Seaborn, a Python data visualization library built on Matplotlib, is your key to creating visually appealing and insightful statistical graphics. Join us in this video as we explore its capabilities!
Here’s what you’ll learn:
Numerical Data Plotting:
– relplot(): Uncover relationships between numerical variables.
– scatterplot(): Craft visually engaging scatter plots.
– lineplot(): Highlight data trends with line plots.
Categorical Data Plotting:
– catplot(): Dive into the world of categorical data visualization.
– boxplot(): Reveal data distribution and outliers.
– stripplot(): Visualize individual data points using strip plots.
– swarmplot(): Explore data distributions with swarm plots and more.
Visualizing Data Distribution:
– distplot(): Analyze data distribution with histograms.
– kdeplot(): Create Kernel Density Estimation plots.
– jointplot(): Investigate bivariate relationships through joint plots.
– rugplot(): Enhance your visualizations with rug plots.
Linear Regression and Relationships:
– regplot(): Perform linear regression analysis.
– lmplot(): Visualize linear relationships between variables.
Controlling Plotted Figure Aesthetics:
Master figure and axes styling.
Explore an array of color palettes for stunning visuals.
Discover additional customization options to make your plots truly shine.
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