42 - Introduction to Seaborn Plotting in Python

Seaborn is a complimentary plotting library to Matplotlib for statistical data visualization. It enhances the visualization capabilities and makes working with Pandas DataFrames easy. This tutorial covers the basics of Seaborn library and goes through a few example plots.
The code from this video is available at: github.com/bnsreenu/python_fo...

[@finnmccool8671]

I've watched only a couple of your videos so far and have found them very helpful and succinct. I will be watching more in the future!

[@DigitalSreeni]

Thanks Finn, the feedback definitely encourages me to create more videos.

[@felip6180]

Quite amazing tutorial and tool this seaborn! I've got curious about one thing. Does seaborn has a residual plot function? I mean, if I input the data, regress it, is there a possibility of it showing a plot of the residual values?

[@DigitalSreeni]

Yes, I believe so. Check the documentation.... seaborn.pydata.org/generated/seaborn.residplot.html

[@felip6180]

@@DigitalSreeni Thank you very much!

[@DigitalSreeni]

You’re welcome 😇

[@chrisphayao]

ChatGPT told me that seaborn doesn't provide the underlying equation for lmplot - there is an indirect way to find that out - but your way is easier ! import seaborn as sns import statsmodels.formula.api as smf # Generate a scatter plot with a regression line using lmplot sns.set(style="ticks") tips = sns.load_dataset("tips") lm = sns.lmplot(x="total_bill", y="tip", data=tips) # Extract the data from the lmplot figure x_data = lm.ax.collections[0].get_offsets()[:, 0] y_data = lm.ax.collections[0].get_offsets()[:, 1] # Fit a linear regression model using statsmodels model = smf.ols(formula='y ~ x', data=pd.DataFrame({'x': x_data, 'y': y_data})).fit() # Extract the coefficients from the model intercept = model.params['Intercept'] slope = model.params['x'] # Build the equation equation = f"y = {slope:.2f} * x + {intercept:.2f}" print(equation)