Data Manipulation Cookbook

Adding a prefix to column values Adding leading zeros to strings of a column Adding new column using lists Adding padding to a column of strings Bit-wise OR Changing column type to string Conditionally updating values of a DataFrame Converting all object-typed columns to categorical type Converting column type to date Converting column type to float Converting column type to integer Converting K and M to numerical form Converting string categories or labels to numeric values Encoding categorical variables Expanding lists vertically in a DataFrame Expanding strings vertically in a DataFrame Extracting numbers from column Filling missing value in Index of DataFrame Filtering column values using boolean masks Logical AND operation Making DataFrame string column lowercase Mapping True and False to 1 and 0 respectively Mapping values of a DataFrame using a dictionary Modifying a single value in a DataFrame Removing characters from columns Removing comma from column values Removing first n characters from column values Removing last n characters from column values Removing leading substring Removing trailing substring Replacing infinities with another value in DataFrame Replacing values in a DataFrame Rounding values Sorting categorical columns Using previous row to create new columns

thumb_up

thumb_down

chat_bubble_outline
0
 auto_stories new 
settings

Sorting categorical columns in Pandas DataFrame

Programming

Python

Pandas

Cookbooks

DataFrame Cookbooks

Data Manipulation Cookbook

Jul 1, 2022

Last updated

local_offer Python●Pandas

Tags

Table of Contents

Consider the following DataFrame:


        
        
            
                
                
                    import pandas as pd

df = pd.DataFrame({
    'class':['economy','first','business','first','economy'],
    'price':[4,10,7,8,3]})
df
                
            
               class     price
0  economy   4
1  first     10
2  business  7
3  first     8
4  economy   3

The class column is a categorical with ordering (economy < business < first).

The goal is to sort the DataFrame based on the class column. To do so, we first convert class into a categorical type, and set the ordering using the second parameter. At this point, the column has not yet been sorted, and so we use sort_values() to perform the sort:


        
        
            
                
                
                    df['class'] = pd.Categorical(df['class'], ["economy", "business", "first"])
df_new = df.sort_values('class')
df_new
                
            
               class     price
0  economy   4
4  economy   3
2  business  7
1  first     10
3  first     8

Published by Isshin Inada

Edited by 0 others

Did you find this page useful?

thumb_up

thumb_down

Comment

Citation

Share on Twitter

Share on Facebook

Ask a question or leave a feedback...

thumb_up

thumb_down

chat_bubble_outline

settings

Enjoy our search

Hit / to insta-search docs and recipes!