How to Plot Multiple Linear Regressions in Python

In this tutorial, we will delve into the world of data analysis and presentation using Python, the powerful, easy-to-use programming language beloved by developers and data scientists alike.

Specifically, we’ll explore how to plot multiple linear regressions, which is key in interpreting and understanding complex data sets. It is a common procedure in statistical analyses, where we try to establish a linear relationship between variables.

But fear not, as Python – in conjunction with libraries such as pandas, numpy, matplotlib, and seaborn – simplifies this process significantly.”

Step 1: Importing the Necessary Libraries

First, we need to import the libraries we’ll be using. These include matplotlib, a data visualization library, Pandas and numpy for data analysis and manipulation, and Seaborn for data visualization.

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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

Step 2: Create or Import Your Data

Next, create or import your data. For this tutorial, we’ll be creating a simple dataframe using pandas. But in a real-world scenario, you may be importing your data from various sources like csv files, databases, etc.

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# Create a simple dataframe with pandas
data = {'Variable1': [1,2,3,4,5,6,7,8,9,10],
        'Variable2': [10,20,30,40,50,60,70,80,90,100],
        'Variable3': [5,10,15,20,25,30,35,40,45,50]}
df = pd.DataFrame(data)

Step 3: Create Linear Regression Plots

To create linear regression plots we will be using Seaborn’s ‘regplot’ function. We’ll create a function that, when passed a dataframe, will plot a linear regression for each pair of variables in the dataframe.

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# Function to create linear regression plots
def plot_regressions(dataframe):
    for x in dataframe:
        for y in dataframe:
            if x != y:
                sns.regplot(x=x, y=y, data=dataframe)
                plt.show()

Now we’ll call our function with the data we’ve created.

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# Call the function with our data
plot_regressions(df)

Step 4: Analyze the Plots

After plotting the graphs, the next step is to analyze them. Ensure you understand the relationship between the variables, check for linearity, and if there are any outliers. With seaborn, the scatter plots also include a line of best fit which is quite beneficial for quick analyses.

Full Code

Let us now review the full Python code that was used in this tutorial

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# Importing the libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
 
# Create a simple dataframe
data = {'Variable1': [1,2,3,4,5,6,7,8,9,10],
        'Variable2': [10,20,30,40,50,60,70,80,90,100],
        'Variable3': [5,10,15,20,25,30,35,40,45,50]}
df = pd.DataFrame(data)
 
# Function to create linear regression plots
def plot_regressions(dataframe):
    for x in dataframe:
        for y in dataframe:
            if x != y:
                sns.regplot(x=x, y=y, data=dataframe)
                plt.show()
 
# Call the function with our data
plot_regressions(df)

Conclusion

In this tutorial, we have seen how to plot multiple linear regressions using Python’s matplotlib and seaborn libraries. Linear regression plots are key in statistical analyses drawing relationships between variables.

Whenever you need to create and analyze multiple linear regressions in Python, you can use the code and steps outlined in this tutorial as your guideline.