How to Slice Columns in Pandas DataFrame (With Examples)

You can use the following methods to slice the columns in a pandas DataFrame:

Method 1: Slice by Specific Column Names

df_new = df.loc[:, ['col1', 'col4']]

Method 2: Slice by Column Names in Range

df_new = df.loc[:, 'col1':'col4']

Method 3: Slice by Specific Column Index Positions

df_new = df.iloc[:, [0, 3]]

Method 4: Slice by Column Index Position Range

df_new = df.iloc[:, 0:3]

Note the subtle difference between loc and iloc in each of these methods:

loc selects rows and columns with specific labels
iloc selects rows and columns at specific integer positions

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: Slice by Specific Column Names

We can use the following syntax to create a new DataFrame that only contains the columns team and rebounds:

#slice columns team and rebounds
df_new = df.loc[:, ['team', 'rebounds']]

#view new DataFrame
print(df_new)

  team  rebounds
0    A        11
1    B         8
2    C        10
3    D         6
4    E         6
5    F         5
6    G         9
7    H        12

Example 2: Slice by Column Names in Range

We can use the following syntax to create a new DataFrame that only contains the columns in the range between team and rebounds:

#slice columns between team and rebounds
df_new = df.loc[:, 'team':'rebounds']

#view new DataFrame
print(df_new)

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

Example 3: Slice by Specific Column Index Positions

We can use the following syntax to create a new DataFrame that only contains the columns in the index positions 0 and 3:

#slice columns in index positions 0 and 3
df_new = df.iloc[:, [0, 3]]

#view new DataFrame
print(df_new)

  team  rebounds
0    A        11
1    B         8
2    C        10
3    D         6
4    E         6
5    F         5
6    G         9
7    H        12

Example 4: Slice by Column Index Position Range

We can use the following syntax to create a new DataFrame that only contains the columns in the index position range between 0 and 3:

#slice columns in index position range between 0 and 3
df_new = df.iloc[:, 0:3]

#view new DataFrame
print(df_new)

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

Note: When using an index position range, the last index position in the range will not be included. For example, the rebounds column in index position 3 is not included in the new DataFrame.