Pandas: sort the DataFrame by column values

In this post, I am going to explain how to sort the DataFrame by a single column values.

To demonstrate the application, I am using the below data set.

        Name   Age       City  Gender
0   Krishna  34.0  Bangalore    Male
1     Sailu  35.0  Hyderabad  Female
2      Joel  29.0       None    Male
3     Chamu   NaN    Chennai  Female
4  Jitendra  52.0       None    Male
5       Raj   NaN    Chennai    Male

 

Example 1: Sort by name in ascending order.

sort_by_name_ascending_1 = df.sort_values('Name')

  Above statement sort the DataFrame by ‘Name’ column and assign the result dataset to the variable ‘sort_by_name_ascending_1’. Above statement do not have an effect on the original DataFrame.

 

sort_by_name_ascending_1 : 
        Name   Age       City  Gender
3     Chamu   NaN    Chennai  Female
4  Jitendra  52.0       None    Male
2      Joel  29.0       None    Male
0   Krishna  34.0  Bangalore    Male
5       Raj   NaN    Chennai    Male
1     Sailu  35.0  Hyderabad  Female

We can achieve the same result with below statements too.

sort_by_name_ascending_2 = df.sort_values(by='Name')
sort_by_name_ascending_3 = df.sort_values(by='Name', ascending=True)
sort_by_name_ascending_4 = df.sort_values('Name', ascending=True)

Example 2: Sort by Name column in descending order.

‘sort_values’ method sort the DataFrame by given column in ascending order by default. You can customize the sorting order by setting the argument ‘ascending’ to False.

sort_by_name_descending_1 = df.sort_values('Name', ascending=False)

sort_by_name_descending_1 : 
        Name   Age       City  Gender
1     Sailu  35.0  Hyderabad  Female
5       Raj   NaN    Chennai    Male
0   Krishna  34.0  Bangalore    Male
2      Joel  29.0       None    Male
4  Jitendra  52.0       None    Male
3     Chamu   NaN    Chennai  Female

Find the below working application.

 

sort_by_name.py

import pandas as pd
import numpy as np

# Create a sample DataFrame
data = {'Name': ['Krishna', 'Sailu', 'Joel', 'Chamu', 'Jitendra', "Raj"],
        'Age': [34, 35, 29, np.nan, 52, np.nan],
        'City': ['Bangalore', 'Hyderabad', None, 'Chennai', None, 'Chennai'],
        'Gender': ['Male', 'Female', 'Male', 'Female', 'Male', 'Male']}
df = pd.DataFrame(data)

sort_by_name_ascending_1 = df.sort_values('Name')
sort_by_name_ascending_2 = df.sort_values(by='Name')
sort_by_name_ascending_3 = df.sort_values(by='Name', ascending=True)
sort_by_name_ascending_4 = df.sort_values('Name', ascending=True)

print('df : \n', df)
print('\nsort_by_name_ascending_1 : \n', sort_by_name_ascending_1)
print('\nsort_by_name_ascending_2 : \n', sort_by_name_ascending_2)
print('\nsort_by_name_ascending_3 : \n', sort_by_name_ascending_3)
print('\nsort_by_name_ascending_4 : \n', sort_by_name_ascending_4)

print('\nSort by Name in descending order\n')
sort_by_name_descending_1 = df.sort_values('Name', ascending=False)
print('\nsort_by_name_descending_1 : \n', sort_by_name_descending_1)

Output

df : 
        Name   Age       City  Gender
0   Krishna  34.0  Bangalore    Male
1     Sailu  35.0  Hyderabad  Female
2      Joel  29.0       None    Male
3     Chamu   NaN    Chennai  Female
4  Jitendra  52.0       None    Male
5       Raj   NaN    Chennai    Male

sort_by_name_ascending_1 : 
        Name   Age       City  Gender
3     Chamu   NaN    Chennai  Female
4  Jitendra  52.0       None    Male
2      Joel  29.0       None    Male
0   Krishna  34.0  Bangalore    Male
5       Raj   NaN    Chennai    Male
1     Sailu  35.0  Hyderabad  Female

sort_by_name_ascending_2 : 
        Name   Age       City  Gender
3     Chamu   NaN    Chennai  Female
4  Jitendra  52.0       None    Male
2      Joel  29.0       None    Male
0   Krishna  34.0  Bangalore    Male
5       Raj   NaN    Chennai    Male
1     Sailu  35.0  Hyderabad  Female

sort_by_name_ascending_3 : 
        Name   Age       City  Gender
3     Chamu   NaN    Chennai  Female
4  Jitendra  52.0       None    Male
2      Joel  29.0       None    Male
0   Krishna  34.0  Bangalore    Male
5       Raj   NaN    Chennai    Male
1     Sailu  35.0  Hyderabad  Female

sort_by_name_ascending_4 : 
        Name   Age       City  Gender
3     Chamu   NaN    Chennai  Female
4  Jitendra  52.0       None    Male
2      Joel  29.0       None    Male
0   Krishna  34.0  Bangalore    Male
5       Raj   NaN    Chennai    Male
1     Sailu  35.0  Hyderabad  Female

Sort by Name in descending order


sort_by_name_descending_1 : 
        Name   Age       City  Gender
1     Sailu  35.0  Hyderabad  Female
5       Raj   NaN    Chennai    Male
0   Krishna  34.0  Bangalore    Male
2      Joel  29.0       None    Male
4  Jitendra  52.0       None    Male
3     Chamu   NaN    Chennai  Female

sort_by_name_na_is_first : 
        Name   Age       City  Gender
3     Chamu   NaN    Chennai  Female
4  Jitendra  52.0       None    Male
2      Joel  29.0       None    Male
0   Krishna  34.0  Bangalore    Male
5       Raj   NaN    Chennai    Male
1     Sailu  35.0  Hyderabad  Female

 

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