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How to Sort Pandas DataFrame

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by ganesh dot icon Updated: Jan 19, 2023

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A DataFrame is a two-dimensional size-mutable tabular data structure with rows and columns that can hold any data type. It is a crucial data structure in the pandas library, a popular library for data manipulation and analysis in Python. 


  • pandas: pandas is a library for data manipulation and analysis in Python. It provides a range of tools for working with data in a variety of formats, including: 
  • importing and exporting data from different formats (e.g. CSV, Excel, JSON) 
  • data cleanup and preparation for analysis 
  • data manipulation and transformation 
  • using plots and charts to represent data 


We can use the sort_values() method of a pandas DataFrame to sort the data by one or more columns. This method returns a new DataFrame sorted by the specified columns. 

  • sort_values(): This will sort the DataFrame by the values in the given column in ascending order. You can use the ascending parameter to specify the sort order and set it to False to sort in descending order. 

Here is how you can sort Pandas dataframe;

Fig 1: Preview of the output that you will get on running this code from your Jupyter notebook



Code


In this solution, we use the sort_values function of the Pandas library

Pandas: DataFrame sorting

PythondotLines of Code : 34dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
df = pd.DataFrame({'TS': [40,1,4], 'a':[1,5,2], 'd':[7,8,9]}, index=list('abc'))
print (df)
   TS  a  d
a  40  1  7
b   1  5  8
c   4  2  9

df.sort_values(by='TS',
               axis=0,
               ascending=True,
               inplace=True,
               kind='quicksort',
               na_position='last') 

print (df)
   TS  a  d
b   1  5  8
c   4  2  9
a  40  1  7

df.sort_values(by='TS', inplace=True) 
print (df)
   TS  a  d
b   1  5  8
c   4  2  9
a  40  1  7

df.sort_values(by='a',axis=1,ascending=True,inplace=True,kind='quicksort',na_position='last') 
print (df)
   a  d  TS
a  1  7  40
b  5  8   1
c  2  9   4
  1. Copy the code using the "Copy" button above, and paste it in a cell of Jupyter notebook.
  2. Run the cell to sort a Pandas dataframe.


I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.


I found this code snippet by searching for "sort pandas dataframe" in kandi. You can try any such use case!

Dependent Libraries


pandasby pandas-dev

Python doticonstar image 38689 doticonVersion:v2.0.2doticon
License: Permissive (BSD-3-Clause)

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

Support
    Quality
      Security
        License
          Reuse

            pandasby pandas-dev

            Python doticon star image 38689 doticonVersion:v2.0.2doticon License: Permissive (BSD-3-Clause)

            Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
            Support
              Quality
                Security
                  License
                    Reuse

                      If you do not have Pandas that is required to run this code, you can install it by clicking on the above link and following the installation instruction from either Github or Pypi links through the Pandas page in kandi.


                      You can search for any dependent library on kandi like Pandas.

                      Environment Tested


                      I tested this solution in the following versions. Be mindful of changes when working with other versions.

                      1. The solution is created in Python3.7.
                      2. The solution is tested on Pandas 1.3.1 version.


                      Using this solution, we are able to sort a Dataframe using the Pandas library in Python with simple steps. This process also facilities an easy to use, hassle free method to create a hands-on working version of code which would help us read data in Pandas.

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