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.
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