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import numpy as np
from bqplot import *

Basic Histogram



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np.random.seed(0)
x_data = np.random.randn(100)



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x_sc = LinearScale()
y_sc = LinearScale()

hist = Hist(sample=x_data, scales={'sample': x_sc, 'count': y_sc})
ax_x = Axis(scale=x_sc, tick_format='0.2f')
ax_y = Axis(scale=y_sc, orientation='vertical')

Figure(marks=[hist], axes=[ax_x, ax_y], padding_y=0)



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hist.count



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# Changing the number of bins
hist.bins = 20

Properties of Histogram



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# normalizing the count

x_sc = LinearScale()
y_sc = LinearScale()

hist = Hist(sample=x_data, scales={'sample': x_sc, 'count': y_sc}, normalized=True)
ax_x = Axis(scale=x_sc, tick_format='0.2f')
ax_y = Axis(scale=y_sc, orientation='vertical')

Figure(marks=[hist], axes=[ax_x, ax_y], padding_y=0)



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# changing the color
hist.colors=['orangered']



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# stroke and opacity update
hist.stroke = 'orange'
hist.opacities = [0.5] * hist.bins

Read-only properties of Histogram



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x_sc = LinearScale()
y_sc = LinearScale()

hist = Hist(sample=x_data, scales={'sample': x_sc, 'count': y_sc})
ax_x = Axis(scale=x_sc, tick_format='0.2f')
ax_y = Axis(scale=y_sc, orientation='vertical')

Figure(marks=[hist], axes=[ax_x, ax_y], padding_y=0)



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# count is the number of elements in each interval
hist.count



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# mid points are the mid points of each interval
hist.midpoints



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