Python Plotting for Exploratory Data Analysis

The simple graph has brought more information to the data analyst's mind than any other device.

John Tukey in The Future of Data Analysis

Introduction
Examples

Introduction

As a data scientist, I spend much of my time making simple plots to understand complex data sets (exploratory data analysis) and help others understand them (presentations).

In particular, I make a lot of bar charts (including histograms), line plots (including time series), scatter plots, and density plots from data in Pandas data frames. I often want to facet these on various categorical variables and layer them on a common grid.

Python Plotting Options

Python has many plotting libraries. Matplotlib is the best known, and several others build on it.

"Matplotlib makes easy things easy and hard things possible." It hands you figures, axes, and drawing primitives. You assemble everything above that level yourself: faceting, stacking, density estimation, smoothing. That assembly is what sends analysts to Stack Overflow.

Put the Matplotlib and ggplot2 versions of the two-variable faceted scatter plot below side by side: eighteen lines of subplot bookkeeping against four lines of grammar. If Matplotlib annoys you and you haven't read Effectively Using Matplotlib by Chris Moffitt, go read it.

Matplotlib-Based Libraries

Pandas plotting provides "the basics ... to easily create decent looking plots" from data frames. That is about 70% of what I do day-to-day. It has no faceting, no categorical color mapping, and no smoothing, so five of the examples below have no pandas column.

Seaborn calls itself "statistical data visualization." Its classic interface is a set of named functions (histplot, scatterplot, countplot, lmplot, kdeplot) plus FacetGrid, which I use for faceting more than anything else in the library. It covers every plot below, once you know which function to reach for.

Seaborn 0.12 added seaborn.objects, a second interface built on the grammar of graphics. It composes a plot from marks and statistical transforms instead of dispatching to a named plotting function. The interface has no loess smoother and no regression confidence band, so those two examples are missing.

"plotnine is a data visualization package for Python based on the grammar of graphics." It tracks ggplot2 closely enough that most R code translates line for line, down to the + for layering. I reach for it when I want ggplot2 semantics without leaving Python.

Interactive Plotting Libraries

These libraries draw in the browser. The examples here are static PNGs, so their tooltips, panning, and linked selection are gone.

"Vega-Altair is a declarative visualization library for Python," built on Vega-Lite. According to Jake Vanderplas, "Declarative visualization lets you think about data and relationships, rather than incidental details." You describe the encoding and Altair chooses the marks, scales, and legend.

"plotly's Python graphing library makes interactive, publication-quality graphs." The examples here use Plotly Express, which the project calls "the recommended starting point for creating most common figures." Express covers most of these plots in one call; the regression and smoothing examples fall back to graph_objects and statsmodels.

JetBrains writes Lets-Plot, which it calls "a faithful port of R's ggplot2 to Python and Kotlin." The claim holds up: most of the examples below are the ggplot2 column with lp. prefixes. Like Altair, it renders to HTML in the notebook.

"Bokeh is a Python library for creating interactive visualizations for modern web browsers." The Bokeh examples below go through hvPlot, which adds an .hvplot accessor to data frames. The accessor echoes the pandas .plot API, so most of these plots are one call plus a few keyword arguments. hvPlot has no regression line or loess smoother, so it is absent from those two examples.

Hearty Thank You

Open source developers do most Python plotting development, an (almost) thankless job. I am grateful for the hours they have spent helping me do mine. Please keep it up!

Why all the talk about ggplot?

Before I started using Python, I did most of my data analysis work in R. Like many Pythonistas, I remain a fan of Hadley Wickham's ggplot2, a "grammar of graphics" implementation in R, for exploratory data analysis.

Like scikit-learn for machine learning in Python, ggplot2 has a consistent API and sane defaults. The consistent interface lets me iterate without stopping to think. The sane defaults make it easy to drop plots right into an email or presentation.

ggplot2 makes basic plots (bar, histogram, line, scatter, density, violin) from data frames with faceting and layering by discrete values.

Hadley Wickham and Garrett Grolemund's R for Data Science teaches ggplot2 well.

Humble Rosetta Stone for Visualization in Exploratory Data Analysis

Below is a list of basic plots for exploratory data analysis, each made with as many libraries as time (and library) permit.

I hope it helps you work with what exists today and inspires what gets built next.

Contributing instructions are on GitHub. General feedback or other plot suggestions are welcome.

Data

ggplot2 ships the datasets used below: the Prices of 50,000 round cut diamonds and Fuel economy data from 1999 and 2008 for 38 popular models of car.

The time series example is a random walk I generate with a quick Python script.

A few rows of each:

ts

date	value
2000-01-01	-1.129240
2000-01-02	-0.713866
2000-01-03	-0.967130
2000-01-04	-0.443428
2000-01-05	1.366886

mpg

manufacturer	model	displ	year	cyl	trans	drv	cty	hwy	fl	class
audi	a4	1.8	1999	4	auto(l5)	f	18	29	p	compact
audi	a4	1.8	1999	4	manual(m5)	f	21	29	p	compact
audi	a4	2.0	2008	4	manual(m6)	f	20	31	p	compact
audi	a4	2.0	2008	4	auto(av)	f	21	30	p	compact
audi	a4	2.8	1999	6	auto(l5)	f	16	26	p	compact

diamonds

carat	cut	color	clarity	depth	table	price	x	y	z
0.23	Ideal	E	SI2	61.5	55.0	326	3.95	3.98	2.43
0.21	Premium	E	SI1	59.8	61.0	326	3.89	3.84	2.31
0.23	Good	E	VS1	56.9	65.0	327	4.05	4.07	2.31
0.29	Premium	I	VS2	62.4	58.0	334	4.20	4.23	2.63
0.31	Good	J	SI2	63.3	58.0	335	4.34	4.35	2.75

Bar Chart#

Code:

(mpg['manufacturer']
 .value_counts(sort=False)
 .plot.barh()
 .set_title('Number of Cars by Make')
)

Code:

counts = mpg['manufacturer'].value_counts(
    sort=False)
fig, ax = pyplot.subplots()
ax.barh(counts.index, counts.values)
ax.set_title('Number of Cars by Make')

Code:

ax = sns.countplot(mpg, y='manufacturer')
ax.set_title('Number of Cars by Make')

Code:

(so.Plot(mpg, y='manufacturer')
 .add(so.Bar(), so.Count())
 .label(title='Number of Cars by Make'))

Note:

seaborn.objects has no coord_flip, so the categorical variable is mapped to y instead.

Code:

(ggplot(mpg) + 
   aes(x='manufacturer') +
   geom_bar(size=20) + 
   coord_flip() +
   ggtitle('Number of Cars by Make')
)

Note:

plotnine gives an error on ggplot(data=mpg).

Code:

(lp.ggplot(mpg) +
   lp.aes(x='manufacturer') +
   lp.geom_bar() +
   lp.coord_flip() +
   lp.ggtitle('Number of Cars by Make'))

Note:

Lets-Plot mirrors the ggplot2 grammar. It is imported as lp here so its names don't collide with plotnine's.

Code:

px.histogram(
    mpg, y='manufacturer', 
    title='Number of Cars by Make'
)

Code:

(mpg['manufacturer']
 .value_counts(sort=False)
 .hvplot.barh(
     title='Number of Cars by Make'))

Note:

hvPlot exposes Bokeh through a .hvplot accessor that mirrors pandas' own .plot.

Code:

(
    alt.Chart(
        mpg, title='Number of Cars by Make'
    )
    .mark_bar()
    .encode(
        x='count()', y=alt.Y('manufacturer')
    )
)

Code:

ggplot(data=mpg) + 
    aes(x=manufacturer) + 
    geom_bar() + 
    coord_flip() +
    ggtitle('Number of Cars by Make')

Histogram#

Code:

(mpg['cty']
 .plot
 .hist(bins=12))

Code:

pyplot.hist('cty', bins=12, data=mpg)

Code:

sns.histplot(mpg, x='cty', binwidth=2)

Code:

(so.Plot(mpg, x='cty')
 .add(so.Bars(), so.Hist(binwidth=2)))

Code:

(ggplot(mpg) + 
    aes(x='cty') +
    geom_histogram(binwidth=2))

Code:

(lp.ggplot(mpg) +
    lp.aes(x='cty') +
    lp.geom_histogram(binwidth=2))

Code:

px.histogram(
    mpg, x='cty'
)

Code:

mpg.hvplot.hist('cty', bins=12)

Code:

(
    alt.Chart(mpg)
    .mark_bar()
    .encode(
        alt.X('cty', bin=alt.Bin(step=2)),
        y='count()',
    )
)

Code:

ggplot(data=mpg) + 
    aes(x=cty) + 
    geom_histogram(binwidth=2)

Scatter Plot#

Code:

(mpg
 .plot
 .scatter(x='displ', y='hwy')
 .set(title='Engine Displacement in Liters vs Highway MPG',
      xlabel='Engine Displacement in Liters',
      ylabel='Highway MPG'))

Code:

fig, ax = pyplot.subplots()
ax.scatter(mpg['displ'], mpg['hwy'])
ax.set_title('Engine Displacement in Liters '
             'vs Highway MPG')
ax.set_xlabel('Engine Displacement in Liters')
ax.set_ylabel('Highway MPG')

Code:

ax = sns.scatterplot(mpg, x='displ', y='hwy')
ax.set(
    title='Engine Displacement in Liters '
          'vs Highway MPG',
    xlabel='Engine Displacement in Liters',
    ylabel='Highway MPG')

Code:

(so.Plot(mpg, x='displ', y='hwy')
 .add(so.Dot())
 .label(
    x='Engine Displacement in Liters',
    y='Highway MPG',
    title='Engine Displacement in Liters vs Highway MPG'))

Code:

(ggplot(mpg) +
    aes(x = 'displ', y = 'hwy') +
    geom_point() + 
    ggtitle('Engine Displacement in Liters vs Highway MPG') +
    xlab('Engine Displacement in Liters') +
    ylab('Highway MPG'))

Code:

(lp.ggplot(mpg) +
    lp.aes(x='displ', y='hwy') +
    lp.geom_point() +
    lp.ggtitle('Engine Displacement in Liters vs Highway MPG') +
    lp.xlab('Engine Displacement in Liters') +
    lp.ylab('Highway MPG'))

Code:

px.scatter(
    mpg, x='displ', y='hwy', 
    title='Engine Displacement in Liters vs Highway MPG',
    labels=dict(
       displ='Engine Displacement in Liters', 
       hwy='Highway MPG')
)

Code:

mpg.hvplot.scatter(
    x='displ', y='hwy',
    xlabel='Engine Displacement in Liters',
    ylabel='Highway MPG',
    title='Engine Displacement in Liters '
          'vs Highway MPG')

Code:

alt.Chart(mpg).mark_circle().encode(
    alt.X(
        'displ',
        title='Engine Displacement in Liters',
    ),
    alt.Y(
        'hwy',
        title='Highway Miles per Gallon',
    ),
).properties(
    title='Engine Displacement in Liters'
)

Code:

ggplot(data = mpg) +
    aes(x = displ, y = hwy) +
    geom_point() + 
    ggtitle('Engine Displacement in Liters vs Highway MPG') +
    xlab('Engine Displacement in Liters') +
    ylab('Highway MPG')

Time Series#

Code:

ts.set_index('date')['value'].plot()

Code:

fig, ax = pyplot.subplots()
ax.plot(ts['date'], ts['value'])

Code:

sns.lineplot(ts, x='date', y='value')

Code:

(so.Plot(ts, x='date', y='value')
 .add(so.Line()))

Code:

(ggplot(ts) 
 + aes('date', 'value') 
 + geom_line())

Code:

(lp.ggplot(ts)
 + lp.aes('date', 'value')
 + lp.geom_line())

Code:

px.line(
    ts, x='date', y='value'
)

Code:

ts.hvplot.line(x='date', y='value')

Code:

alt.Chart(ts).mark_line().encode(
    x='date', y='value'
)

Code:

ggplot(ts) + aes(date, value) + geom_line()

Scatter Plot with Faceted with Color#

Code:

fig, ax = pyplot.subplots()
for c, df in mpg.groupby('class'):
    ax.scatter(df['displ'], df['hwy'], label=c)
ax.legend()
ax.set_title('Engine Displacement in Liters vs Highway MPG')
ax.set_xlabel('Engine Displacement in Liters')
ax.set_ylabel('Highway MPG')

Scatter Plot with Faceted with Color drawn with Seaborn

Code:

(sns
 .FacetGrid(mpg, hue='class', height=10)
 .map(pyplot.scatter, 'displ', 'hwy')
 .add_legend()
 .set(
    title='Engine Displacement in Liters vs Highway MPG',
    xlabel='Engine Displacement in Liters',
    ylabel='Highway MPG'
))

Note:

seaborn.FacetGrid overrides the rcParams['figure.figsize'] global parameter. You have to set the size in the size withheight=inFacetGrid`

Scatter Plot with Faceted with Color drawn with seaborn.objects

Code:

(so.Plot(mpg, x='displ', y='hwy',
         color='class')
 .add(so.Dot())
 .label(
    x='Engine Displacement in Liters',
    y='Highway MPG',
    title='Engine Displacement in Liters vs Highway MPG'))

Scatter Plot with Faceted with Color drawn with plotnine

Code:

(ggplot(mpg) + 
    aes(x = 'displ', y = 'hwy', color = 'class') +
    geom_point() + 
    ggtitle('Engine Displacement in Liters vs Highway MPG') +
    xlab('Engine Displacement in Liters') +
    ylab('Highway MPG'))

Scatter Plot with Faceted with Color drawn with lets-plot

Code:

(lp.ggplot(mpg) +
    lp.aes(x='displ', y='hwy', color='class') +
    lp.geom_point() +
    lp.ggtitle('Engine Displacement in Liters vs Highway MPG') +
    lp.xlab('Engine Displacement in Liters') +
    lp.ylab('Highway MPG'))

Scatter Plot with Faceted with Color drawn with plotly

Code:

px.scatter(
    mpg, x='displ', y='hwy', color='class', 
    title='Engine Displacement in Liters vs Highway MPG',
    labels=dict(
       displ='Engine Displacement in Liters', 
       hwy='Highway MPG')
)

Scatter Plot with Faceted with Color drawn with hvPlot (Bokeh)

Code:

mpg.hvplot.scatter(
    x='displ', y='hwy', by='class')

Scatter Plot with Faceted with Color drawn with Altair

Code:

(
    alt.Chart(
        mpg,
        title='Engine Displacement in Liters vs Highway MPG',
    )
    .mark_circle()
    .encode(
        alt.X(
            'displ',
            title='Engine Displacament in Liters',
        ),
        alt.Y('hwy', title='Highway MPG'),
        color='class',
    )
)

Scatter Plot with Faceted with Color drawn with ggplot2 (R)

Code:

ggplot(data = mpg) + 
    aes(x = displ, y = hwy, color = class) +
    geom_point() + 
    ggtitle('Engine Displacement in Liters vs Highway MPG') +
    xlab('Engine Displacement in Liters') +
    ylab('Highway MPG')

Scatter Plot with Points Sized by Continuous Value#

Code:

ax = (mpg
    .plot
    .scatter(x='cty', 
             y='hwy', 
             s=10*mpg['cyl'],
             alpha=.5))
ax.set_title('City MPG vs Highway MPG')
ax.set_xlabel('City MPG')
ax.set_ylabel('Highway MPG')

Scatter Plot with Points Sized by Continuous Value drawn with Matplotlib

Code:

fig, ax = pyplot.subplots()
ax.scatter(mpg['cty'], mpg['hwy'],
           s=10 * mpg['cyl'], alpha=.5)
ax.set_title('City MPG vs Highway MPG')
ax.set_xlabel('City MPG')
ax.set_ylabel('Highway MPG')

Scatter Plot with Points Sized by Continuous Value drawn with Seaborn

Code:

ax = sns.scatterplot(mpg, x='cty', y='hwy',
                     size='cyl', alpha=.5)
ax.set(title='City MPG vs Highway MPG',
       xlabel='City MPG',
       ylabel='Highway MPG')

Scatter Plot with Points Sized by Continuous Value drawn with seaborn.objects

Code:

(so.Plot(mpg, x='cty', y='hwy',
         pointsize='cyl')
 .add(so.Dots(alpha=.5))
 .scale(pointsize=(4, 12))
 .label(x='City MPG', y='Highway MPG'))

Scatter Plot with Points Sized by Continuous Value drawn with plotnine

Code:

(ggplot(mpg) +
    aes(x='cty', y='hwy', size='cyl') +
    geom_point(alpha=.5))

Scatter Plot with Points Sized by Continuous Value drawn with lets-plot

Code:

(lp.ggplot(mpg) +
    lp.aes(x='cty', y='hwy', size='cyl') +
    lp.geom_point(alpha=.5))

Scatter Plot with Points Sized by Continuous Value drawn with plotly

Code:

px.scatter(
    mpg, x='cty', y='hwy', 
    size='cyl', size_max=10,
    title='City MPG vs Highway MPG',
    labels=dict(cty='City MPG', hwy='Highway MPG')
)

Scatter Plot with Points Sized by Continuous Value drawn with hvPlot (Bokeh)

Code:

mpg.hvplot.scatter(
    x='cty', y='hwy',
    s='cyl', scale=4, alpha=0.5)

Scatter Plot with Points Sized by Continuous Value drawn with Altair

Code:

(
    alt.Chart(
        mpg,
        title='City MPG vs Highway MPG',
    )
    .mark_circle(opacity=0.3)
    .encode(
        x=alt.X(
            'cty',
            axis=alt.Axis(title='City MPG'),
        ),
        y=alt.Y(
            'hwy',
            axis=alt.Axis(
                title='Highway MPG'
            ),
        ),
        size='cyl',
    )
)

Scatter Plot with Points Sized by Continuous Value drawn with ggplot2 (R)

Code:

ggplot(data = mpg) +
    aes(x = cty, y = hwy, size = cyl) +
    geom_point(alpha=.5)

Scatter Plot Faceted on One Variable#

Code:

classes = sorted(mpg['class'].unique())
fig, axes = pyplot.subplots(
    2, 4, sharex=True, sharey=True)
for ax, c in zip(axes.flat, classes):
    d = mpg[mpg['class'] == c]
    ax.scatter(d['displ'], d['hwy'], s=20)
    ax.set_title(c, fontsize=16)
    ax.tick_params(labelsize=12)
for ax in axes.flat[len(classes):]:
    ax.remove()

Note:

Matplotlib has no faceting. subplots makes the grid splitting the data, titling each panel and hiding the leftover axes is manual.

Scatter Plot Faceted on One Variable drawn with Seaborn

Code:

(mpg
 .pipe(sns.FacetGrid, 
       col='class', 
       col_wrap=4, 
       aspect=.5, 
       height=6)
 .map(pyplot.scatter, 'displ', 'hwy', s=20)
 .fig.subplots_adjust(wspace=.2, hspace=.2)
)

Scatter Plot Faceted on One Variable drawn with seaborn.objects

Code:

(so.Plot(mpg, x='displ', y='hwy')
 .add(so.Dot())
 .facet('class', wrap=4))

Scatter Plot Faceted on One Variable drawn with plotnine

Code:

(ggplot(mpg.assign(c=mpg['class'])) + 
  aes(x='displ', y='hwy') +
  geom_point() +
  facet_wrap(' ~ c', nrow = 2))

Scatter Plot Faceted on One Variable drawn with lets-plot

Code:

(lp.ggplot(mpg) +
  lp.aes(x='displ', y='hwy') +
  lp.geom_point() +
  lp.facet_wrap(facets='class', nrow=2))

Scatter Plot Faceted on One Variable drawn with plotly

Code:

px.scatter(
    mpg, x='displ', y='hwy', 
    facet_col='class', facet_col_wrap=4
)

Scatter Plot Faceted on One Variable drawn with hvPlot (Bokeh)

Code:

(mpg.hvplot.scatter(
    x='displ', y='hwy', by='class',
    subplots=True, fontscale=0.65,
    width=185, height=185)
 .cols(4))

Scatter Plot Faceted on One Variable drawn with Altair

Code:

alt.Chart(mpg).mark_circle().encode(
    x=alt.X('displ'),
    y=alt.Y('hwy'),
    facet=alt.Facet('class:O', columns=4),
).properties(width=200, height=300)

Scatter Plot Faceted on One Variable drawn with ggplot2 (R)

Code:

ggplot(data = mpg) + 
  aes(x=displ, y=hwy) +
  geom_point() + 
  facet_wrap(~ class, nrow = 2)

Scatter Plot Faceted on Two Variables#

Code:

drvs = sorted(mpg['drv'].unique())
cyls = sorted(mpg['cyl'].unique())
fig, axes = pyplot.subplots(
    len(drvs), len(cyls),
    sharex=True, sharey=True)
for i, drv in enumerate(drvs):
    for j, cyl in enumerate(cyls):
        d = mpg[(mpg['drv'] == drv)
                & (mpg['cyl'] == cyl)]
        ax = axes[i, j]
        ax.scatter(d['displ'], d['hwy'], s=20)
        ax.tick_params(labelsize=12)
for j, cyl in enumerate(cyls):
    axes[0, j].set_title(cyl, fontsize=16)
for i, drv in enumerate(drvs):
    ax = axes[i, -1]
    ax.set_ylabel(drv, fontsize=16)
    ax.yaxis.set_label_position('right')

Note:

The drv by cyl grid is indexed by hand. The strip labels are axis titles on the top row and the right column.

Scatter Plot Faceted on Two Variables drawn with Seaborn

Code:

(mpg
 .pipe(sns.FacetGrid, 
       col='cyl', 
       row='drv', 
       aspect=.9, 
       height=4)
 .map(pyplot.scatter, 'displ', 'hwy', s=20)
 .fig.subplots_adjust(wspace=.02, hspace=.02)
)

Scatter Plot Faceted on Two Variables drawn with seaborn.objects

Code:

(so.Plot(mpg, x='displ', y='hwy')
 .add(so.Dot())
 .facet(col='cyl', row='drv'))

Scatter Plot Faceted on Two Variables drawn with plotnine

Code:

(ggplot(mpg) + 
  aes(x='displ', y='hwy') +
  geom_point() + 
  facet_grid('drv ~ cyl'))

Scatter Plot Faceted on Two Variables drawn with lets-plot

Code:

(lp.ggplot(mpg) +
  lp.aes(x='displ', y='hwy') +
  lp.geom_point() +
  lp.facet_grid(x='cyl', y='drv'))

Scatter Plot Faceted on Two Variables drawn with plotly

Code:

px.scatter(
    mpg, x='displ', y='hwy', 
    facet_col='cyl', facet_row='drv',
    category_orders=dict(cyl=[4,5,6,8])
)

Scatter Plot Faceted on Two Variables drawn with hvPlot (Bokeh)

Code:

mpg.hvplot.scatter(
    x='displ', y='hwy',
    row='cyl', col='drv', subplots=True,
    fontscale=0.8, width=230, height=180)

Note:

row and col build a HoloViews GridSpace, which sorts its panels by the facet values.

Scatter Plot Faceted on Two Variables drawn with Altair

Code:

(alt
 .Chart(mpg)
 .mark_circle()
 .encode(x='displ', y='hwy',)
 .properties(
    width=100, height=150
  )
 .facet(column='cyl', row='drv')
)

Scatter Plot Faceted on Two Variables drawn with ggplot2 (R)

Code:

ggplot(data = mpg) + 
  aes(x = displ, y = hwy) +
  geom_point() + 
  facet_grid(drv ~ cyl)

Scatter Plot and Regression Line with 95% Confidence Interval Layered#

Code:

sns.lmplot(x='displ', y='hwy', 
           data=mpg, height=12)

Scatter Plot and Regression Line with 95% Confidence Interval Layered drawn with plotnine

Code:

(ggplot(mpg) +
    aes('displ', 'hwy') +
    geom_point() +
    geom_smooth(method='lm'))

Scatter Plot and Regression Line with 95% Confidence Interval Layered drawn with lets-plot

Code:

(lp.ggplot(mpg) +
    lp.aes('displ', 'hwy') +
    lp.geom_point() +
    lp.geom_smooth(method='lm'))

Scatter Plot and Regression Line with 95% Confidence Interval Layered drawn with plotly

Code:

import statsmodels.api as sm
from statsmodels.stats.outliers_influence import summary_table

y=mpg.hwy
x=mpg.displ
X = sm.add_constant(x)
res = sm.OLS(y, X).fit()

st, data, ss2 = summary_table(res, alpha=0.05)
preds = pd.DataFrame.from_records(data, columns=[s.replace('\n', ' ') for s in ss2])
preds['displ'] = mpg.displ
preds = preds.sort_values(by='displ')

fig = graph_objects.Figure(layout={
    'title' : 'Engine Displacement in Liters vs Highway MPG',
    'xaxis' : {
        'title' : 'Engine Displacement in Liters'
    },
    'yaxis' : {
        'title' : 'Highway MPG'
    }
})
p1 = graph_objects.Scatter(**{
    'mode' : 'markers',
    'x' : mpg.displ,
    'y' : mpg.hwy,
    'name' : 'Points'
})
p2 = graph_objects.Scatter({
    'mode' : 'lines',
    'x' : preds['displ'],
    'y' : preds['Predicted Value'],
    'name' : 'Regression',
})
#Add a lower bound for the confidence interval, white
p3 = graph_objects.Scatter({
    'mode' : 'lines',
    'x' : preds['displ'],
    'y' : preds['Mean ci 95% low'],
    'name' : 'Lower 95% CI',
    'showlegend' : False,
    'line' : {
        'color' : 'white'
    }
})
# Upper bound for the confidence band, transparent but with fill
p4 = graph_objects.Scatter( {
    'type' : 'scatter',
    'mode' : 'lines',
    'x' : preds['displ'],
    'y' : preds['Mean ci 95% upp'],
    'name' : '95% CI',
    'fill' : 'tonexty',
    'line' : {
        'color' : 'white'
    },
    'fillcolor' : 'rgba(255, 127, 14, 0.3)'
})
fig.add_trace(p1)
fig.add_trace(p2)
fig.add_trace(p3)
fig.add_trace(p4)

Note:

No built in method to calculate and display confidence intervals. Must calculate manually and utilise existing features to build the confidence band.

Scatter Plot and Regression Line with 95% Confidence Interval Layered drawn with ggplot2 (R)

Code:

ggplot(data = mpg) +
    aes(x = displ, y = hwy) +
    geom_point() +
    geom_smooth(method=lm)

Smoothed Line Plot and Scatter Plot Layered#

Code:

import statsmodels.api as sm

fig, ax = pyplot.subplots()
for c, d in mpg.groupby('class'):
    ax.scatter(d['displ'], d['hwy'], label=c)
sub = mpg[mpg['class'] == 'subcompact']
fit = sm.nonparametric.lowess(sub['hwy'],
                              sub['displ'])
ax.plot(fit[:, 0], fit[:, 1], color='black')
ax.legend()

Note:

Matplotlib has no smoother, so the loess fit comes from statsmodels.

Smoothed Line Plot and Scatter Plot Layered drawn with Seaborn

Code:

ax = sns.scatterplot(mpg, x='displ', y='hwy',
                     hue='class')
sns.regplot(mpg[mpg['class'] == 'subcompact'],
            x='displ', y='hwy', lowess=True,
            scatter=False, ax=ax)

Note:

lowess=True fits the loess curve. Seaborn draws no confidence band around it.

Smoothed Line Plot and Scatter Plot Layered drawn with plotnine

Code:

(ggplot(data=mpg, 
        mapping=aes(x='displ', y='hwy')) + 
  geom_point(mapping=aes(color = 'class')) + 
  geom_smooth(data=mpg[mpg['class'] == 'subcompact'], 
              se=False,
              method = 'loess'
             ))

Note:

Notice the smoothed line isn't as smooth as it is in ggplot2.

Smoothed Line Plot and Scatter Plot Layered drawn with lets-plot

Code:

(lp.ggplot(mpg) +
  lp.aes(x='displ', y='hwy') +
  lp.geom_point(lp.aes(color='class')) +
  lp.geom_smooth(data=mpg[mpg['class'] == 'subcompact'],
                 se=False,
                 method='loess'))

Smoothed Line Plot and Scatter Plot Layered drawn with plotly

Code:

traces = []
for cls in mpg['class'].unique():
    traces.append(graph_objects.Scatter({
        'mode' : 'markers',
        'x' : mpg.displ[mpg['class'] == cls],
        'y' : mpg.hwy[mpg['class'] == cls],
        'name' : cls
    }))

    
subcompact = mpg[mpg['class'] == 'subcompact'].sort_values(by='displ')

traces.append(graph_objects.Scatter({
    'mode' : 'lines',
    'x' : subcompact.displ,
    'y' : subcompact.hwy,
    'name' : 'smoothing',
    'line' : {
        'shape' : 'spline',
        'smoothing' : 1.3
    }
}))
    
fig = graph_objects.Figure(**{
    'data' : traces,
    'layout' : {
        'title' : 'Engine Displacement in Liters vs Highway MPG',
        'xaxis' : {
            'title' : 'Engine Displacement in Liters',
        },
        'yaxis' : {
            'title' : 'Highway MPG'
        }
    }
})

Note:

Plotly's builtin smoothing function is very weak

Smoothed Line Plot and Scatter Plot Layered drawn with Altair

Code:

scatter = (
    alt.Chart(
        mpg,
        title='Engine Displacement in Liters vs Highway MPG',
    )
    .mark_circle()
    .encode(
        x=alt.X(
            'displ',
            axis=alt.Axis(
                title='Engine Displacament in Liters'
            ),
        ),
        y=alt.Y(
            'hwy',
            axis=alt.Axis(
                title='Highway MPG'
            ),
        ),
        color='class',
    )
)

line = (
    alt.Chart(
        mpg[mpg['class'] == 'subcompact']
    )
    .transform_loess('displ', 'hwy')
    .mark_line()
    .encode(x=alt.X('displ'), y=alt.Y('hwy'))
)

scatter + line

Smoothed Line Plot and Scatter Plot Layered drawn with ggplot2 (R)

Code:

subcompact = mpg[mpg$`class` == 'subcompact', ]
ggplot(data = mpg, 
       mapping = aes(x = displ, y = hwy)) + 
  geom_point(mapping = aes(color = class)) + 
  geom_smooth(data = subcompact, 
              se = FALSE,
              method = 'loess')

Stacked Bar Chart#

Code:

(diamonds
 .groupby(['cut', 'clarity'])
 .size()
 .unstack()
 .plot.bar(stacked=True)
)

Code:

counts = (diamonds
          .groupby(['cut', 'clarity'])
          .size()
          .unstack())
fig, ax = pyplot.subplots()
bottom = np.zeros(len(counts))
for clarity in counts.columns:
    ax.bar(counts.index, counts[clarity],
           bottom=bottom, label=clarity)
    bottom += counts[clarity].values
ax.legend()

Note:

Matplotlib stacks bars by carrying the running total of each series in bottom.

Code:

sns.histplot(diamonds, x='cut',
             hue='clarity',
             multiple='stack', shrink=.8)

Note:

Classic seaborn has no bar-stacking function. histplot counts the categories and stacks the hue levels.

Stacked Bar Chart drawn with seaborn.objects

Code:

(so.Plot(diamonds, x='cut', color='clarity')
 .add(so.Bar(), so.Count(), so.Stack()))

Code:

(ggplot(diamonds) + 
  aes(x='cut', fill='clarity') +
  geom_bar())

Code:

(lp.ggplot(diamonds) +
  lp.aes(x='cut', fill='clarity') +
  lp.geom_bar())

Code:

px.histogram(
    diamonds, x='cut', color='clarity',
    category_orders=dict(cut=[
     'Fair', 'Good',  'Very Good', 
     'Premium', 'Ideal'])
)

Stacked Bar Chart drawn with hvPlot (Bokeh)

Code:

(diamonds
 .groupby(['cut', 'clarity'])
 .size()
 .unstack()
 .hvplot.bar(stacked=True, rot=45,
             legend='top_left'))

Code:

alt.data_transformers.disable_max_rows()
alt.Chart(diamonds).mark_bar().encode(
    x='cut', y='count(cut)', color='clarity'
).properties(width=300)

Stacked Bar Chart drawn with ggplot2 (R)

Code:

ggplot(data = diamonds) + 
  aes(x = cut, fill = clarity) +
  geom_bar()

Dodged Bar Chart#

Code:

(diamonds
 .groupby(['cut', 'clarity'])
 .size()
 .unstack()
 .plot.bar()
)

Code:

counts = (diamonds
          .groupby(['cut', 'clarity'])
          .size()
          .unstack())
x = np.arange(len(counts))
width = .8 / len(counts.columns)
fig, ax = pyplot.subplots()
for i, clarity in enumerate(counts.columns):
    ax.bar(x + i * width, counts[clarity],
           width=width, label=clarity)
ax.set_xticks(x + .4 - width / 2)
ax.set_xticklabels(counts.index, rotation=45)
ax.legend()

Note:

Dodging is manual: shift each series by its own offset and move the ticks back to the group centers.

Code:

sns.countplot(diamonds, x='cut',
              hue='clarity')

Dodged Bar Chart drawn with seaborn.objects

Code:

(so.Plot(diamonds, x='cut', color='clarity')
 .add(so.Bar(), so.Count(), so.Dodge()))

Code:

(ggplot(diamonds) + 
  aes(x='cut', fill='clarity') +
  geom_bar(position = 'dodge'))

Code:

(lp.ggplot(diamonds) +
  lp.aes(x='cut', fill='clarity') +
  lp.geom_bar(position='dodge'))

Code:

px.histogram(
    diamonds, x='cut', color='clarity', barmode='group',
    category_orders=dict(cut=[
     'Fair', 'Good',  'Very Good', 
     'Premium', 'Ideal'])
)

Dodged Bar Chart drawn with hvPlot (Bokeh)

Code:

(diamonds
 .groupby(['cut', 'clarity'])
 .size()
 .unstack()
 .hvplot.bar(stacked=False, rot=90,
             fontscale=0.9))

Code:

alt.data_transformers.disable_max_rows()
alt.Chart(diamonds).mark_bar().encode(
    x='clarity',
    y='count(cut)',
    color='clarity',
    column='cut',
).properties(width=100)

Code:

ggplot(data = diamonds) + 
  aes(x = cut, fill = clarity) +
  geom_bar(position = 'dodge')

Stacked KDE Plot#

Code:

fig, ax = pyplot.subplots()
ax.set_xlim(55, 70)
for cut in diamonds['cut'].unique():
    s = diamonds[diamonds['cut'] == cut]['depth']
    s.plot.kde(ax=ax, label=cut)
ax.legend()

Note:

I don't know whether Pandas can fill a KDE curve.

This requires using some Matplotlib to get them to stack and to have a legend.

Code:

from scipy.stats import gaussian_kde

grid = np.linspace(55, 70, 200)
fig, ax = pyplot.subplots()
for cut, d in diamonds.groupby('cut')['depth']:
    density = gaussian_kde(d)(grid)
    ax.fill_between(grid, density, alpha=.1)
    ax.plot(grid, density, label=cut)
ax.set_xlim(55, 70)
ax.legend()

Note:

Matplotlib has no density estimator, so the KDE comes from scipy. set_xlim clips the axis ggplot2's xlim() drops rows first.

Code:

(sns
  .FacetGrid(diamonds, 
             hue='cut', 
             height=10, 
             xlim=(55, 70))
  .map(sns.kdeplot, 'depth', fill=True)
 .add_legend()
)

Stacked KDE Plot drawn with seaborn.objects

Code:

(so.Plot(diamonds, x='depth', color='cut')
 .add(so.Area(alpha=.1), so.KDE(gridsize=500))
 .limit(x=(55, 70)))

Note:

.limit() only clips the axis unlike ggplot2's xlim() it does not drop rows before the density is estimated.

Code:

(ggplot(diamonds) +
  aes('depth', fill='cut', color='cut') +
  geom_density(alpha=0.1))

Note:

+ xlim(55, 70) results in an error.

Code:

(lp.ggplot(diamonds) +
  lp.aes('depth', fill='cut', color='cut') +
  lp.geom_density(alpha=0.1) +
  lp.xlim(55, 70))

Code:

fig = figure_factory.create_distplot(
    [diamonds['depth'][diamonds['cut'] == c].values 
     for c in diamonds.cut.unique()
    ],
    diamonds.cut.unique(),
    show_hist=False,
    show_rug=False,
)
for d in fig['data']:
    d.update({'fill': 'tozeroy'})

Stacked KDE Plot drawn with hvPlot (Bokeh)

Code:

diamonds.hvplot.kde(
    y='depth', by='cut',
    alpha=0.1, xlim=(55, 70))

Note:

xlim only clips the axis. Unlike ggplot2's xlim() it doesn't drop rows before the densities are estimated.

Code:

alt.data_transformers.disable_max_rows()
alt.Chart(diamonds).transform_density(
    'depth',
    as_=['depth', 'density'],
    groupby=['cut'],
    extent=[55, 70],
).mark_area(fillOpacity=0.3,).encode(
    x='depth',
    y='density:Q',
    color='cut',
    stroke='cut',
)

Code:

ggplot(diamonds) +
  aes(depth, fill = cut, colour = cut) +
  geom_density(alpha = 0.1) +
  xlim(55, 70)

Python Plotting for Exploratory Data Analysis

Contents

Introduction

Python Plotting Options

Matplotlib-Based Libraries

Interactive Plotting Libraries

Further Reading

Hearty Thank You

Why all the talk about ggplot?

Humble Rosetta Stone for Visualization in Exploratory Data Analysis

Data

ts

mpg

diamonds

Bar Chart#

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Scatter Plot#

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Scatter Plot with Faceted with Color#

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Scatter Plot with Points Sized by Continuous Value#

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