A common use for notebooks is data visualization using charts. Colaboratory makes this easy with several charting tools available as Python imports.
Matplotlib is the most common charting package, see its documentation for details, and its examples for inspiration.
import matplotlib.pyplot as plt
plt.figure(figsize=(4, 2))
x = [1, 2, 3, 4, 5, 6, 7, 8, 9]
y1 = [1, 3, 5, 3, 1, 3, 5, 3, 1]
y2 = [2, 4, 6, 4, 2, 4, 6, 4, 2]
plt.plot(x, y1, label="line L")
plt.plot(x, y2, label="line H")
plt.plot()
plt.xlabel("x axis")
plt.ylabel("y axis")
plt.title("Line Graph Example")
plt.legend()
plt.show()
import matplotlib.pyplot as plt
plt.figure(figsize=(4, 2))
# Look at index 4 and 6, which demonstrate overlapping cases.
x1 = [1, 3, 4, 5, 6, 7, 9]
y1 = [4, 7, 2, 4, 7, 8, 3]
x2 = [2, 4, 6, 8, 10]
y2 = [5, 6, 2, 6, 2]
# Colors: https://matplotlib.org/api/colors_api.html
plt.bar(x1, y1, label="Blue Bar", color='b')
plt.bar(x2, y2, label="Green Bar", color='g')
plt.plot()
plt.xlabel("bar number")
plt.ylabel("bar height")
plt.title("Bar Chart Example")
plt.legend()
plt.show()
import matplotlib.pyplot as plt
import numpy as np
plt.figure(figsize=(4, 2))
# Use numpy to generate a bunch of random data in a bell curve around 5.
n = 5 + np.random.randn(1000)
m = [m for m in range(len(n))]
plt.bar(m, n)
plt.title("Raw Data")
plt.show()
plt.hist(n, bins=20)
plt.title("Histogram")
plt.show()
plt.hist(n, cumulative=True, bins=20)
plt.title("Cumulative Histogram")
plt.show()
import matplotlib.pyplot as plt
x1 = [2, 3, 4]
y1 = [5, 5, 5]
x2 = [1, 2, 3, 4, 5]
y2 = [2, 3, 2, 3, 4]
y3 = [6, 8, 7, 8, 7]
# Markers: https://matplotlib.org/api/markers_api.html
plt.scatter(x1, y1)
plt.scatter(x2, y2, marker='v', color='r')
plt.scatter(x2, y3, marker='^', color='m')
plt.title('Scatter Plot Example')
plt.show()
import matplotlib.pyplot as plt
idxes = [ 1, 2, 3, 4, 5, 6, 7, 8, 9]
arr1 = [23, 40, 28, 43, 8, 44, 43, 18, 17]
arr2 = [17, 30, 22, 14, 17, 17, 29, 22, 30]
arr3 = [15, 31, 18, 22, 18, 19, 13, 32, 39]
# Adding legend for stack plots is tricky.
plt.plot([], [], color='r', label = 'D 1')
plt.plot([], [], color='g', label = 'D 2')
plt.plot([], [], color='b', label = 'D 3')
plt.stackplot(idxes, arr1, arr2, arr3, colors= ['r', 'g', 'b'])
plt.title('Stack Plot Example')
plt.legend()
plt.show()
import matplotlib.pyplot as plt
labels = 'S1', 'S2', 'S3'
sections = [56, 66, 24]
colors = ['c', 'g', 'y']
plt.pie(sections, labels=labels, colors=colors,
startangle=90,
explode = (0, 0.1, 0),
autopct = '%1.2f%%')
plt.axis('equal') # Try commenting this out.
plt.title('Pie Chart Example')
plt.show()
import matplotlib.pyplot as plt
import numpy as np
ys = 200 + np.random.randn(100)
x = [x for x in range(len(ys))]
plt.plot(x, ys, '-')
plt.fill_between(x, ys, 195, where=(ys > 195), facecolor='g', alpha=0.6)
plt.title("Fills and Alpha Example")
plt.show()
import matplotlib.pyplot as plt
import numpy as np
def random_plots():
xs = []
ys = []
for i in range(20):
x = i
y = np.random.randint(10)
xs.append(x)
ys.append(y)
return xs, ys
fig = plt.figure()
ax1 = plt.subplot2grid((5, 2), (0, 0), rowspan=1, colspan=2)
ax2 = plt.subplot2grid((5, 2), (1, 0), rowspan=3, colspan=2)
ax3 = plt.subplot2grid((5, 2), (4, 0), rowspan=1, colspan=1)
ax4 = plt.subplot2grid((5, 2), (4, 1), rowspan=1, colspan=1)
x, y = random_plots()
ax1.plot(x, y)
x, y = random_plots()
ax2.plot(x, y)
x, y = random_plots()
ax3.plot(x, y)
x, y = random_plots()
ax4.plot(x, y)
plt.tight_layout()
plt.show()
Colaboratory charts use Seaborn’s custom styling by default. To customize styling further please see the matplotlib docs.
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import axes3d
fig = plt.figure()
ax = fig.add_subplot(111, projection = '3d')
x1 = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
y1 = np.random.randint(10, size=10)
z1 = np.random.randint(10, size=10)
x2 = [-1, -2, -3, -4, -5, -6, -7, -8, -9, -10]
y2 = np.random.randint(-10, 0, size=10)
z2 = np.random.randint(10, size=10)
ax.scatter(x1, y1, z1, c='b', marker='o', label='blue')
ax.scatter(x2, y2, z2, c='g', marker='D', label='green')
ax.set_xlabel('x axis')
ax.set_ylabel('y axis')
ax.set_zlabel('z axis')
plt.title("3D Scatter Plot Example")
plt.legend()
plt.tight_layout()
plt.show()
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
ax = fig.add_subplot(111, projection = '3d')
x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
y = np.random.randint(10, size=10)
z = np.zeros(10)
dx = np.ones(10)
dy = np.ones(10)
dz = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
ax.bar3d(x, y, z, dx, dy, dz, color='g')
ax.set_xlabel('x axis')
ax.set_ylabel('y axis')
ax.set_zlabel('z axis')
plt.title("3D Bar Chart Example")
plt.tight_layout()
plt.show()
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111, projection = '3d')
x, y, z = axes3d.get_test_data()
ax.plot_wireframe(x, y, z, rstride = 2, cstride = 2)
plt.title("Wireframe Plot Example")
plt.tight_layout()
plt.show()
There are several libraries layered on top of Matplotlib that you can use in Colab. One that is worth highlighting is Seaborn:
That’s a simple scatterplot with a nice regression line fit to it, all with just one call to Seaborn’s regplot.
Here’s a Seaborn heatmap:
import matplotlib.pyplot as plt
import numpy as np
# Make a 10 x 10 heatmap of some random data
side_length = 10
# Start with a 10 x 10 matrix with values randomized around 5
data = 5 + np.random.randn(side_length, side_length)
# The next two lines make the values larger as we get closer to (9, 9)
data += np.arange(side_length)
data += np.reshape(np.arange(side_length), (side_length, 1))
# Generate the heatmap
sns.heatmap(data)
plt.show()
Altair is a declarative visualization library for creating interactive visualizations in Python, and is installed and enabled in Colab by default.
For example, here is an interactive scatter plot:
!pip install vega_datasetsCollecting vega_datasets
Using cached vega_datasets-0.9.0-py3-none-any.whl.metadata (5.5 kB)
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Using cached vega_datasets-0.9.0-py3-none-any.whl (210 kB)
Installing collected packages: vega_datasets
Successfully installed vega_datasets-0.9.0import altair as alt
from vega_datasets import data
cars = data.cars()
alt.Chart(cars).mark_point().encode(
x='Horsepower',
y='Miles_per_Gallon',
color='Origin',
).interactive()For more examples of Altair plots, see the Altair snippets notebook or the external Altair Example Gallery.
import numpy as np
from bokeh.plotting import figure, show
from bokeh.io import output_notebook
# Call once to configure Bokeh to display plots inline in the notebook.
output_notebook()N = 4000
x = np.random.random(size=N) * 100
y = np.random.random(size=N) * 100
radii = np.random.random(size=N) * 1.5
colors = ["#%02x%02x%02x" % (r, g, 150) for r, g in zip(np.floor(50+2*x).astype(int), np.floor(30+2*y).astype(int))]
p = figure()
p.circle(x, y, radius=radii, fill_color=colors, fill_alpha=0.6, line_color=None)
show(p)