Graphics in Python

The foundational package for most graphics in Python is `matplotlib <http://matplotlib.org>`__, and the `seaborn <http://stanford.edu/~mwaskom/software/seaborn/>`__ package builds on this to provide more statistical graphing options. We will focus on these two packages, but there are many others if these don’t meet your needs.

There are also several specialized packages that might come in useful:

• `ggplot <http://ggplot.yhathq.com>`__ is a port of the R ggplto2 library to Python
• `vispy <http://vispy.org>`__ and `lightning <http://lightning-viz.org>`__ for interactive visualization of large data sets
• `bokeh <http://bokeh.pydata.org/en/latest/>`__ for web-friendly interactive graphics
• `pyxley <pyxley%20github>`__ and `spyre <spyre%20pyton>`__ for R shiny fans and several others.

Resources

• Matplotlib tutorial
• Matplotlib gallery
• Seaborn gallery

import warnings
warnings.filterwarnings("ignore")

Matplotlib

Matplotlib has a “functional” interface similar to Matlab via the pyplot module for simple interactive use, as well as an object-oriented interface that is useful for more complex graphic creations.

Types of plots

plt.hist(np.random.randn(1000), bins=np.linspace(-4,4,11))
pass

plt.boxplot(np.random.random((6,10)))
pass

plt.scatter(*np.random.uniform(0.1, 0.9, (2,100)),
            s=np.random.randint(10, 200, 100),
            c=np.random.random(100))
pass

plt.stem(np.random.random(8))
plt.margins(0.05)
pass

x = np.linspace(0, 2*np.pi, 100)
y = np.sin(x)

plt.plot(x, y)
plt.axis([0, 2*np.pi, -1.05, 1.05,])
pass

Colors

plt.scatter(*np.random.uniform(0.1, 0.9, (2,100)),
            s=np.random.randint(10, 200, 100),
            c=np.random.random(100))
pass

plt.scatter(*np.random.uniform(0.1, 0.9, (2,100)),
            s=np.random.randint(10, 200, 100),
            c=np.random.random(100), cmap='summer')
pass

plt.scatter(*np.random.uniform(0.1, 0.9, (2,100)),
            s=np.random.randint(10, 200, 100),
            c=np.random.random(100), cmap='hsv')
pass

Gettting a list of colors from a colormap

Giving an argument of 0.0 < x < 1.0 to a colormap gives the appropriate interpolated color.

# find the bottom, middle and top colors of the winter colormap
colors = plt.cm.winter(np.linspace(0, 1, 3))
colors

array([[ 0.        ,  0.        ,  1.        ,  1.        ],
       [ 0.        ,  0.50196078,  0.74901961,  1.        ],
       [ 0.        ,  1.        ,  0.5       ,  1.        ]])

plt.scatter(*np.random.uniform(0.1, 0.9, (2,100)),
            s=np.random.randint(10, 200, 100),
            c=colors)
pass

Styles

plt.style.available

['seaborn-ticks',
 'seaborn-white',
 'seaborn-whitegrid',
 'seaborn-colorblind',
 'seaborn-pastel',
 'seaborn-poster',
 'seaborn-paper',
 'ggplot',
 'seaborn-deep',
 'bmh',
 'seaborn-talk',
 'seaborn-dark',
 'dark_background',
 'seaborn-bright',
 'fivethirtyeight',
 'seaborn-notebook',
 'classic',
 'presentation',
 'seaborn-muted',
 'seaborn-dark-palette',
 'grayscale',
 'seaborn-darkgrid']

with plt.style.context('classic'):
    plt.plot(x, y)
    plt.axis([0, 2*np.pi, -1.05, 1.05,])

with plt.style.context('fivethirtyeight'):
    plt.plot(x, y)
    plt.axis([0, 2*np.pi, -1.05, 1.05,])

with plt.style.context('ggplot'):
    plt.plot(x, y)
    plt.axis([0, 2*np.pi, -1.05, 1.05,])

with plt.xkcd():
    plt.plot(x, y)
    plt.axis([0, 2*np.pi, -1.05, 1.05,])

Creating your onw style

Many, many options can be configured.

plt.rcParams

RcParams({'agg.path.chunksize': 0,
          'animation.avconv_args': [],
          'animation.avconv_path': 'avconv',
          'animation.bitrate': -1,
          'animation.codec': 'mpeg4',
          'animation.convert_args': [],
          'animation.convert_path': 'convert',
          'animation.ffmpeg_args': [],
          'animation.ffmpeg_path': 'ffmpeg',
          'animation.frame_format': 'png',
          'animation.html': 'none',
          'animation.mencoder_args': [],
          'animation.mencoder_path': 'mencoder',
          'animation.writer': 'ffmpeg',
          'axes.axisbelow': True,
          'axes.edgecolor': 'white',
          'axes.facecolor': '#EAEAF2',
          'axes.formatter.limits': [-7, 7],
          'axes.formatter.use_locale': False,
          'axes.formatter.use_mathtext': False,
          'axes.formatter.useoffset': True,
          'axes.grid': True,
          'axes.grid.axis': 'both',
          'axes.grid.which': 'major',
          'axes.hold': True,
          'axes.labelcolor': '.15',
          'axes.labelpad': 5.0,
          'axes.labelsize': 16.5,
          'axes.labelweight': 'normal',
          'axes.linewidth': 0.0,
          'axes.prop_cycle': cycler('color', [(0.2980392156862745, 0.4470588235294118, 0.6901960784313725), (0.3333333333333333, 0.6588235294117647, 0.40784313725490196), (0.7686274509803922, 0.3058823529411765, 0.3215686274509804), (0.5058823529411764, 0.4470588235294118, 0.6980392156862745), (0.8, 0.7254901960784313, 0.4549019607843137), (0.39215686274509803, 0.7098039215686275, 0.803921568627451)]),
          'axes.spines.bottom': True,
          'axes.spines.left': True,
          'axes.spines.right': True,
          'axes.spines.top': True,
          'axes.titlesize': 18.0,
          'axes.titleweight': 'normal',
          'axes.unicode_minus': True,
          'axes.xmargin': 0.0,
          'axes.ymargin': 0.0,
          'axes3d.grid': True,
          'backend': 'module://ipykernel.pylab.backend_inline',
          'backend.qt4': 'PyQt4',
          'backend.qt5': 'PyQt5',
          'backend_fallback': True,
          'boxplot.bootstrap': None,
          'boxplot.boxprops.color': 'b',
          'boxplot.boxprops.linestyle': '-',
          'boxplot.boxprops.linewidth': 1.0,
          'boxplot.capprops.color': 'k',
          'boxplot.capprops.linestyle': '-',
          'boxplot.capprops.linewidth': 1.0,
          'boxplot.flierprops.color': 'b',
          'boxplot.flierprops.linestyle': 'none',
          'boxplot.flierprops.linewidth': 1.0,
          'boxplot.flierprops.marker': '+',
          'boxplot.flierprops.markeredgecolor': 'k',
          'boxplot.flierprops.markerfacecolor': 'b',
          'boxplot.flierprops.markersize': 6.0,
          'boxplot.meanline': False,
          'boxplot.meanprops.color': 'r',
          'boxplot.meanprops.linestyle': '-',
          'boxplot.meanprops.linewidth': 1.0,
          'boxplot.medianprops.color': 'r',
          'boxplot.medianprops.linestyle': '-',
          'boxplot.medianprops.linewidth': 1.0,
          'boxplot.notch': False,
          'boxplot.patchartist': False,
          'boxplot.showbox': True,
          'boxplot.showcaps': True,
          'boxplot.showfliers': True,
          'boxplot.showmeans': False,
          'boxplot.vertical': True,
          'boxplot.whiskerprops.color': 'b',
          'boxplot.whiskerprops.linestyle': '--',
          'boxplot.whiskerprops.linewidth': 1.0,
          'boxplot.whiskers': 1.5,
          'contour.corner_mask': True,
          'contour.negative_linestyle': 'dashed',
          'datapath': '/Users/cliburn/anaconda/envs/py35/lib/python3.5/site-packages/matplotlib/mpl-data',
          'docstring.hardcopy': False,
          'errorbar.capsize': 3.0,
          'examples.directory': '',
          'figure.autolayout': False,
          'figure.dpi': 80.0,
          'figure.edgecolor': (1, 1, 1, 0),
          'figure.facecolor': (1, 1, 1, 0),
          'figure.figsize': [6.0, 4.0],
          'figure.frameon': True,
          'figure.max_open_warning': 20,
          'figure.subplot.bottom': 0.125,
          'figure.subplot.hspace': 0.2,
          'figure.subplot.left': 0.125,
          'figure.subplot.right': 0.9,
          'figure.subplot.top': 0.9,
          'figure.subplot.wspace': 0.2,
          'figure.titlesize': 'medium',
          'figure.titleweight': 'normal',
          'font.cursive': ['Apple Chancery',
                           'Textile',
                           'Zapf Chancery',
                           'Sand',
                           'Script MT',
                           'Felipa',
                           'cursive'],
          'font.family': ['sans-serif'],
          'font.fantasy': ['Comic Sans MS',
                           'Chicago',
                           'Charcoal',
                           'ImpactWestern',
                           'Humor Sans',
                           'fantasy'],
          'font.monospace': ['Bitstream Vera Sans Mono',
                             'DejaVu Sans Mono',
                             'Andale Mono',
                             'Nimbus Mono L',
                             'Courier New',
                             'Courier',
                             'Fixed',
                             'Terminal',
                             'monospace'],
          'font.sans-serif': ['Arial',
                              'Liberation Sans',
                              'Bitstream Vera Sans',
                              'sans-serif'],
          'font.serif': ['Bitstream Vera Serif',
                         'DejaVu Serif',
                         'New Century Schoolbook',
                         'Century Schoolbook L',
                         'Utopia',
                         'ITC Bookman',
                         'Bookman',
                         'Nimbus Roman No9 L',
                         'Times New Roman',
                         'Times',
                         'Palatino',
                         'Charter',
                         'serif'],
          'font.size': 10.0,
          'font.stretch': 'normal',
          'font.style': 'normal',
          'font.variant': 'normal',
          'font.weight': 'normal',
          'grid.alpha': 1.0,
          'grid.color': 'white',
          'grid.linestyle': '-',
          'grid.linewidth': 1.0,
          'image.aspect': 'equal',
          'image.cmap': 'Greys',
          'image.composite_image': True,
          'image.interpolation': 'bilinear',
          'image.lut': 256,
          'image.origin': 'upper',
          'image.resample': False,
          'interactive': True,
          'keymap.all_axes': ['a'],
          'keymap.back': ['left', 'c', 'backspace'],
          'keymap.forward': ['right', 'v'],
          'keymap.fullscreen': ['f', 'ctrl+f'],
          'keymap.grid': ['g'],
          'keymap.home': ['h', 'r', 'home'],
          'keymap.pan': ['p'],
          'keymap.quit': ['ctrl+w', 'cmd+w'],
          'keymap.save': ['s', 'ctrl+s'],
          'keymap.xscale': ['k', 'L'],
          'keymap.yscale': ['l'],
          'keymap.zoom': ['o'],
          'legend.borderaxespad': 0.5,
          'legend.borderpad': 0.4,
          'legend.columnspacing': 2.0,
          'legend.edgecolor': 'inherit',
          'legend.facecolor': 'inherit',
          'legend.fancybox': False,
          'legend.fontsize': 15.0,
          'legend.framealpha': None,
          'legend.frameon': False,
          'legend.handleheight': 0.7,
          'legend.handlelength': 2.0,
          'legend.handletextpad': 0.8,
          'legend.isaxes': True,
          'legend.labelspacing': 0.5,
          'legend.loc': 'upper right',
          'legend.markerscale': 1.0,
          'legend.numpoints': 1,
          'legend.scatterpoints': 1,
          'legend.shadow': False,
          'lines.antialiased': True,
          'lines.color': 'b',
          'lines.dash_capstyle': 'butt',
          'lines.dash_joinstyle': 'round',
          'lines.linestyle': '-',
          'lines.linewidth': 1.75,
          'lines.marker': 'None',
          'lines.markeredgewidth': 0.0,
          'lines.markersize': 7.0,
          'lines.solid_capstyle': 'round',
          'lines.solid_joinstyle': 'round',
          'markers.fillstyle': 'full',
          'mathtext.bf': 'serif:bold',
          'mathtext.cal': 'cursive',
          'mathtext.default': 'it',
          'mathtext.fallback_to_cm': True,
          'mathtext.fontset': 'cm',
          'mathtext.it': 'serif:italic',
          'mathtext.rm': 'serif',
          'mathtext.sf': 'sans\-serif',
          'mathtext.tt': 'monospace',
          'nbagg.transparent': True,
          'patch.antialiased': True,
          'patch.edgecolor': 'k',
          'patch.facecolor': (0.2980392156862745,
                              0.4470588235294118,
                              0.6901960784313725),
          'patch.linewidth': 0.3,
          'path.effects': [],
          'path.simplify': True,
          'path.simplify_threshold': 0.1111111111111111,
          'path.sketch': None,
          'path.snap': True,
          'pdf.compression': 6,
          'pdf.fonttype': 3,
          'pdf.inheritcolor': False,
          'pdf.use14corefonts': False,
          'pgf.debug': False,
          'pgf.preamble': [],
          'pgf.rcfonts': True,
          'pgf.texsystem': 'xelatex',
          'plugins.directory': '.matplotlib_plugins',
          'polaraxes.grid': True,
          'ps.distiller.res': 6000,
          'ps.fonttype': 3,
          'ps.papersize': 'letter',
          'ps.useafm': False,
          'ps.usedistiller': False,
          'savefig.bbox': None,
          'savefig.directory': '~',
          'savefig.dpi': 72.0,
          'savefig.edgecolor': 'w',
          'savefig.facecolor': 'w',
          'savefig.format': 'png',
          'savefig.frameon': True,
          'savefig.jpeg_quality': 95,
          'savefig.orientation': 'portrait',
          'savefig.pad_inches': 0.1,
          'savefig.transparent': False,
          'svg.fonttype': 'path',
          'svg.image_inline': True,
          'svg.image_noscale': False,
          'text.antialiased': True,
          'text.color': '.15',
          'text.dvipnghack': None,
          'text.hinting': 'auto',
          'text.hinting_factor': 8,
          'text.latex.preamble': [],
          'text.latex.preview': False,
          'text.latex.unicode': False,
          'text.usetex': False,
          'timezone': 'UTC',
          'tk.window_focus': False,
          'toolbar': 'toolbar2',
          'verbose.fileo': 'sys.stdout',
          'verbose.level': 'silent',
          'webagg.open_in_browser': True,
          'webagg.port': 8988,
          'webagg.port_retries': 50,
          'xtick.color': '.15',
          'xtick.direction': 'out',
          'xtick.labelsize': 15.0,
          'xtick.major.pad': 7.0,
          'xtick.major.size': 0.0,
          'xtick.major.width': 1.0,
          'xtick.minor.pad': 4.0,
          'xtick.minor.size': 0.0,
          'xtick.minor.visible': False,
          'xtick.minor.width': 0.5,
          'ytick.color': '.15',
          'ytick.direction': 'out',
          'ytick.labelsize': 15.0,
          'ytick.major.pad': 7.0,
          'ytick.major.size': 0.0,
          'ytick.major.width': 1.0,
          'ytick.minor.pad': 4.0,
          'ytick.minor.size': 0.0,
          'ytick.minor.visible': False,
          'ytick.minor.width': 0.5})

%%file foo.mplstyle
axes.grid: True
axes.titlesize : 24
axes.labelsize : 20
lines.linewidth : 3
lines.markersize : 10
xtick.labelsize : 16
ytick.labelsize : 16

Overwriting foo.mplstyle

with plt.style.context('foo.mplstyle'):
    plt.plot(x, y)
    plt.axis([0, 2*np.pi, -1.05, 1.05,])

Customizing plots

plt.rcParams.update({'font.size': 22})

fig = plt.figure(figsize=(8,6))
ax = plt.subplot(1,1,1)
plt.plot(x, y, color='red', linewidth=2, linestyle='dashed', label='sine curve')
plt.plot(x, np.cos(x), 'b-', label='cosine curve')
plt.legend(loc='best', fontsize=14)
plt.axis([0, 2*np.pi, -1.05, 1.05,])
plt.xlabel('x')
plt.ylabel('sin(x)')
plt.xticks([0,0.5*np.pi,np.pi,1.5*np.pi,2*np.pi],
           [0, r'$\frac{\pi}{2}$', r'$\pi$', r'$\frac{3\pi}{2}$', r'$2\pi$'])
plt.title('Sine and Cosine Plots')
plt.text(0.45, 0.9, 'Empty space', transform=ax.transAxes, ha='left', va='top')
pass

Plot layouts

fig, axes = plt.subplots(2,2,figsize=(8,8))
axes[0,0].plot(x,y, 'r')
axes[0,1].plot(x,y, 'g')
axes[1,0].plot(x,y, 'b')
axes[1,1].plot(x,y, 'k')
for ax in axes.ravel():
    ax.margins(0.05)
pass

ax1 = plt.subplot2grid((3,3), (0,0), colspan=3)
ax2 = plt.subplot2grid((3,3), (1,0), colspan=2)
ax3 = plt.subplot2grid((3,3), (1,2), rowspan=2)
ax4 = plt.subplot2grid((3,3), (2,0), colspan=2)
axes = [ax1, ax2, ax3, ax4]
colors = ['r', 'g', 'b', 'k']
for ax, c in zip(axes, colors):
    ax.plot(x, y, c)
    ax.margins(0.05)
plt.tight_layout()

Seaborn

sns.set_context("notebook", font_scale=1.5, rc={"lines.linewidth": 2.5})

import numpy.random as rng

Density plots

xs = rng.normal(0,1,100)

fig, axes = plt.subplots(1, 2, figsize=(8,4))
sns.distplot(xs, hist=False, rug=True, ax=axes[0]);
sns.distplot(xs, hist=True, ax=axes[1])
pass

Kernel density estimate

sns.kdeplot(np.r_[rng.normal(0,1,50), rng.normal(4,0.8,100)])
pass

iris = sns.load_dataset('iris')

iris.head()

	sepal_length	sepal_width	petal_length	petal_width	species
0	5.1	3.5	1.4	0.2	setosa
1	4.9	3.0	1.4	0.2	setosa
2	4.7	3.2	1.3	0.2	setosa
3	4.6	3.1	1.5	0.2	setosa
4	5.0	3.6	1.4	0.2	setosa

Joint distribution plot

sns.jointplot(x='petal_length', y='petal_width', data=iris, kind='kdeplot')
pass

Box and violin plots

fig, axes = plt.subplots(1, 2, figsize=(8,4))

sns.boxplot(x='species', y='petal_length', data=iris, ax=axes[0])
sns.violinplot(x='species', y='petal_length', data=iris, ax=axes[1])
pass

Composite plots

url = 'https://raw.githubusercontent.com/mwaskom/seaborn-data/master/titanic.csv'
titanic = pd.read_csv(url)

titanic.head()

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
0	0	3	male	22	1	0	7.2500	S	Third	man	True	NaN	Southampton	no	False
1	1	1	female	38	1	0	71.2833	C	First	woman	False	C	Cherbourg	yes	False
2	1	3	female	26	0	0	7.9250	S	Third	woman	False	NaN	Southampton	yes	True
3	1	1	female	35	1	0	53.1000	S	First	woman	False	C	Southampton	yes	False
4	0	3	male	35	0	0	8.0500	S	Third	man	True	NaN	Southampton	no	True

sns.set_context('notebook', font_scale=1.5)

sns.lmplot(x='fare', y='survived', col='alone', row='sex', data=titanic, logistic=True)
pass

g = sns.PairGrid(titanic,
                 y_vars=['fare', 'age'],
                 x_vars=['sex', 'class', 'embark_town' ],
                 aspect=1, size=5.5)
g.map(sns.stripplot, jitter=True, palette="bright")
pass

Using ggplot as an alternative to seaborn.

The ggplot module is a port of R’s ggplot2 - usage is very similar except for the following minor differences:

• Pass in a pandas dataframe
• aethetics comes before data in the argument list ot ggplot
• Give column names and other arugments (e.g.. function to call) as strings
• You need to use the line continuation character \ to extend over multiple lines

Only the most elementary examples are shown below. The ggplot module is extremely rich and sophisticated with a steep learning curve if you’re not already familiar with it from R. Please see documentation for details.

from ggplot import *

Interacting with R

%load_ext rpy2.ipython

Note that we are exporting the R mtcars dataframe to Python (converts to pandas DataFrame)

%R -o mtcars

mtcars.head()

	mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb
Mazda RX4	21.0	6	160	110	3.90	2.620	16.46	0	1	4	4
Mazda RX4 Wag	21.0	6	160	110	3.90	2.875	17.02	0	1	4	4
Datsun 710	22.8	4	108	93	3.85	2.320	18.61	1	1	4	1
Hornet 4 Drive	21.4	6	258	110	3.08	3.215	19.44	1	0	3	1
Hornet Sportabout	18.7	8	360	175	3.15	3.440	17.02	0	0	3	2

ggplot(aes(x='wt', y='mpg'), data=mtcars,) + geom_point()

<ggplot: (292414163)>

ggplot(aes(x='wt', y='mpg'), data=mtcars) + geom_point() + geom_smooth(method='loess')

<ggplot: (292201757)>

ggplot(aes(x='wt', y='mpg'), data=mtcars) + geom_point() + geom_line()

<ggplot: (287265863)>

ggplot(aes(x='mpg'), data=mtcars) + geom_histogram(binwidth=2)

<ggplot: (-9223372036566578744)>

ggplot(aes(x='mpg'), mtcars) + \
geom_line(stat="density") + \
xlim(2.97, 41.33) + \
labs(title="Density plot")

<ggplot: (288465612)>

Use ggplot in R directly with %R magic

cars = mtcars

Note that we pass in Python variables with the -i optin and using the %%R cell magic

%%R -i cars
library('ggplot2')
ggplot(cars, aes(x=mpg, y=am)) +
geom_point(position=position_jitter(width=.3, height=.08), shape=21, alpha=0.6, size=3) +
stat_smooth(method=glm, method.args=list(family="binomial"), color="red")