Matplotlib and seaborn 3 Cheatsheet

Basics

• Install the libraries — Install Matplotlib and seaborn.
• Import pyplot — The conventional import names.
• Draw a line chart — Plot y against x and show the figure.
• Add a title and axis labels — Label the chart so it can be understood on its own.
• Set the figure size — Create a figure with a size in inches and a resolution.
• Save a chart to a file — Write the figure as PNG, SVG or PDF. bbox_inches="tight" trims the margins.
• Draw without a screen — Use the Agg backend on servers and in CI, where there is no display.

Chart types

• Scatter plot — Plot two variables as points, colored by a third.
• Bar chart — Compare values across categories.
• Horizontal bar chart — A better choice when category names are long.
• Histogram — Show the distribution of a numeric variable in 30 bins.
• Box plot — Compare distributions with medians, quartiles and outliers.
• Pie chart — Show parts of a whole. Use sparingly: bars are easier to compare.
• Heatmap with imshow — Show a matrix as colors, with a color scale.
• Error bars — Plot values with uncertainty.

Layout and styling

• Draw several charts — Create a grid of axes and plot on each one.
• Two y-axes — Plot two series with different scales on the same chart.
• Use a log scale — Plot values that span several orders of magnitude.
• Annotate a point — Add a label with an arrow pointing at a data point.
• Apply a style — Use a built-in style sheet for consistent looks.
• Rotate tick labels — Rotate crowded labels on the x-axis.

seaborn

• Set a theme — Apply seaborn's default look to all charts.
• Histogram with density — A histogram with a smooth density curve on top.
• Scatter plot by group — Color points by category with hue.
• Box plot by category — Compare a variable across groups.
• Count categories — Show how many rows fall in each category.
• Correlation heatmap — Show how numeric columns relate, with values written in the cells.
• Plot every pair of variables — A grid of scatter plots and histograms for exploring a dataset.
• Line chart with confidence interval — Plot a trend with an interval around the mean.

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