18 Ggplot2 plots in python cheat sheet tutorial

Braden Huffman

18.1 Motivation

In R, ggplot2 is a powerful visualization tool that all data scientists should have knowledge of. Ggplot2 might even be the most useful visualization tool, but in the event that ggplot2 cannot be used, a data scientist needs to be able to visualize the data. I created a cheat sheet that provides documentation and examples to create some of the most popular R graphs according to http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html, and what their equivalents are in Python. I also included a couple of other graphs that we discussed in class.

18.2 Table

data %>% 
  kbl(caption = "Cheat sheet", width=10) %>% 
  kable_material_dark(full_width=F, font_size=8)

Table 18.1: Cheat sheet

Type of Graph	ggplot2	Python	Python documentation/example
Scatter Plot	geom_point	pyplot.scatter	https://matplotlib.org/stable/gallery/shapes_and_collections/scatter.html
Bubble Plot	geom_jitter	plotly.express.scatter	https://plotly.com/python/bubble-charts/
Marginal Histogram	ggMarginal	seaborn.jointplot	https://seaborn.pydata.org/generated/seaborn.jointplot.html
Correlogram	ggcorrplot	seaborn.diverging_palette	https://seaborn.pydata.org/examples/many_pairwise_correlations.html
Diverging Bar Chart	ggbar	pyplot.hlines	https://www.geeksforgeeks.org/diverging-bar-chart-using-python/
Diverging Dot Plot	geom_point	pyplot.scatter	https://www.machinelearningplus.com/plots/top-50-matplotlib-visualizations-the-master-plots-python/#12.-Diverging-Dot-Plot
Area Plot	geom_area	pyplot.fill_between	https://www.python-graph-gallery.com/area-plot/
Bar Graph	geom_bar	pyplot.bar	https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.bar.html
Lolipop Chart	geom_point + geom_segment	pyplot.stem	https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.stem.html
Dot Plot	geom_point	plotly.express.scatter	https://lifewithdata.com/2022/02/28/how-to-create-a-dot-plot-in-plotly-python/
Slope Chart	geom_vline	plt.plot	https://towardsdatascience.com/slope-charts-with-pythons-matplotlib-2c3456c137b8
Dumbbell Plot	geom_dumbbell	plotly.express.scatter + fig.add_shape	https://medium.com/@ginoasuncion/creating-a-dumbbell-plot-with-plotly-python-570ff768ff7e
Histogram	geom_histogram	pyplot.hist	https://matplotlib.org/stable/gallery/statistics/hist.html
Density Plot	geom_density	seaborn.displot(kind=“kde”)	https://seaborn.pydata.org/tutorial/distributions.html#kernel-density-estimation
Boxplot	geom_boxplot	pyplot.boxplot	https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.boxplot.html
Violin Plot	geom_violin	seaborn.violinplot	https://seaborn.pydata.org/generated/seaborn.violinplot.html
Waffle Chart	geom_tile	pyplot.figure(FigureClass=Waffle)	https://github.com/gyli/PyWaffle
Pie Chart	geom_bar + coord_polar	pyplot.pie	https://matplotlib.org/stable/gallery/pie_and_polar_charts/pie_features.html
Treemap	treemapify + ggplotify	squarify.plot	https://www.python-graph-gallery.com/treemap/
Categorywise Bar Chart	geom_bar	pyplot.bar	https://www.geeksforgeeks.org/bar-plot-in-matplotlib/
Line graph	geom_line	pyplot.plot	https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html
Time Series Calendar Heatmap	geom_tile	calmap.yearplot	https://pythonhosted.org/calmap/
Hierarchical Dendrogram	ggdendrogram	scipy.cluster.hierarchy.dendrogram	https://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.hierarchy.dendrogram.html
Ridgeline Plot	geom_density_ridges2	seaborn.FacetGrid.map(seaborn.kdeplot)	https://seaborn.pydata.org/examples/kde_ridgeplot
Alluvium Graph	geom_alluvium	plotly.graphical_objects.Figure(data=[go.Sankey(…)])	https://plotly.com/python/sankey-diagram/
Radar Chart	ggradar	plotly.express.line_polar	https://plotly.com/python/radar-chart/

18.3 Example

Consider the following time series data:

x: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10

y: 5, 4, 7, 6, 6, 19, 20, 15, 10, 8

Imagine for a moment that your boss instructed you to create an Area Plot of the above data. You know how to do this easily. In fact, you can do it in five lines of code.

x <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
y <- c(5, 4, 7, 6, 6, 19, 20, 15, 10, 8)
df <- data.frame(x,y)

ggplot(data = df) +
  geom_area(mapping=aes(x,y))

You smile for a moment, knowing that you will actually be home for dinner with the family today. You keep smiling until he utters the words, “in Python.” While area plots are a fairly popular type of graph, you realize that you don’t even know if python has a way to create an area chart, so you decide to consult the above cheat sheet.

The cheat sheet takes you to the following website https://www.python-graph-gallery.com/area-plot/. Fortune shine’s down on you today. Creating an Area Plot in Python isn’t as hard as you had thought it would have been.

https://colab.research.google.com/drive/1DxD5UZolQxcphI44kvK8w4YR-RkZNYgc#scrollTo=UdOui0Fhk24e.

You show your boss the graph linked in the above iPython notebook, and while he isn’t impressed with your artistic ability, he is impressed by your speed.

Clearly Area Plots aren’t the worlds hardest graphs to create, and many of the graphs in the table are more difficult to find and create in Python. I hope this tutorial and cheat sheet make it easier to start creating your favorite ggplot2 graphs in Python.