Data visualization and graphics in Python;

or, Where is my ggplot2?!

(for any scientists making the switch from R)

---

Hana Lee
17 August 2016
ChiPy Scientific SIG

Data communication

"Big Data" is....

• multidimensional
• continuous & discrete
• nonlinear
• noisy

...complicated.

Wise words

"Graphical excellence is that which gives to the viewer the greatest number of ideas in the shortest time with the least ink in the smallest space."

Visual Display of Quantitative Information, by Edward Tufte

Processing visual information

• quantities
  • length
  • area
  • scale
• relationships
  • proximity
  • similarity
  • connection or boundary
• attention
• narrative

A grammar of graphics?

• aesthetics
• geometric objects
• scales
• coordinates
• statistics
• facets
• annotations

Grammar of Graphics, by Leland Wilkinson

ggplot2

• Clear, consistent syntax based on "grammar of graphics"
• Layer system for adding new elements to a plot
• Facet system for subsetting data into series of related plots

%%R -w 10 -h 6 -u in
mpg_plt <- ggplot(data = vehicles_mpg, aes(y = mpg, x = year, color = mpgtype)) +
           geom_jitter(alpha=0.5, width=1) + 
           xlab("Year") + ylab("Miles per gallon (mpg)") +
           scale_fill_discrete(labels = c("city", "highway"), 
                               name = "Mileage type") +
           ggtitle("Gas mileage of car models over time")
mpg_plt

%%R -w 10 -h 6 -u in
mpg_plt <- ggplot(data = vehicles_mpg, aes(y = mpg, x = year, color = mpgtype)) + 
           geom_point(stat = "summary", fun.y = "mean") + 
           stat_summary(fun.data = "mean_se", geom = "errorbar") + 
           xlab("Year") + ylab("Mean miles per gallon (mpg)") +
           scale_color_discrete(labels = c("city", "highway"), 
                                name = "Mileage type") +
           ggtitle("Gas mileage of car models over time")
mpg_plt

%%R -w 10 -h 6 -u in
mpg_plt <- ggplot(data = vehicles_mpg, aes(y = mpg, x = year, color = mpgtype)) + 
           geom_smooth() + xlab("Year") + ylab("Mean miles per gallon (mpg)") +
           scale_color_discrete(labels = c("city", "highway"), 
                                name = "Mileage type") +
           ggtitle("Gas mileage of car models over time")
mpg_plt

%%R -w 10 -h 6 -u in
displ_plt <- ggplot(aes(y = mpg, x = year, color = mpgtype),  
                    data = vehicles_displ) + geom_smooth() +
                    facet_wrap(~ displ_bin, nrow = 2, 
                               labeller = label_bquote("Displacement" 
                                                       >= .(displ_bin))) +
                    xlab("Year") + ylab("Mean miles per gallon (mpg)") +
                    scale_color_discrete(labels = c("city", "highway"), 
                                         name = "Mileage type") +
                    ggtitle("Gas mileage of car models by engine displacement")
displ_plt

Switching to Python

• Readable code
• Faster and more powerful
• General-purpose language
• More widely used in industry
• Testing framework
• Easier to deploy and release

Data visualization packages in Python

• matplotlib
• seaborn
• ggplot

matplotlib

Python port of Matlab plotting library

Pros	Cons
Based on widely used Matlab library	Hard-to-parse syntax
Functionality for interactive plots	Ugly default style
Specialized plot types, including 3D	Difficult to customize style
	Manual subsetting of data for plotting

fig, mpg_plt = plt.subplots()
colors = {"hwy": "blue", "cty": "red"}
for mpgtype, color in colors.items():
    type_filter = vehicles_mpg["mpgtype"] == mpgtype
    mpg_plt.scatter(x = vehicles_mpg[type_filter]["year"], 
                    y = vehicles_mpg[type_filter]["mpg"], 
                    c = color, alpha = 0.5, edgecolor = "none")
mpg_plt.legend(["highway", "city"], title = "Mileage type", 
               loc = 2, frameon = True)
mpg_plt.set_title("Gas mileage of car models over time")
mpg_plt.set_xlabel("Year")
mpg_plt.set_ylabel("Miles per gallon (mpg)")

<matplotlib.text.Text at 0x119d27080>

fig, mpg_plt = plt.subplots()
for mpgtype, color in colors.items():
    type_filter = vehicles_mpg["mpgtype"] == mpgtype
    mpg_means = vehicles_mpg[type_filter].groupby(["year"])["mpg"].mean()
    mpg_sem = vehicles_mpg[type_filter].groupby(["year"])["mpg"].sem()
    mpg_plt.errorbar(x = mpg_means.index, y = mpg_means.values, 
                     yerr = mpg_sem.values, c = color)
mpg_plt.legend(["highway", "city"], title = "Mileage type", 
            scatterpoints = 1, loc = 2, frameon = True)
mpg_plt.set_title("Gas mileage of car models over time")
mpg_plt.set_xlabel("Year")
mpg_plt.set_ylabel("Mean miles per gallon (mpg)")

<matplotlib.text.Text at 0x119f89c18>

fig, mpg_displ = plt.subplots(4, 2, sharex='col', sharey='row')
for n in range(8):
    plt.subplot(2, 4, n + 1)
    for mpgtype, color in colors.items():
        vehicles_filter = vehicles_displ.query("displ_bin == %i & mpgtype == '%s'"
                                               % (n + 1, mpgtype))
        mpg_means = vehicles_filter.groupby(["year"])["mpg"].mean()
        mpg_sem = vehicles_filter.groupby(["year"])["mpg"].sem()
        plt.errorbar(x = mpg_means.index, y = mpg_means.values, 
                     yerr = mpg_sem.values, c = color)

seaborn

Wrapper for matplotlib

Pros	Cons
Attractive default style	Difficult to customize and label
Simplified syntax	Limited range of plot types
Easy subsetting of data	Incomplete documentation

mpg_plt = sns.stripplot(x = "year", y = "mpg", hue="mpgtype", data=vehicles_mpg,
                        jitter = 1.0, alpha = 0.5, split = True)

mpg_plt = sns.factorplot(x = "year", y = "mpg", hue="mpgtype", data=vehicles_mpg,
                         join = False, aspect = 1.5, size = 6)
mpg_plt.set_axis_labels("Year", "Mean miles per gallon (mpg)")
mpg_plt.set_xticklabels(map(lambda x: str(x) if x % 5 == 0 else "", 
                            sorted(vehicles_mpg["year"].unique())))

<seaborn.axisgrid.FacetGrid at 0x118c3edd8>

mpg_plt = sns.factorplot(x = "year", y = "mpg", hue = "mpgtype", 
                         data=vehicles_displ, markers = [None, None],
                         col = "displ_bin", col_wrap = 4)
mpg_plt.set_axis_labels("Year", "Mean miles per gallon (mpg)")
mpg_plt.set_xticklabels(map(lambda x: str(x) if x % 5 == 0 else "", 
                            sorted(vehicles_displ["year"].unique())))
mpg_plt.set_titles("Displacement >= {col_name}")

<seaborn.axisgrid.FacetGrid at 0x1115ef160>

ggplot

Python port of ggplot2 by Ŷhat

Pros	Cons
Implements layering and faceting	Missing several statistical transformations
Faithful to ggplot2 syntax	Missing several plot types
	Incomplete documentation

mpg_plt = ggplot(aes(y = "mpg", x = "year", color = "mpgtype"), 
                 data = vehicles_mpg) + geom_jitter(alpha = 0.5) + \
          xlab("Year") + ylab("Miles per gallon (mpg)") + \
          ggtitle("Gas mileage of car models over time")
mpg_plt

<ggplot: (306494570)>

mpg_plt = ggplot(aes(y = "mpg", x = "year", color = "mpgtype"), 
                 data = vehicles_mpg) + stat_smooth(method = "loess") + \
          xlab("Year") + ylab("Miles per gallon (mpg)") + \
          ggtitle("Gas mileage of car models over time")
mpg_plt

<ggplot: (289478576)>

displ_plt = ggplot(aes(y = "mpg", x = "year", color = "mpgtype"), 
                       data = vehicles_displ) + stat_smooth(method = "loess") + \
            facet_wrap("displ_bin", nrow = 2) + \
            xlab("Year") + ylab("Miles per gallon (mpg)") + \
            ggtitle("Gas mileage of car models by engine displacement")
displ_plt

<ggplot: (300849405)>

...vs ggplot2 in R

%%R -w 10 -h 6 -u in
displ_plt

Next steps?

• Contribute:
  • matplotlib: https://github.com/matplotlib/matplotlib
  • seaborn: https://github.com/mwaskom/seaborn
  • ggplot: https://github.com/yhat/ggplot
• Use R and Python together in Jupyter notebooks
• For interactive visualizations, see ggvis (R) and bokeh (Python)

Endnotes

Notebook: https://github.com/hnlee/talks/blob/master/pyviz/pyviz.ipynb

Slides: http://hnlee.github.io/talks/pyviz/

hanalee.info or @lee_hn