Subplots¶
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%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
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plt.figure()
# subplot with 1 row, 2 columns, and current axis is 1st subplot axes
plt.subplot(1, 2, 1)
linear_data = np.array([1,2,3,4,5,6,7,8])
plt.plot(linear_data, '-o')
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plt.figure()
# subplot with 1 row, 2 columns, and current axis is 1st subplot axes
plt.subplot(1, 2, 1)
linear_data = np.array([1,2,3,4,5,6,7,8])
plt.plot(linear_data, '-o')
exponential_data = linear_data**2
# subplot with 1 row, 2 columns, and current axis is 2nd subplot axes
plt.subplot(1, 2, 2)
plt.plot(exponential_data, '-o')
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plt.figure()
ax1 = plt.subplot(1, 2, 1)
plt.plot(linear_data, '-o')
# pass sharey=ax1 to ensure the two subplots share the same y axis
ax2 = plt.subplot(1, 2, 2, sharey=ax1)
plt.plot(exponential_data, '-x')
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# create a 3x3 grid of subplots
fig, ((ax1,ax2,ax3), (ax4,ax5,ax6), (ax7,ax8,ax9)) = plt.subplots(3, 3, sharex=True, sharey=True)
# plot the linear_data on the 5th subplot axes
ax5.plot(linear_data, '-')
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Histogram¶
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# create 2x2 grid of axis subplots
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, sharex=True)
axs = [ax1,ax2,ax3,ax4]
# draw n = 10, 100, 1000, and 10000 samples from the normal distribution and plot corresponding histograms
for n in range(0,len(axs)):
sample_size = 10**(n+1)
sample = np.random.normal(loc=0.0, scale=1.0, size=sample_size)
axs[n].hist(sample)
axs[n].set_title('n={}'.format(sample_size))
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# repeat with number of bins set to 100
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, sharex=True)
axs = [ax1,ax2,ax3,ax4]
for n in range(0,len(axs)):
sample_size = 10**(n+1)
sample = np.random.normal(loc=0.0, scale=1.0, size=sample_size)
axs[n].hist(sample, bins=100)
axs[n].set_title('n={}'.format(sample_size))
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plt.figure()
Y = np.random.normal(loc=0.0, scale=1.0, size=10000)
X = np.random.random(size=10000)
plt.scatter(X,Y)
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# use gridspec to partition the figure into subplots
import matplotlib.gridspec as gridspec
plt.figure()
gspec = gridspec.GridSpec(3, 3)
top_histogram = plt.subplot(gspec[0, 1:])
side_histogram = plt.subplot(gspec[1:, 0])
lower_right = plt.subplot(gspec[1:, 1:])
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# use gridspec to partition the figure into subplots
import matplotlib.gridspec as gridspec
plt.figure()
gspec = gridspec.GridSpec(3, 3)
top_histogram = plt.subplot(gspec[0, 1:])
side_histogram = plt.subplot(gspec[1:, 0])
lower_right = plt.subplot(gspec[1:, 1:])
Y = np.random.normal(loc=0.0, scale=1.0, size=10000)
X = np.random.random(size=10000)
lower_right.scatter(X, Y)
top_histogram.hist(X, bins=100)
s = side_histogram.hist(Y, bins=100, orientation='horizontal')
# clear the histograms and plot normed histograms
top_histogram.clear()
top_histogram.hist(X, bins=100, normed=True)
side_histogram.clear()
side_histogram.hist(Y, bins=100, orientation='horizontal', normed=True)
# flip the side histogram's x axis
side_histogram.invert_xaxis()
# change axes limits
for ax in [top_histogram, lower_right]:
ax.set_xlim(0, 1)
for ax in [side_histogram, lower_right]:
ax.set_ylim(-5, 5)
plt.tight_layout()
Box plots¶
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import pandas as pd
normal_sample = np.random.normal(loc=0.0, scale=1.0, size=10000)
random_sample = np.random.random(size=10000)
gamma_sample = np.random.gamma(2, size=10000)
df = pd.DataFrame({'normal': normal_sample,
'random': random_sample,
'gamma': gamma_sample})
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df.describe()
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df.plot()
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df.plot(kind = "box")
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_ = plt.boxplot([ df['normal'], df['random'], df['gamma'] ], whis='range')
Heatmaps¶
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plt.figure()
Y = np.random.normal(loc=0.0, scale=1.0, size=10000)
X = np.random.random(size=10000)
_ = plt.hist2d(X, Y, bins=25)
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plt.figure()
_ = plt.hist2d(X, Y, bins=100)
# add a colorbar legend
plt.colorbar()
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Animations¶
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%matplotlib notebook
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import matplotlib.animation as animation
n = 100
x = np.random.randn(n)
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# create the function that will do the plotting, where curr is the current frame
def update(curr):
# check if animation is at the last frame, and if so, stop the animation a
if curr == n:
a.event_source.stop()
plt.cla()
bins = np.arange(-4, 4, 0.5)
plt.hist(x[:curr], bins=bins)
plt.axis([-4,4,0,30])
plt.gca().set_title('Sampling the Normal Distribution')
plt.gca().set_ylabel('Frequency')
plt.gca().set_xlabel('Value')
plt.annotate('n = {}'.format(curr), [3,27])
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fig = plt.figure()
a = animation.FuncAnimation(fig, update, interval=100)
Interactivity¶
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plt.figure()
data = np.random.rand(10)
plt.plot(data)
def onclick(event):
plt.cla()
plt.plot(data)
plt.gca().set_title('Event at pixels {},{} \nand data {},{}'.format(event.x, event.y, event.xdata, event.ydata))
# tell mpl_connect we want to pass a 'button_press_event' into onclick when the event is detected
plt.gcf().canvas.mpl_connect('button_press_event', onclick)
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from random import shuffle
origins = ['China', 'Brazil', 'India', 'USA', 'Canada', 'UK', 'Germany', 'Iraq', 'Chile', 'Mexico']
shuffle(origins)
df = pd.DataFrame({'height': np.random.rand(10),
'weight': np.random.rand(10),
'origin': origins})
df
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plt.figure()
# picker=5 means the mouse doesn't have to click directly on an event, but can be up to 5 pixels away
plt.scatter(df['height'], df['weight'], picker=5)
plt.gca().set_ylabel('Weight')
plt.gca().set_xlabel('Height')
def onpick(event):
origin = df.iloc[event.ind[0]]['origin']
plt.gca().set_title('Selected item came from {}'.format(origin))
# tell mpl_connect we want to pass a 'pick_event' into onpick when the event is detected
plt.gcf().canvas.mpl_connect('pick_event', onpick)
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