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Python | Pandas Dataframe.sort_values() | Set-1

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Python is a great language for data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages, making importing and analyzing data much easier. Pandas sort_values() function sorts a data frame in Ascending or Descending order of passed Column. It’s different than the sorted Python function since it cannot sort a data frame and a particular column cannot be selected.

Let’s discuss the Dataframe.sort_values() Single Parameter Sorting

Python Pandas DataFrame.sort_values() Syntax

Syntax: DataFrame.sort_values(by, axis=0, ascending=True, inplace=False, kind=’quicksort’, na_position=’last’)

Note: Every parameter has some default values except the ‘by’ parameter.

Parameters: 

  • by: Single/List of column names to sort Data Frame by. 
  • axis: 0 or ‘index’ for rows and 1 or ‘columns’ for Column. 
  • ascending: Boolean value which sorts Data frame in ascending order if True. 
  • inplace: Boolean value. Makes the changes in passed data frame itself if True. 
  • kind: String which can have three inputs(‘quicksort’, ‘mergesort’ or ‘heapsort’) of algorithm used to sort data frame. 
  • na_position: Takes two string input ‘last’ or ‘first’ to set position of Null values. Default is ‘last’.


Return Type: 

Returns a sorted Data Frame with Same dimensions as of the function caller DataFrame.

For link to CSV file Used in Code, click here.

Python Pandas DataFrame sort_values() Examples

Below are some examples by which we can use dataframe.sort_values() function in Python:

Example 1: Sorting by Name 

In the following example, we will perform sorting by values in Pandas. Here, a Pandas dataframe is made from the csv file and the data frame is sorted in ascending order of Names of Players.

Before Sorting

In this example, simple dataframe is printed without sorting.

Python

# importing pandas package
import pandas as pd
 
# making data frame from csv file
data = pd.read_csv("nba.csv")
 
# display
data

                    

Output: 
 


After Sorting

In this example, sorting by values in Pandas is performed and dataframe is sorted as a result and it is then printed.

Python

# importing pandas package
import pandas as pd
 
# making data frame from csv file
data = pd.read_csv("nba.csv")
 
# sorting data frame by name
data.sort_values("Name", axis=0, ascending=True,
                 inplace=True, na_position='last')
 
# display
data

                    

As shown in the image, index column is now jumbled since the data frame is sorted by Name.

Output: 

Example 2: Changing Position of Null Values

In the given data, there are many null values in different columns which are put in the last by default. In this example, Pandas Data Frame is sorted with respect to Salary column and Null values are kept at the top. 

Python

# importing pandas package
import pandas as pd
 
# making data frame from csv file
data = pd.read_csv("nba.csv")
 
# sorting data frame by name
data.sort_values("Salary", axis=0, ascending=True,
                 inplace=True, na_position='first')
 
data
# display

                    

As shown in output image, The NaN values are at the top and after that comes the sorted value of Salary.

Output:



Last Updated : 22 Dec, 2023
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