How you can Trade the Series of Columns in Pandas DataFrame

You’ll be able to significance please see syntax to temporarily trade the line of columns in a pandas DataFrame:

df[['column2', 'column3', 'column1']]

Please see examples display significance this syntax with please see pandas DataFrame:

import pandas as pd

#build unutilized DataFrame
df = pd.DataFrame({'issues': [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]})

#show DataFrame
df

	issues	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

Instance 1: Trade the Series of Columns by means of Identify

Please see code presentations trade the line of the columns within the DataFrame according to title:

#trade line of columns by means of title
df[['rebounds', 'assists', 'points']]

	rebounds assists issues
0	11	 5	 25
1	8	 7	 12
2	10	 7	 15
3	6	 9	 14
4	6	 12	 19
5	5	 9	 23
6	9	 9	 25
7	12	 4	 29

Instance 2: Trade the Series by means of Including Brandnew First Column

Please see code presentations trade the line of the columns within the DataFrame by means of putting a unutilized column within the first place:

#outline unutilized column so as to add
steals = [2, 3, 3, 4, 3, 2, 1, 2]

#insert unutilized column in first place
df.insert(0, 'steals', steals)

#show dataFrame
df
        steals	issues	assists	rebounds
0	2	25	5	11
1	3	12	7	8
2	3	15	7	10
3	4	14	9	6
4	3	19	12	6
5	2	23	9	5
6	1	25	9	9
7	2	29	4	12

Instance 3: Trade the Series by means of Including Brandnew Utmost Column

Please see code presentations trade the line of the columns within the DataFrame by means of putting a unutilized column within the closing place of the DataFrame:

#outline unutilized column so as to add
steals = [2, 3, 3, 4, 3, 2, 1, 2]

#insert unutilized column in closing place
df.insert(len(df.columns), 'steals', steals)

#show dataFrame
df

	issues	assists	rebounds steals
0	25	5	11	 2
1	12	7	8	 3
2	15	7	10	 3
3	14	9	6	 4
4	19	12	6	 3
5	23	9	5	 2
6	25	9	9	 1
7	29	4	12	 2

Supplementary Sources

How you can Insert a Column Right into a Pandas DataFrame
How you can Reduce the Index Column in Pandas
How you can Mix Two Columns in Pandas