Pandas dataframe ordering with examples using sort_values().

Often times, you need some form of ordering in a result set. In the SQL world, without an ORDER BY clause, query results order is not guaranteed. What if you are working in the pandas world? Fear not. You can order DataFrame results in a similar fashion as that of ORDER BY using the sort_values() function. Let’s learn together by example…

Photo by Edu Grande on Unsplash

Note: All data, names or naming found within the database presented in this post, are strictly used for practice, learning, instruction, and testing purposes. It by no means depicts actual data belonging to or being used by any party or organization.

OS and DB used:
  • Xubuntu Linux 18.04.2 LTS (Bionic Beaver)
  • PostgreSQL 11.5

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For a working data set, I’ll use the pandas read_csv() function and load a csv files’ contents into a DataFrame object:

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>>> import pandas as pd
>>> df = pd.read_csv('/home/linux_user/Practice Data/Fitness_DB_Data/july_2019_hiking_stats.csv')
(To get started with read_csv(), see this post I wrote using a simple example)

Calling the head() function, returns the first 5 rows of data, allowing us to gain a sense of the DataFrame’s contents:

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>>> df.head()
   day_walked  cal_burned  miles_walked  duration  mph  shoe_id
0  2019-07-01       330.5          3.27  01:00:45  3.2        4
1  2019-07-03       306.1          2.98  00:56:17  3.2        4
2  2019-07-04       330.4          3.17  01:00:45  3.1        4
3  2019-07-05       326.9          3.19  01:00:06  3.2        4
4  2019-07-06       327.2          3.23  01:00:06  3.2        4

One of several DataFrame attributes you can access for information is the columns label, which returns a list of the DataFrame’s column names:

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>>> df.columns
Index(['day_walked', 'cal_burned', 'miles_walked', 'duration', 'mph',
       'shoe_id'],
      dtype='object')

Suppose you want the DataFrame results sorted by the ‘day_walked’ column, starting at the earliest date to the latest? Simply pass in that column name to the sort_values() by parameter:

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>>> dw_sort = df.sort_values(by=['day_walked'])
>>> dw_sort
    day_walked  cal_burned  miles_walked  duration  mph  shoe_id
0   2019-07-01       330.5          3.27  01:00:45  3.2        4
1   2019-07-03       306.1          2.98  00:56:17  3.2        4
2   2019-07-04       330.4          3.17  01:00:45  3.1        4
3   2019-07-05       326.9          3.19  01:00:06  3.2        4
4   2019-07-06       327.2          3.23  01:00:06  3.2        4
5   2019-07-08       330.4          3.31  01:00:45  3.3        4
6   2019-07-09       337.8          3.33  01:02:07  3.2        4
7   2019-07-10       323.1          3.18  00:59:24  3.2        4
8   2019-07-11       327.3          3.22  01:00:11  3.2        4
9   2019-07-12       327.0          3.21  01:00:08  3.2        4
10  2019-07-14       368.0          3.65  01:07:40  3.2        4
11  2019-07-15       359.4          3.55  01:06:05  3.2        5
12  2019-07-16       356.4          3.50  01:05:31  3.2        5
13  2019-07-17       354.7          3.54  01:05:13  3.3        5
14  2019-07-18       332.4          3.31  01:01:07  3.2        5
15  2019-07-19       358.8          3.52  01:05:58  3.2        5
16  2019-07-21       356.2          3.60  01:05:29  3.3        5
17  2019-07-22       355.7          3.50  01:05:24  3.2        5
18  2019-07-23       349.8          3.49  01:04:19  3.3        5
19  2019-07-24       352.7          3.49  01:04:51  3.2        5
20  2019-07-25       350.8          3.44  01:04:30  3.2        5
21  2019-07-26       358.5          3.52  01:05:54  3.2        5
22  2019-07-28       361.0          3.55  01:06:22  3.2        4
23  2019-07-29       359.9          3.52  01:06:11  3.2        6
24  2019-07-30       358.1          3.53  01:05:51  3.2        6
25  2019-07-31       224.0          2.22  00:41:11  3.2        6

Based on the next example, we can see that ascended sorting (from least to greatest) is the default. In specifying False as the value for the ascending parameter, the sort order is reversed to a descending (greatest to least) ordering:

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>>> dw_sort = df.sort_values(by=['day_walked'], ascending=False)
>>> dw_sort
    day_walked  cal_burned  miles_walked  duration  mph  shoe_id
25  2019-07-31       224.0          2.22  00:41:11  3.2        6
24  2019-07-30       358.1          3.53  01:05:51  3.2        6
23  2019-07-29       359.9          3.52  01:06:11  3.2        6
22  2019-07-28       361.0          3.55  01:06:22  3.2        4
21  2019-07-26       358.5          3.52  01:05:54  3.2        5
20  2019-07-25       350.8          3.44  01:04:30  3.2        5
19  2019-07-24       352.7          3.49  01:04:51  3.2        5
18  2019-07-23       349.8          3.49  01:04:19  3.3        5
17  2019-07-22       355.7          3.50  01:05:24  3.2        5
16  2019-07-21       356.2          3.60  01:05:29  3.3        5
15  2019-07-19       358.8          3.52  01:05:58  3.2        5
14  2019-07-18       332.4          3.31  01:01:07  3.2        5
13  2019-07-17       354.7          3.54  01:05:13  3.3        5
12  2019-07-16       356.4          3.50  01:05:31  3.2        5
11  2019-07-15       359.4          3.55  01:06:05  3.2        5
10  2019-07-14       368.0          3.65  01:07:40  3.2        4
9   2019-07-12       327.0          3.21  01:00:08  3.2        4
8   2019-07-11       327.3          3.22  01:00:11  3.2        4
7   2019-07-10       323.1          3.18  00:59:24  3.2        4
6   2019-07-09       337.8          3.33  01:02:07  3.2        4
5   2019-07-08       330.4          3.31  01:00:45  3.3        4
4   2019-07-06       327.2          3.23  01:00:06  3.2        4
3   2019-07-05       326.9          3.19  01:00:06  3.2        4
2   2019-07-04       330.4          3.17  01:00:45  3.1        4
1   2019-07-03       306.1          2.98  00:56:17  3.2        4
0   2019-07-01       330.5          3.27  01:00:45  3.2        4

Multiple column sorting is possible with sort_values() as well:

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>>> df.sort_values(by=['day_walked','cal_burned'], ascending=[False,False], inplace=True)
>>> df
    day_walked  cal_burned  miles_walked  duration  mph  shoe_id
25  2019-07-31       224.0          2.22  00:41:11  3.2        6
24  2019-07-30       358.1          3.53  01:05:51  3.2        6
23  2019-07-29       359.9          3.52  01:06:11  3.2        6
22  2019-07-28       361.0          3.55  01:06:22  3.2        4
21  2019-07-26       358.5          3.52  01:05:54  3.2        5
20  2019-07-25       350.8          3.44  01:04:30  3.2        5
19  2019-07-24       352.7          3.49  01:04:51  3.2        5
18  2019-07-23       349.8          3.49  01:04:19  3.3        5
17  2019-07-22       355.7          3.50  01:05:24  3.2        5
16  2019-07-21       356.2          3.60  01:05:29  3.3        5
15  2019-07-19       358.8          3.52  01:05:58  3.2        5
14  2019-07-18       332.4          3.31  01:01:07  3.2        5
13  2019-07-17       354.7          3.54  01:05:13  3.3        5
12  2019-07-16       356.4          3.50  01:05:31  3.2        5
11  2019-07-15       359.4          3.55  01:06:05  3.2        5
10  2019-07-14       368.0          3.65  01:07:40  3.2        4
9   2019-07-12       327.0          3.21  01:00:08  3.2        4
8   2019-07-11       327.3          3.22  01:00:11  3.2        4
7   2019-07-10       323.1          3.18  00:59:24  3.2        4
6   2019-07-09       337.8          3.33  01:02:07  3.2        4
5   2019-07-08       330.4          3.31  01:00:45  3.3        4
4   2019-07-06       327.2          3.23  01:00:06  3.2        4
3   2019-07-05       326.9          3.19  01:00:06  3.2        4
2   2019-07-04       330.4          3.17  01:00:45  3.1        4
1   2019-07-03       306.1          2.98  00:56:17  3.2        4
0   2019-07-01       330.5          3.27  01:00:45  3.2        4
(Note: It appears that precedence is given to the ‘day_walked’ column in the sorting order. On this, I am still learning. Feel free to share thoughts, comments, and more information about multiple column sorts…)

Information Links…

Visit the below links for more information on many of the topics covered in this post:

Have a look at sort_values() when you need a sure sorting order for pandas DataFrame contents.

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Josh Otwell has a passion to study and grow as a SQL Developer and blogger. Other favorite activities find him with his nose buried in a good book, article, or the Linux command line. Among those, he shares a love of tabletop RPG games, reading fantasy novels, and spending time with his wife and two daughters.

Disclaimer: The examples presented in this post are hypothetical ideas of how to achieve similar types of results. They are not the utmost best solution(s). The majority, if not all, of the examples provided, is performed on a personal development/learning workstation-environment and should not be considered production quality or ready. Your particular goals and needs may vary. Use those practices that best benefit your needs and goals. Opinions are my own.

One thought on “Pandas dataframe ordering with examples using sort_values().”

  1. Pingback: Pandas and the python os module use case – appending source file information from CSV’s. | Digital Owl's Prose

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