Pandas DataFrame sort_index() 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 sort_index()

The sort_index() method sorts the DataFrame.

The syntax for this method is as follows:

DataFrame.sort_index(axis=0, level=None, ascending=True, inplace=False, kind='quicksort', na_position='last', sort_remaining=True, ignore_index=False, key=None)

Parameter	Description
`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.
`level`	This parameter is an integer, level name, or a list of integers/level name(s). If not empty, a sort is performed on values in the selected index level(s).
`ascending`	By default, `True`. Sort is conducted in ascending order. If `False`, descending order.
`inplace`	If `False`, create a copy of the object. If `True`, the original object updates. By default, `False`.
`kind`	Available options are `quicksort`, `mergesort`, `heapsort`, or `stable`. By default, `quicksort`. See `numpy.sort` for additional details.
`na_position`	Available options are `first` and `last` (default). If the option is `first`, all `NaN` values move to the beginning, `last` to the end.
`ignore_index`	If `True`, the axis numbering is 0, 1, 2, etc. By default, `False`.
`key`	This parameter applies the function to the values before a sort. The data must be in a Series format and applies to each column.

For this example, a comma-delimited CSV file is read into a DataFrame. This DataFrame sorts on the index Country column.

df = pd.read_csv('countries.csv')
df = df.set_index('Country')
result = df.sort_index()
print(result)

Line [1] reads in a comma-delimited CSV file and saves to df.
Line [2] sets the index of the DataFrame to Country. The output saves to df (over-writing original df).
Line [3] sorts the DataFrame (df) on the indexed column (Country) in ascending order (default). The output saves to result.
Line [4] outputs the result to the terminal.

Output

	Country	Population	Area
China	Beijing	1400050000	9596961
France	Paris	67081000	551695
Germany	Berlin	83783942	357021
India	Dheli	1352642280	3287263
Italy	Rome	60317116	301338
Poland	Warsaw	38383000	312685
Russia	Moscow	146748590	17098246
Spain	Madrid	47431256	498511
USA	Washington	328239523	9833520

More Pandas DataFrame Methods

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

👈 df.slice_shift()

df.sort_values() 👉

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