Small multiples (facet grid) in Matplotlib - Cheat Sheet & Quick Revision

Recall & Review

beginner

What are small multiples (facet grids) in data visualization?

Small multiples are a series of similar graphs or charts using the same scale and axes, arranged in a grid. They help compare different subsets of data side by side clearly.

beginner

Why use small multiples instead of one combined plot?

Small multiples separate data into smaller plots, making it easier to see patterns and differences across groups without clutter or confusion from overlapping data.

intermediate

Which matplotlib function helps create small multiples (facet grids)?

Matplotlib itself does not have a direct facet grid function, but you can use subplots with loops or use seaborn's 'FacetGrid' which is built on matplotlib for easy small multiples.

beginner

How do you arrange multiple plots in matplotlib to create a grid?

Use plt.subplots(nrows, ncols) to create a grid of axes, then plot each subset of data on each axis to form small multiples.

beginner

What is a real-life example where small multiples help?

Comparing monthly sales trends for different stores side by side helps managers quickly spot which stores perform better or worse over time.

What is the main benefit of using small multiples?

A To combine all data into one plot

B To compare multiple subsets of data clearly

C To reduce the number of plots shown

D To hide differences between groups

Small multiples allow clear comparison of different data subsets by showing them in separate but similar plots.

Which matplotlib function is commonly used to create a grid of plots?

A plt.subplots()

B plt.plot_grid()

C plt.grid()

D plt.facetgrid()

plt.subplots() creates a grid of axes where you can plot multiple charts side by side.

Which library provides a direct FacetGrid function built on matplotlib?

A seaborn

B pandas

C numpy

D scikit-learn

Seaborn offers FacetGrid, which simplifies creating small multiples using matplotlib.

Small multiples help to avoid which common problem in data visualization?

A Changing axis labels

B Using too many colors

C Adding legends

D Overlapping data points

By splitting data into multiple plots, small multiples reduce clutter and overlapping points.

If you want to compare sales trends for 4 regions, how would small multiples help?

A Show only the top region's sales

B Show all regions in one plot with different colors

C Create 4 separate plots arranged in a grid, one per region

D Combine all data into a single number

Small multiples create separate plots for each region, making it easier to compare trends visually.

Explain what small multiples (facet grids) are and why they are useful in data visualization.

Think about how showing many small charts side by side helps compare groups.

Describe how you would create a small multiples grid using matplotlib for different categories in your data.

Consider how to arrange multiple plots and assign data to each.