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Pandas Sort by Column : How to do it using the Best Examples

Pandas is a great python module for data manipulation. In this entire tutorial, I will show you how to do pandas sort by column using different cases. The method for doing this task is done by pandas.sort_values(). The syntax for this method is given below.

by: Names of columns you want to do the sorting.

axis: It has 0 and 1 value. To sort row-wise use 0 and to sort column-wise use 1. The default value of it is 0.

ascending: True or false value. The default is True. If you set False then sorting will be done in descending order.

kind: Kind of sorting method. You can use it from anyone in ‘quicksort’, ‘mergesort’, ‘heapsort’.

na_position: It allows you to put the null values or empty values at the end or start after sorting. The default value is “last”.

Before demonstrating all the examples let’s first create a very simple dataframe. It will be helpful in explaining in the entire example I have used it here. Use the following code for that.

Sample DataFrame Creation for Sorting

There are two ways you can sort the pandas dataframe. In ascending order and descending order. Below are the examples for each order.

How to sort Pandas Dataframe by column an ascending order?

Here I will try to sort the pandas dataframe in ascending order on the age columns. To do so you have to use thesort_values()method on the dataframe.

Run the following code.


Sorting Pandas Dataframe by column in an ascending order

How to sort Pandas Dataframe by column in descending order?

To sort the dataframe in descending order you have to set the ascending=False inside the sort_values() method.


Sorting Pandas Dataframe by column in an Descending order

Sort Dataframe by two columns

The above example was to sort the values of the dataframe using one column. In this section, you will learn how to sort a pandas dataframe by two columns. This type of sorting is useful when there is a repetition of the same values.

For example In our sample dataframe there is two same values in the “Year ” column that is 2018. If you make sorting by two columns then the sort_values()method takes care of it.


Sorting Pandas Dataframe by age and year


Sorting Pandas Dataframe by Name and Year

You can see in both the output the values that are is lower or alphabetically order are printed first in the corresponding 2018 values.

How to permanently sort pandas dataframe by column?

There is a lot of cases when the coder forgets to pass inplace= True inside the sort_values() method. Due to this sorted values are not permanently sorted. To Permanently sort it you have to set the inplaceparameter to True.

You can see in all the above examples I have used inplace= True.

How to sort columns by mean in pandas?

If your dataframe have integer or float values. Then you can sort columns by meanof it. For example, I can sort the dataframe using the reindex()method. Just execute the following code.


Sort columns by mean in pandas

End Notes

These are some good examples of doing Pandas Sort by Column. Hope you have liked all the example above. In case you have any query then you can contact us for more information. Full code and Output of the entire tutorial is below.

Full Code


Outptut For the Pandas Sort by Column


Official Numpy Sorting Documentation

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You may use df.sort_values in order to sort Pandas DataFrame.

In this short tutorial, you’ll see 4 examples of sorting:

  1. A column in an ascending order
  2. A column in a descending order
  3. By multiple columns – Case 1
  4. By multiple columns – Case 2

To start with a simple example, let’s say that you have the following data about cars:


You can then capture that data in Python by creating the following DataFrame:

import pandas as pd data = {'Brand': ['HH','TT','FF','AA'], 'Price': [22000,25000,27000,35000], 'Year': [2015,2013,2018,2018] } df = pd.DataFrame(data, columns=['Brand','Price','Year']) print (df)

And if you run the above Python code, you’ll get the following DataFrame:

Next, you’ll see how to sort that DataFrame using 4 different examples.

Example 1: Sort Pandas DataFrame in an ascending order

Let’s say that you want to sort the DataFrame, such that the Brand will be displayed in an ascending order. In that case, you’ll need to add the following syntax to the code:

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

Note that unless specified, the values will be sorted in an ascending order by default.

The full Python code would look like this:

import pandas as pd data = {'Brand': ['HH','TT','FF','AA'], 'Price': [22000,25000,27000,35000], 'Year': [2015,2013,2018,2018] } df = pd.DataFrame(data, columns=['Brand','Price','Year']) # sort Brand in an ascending order df.sort_values(by=['Brand'], inplace=True) print (df)

When you run the code, you’ll notice that the Brand will indeed get sorted in an ascending order, where ‘AA’ would be the first record, while ‘TT’ would be the last:

Example 2: Sort Pandas DataFrame in a descending order

Alternatively, you can sort the Brand column in a descending order. To do that, simply add the condition of ascending=False in the following manner:

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

And the complete Python code would be:

import pandas as pd data = {'Brand': ['HH','TT','FF','AA'], 'Price': [22000,25000,27000,35000], 'Year': [2015,2013,2018,2018] } df = pd.DataFrame(data, columns=['Brand','Price','Year']) # sort Brand in a descending order df.sort_values(by=['Brand'], inplace=True, ascending=False) print (df)

You’ll now notice that ‘TT’ would be the first record, while ‘AA’ would be the last (as you would expect to get when applying a descending order for our sample):

Example 3: Sort by multiple columns – case 1

But what if you want to sort by multiple columns?

In that case, you may use the following template to sort by multiple columns:

df.sort_values(by=['First Column','Second Column',...], inplace=True)

Suppose that you want to sort by both the ‘Year’ and the ‘Price.’ Since you have two records where the Year is 2018 (i.e., for the ‘FF’ and the ‘AA’ brands), then sorting by a second column – the ‘Price’ column –  would be useful:

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

Here is the Python code that you may use:

import pandas as pd data = {'Brand': ['HH','TT','FF','AA'], 'Price': [22000,25000,27000,35000], 'Year': [2015,2013,2018,2018] } df = pd.DataFrame(data, columns=['Brand','Price','Year']) # sort by multiple columns: Year and Price df.sort_values(by=['Year','Price'], inplace=True) print (df)

Notice that all the records are now sorted by both the year and the price in an ascending order, so ‘FF’ would appear before ‘AA’:

Also note that the ‘Year’ column takes the priority when performing the sorting, as it was placed in the df.sort_values before the ‘Price’ column.

Example 4: Sort by multiple columns – case 2

Finally, let’s sort by the columns of ‘Year’ and ‘Brand’ as follows:

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

The complete Python code would look like this:

import pandas as pd data = {'Brand': ['HH','TT','FF','AA'], 'Price': [22000,25000,27000,35000], 'Year': [2015,2013,2018,2018] } df = pd.DataFrame(data, columns=['Brand','Price','Year']) # sort by multiple columns: Year and Brand df.sort_values(by=['Year','Brand'], inplace=True) print (df)

You’ll now see that all the records are sorted by both the year and the brand in an ascending order, so this time ‘AA’ would appear prior to ‘FF’:

You may want to check the Pandas Documentation to learn more about sorting values in Pandas DataFrame.

Categories PythonSours:
Pandas Sort Values - pd.DataFrame.sort_values()

Pandas DataFrame – Sort by Index

To sort a Pandas DataFrame by index, you can use DataFrame.sort_index() method.

To specify whether the method has to sort the DataFrame in ascending or descending order of index, you can set the named boolean argument to True or False respectively.

When the index is sorted, respective rows are rearranged.

Pandas DataFrame - Sort by Index

Example 1: Sort DataFrame by Index in Ascending Order

In this example, we shall create a dataframe with some rows and index with an array of numbers. We shall sort the rows of this dataframe, so that the index shall be in ascending order.

To sort the index in ascending order, we call sort_index() method with the argument as shown in the following Python program. Or you may ignore the ascending parameter, since the default value for argument is .

Python Program



Run the above program. We have printed the original DataFrame to the console, followed by sorted DataFrame.

Example 2: Sort DataFrame by Index in Descending Order

In this example, we shall sort the DataFrame based on the descending order of index. For that, we shall pass to the sort_index() method.

Python Program



Run the program. The sorted dataframe has index in descending order.


In this tutorial of Python Examples, we learned how to sort a Pandas DataFrame by index in ascending and descending orders.


Pandas sortby

Python | Pandas Dataframe.sort_values() | Set-1

Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages, and makes importing and analyzing data much easier.
Pandas sort_values() function sorts a data frame in Ascending or Descending order of passed Column. It’s different than the sorted Python function since it cannot sort a data frame and particular column cannot be selected.
Let’s discuss Dataframe.sort_values() Single Parameter Sorting:

DataFrame.sort_values(by, axis=0, ascending=True, inplace=False, kind=’quicksort’, na_position=’last’)

Every parameter has some default values except the ‘by’ parameter.

by: Single/List of column names to sort Data Frame by. 
axis: 0 or ‘index’ for rows and 1 or ‘columns’ for Column. 
ascending: Boolean value which sorts Data frame in ascending order if True. 
inplace: Boolean value. Makes the changes in passed data frame itself if True. 
kind: String which can have three inputs(‘quicksort’, ‘mergesort’ or ‘heapsort’) of algorithm used to sort data frame. 
na_position: Takes two string input ‘last’ or ‘first’ to set position of Null values. Default is ‘last’.

Return Type: 

Returns a sorted Data Frame with Same dimensions as of the function caller Data Frame.

For link to CSV file Used in Code, click here.
Example #1: Sorting by Name 
In the following example, A data frame is made from the csv file and the data frame is sorted in ascending order of Names of Players.
Before Sorting-





After Sorting-





As shown in the image, index column is now jumbled since the data frame is sorted by Name.

Example #2: Changing position of Null values
In the give data, there are many null values in different columns which are put in the last by default. In this example, the Data Frame is sorted with respect to Salary column and Null values are kept at the top.





As shown in output image, The NaN values are at the top and after that comes the sorted value of Salary.


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