Learn Visualizing Linear Regression

The regplot (regression plot) is used to plot data and a linear regression model fit.

It draws a scatter plot of two variables, x and y, and then fits a linear regression line (best prediction line) through them to visualize the correlation.

Key Parameters

fit_reg: this is the most important switch;
True (default): draws the regression line and the confidence interval (the shaded area);
False: draws only the scatter plot. This is useful when you want the styling of regplot but don't need the model;
marker: changes the symbol of the data points (e.g., '+', 'x', 'o');
color: sets the color for both the points and the line.

Example

Here is a regression plot showing the strong relationship between total bill and tip amount.


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import seaborn as sns
import matplotlib.pyplot as plt

# Load dataset
df = sns.load_dataset('tips')

# Create a regression plot
sns.regplot(
    data=df,
    x='total_bill',
    y='tip',
    color='b',       # Blue color
    marker='x',      # Use 'x' as marker
    fit_reg=True     # Show the line
)

plt.show()

Task

Swipe to start coding

Analyze the tips dataset, but this time just visualize the raw data points using specific styling.

Set the style to 'darkgrid'. Configure the colors by passing a dictionary: set 'figure.facecolor' to 'tan' and 'axes.facecolor' to 'cornsilk'.
Create a regplot using the tips dataset (df):
- Map 'total_bill' to x and 'tip' to y.
- Set the point symbol (marker) to '+'.
- Set the color to 'green'.
- Disable the regression line by setting fit_reg=False.
Display the plot.

Solution

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Section 1. Chapter 21

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