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

In [1]:

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

In [2]:

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

In [3]:

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

In [4]:

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

In [5]:

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

In [6]:

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

In [7]:

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

Colors

In [8]:

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

In [9]:

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

In [10]:

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.

In [11]:

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

Out[11]:

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

In [12]:

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

Styles

In [13]:

plt.style.available

Out[13]:

['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']

In [14]:

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

In [15]:

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

In [16]:

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

In [17]:

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.

In [18]:

plt.rcParams

Out[18]:

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})

In [19]:

%%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

In [20]:

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

Customizing plots

In [21]:

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

In [22]:

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

In [23]:

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

In [24]:

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

In [25]:

import numpy.random as rng

Density plots

In [26]:

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

In [27]:

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

In [28]:

iris = sns.load_dataset('iris')

In [29]:

iris.head()

Out[29]:

	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

In [30]:

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

Box and violin plots

In [31]:

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

In [32]:

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

In [33]:

titanic.head()

Out[33]:

	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

In [34]:

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

In [35]:

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

In [36]:

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 befor 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.

In [37]:

from ggplot import *

Interacting with R

In [38]:

%load_ext rpy2.ipython

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

In [39]:

%R -o mtcars

In [40]:

mtcars.head()

Out[40]:

	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

In [41]:

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

Out[41]:

<ggplot: (292414163)>

In [42]:

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

Out[42]:

<ggplot: (292201757)>

In [43]:

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

Out[43]:

<ggplot: (287265863)>

In [44]:

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

Out[44]:

<ggplot: (-9223372036566578744)>

In [45]:

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

Out[45]:

<ggplot: (288465612)>

Use ggplot in R directly with %R magic

In [46]:

cars = mtcars

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

In [52]:

%%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")

Version Information

In [48]:

%load_ext version_information
%version_information numpy, matplotlib, seaborn

Out[48]:

Software	Version
Python	3.5.1 64bit [GCC 4.2.1 (Apple Inc. build 5577)]
IPython	4.0.1
OS	Darwin 15.3.0 x86_64 i386 64bit
numpy	1.10.4
matplotlib	1.5.1
seaborn	0.7.0
Tue Feb 09 15:58:21 2016 EST

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