notice that only the points and blocks columns remain.

All other columns have been dropped.

Example 2: Drop All Columns Except Specific Ones Using .loc

We can also use the .loc function to drop all columns in the DataFrame except the ones called points and blocks:

#drop all columns except points and blocks
df = df.loc[:, ['points', 'blocks']]

#view updated DataFrame
print(df)

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

Notice that only the points and blocks columns remain.

This matches the results from the previous example.

Related: Pandas loc vs. iloc: What’s the Difference?

Additional Resources

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

How to Drop First Row in Pandas DataFrame
How to Drop First Column in Pandas DataFrame
How to Drop Duplicate Columns in Pandas

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