

In [1]:

    
import numpy as np
import bqplot.pyplot as plt

Basic Histogram



In [2]:

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



In [3]:

    
fig = plt.figure(padding_y=0)
hist = plt.hist(x_data)
fig



In [4]:

    
hist.count









    Out[4]:





array([], dtype=float64)



In [5]:

    
# Changing the number of bins
hist.bins = 20

Properties of Histogram



In [6]:

    
# normalizing the count
fig = plt.figure(padding_y=0)
hist = plt.hist(x_data, normalized=True)
fig



In [7]:

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



In [8]:

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



In [9]:

    
fig = plt.figure(padding_y=0)
hist = plt.hist(x_data, normalized=True)
fig



In [10]:

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









    Out[10]:





array([], dtype=float64)



In [11]:

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









    Out[11]:





[]