In [31]:

x = np.array([10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5])
y = np.array([8.04, 6.95, 7.58, 8.81, 8.33, 9.96, 7.24, 4.26, 10.84, 4.82, 5.68])

plt.plot(x, y, 'o')
plt.xlabel('x')
plt.ylabel('y')
plt.title('Anscombe dataset')

Out[31]:

<matplotlib.text.Text at 0x1073584d0>

In [32]:

a, b = np.polyfit(x, y, 1)
plt.plot(x, y, 'o')
plt.plot(x, a * x + b, '-')

Out[32]:

[<matplotlib.lines.Line2D at 0x1070af490>]

In [33]:

xvec =  np.array([[10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5],
               [10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5],
               [10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5],
               [8,8,8,8,8,8,8,19,8,8,8]]).T
yvec = np.array([[8.04, 6.95, 7.58, 8.81, 8.33, 9.96, 7.24, 4.26, 10.84, 4.82, 5.68],
              [9.14, 8.14, 8.74, 8.77, 9.26, 8.10, 6.13, 3.10, 9.13, 7.26, 4.74],
              [7.46, 6.77, 12.74, 7.11, 7.81, 8.84, 6.08, 5.39, 8.15, 6.42, 5.73],
              [6.58, 5.76, 7.71, 8.84, 8.47, 7.04, 5.25, 12.50, 5.56, 7.91, 6.89]]).T

plt.plot(xvec, yvec, 'o');

Visual encodings

Length (`plt.bar`)

In [34]:

plt.title('length')
plt.bar(x, y, 1)

Out[34]:

<Container object of 11 artists>

Position and color (`plt.scatter`)

In [35]:

plt.title('position and color')
plt.scatter(x, y, c=y, s=100)

Out[35]:

<matplotlib.collections.PathCollection at 0x10790ba90>

Position and size (`plt.scatter`)

In [36]:

plt.title('position and size')
plt.scatter(x, y, s=y*20, facecolor='none')

Out[36]:

<matplotlib.collections.PathCollection at 0x107931050>

Length and angle (`plt.polar`)

In [37]:

theta = (x - x.min())*1./(x.max()-x.min())*2*np.pi

theta = np.vstack([theta, theta])
radius = np.vstack([np.zeros(len(y)), y])

plt.polar(theta, radius, 'b');

Creating visual style

Using style arguments

In [38]:

plt.plot(x, y, 'o', markersize=10, mfc='k')
plt.xlabel('x', size=16)
plt.ylabel('y', size=16)
plt.title('Anscombe dataset', size=16, weight='bold')

Out[38]:

<matplotlib.text.Text at 0x107364c50>

In [39]:

rc_defaults = matplotlib.rcParams.copy()

In [63]:

import matplotlib
matplotlib.rcParams.update({'xtick.labelsize' : 16,
                            'ytick.labelsize' : 16,
                            'axes.labelsize' : 16,
                            'axes.titlesize' : 18,
                            'lines.markersize' : 10,
                            'lines.linestyle' : 'None',
                            'lines.marker' : 'o',
                            'lines.markerfacecolor' : 'k'})

plt.plot(x, y)
plt.xlabel('x')
plt.ylabel('y')
plt.title('Anscombe dataset')

Out[63]:

<matplotlib.text.Text at 0x10aa3cf90>

In [71]:

matplotlib.rcParams.update(rc_defaults)

Using object-oriented API

In [42]:

ax = plt.subplot(111)
line, = plt.plot(x, y, 'o')
line.set_markersize(10)
line.set_mfc('k')

ticklabels = ax.get_xticklabels()
ticklabels[0].set_size(15)
ticklabels[-1].set_size(15)

plt.setp(ticklabels[1:-1], visible=False)

Out[42]:

[None, None, None, None]

In [75]:

plt.plot(x, y, 'ko')
plt.grid(axis='y', which='major', ls='-', lw=0.5)

In [66]:

ax = plt.subplot(111)
plt.plot(x, y, 'ko', ms=10, clip_on=False)

ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_position(('outward', 20))
ax.spines['left'].set_position('center')

ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')

In [103]:

p1, = plt.plot(xvec[:,0], yvec[:, 0], 'bo', label='dataset 1')
p2, = plt.plot(xvec[:,1], yvec[:, 1], 'ro', label='dataset 2')

#inset legend
l1 = plt.legend(frameon=False, loc='lower right')
#external legend
l2 = plt.legend((p1, p2), ('new label 1', 'new label 2'),
                ncol=2, numpoints=1,
                loc='upper left', bbox_to_anchor=(0, -0.1))
plt.gca().add_artist(l1) # re-insert first legend

Out[103]:

<matplotlib.legend.Legend at 0x10b057bd0>

In [67]:

ax = plt.subplot(111)

plt.plot(x, y, 'bo')

plt.text(5, 10, 'Text in\ndata coords', 
         transform=ax.transData, # data coordinates (default)
         size=15, va='top')
plt.text(0.8, 0.1, 'Text in\naxes coords', 
         transform=ax.transAxes, # axes coordinates
         ma='right', ha='right', size=15)

Out[67]:

<matplotlib.text.Text at 0x106b7edd0>

In [46]:

ax = plt.subplot(111)

plt.plot(xvec[:,2], yvec[:,2], 'ko')

ax.annotate("Outlier",
                xy=(13, 12.74), xycoords='data',
                xytext=(10, 11), textcoords='data',
                arrowprops=dict(arrowstyle="->",
                                color='r',
                                shrinkA=2, shrinkB=5,
                                connectionstyle='angle,angleA=180,angleB=90,rad=0'
                                )
            )

Out[46]:

<matplotlib.text.Annotation at 0x107d1c210>

Heatmaps and colorbars

In [47]:

plt.imshow(yvec.T, interpolation='nearest')
plt.gray()
plt.colorbar(orientation='horizontal')

Out[47]:

<matplotlib.colorbar.Colorbar instance at 0x107da1128>

In [68]:

fig, axes = plt.subplots(nrows=3, figsize=(6,2))
fig.subplots_adjust(hspace=0.4)
c = np.linspace(0, 1, 50)[None, :]

cmaps = [('Sequential','PuBu'),
         ('Diverging', 'RdBu'),
         ('Qualitative', 'Accent')]

for ax, (cname, cmap) in zip(axes, cmaps):
    ax.imshow(c, interpolation='nearest', aspect='auto', cmap=cmap)
    ax.set_ylabel(cname, rotation=0)
    ax.set_xticks([])
    ax.set_yticks([])

In [49]:

from matplotlib import colors

cmap1 = colors.LinearSegmentedColormap.from_list('smooth', ['r', 'y', 'b'])
cmap2 = colors.LinearSegmentedColormap.from_list('segmented', ['r', 'y', 'b'], N=6)

fig, axes = plt.subplots(nrows=2, figsize=(6,1))
for ax, cmap in zip(axes, [cmap1, cmap2]):
    ax.imshow(c, interpolation='nearest', aspect='auto', cmap=cmap)
    ax.set_xticks([]), ax.set_yticks([])
    ax.set_ylabel(cmap.name, rotation=0)

Discretise color maps

In [50]:

def cmap_discretise(cmap, n_levels):
    original_cmap = plt.get_cmap(cmap)
    colors_i = np.concatenate((np.linspace(0, 1., n_levels), (0.,0.,0.,0.)))
    colors_rgba = original_cmap(colors_i)
    indices = np.linspace(0, 1., n_levels+1)
    cdict = {}
    for ki,key in enumerate(('red','green','blue')):
        cdict[key] = [(indices[i], colors_rgba[i-1,ki], colors_rgba[i,ki]) 
                      for i in xrange(n_levels+1)] 
    discrete_cmap = colors.LinearSegmentedColormap('discrete', cdict, 1024)
    return discrete_cmap

fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10, 5))
cmap = cmap_discretise('PuBu', 4)
img1 = ax1.imshow(yvec.T, interpolation='nearest', cmap='PuBu')
img2 = ax2.imshow(yvec.T, interpolation='nearest',  cmap=cmap)

Figure layout

Subplots

In [51]:

fig, (ax1, ax2) = plt.subplots(ncols=2, sharey=True, figsize=(8,4))

ax1.plot(xvec[:, 1], yvec[:, 1], 'ko')
ax2.plot(xvec[:, 2], yvec[:, 2], 'ko')

Out[51]:

[<matplotlib.lines.Line2D at 0x106756090>]

In [52]:

from matplotlib import gridspec
gs = gridspec.GridSpec(4,4)

for i in range(4):
    for j in range(4):
        if i == j:
            continue
        ax = plt.subplot(gs[i, j])
        ax.plot(yvec[:, i], yvec[:, j], '.')
        plt.setp(ax.get_xticklabels(), visible=False)
        plt.setp(ax.get_yticklabels(), visible=False)

In [53]:

gs = gridspec.GridSpec(2, 2)

gs_topright = gridspec.GridSpecFromSubplotSpec(2, 2, gs[0,0])

for i in range(3):
    plt.subplot(gs[i+1])
for i in range(4):
    plt.subplot(gs_topright[i])

Escaping the flatland

Subplots

In [54]:

for i in range(4):
    plt.subplot(2, 2, i+1)
    plt.plot(xvec[:, i], yvec[:, i], 'o')

In [55]:

plt.scatter(xvec[:, 0], yvec[:, 2], s=yvec[:, 1]*20, c=yvec[:, 0])
plt.gray()

Parallel coordinates

In [56]:

plt.subplots_adjust()

<matplotlib.figure.Figure at 0x1079dc710>

Matplotlib Tutorial

Advanced Python School in Zurich, September 2013

Basic plotting

Anscombe dataset

Linear regression

Multiple datasets

Visual encodings

Length (`plt.bar`)

Position and color (`plt.scatter`)

Position and size (`plt.scatter`)

Length and angle (`plt.polar`)

Creating visual style

Using style arguments

Customising globals

Using object-oriented API

Adding grid

Configuring spines

Annotating plots

Adding legend

Placing text

Arrows

Color

Heatmaps and colorbars

Color scales

Custom color maps

Discretise color maps

Figure layout

Subplots

Gridplot

Complex layouts

Escaping the flatland

Subplots

Multiple encodings

Parallel coordinates

Matplotlib Tutorial

Advanced Python School in Zurich, September 2013

Basic plotting

Anscombe dataset

Linear regression

Multiple datasets

Visual encodings

Length (plt.bar)

Position and color (plt.scatter)

Position and size (plt.scatter)

Length and angle (plt.polar)

Creating visual style

Using style arguments

Customising globals

Using object-oriented API

Adding grid

Configuring spines

Annotating plots

Adding legend

Placing text

Arrows

Color

Heatmaps and colorbars

Color scales

Custom color maps

Discretise color maps

Figure layout

Subplots

Gridplot

Complex layouts

Escaping the flatland

Subplots

Multiple encodings

Parallel coordinates

Length (`plt.bar`)

Position and color (`plt.scatter`)

Position and size (`plt.scatter`)

Length and angle (`plt.polar`)