Pandas DataFrame reorder_levels() Method – Be on the Right Side of Change

Preparation

Before any data manipulation can occur, two (2) new libraries will require installation.

The Pandas library enables access to/from a DataFrame.
The NumPy library supports multi-dimensional arrays and matrices in addition to a collection of mathematical functions.

To install these libraries, navigate to an IDE terminal. At the command prompt ($), execute the code below. For the terminal used in this example, the command prompt is a dollar sign ($). Your terminal prompt may be different.

$ pip install pandas

Hit the <Enter> key on the keyboard to start the installation process.

$ pip install numpy

Hit the <Enter> key on the keyboard to start the installation process.

If the installations were successful, a message displays in the terminal indicating the same.

Feel free to view the PyCharm installation guide for the required libraries.

Add the following code to the top of each code snippet. This snippet will allow the code in this article to run error-free.

import pandas as pd
import numpy as np

DataFrame reorder_levels()

The reorder_levels() method re-arranges the index of a DataFrame/Series. This method can not contain any duplicate level(s) or drop level(s).

The syntax for this method is as follows:

DataFrame.reorder_levels(order, axis=0)

Parameter	Description
`order`	This parameter is a list containing the new order levels. These levels can be a position or a label.
`axis`	If zero (0) or index is selected, apply to each column. Default is 0 (column). If zero (1) or columns, apply to each row.

For this example, there are five (5) students. Each student has some associated data with it. Grades generate by using np.random.randint().

index = [(1001, 'Micah Smith', 14), (1001, 'Philip Jones', 15), 
         	(1002, 'Ben Grimes', 16), (1002, 'Alicia Heath', 17), (1002, 'Arch Nelson', 18)]
m_index = pd.MultiIndex.from_tuples(index)
grades_lst = np.random.randint(45,100,size=5)
df = pd.DataFrame({"Grades": grades_lst}, index=m_index)
print(df)

result = df.reorder_levels([1,2,0])
print(result)

Line [1] creates a List of tuples. Each tuple contains three (3) values. The output saves to index.
Line [2] creates a MultiIndex from the List of Tuples created on line [1] and saves to m_index.
Line [3] generates five (5) random grades between the specified range and saves to grades_lst.
Line [4] creates a DataFrame from the variables on lines [1-3] and saves to df.
Line [5] outputs the DataFrame to the terminal.
Line [6] re-orders the levels as specified. The output saves to result.
Line [7] outputs the result to the terminal.

Output

df

			Grades
1001	Micah Smith	14	52
	Philip Jones	15	65
1002	Ben Grimes	16	83
	Alicia Heath	17	99
	Arch Nelson	18	78

result

			Grades
Micah Smith	14	1001	52
Philip Jones	15	1001	65
Ben Grimes	16	1002	83
Alicia Heath	17	1002	99
Arch Nelson	18	1002	78

More Pandas DataFrame Methods

Feel free to learn more about the previous and next pandas DataFrame methods (alphabetically) here:

👈 df.rank()

df.replace() 👉

Also, check out the full cheat sheet overview of all Pandas DataFrame methods.