Pandas DataFrame quantile() Method – Be on the Right Side of Change (original) (raw)


Preparation

Before any data manipulation can occur, two (2) new libraries will require installation.

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


The quantile() method returns the values from a DataFrame/Series at the specified quantile and axis.

The syntax for this method is as follows:

DataFrame.quantile(q=0.5, axis=0, numeric_only=True, interpolation='linear')

Parameter Description
q This is a value 0 <= q <= 1 and is the quantile(s) to calculate.
axis If zero (0) or index is selected, apply to each column. Default 0.If one (1) apply to each row.
numeric_only Only include columns that contain integers, floats, or boolean values.
interpolation Calculates the estimated median or quartiles for the DataFrame/Series.

To fully understand the interpolation parameter from a mathematical point of view, feel free to check out this tutorial:

This example uses the same stock DataFrame as noted above to determine the quantile(s).

df = pd.DataFrame({'ASL': [18.93, 17.03, 14.87], 'DBL': [39.91, 41.46, 40.99], 'UXL': [44.01, 43.67, 41.98]})

result = df.quantile(0.15) print(result)

Output

ASL 15.518
DBL 40.234
USL 42.487
Name: 0.15, dtype: float64

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.