Python Pandas Sorting: Types and Examples

In Python, sorting is a common operation that is used to organize data in a specific order. Pandas, a Python library for data analysis, provides several methods for sorting DataFrames.

In this tutorial, we will discuss the different types of sorting that can be performed in Pandas, along with examples of how to use them.

Types of Sorting in Pandas

Sorting refers to the arrangement of data in a particular format or order. This could be in ascending or descending order, based on one or more columns. Python Pandas provides two kinds of sorting techniques:

Label-based sorting using sort_index(): This method sorts a DataFrame by its index. The default sorting order is ascending, but you can also specify descending order by passing the ascending=False argument.
Value-based sorting using sort_values(): This method sorts a DataFrame by the values in a specified column. The default sorting order is ascending, but you can also specify descending order by passing the ascending=False argument.

sort_index()

The sort_index() function is used to sort the data frame or series by its index in ascending or descending order.

Here's an example of how we can use sort_index().

import pandas as pd

data = pd.Series([1,5,3,2,4], index=['A','E','C','B','D'])

# Sorting the data by index
sorted_data = data.sort_index()
print(sorted_data)

Output:

# A    1
# B    2
# C    3
# D    4
# E    5
# dtype: int64

Our series data is sorted by index in ascending order. You can also sort it in descending order by passing ascending=False to the sort_index() function.

sort_values()

The sort_values() function is used to sort the data frame or series by its values in either ascending or descending order.

Let's understand sort_values() with a simple DataFrame example:

df = pd.DataFrame({
    'Name': ['Mike', 'Brad', 'Chan', 'Alex'],
    'Age': [34, 30, 22, 41]
})

# Sorting the data by Age
sorted_df = df.sort_values(by='Age')
print(sorted_df)

Output:

#    Name  Age
# 2  Chan  22
# 1  Brad  30
# 0  Mike  34
# 3  Alex  41