Preparation
Before any data manipulation can occur, two (2) new libraries will require installation.
- The Pandas library enables access to/from a DataFrame.
- The NumPy library supports multi-dimensional arrays and matrices in addition to a collection of mathematical functions.
To install these libraries, navigate to an IDE terminal. At the command prompt ($), execute the code below. For the terminal used in this example, the command prompt is a dollar sign ($). Your terminal prompt may be different.
$ pip install pandas
Hit the <Enter> key on the keyboard to start the installation process.
$ pip install numpy
Hit the <Enter> key on the keyboard to start the installation process.
If the installations were successful, a message displays in the terminal indicating the same.
Feel free to view the PyCharm installation guide for the required libraries.
Add the following code to the top of each code snippet. This snippet will allow the code in this article to run error-free.
import pandas as pd import numpy as np
DataFrame sort_values()
The sort_values() method sorts (re-arranges) the elements of a DataFrame.
The syntax for this method is as follows:
DataFrame.sort_values(by, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last', ignore_index=False, key=None)
| Parameter | Description |
|---|---|
by | This parameter is a string or a list of strings. These comprise the index levels/columns to sort. Dependent on the selected axis. |
axis | If zero (0) or index is selected, apply to each column. Default is 0 (column). If zero (1) or columns, apply to each row. |
ascending | By default, True. Sort is conducted in ascending order. If False, descending order. |
inplace | If False, create a copy of the object. If True, the original object updates. By default, False. |
kind | Available options are quicksort, mergesort, heapsort, or stable. By default, quicksort. See numpy.sort for additional details. |
na_position | Available options are first and last (default). If the option is first, all NaN values move to the beginning, last to the end. |
ignore_index | If True, the axis numbering is 0, 1, 2, etc. By default, False. |
key | This parameter applies the function to the values before a sort. The data must be in a Series format and applies to each column. |
For this example, a comma-delimited CSV file is read in. This DataFrame sorts on the Capital column in descending order.
df = pd.read_csv('countries.csv')
result = df.sort_values(by=['Capital'], ascending=False)
print(result)- Line [1] reads in a comma-delimited CSV file and saves to
df. - Line [2] sorts the DataFrame on the Capital column in descending order. The output saves to
result. - Line [3] outputs the result to the terminal.
Output
| Country | Capital | Population | Area | |
| 6 | USA | Washington | 328239523 | 9833520 |
| 4 | Poland | Warsaw | 38383000 | 312685 |
| 3 | Italy | Rome | 60317116 | 301338 |
| 1 | France | Paris | 67081000 | 551695 |
| 5 | Russia | Moscow | 146748590 | 17098246 |
| 2 | Spain | Madrid | 47431256 | 498511 |
| 8 | India | Dheli | 1352642280 | 3287263 |
| 0 | Germany | Berlin | 83783942 | 357021 |
| 7 | India | Beijing | 1400050000 | 9596961 |
More Pandas DataFrame Methods
Feel free to learn more about the previous and next pandas DataFrame methods (alphabetically) here:
Also, check out the full cheat sheet overview of all Pandas DataFrame methods.