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5 Examples of Sorting Pandas Data Frame by sort_values method

How to Sort Data Frames in Pandas

Pandas data frame has a sort_values method that is used to sort the data frame’s data in ascending or descending order.

The sorting can be based on column labels.

The sort_values method has different parameters as shown in the syntax below.


DataFrame.sort_values(by, *, axis=0, ascending=True, inplace=False, kind=’quicksort’, na_position=’last’, ignore_index=False, key=None)

Let us show you examples of using sort_values with its parameters below.

An example of sorting results by a column label in ascending order

In the Python program below, we created a data frame with three columns and five rows of data.

Then we displayed the data frame in its original form.

This is followed by using the sort_values method where we only specified the column label to sort the results.

Python program:


You can see, the results are sorted by Employee names in the data frame.

Sorting data frame in descending order

As it can be seen in the syntax, the default value for sorting order is ascending i.e.


By using the False value, you may get the data sorted in descending order.

See the example below where we used the same data frame and sorted results in descending order.




The example of sorting by salary column

The example below sorts the result in descending order based on the salary column:


Sort result by two columns example

You may also sort the results by providing two or more columns.

The example below sorts the results by name and salary columns in our example data frame:


Using ignore_index parameter example

By default, the ignore_index= False. That means the index column numbers remain in place after sorting the results. In the above example output, you can see 3, 4, 0, 1, 2, and the numbers that are for the original DF.

If you may ignore_index=True then the resulting axis after sorting is labeled as 0,1, 2…

See the difference in the example below:





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