Pandas DataFrame quantile() Method | Find Quantile Values

Python is a great language for data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages, making importing and analyzing data much easier. 

Pandas quantile() function returns values at the given quantile over the requested axis. 

Note: In each of any set of values of a variate which divides a frequency distribution into equal groups, each containing the same fraction of the total population.

Example:

Output:

Syntax

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

Parameters : 

• q : float or array-like, default 0.5 (50% quantile). 0 <= q <= 1, the quantile(s) to compute 
• axis : [{0, 1, ‘index’, ‘columns’} (default 0)] 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise 
• numeric_only : If False, the quantile of datetime and timedelta data will be computed as well 
• interpolation : {‘linear’, ‘lower’, ‘higher’, ‘midpoint’, ‘nearest’} 

Returns : quantiles : Series or DataFrame -> If q is an array, a DataFrame will be returned where the index is q, the columns are the columns of self, and the values are the quantiles. -> If q is a float, a Series will be returned where the index is the columns of self and the values are the quantiles.

Examples

Let's see some examples of how to find values of a given quantile using the quantile() function of the Pandas library. Below are the Python codes that illustrates the working of the quantile method on a DataFrame with output.

Example 1 

Let's use the dataframe.quantile() function to find the quantile of '.2' for each column in the DataFrame. 

Output :

 👁 Image
 

Example 2

Use the quantile() function to find the (.1, .25, .5, .75) quantiles along the index axis. 

Output :

 👁 Image