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matplotlib Part 20 – 2D Histograms

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2D Histograms

In the previous article we were talking about histograms. Today we’ll be talking about two-dimensional histograms, which we create using the hist2d method. Let’s define some input values and then create a 2D histogram:

In [1]:
%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np

# random data for x and y
x = np.random.randn(1000)
y = np.random.randn(1000)

# Let's plot the 2D histogram.
fig, ax = plt.subplots()
ax.hist2d(x, y)
Out[1]:
(array([[ 0.,  0.,  0.,  0.,  1.,  1.,  0.,  0.,  0.,  0.],
        [ 0.,  0.,  1.,  0.,  3.,  2.,  3.,  0.,  0.,  0.],
        [ 0.,  0.,  1.,  1., 11., 13.,  7.,  5.,  6.,  1.],
        [ 0.,  0.,  3., 11., 19., 33., 43., 28.,  7.,  2.],
        [ 0.,  0.,  7., 21., 31., 59., 69., 35., 13.,  1.],
        [ 0.,  0.,  7., 23., 41., 68., 60., 41., 15.,  6.],
        [ 2.,  1.,  3., 12., 32., 39., 42., 42., 13.,  2.],
        [ 0.,  0.,  0.,  4., 14., 22., 24.,  7.,  1.,  1.],
        [ 0.,  0.,  0.,  1.,  7.,  9.,  7.,  3.,  1.,  1.],
        [ 0.,  0.,  0.,  1.,  0.,  2.,  3.,  3.,  1.,  1.]]),
 array([-3.55448897, -2.8764872 , -2.19848542, -1.52048365, -0.84248187,
        -0.1644801 ,  0.51352168,  1.19152346,  1.86952523,  2.54752701,
         3.22552878]),
 array([-4.00630263, -3.33061748, -2.65493233, -1.97924719, -1.30356204,
        -0.62787689,  0.04780826,  0.7234934 ,  1.39917855,  2.0748637 ,
         2.75054885]),
 <matplotlib.collections.QuadMesh at 0x1d9165638c8>)

Just like one-dimensional histograms, the default number of bins is 10. It’s 10 for each dimension. Let’s change it to be 40 bins for each dimension:

In [2]:
fig, ax = plt.subplots()
ax.hist2d(x, y, bins=40)
Out[2]:
(array([[0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        ...,
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.]]),
 array([-3.55448897, -3.38498853, -3.21548809, -3.04598764, -2.8764872 ,
        -2.70698675, -2.53748631, -2.36798587, -2.19848542, -2.02898498,
        -1.85948453, -1.68998409, -1.52048365, -1.3509832 , -1.18148276,
        -1.01198232, -0.84248187, -0.67298143, -0.50348098, -0.33398054,
        -0.1644801 ,  0.00502035,  0.17452079,  0.34402124,  0.51352168,
         0.68302212,  0.85252257,  1.02202301,  1.19152346,  1.3610239 ,
         1.53052434,  1.70002479,  1.86952523,  2.03902567,  2.20852612,
         2.37802656,  2.54752701,  2.71702745,  2.88652789,  3.05602834,
         3.22552878]),
 array([-4.00630263, -3.83738134, -3.66846006, -3.49953877, -3.33061748,
        -3.16169619, -2.99277491, -2.82385362, -2.65493233, -2.48601105,
        -2.31708976, -2.14816847, -1.97924719, -1.8103259 , -1.64140461,
        -1.47248333, -1.30356204, -1.13464075, -0.96571946, -0.79679818,
        -0.62787689, -0.4589556 , -0.29003432, -0.12111303,  0.04780826,
         0.21672954,  0.38565083,  0.55457212,  0.7234934 ,  0.89241469,
         1.06133598,  1.23025727,  1.39917855,  1.56809984,  1.73702113,
         1.90594241,  2.0748637 ,  2.24378499,  2.41270627,  2.58162756,
         2.75054885]),
 <matplotlib.collections.QuadMesh at 0x1d913d7c348>)

You can also specify the number of bins individually for each dimension. Let’s make it 30 and 10 respectively:

In [3]:
fig, ax = plt.subplots()
ax.hist2d(x, y, bins=[30, 10])
Out[3]:
(array([[ 0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.],
        [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
        [ 0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.],
        [ 0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.],
        [ 0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.],
        [ 0.,  0.,  1.,  0.,  1.,  2.,  3.,  0.,  0.,  0.],
        [ 0.,  0.,  0.,  0.,  1.,  1.,  3.,  2.,  1.,  1.],
        [ 0.,  0.,  1.,  1.,  4.,  5.,  3.,  1.,  1.,  0.],
        [ 0.,  0.,  0.,  0.,  6.,  7.,  1.,  2.,  4.,  0.],
        [ 0.,  0.,  2.,  1.,  2.,  7., 10.,  7.,  1.,  0.],
        [ 0.,  0.,  0.,  3.,  5., 11., 11.,  7.,  3.,  0.],
        [ 0.,  0.,  1.,  7., 12., 15., 22., 14.,  3.,  2.],
        [ 0.,  0.,  2.,  7.,  7., 13., 22., 10.,  7.,  1.],
        [ 0.,  0.,  1.,  8., 11., 20., 25., 11.,  4.,  0.],
        [ 0.,  0.,  4.,  6., 13., 26., 22., 14.,  2.,  0.],
        [ 0.,  0.,  2.,  6., 16., 28., 18., 14.,  5.,  1.],
        [ 0.,  0.,  2., 10., 12., 22., 23., 17.,  6.,  3.],
        [ 0.,  0.,  3.,  7., 13., 18., 19., 10.,  4.,  2.],
        [ 1.,  0.,  1.,  7., 16., 14., 18., 16.,  4.,  1.],
        [ 1.,  1.,  1.,  2.,  7., 12., 19., 10.,  2.,  1.],
        [ 0.,  0.,  1.,  3.,  9., 13.,  5., 16.,  7.,  0.],
        [ 0.,  0.,  0.,  1.,  7.,  8., 11.,  4.,  1.,  0.],
        [ 0.,  0.,  0.,  3.,  3.,  5.,  8.,  2.,  0.,  0.],
        [ 0.,  0.,  0.,  0.,  4.,  9.,  5.,  1.,  0.,  1.],
        [ 0.,  0.,  0.,  1.,  6.,  4.,  3.,  2.,  1.,  1.],
        [ 0.,  0.,  0.,  0.,  0.,  2.,  2.,  1.,  0.,  0.],
        [ 0.,  0.,  0.,  0.,  1.,  3.,  2.,  0.,  0.,  0.],
        [ 0.,  0.,  0.,  1.,  0.,  0.,  1.,  1.,  0.,  0.],
        [ 0.,  0.,  0.,  0.,  0.,  1.,  1.,  1.,  1.,  0.],
        [ 0.,  0.,  0.,  0.,  0.,  1.,  1.,  1.,  0.,  1.]]),
 array([-3.55448897, -3.32848838, -3.10248779, -2.8764872 , -2.65048661,
        -2.42448601, -2.19848542, -1.97248483, -1.74648424, -1.52048365,
        -1.29448305, -1.06848246, -0.84248187, -0.61648128, -0.39048069,
        -0.1644801 ,  0.0615205 ,  0.28752109,  0.51352168,  0.73952227,
         0.96552286,  1.19152346,  1.41752405,  1.64352464,  1.86952523,
         2.09552582,  2.32152641,  2.54752701,  2.7735276 ,  2.99952819,
         3.22552878]),
 array([-4.00630263, -3.33061748, -2.65493233, -1.97924719, -1.30356204,
        -0.62787689,  0.04780826,  0.7234934 ,  1.39917855,  2.0748637 ,
         2.75054885]),
 <matplotlib.collections.QuadMesh at 0x1d91715de48>)

There are some more options you can tweak, but we’re not going to cover them in this series.

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