A Comprehensive Guide to Sorting in Pandas

In order to sort rows in Pandas, you can use the sort_values() function. I have taken examples from this excellant blog. Although it shows a lot of the use cases covered here, the last case is absent pretty much everywhere.

In this tutorial, I will be showing how to use Pandas to sort rows based on different criterion.

You can run a colab notebook containing all code present in this post by clicking here.

Let us create our DataFrame (df) in pandas

from pandas import DataFrame
 
Cars = {'Brand': ['Honda Civic','Toyota Corolla','Ford Focus','Audi A4'],
        'Price': [22000, 25000, 27000, 35000],
        'Year': [2015, 2013, 2018, 2018]
        }
 
df = DataFrame(Cars, columns= ['Brand', 'Price','Year'])

df

	Brand	Price	Year
0	Honda Civic	22000	2015
1	Toyota Corolla	25000	2013
2	Ford Focus	27000	2018
3	Audi A4	35000	2018

Column in an ascending order⌗

To sort this df based on the column Brand, we can use the sort_values() function in this manner. Here, inplace=True means that the df will be sorted in-memory. Also, by default, the df is sorted in ascending order. But we can change it easily, as will be shown later.

df.sort_values(by=['Brand'], inplace=True)
df

	Brand	Price	Year
3	Audi A4	35000	2018
2	Ford Focus	27000	2018
0	Honda Civic	22000	2015
1	Toyota Corolla	25000	2013

The complete code to perform the task above is shown below.

from pandas import DataFrame
 
Cars = {'Brand': ['Honda Civic','Toyota Corolla','Ford Focus','Audi A4'],
        'Price': [22000,25000,27000,35000],
        'Year': [2015,2013,2018,2018]
        }
 
df = DataFrame(Cars, columns= ['Brand', 'Price','Year'])

# sort Brand - ascending order
df.sort_values(by=['Brand'], inplace=True)

df

	Brand	Price	Year
3	Audi A4	35000	2018
2	Ford Focus	27000	2018
0	Honda Civic	22000	2015
1	Toyota Corolla	25000	2013

Column in an descending order⌗

Now, suppose we would like to sort df in descending order. Well, sort_values() accepts a paramter named ascending. By passing ascending=True, you can sort the df in descending order. The code is given below.

df.sort_values(by=['Brand'], inplace=True, ascending=False)

# sort - descending order
from pandas import DataFrame
 
Cars = {'Brand': ['Honda Civic','Toyota Corolla','Ford Focus','Audi A4'],
        'Price': [22000,25000,27000,35000],
        'Year': [2015,2013,2018,2018]
        }
 
df = DataFrame(Cars, columns= ['Brand', 'Price','Year'])

# sort Brand - descending order
df.sort_values(by=['Brand'], inplace=True, ascending=False)

df

	Brand	Price	Year
1	Toyota Corolla	25000	2013
0	Honda Civic	22000	2015
2	Ford Focus	27000	2018
3	Audi A4	35000	2018

By multiple columns - Same data type⌗

In the next step, we would like to sort based on two columns. So, here, we first sort by Year and then by the Price of the car. sort_values() accepts a parameter called by. The by paramters can accept a string literal or it can accept a list. We simply need to pass the list containing the columns by which we want the df to be sorted. This is shown below.

 df.sort_values(by=['Year','Price'], inplace=True)

# sort by multiple columns
from pandas import DataFrame
 
Cars = {'Brand': ['Honda Civic','Toyota Corolla','Ford Focus','Audi A4'],
        'Price': [22000,25000,27000,35000],
        'Year': [2015,2013,2018,2018]
        }
 
df = DataFrame(Cars, columns= ['Brand', 'Price','Year'])

# sort by multiple columns: Year and Price
df.sort_values(by=['Year','Price'], inplace=True)

df

	Brand	Price	Year
1	Toyota Corolla	25000	2013
0	Honda Civic	22000	2015
2	Ford Focus	27000	2018
3	Audi A4	35000	2018

By multiple columns - Mixed data type⌗

It is also possible to pass mixed type of columns to be sorted. For example, we can pass the Year column (integer) and the Brand column (string). Pandas accepts this without any problem.

df.sort_values(by=['Year','Brand'], inplace=True)

# sort by multiple columns
from pandas import DataFrame
 
Cars = {'Brand': ['Honda Civic','Toyota Corolla','Ford Focus','Audi A4'],
        'Price': [22000,25000,27000,35000],
        'Year': [2015,2013,2018,2018]
        }
 
df = DataFrame(Cars, columns= ['Brand', 'Price','Year'])

# sort by multiple columns: Year and Brand
df.sort_values(by=['Year','Brand'], inplace=True)

df

	Brand	Price	Year
1	Toyota Corolla	25000	2013
0	Honda Civic	22000	2015
3	Audi A4	35000	2018
2	Ford Focus	27000	2018

By multiple columns and mixed order⌗

Last but not the least, it is also possible to sort multiple columns in mixed order. For example, we may want to sort by Year and Brand where the Year column needs to be sorted in descending order whereas the Brand column needs to be sorted in ascening order. Pandas performs this by accepting list in addition to a boolean variable to the ascending parameter. The correct intuitive way of doing this is shown below.

df.sort_values(by=['Year','Brand'], ascending=[False, True], inplace=True)

# sort by multiple columns but different order
from pandas import DataFrame
 
Cars = {'Brand': ['Honda Civic','Toyota Corolla','Ford Focus','Audi A4'],
        'Price': [22000,25000,27000,35000],
        'Year': [2015,2013,2018,2018]
        }
 
df = DataFrame(Cars, columns= ['Brand', 'Price','Year'])

# sort by multiple columns: Year and Brand
df.sort_values(by=['Year','Brand'], ascending=[False, True], inplace=True)

df

	Brand	Price	Year
3	Audi A4	35000	2018
2	Ford Focus	27000	2018
0	Honda Civic	22000	2015
1	Toyota Corolla	25000	2013

You can know more about sorting in Pandas by visiting the official documentation.