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Pandas allows you to select multiple columns from a DataFrame by passing in a list of column names. This can be done using the .loc or .iloc methods. Using .loc will allow you to select columns by their labels, while .iloc will allow you to select by their integer positions. Both methods return a DataFrame with the selected columns, which can be used for further analysis. You can also use the Python slicing syntax to select multiple columns in Pandas.
There are three basic methods you can use to select multiple columns of a pandas DataFrame:
Method 1: Select Columns by Index
df_new = df.iloc[:, [0,1,3]]
Method 2: Select Columns in Index Range
df_new = df.iloc[:, 0:3]
Method 3: Select Columns by Name
df_new = df[['col1', 'col2']]
The following examples show how to use each method with the following pandas DataFrame:
import pandas as pd
#create DataFrame
df = pd.DataFrame({'points': [25, 12, 15, 14, 19, 23, 25, 29],
'assists': [5, 7, 7, 9, 12, 9, 9, 4],
'rebounds': [11, 8, 10, 6, 6, 5, 9, 12],
'blocks': [4, 7, 7, 6, 5, 8, 9, 10]})
#view DataFrame
df
points assists rebounds blocks
0 25 5 11 4
1 12 7 8 7
2 15 7 10 7
3 14 9 6 6
4 19 12 6 5
5 23 9 5 8
6 25 9 9 9
7 29 4 12 10
Method 1: Select Columns by Index
The following code shows how to select columns in index positions 0, 1, and 3:
#select columns in index positions 0, 1, and 3
df_new = df.iloc[:, [0,1,3]]
#view new DataFrame
df_new
points assists blocks
0 25 5 4
1 12 7 7
2 15 7 7
3 14 9 6
4 19 12 5
5 23 9 8
6 25 9 9
7 29 4 10
Notice that the columns in index positions 0, 1, and 3 are selected.
Note: The first column in a pandas DataFrame is located in position 0.
Method 2: Select Columns in Index Range
The following code shows how to select columns in the index range 0 to 3:
#select columns in index range 0 to 3
df_new = df.iloc[:, 0:3]
#view new DataFrame
df_new
points assists rebounds
0 25 5 11
1 12 7 8
2 15 7 10
3 14 9 6
4 19 12 6
5 23 9 5
6 25 9 9
7 29 4 12
Method 3: Select Columns by Name
The following code shows how to select columns by name:
#select columns called 'points' and 'blocks'
df_new = df[['points', 'blocks']]
#view new DataFrame
df_new
points blocks
0 25 4
1 12 7
2 15 7
3 14 6
4 19 5
5 23 8
6 25 9
7 29 10