QuantileQuantile Plot in Python

QuantileQuantile Plot With the
statsmodels
Package in Python 
QuantileQuantile Plot With the
openturns
Package in Python
This tutorial will introduce the methods to draw quantilequantile plots in Python.
QuantileQuantile Plot With the statsmodels
Package in Python
A quantilequantile plot is used to assess whether our data conforms to a particular distribution or not. It can be used to check whether the given dataset is normally distributed or not. We can use the statsmodels
package to plot a quantilequantile graph in Python. The command to install statsmodels
is given below.
pip install statsmodels
The qqplot()
function inside the statsmodels
package plots quantilequantile graphs. This function takes our data and the type of line to draw. The following code snippet shows us how to plot a quantilequantile graph with the statsmodels
package.
import numpy as np
import statsmodels.api as smi
import pylab
sample_data = np.random.normal(0, 1, 1000)
smi.qqplot(sample_data, line="r")
pylab.show()
Output:
We plotted a quantilequantile graph with the smi.qqplot(sample_data, line = "r")
function in statsmodels
package in the above code. We generated our normal data containing a 1000 entries with sample_data = np.random.normal(0,1, 1000)
function. In the end, we used the pylab
package to display our graph with pylab.show()
function.
This method gives us complete control over the type of reference line to be plotted. In the above graph, we set the reference line to be a regression line.
QuantileQuantile Plot With the openturns
Package in Python
Another method for plotting a quantilequantile graph in Python is by using the openturns
package. It is an external package, so we need to install it before using it in our code. The command to install the openturns
package is given below.
pip install openturns
The VisualTest.DrawQQplot()
function is used to plot quantilequantile graphs inside the openturns
package. This function’s first parameter needs to be sample data; the second parameter can either be another sample data or a distribution followed by another third parameter specifying the number of points. For this example, we will plot a normal distribution sample against a uniform distribution sample. The following code snippet shows us how to plot a quantilequantile graph with the openturns
package.
import openturns as ot
x = ot.Normal().getSample(1000000)
y = ot.Uniform().getSample(1000000)
g = ot.VisualTest.DrawQQplot(x, y)
g
Output:
We tested a sample from a normal distribution against a sample from a uniform distribution with the VisualTest.DrawQQplot(x, y)
function in openturns
package in the above code. We generated our sample data from normal distribution containing one million entries with x = ot.Normal().getSample(1000000)
. We generated our sample data from uniform distribution containing one million entries with y = ot.Uniform().getSample(1000000)
.
This method does not provide control over the reference/test line.
Maisam is a highly skilled and motivated Data Scientist. He has over 4 years of experience with Python programming language. He loves solving complex problems and sharing his results on the internet.
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