Matplotlib pyplot hist

plt.hist

plt.hist

• Returns:

  • n : array or list of arrays

    The values of the histogram bins. See normed or density and weights for a description of the possible semantics. If input x is an array, then this is an array of length nbins. If input is a sequence arrays [data1, data2,..], then this is a list of arrays with the values of the histograms for each of the arrays in the same order.

  • bins : array

    The edges of the bins. Length nbins + 1 (nbins left edges and right edge of last bin). Always a single array even when multiple data sets are passed in.

  • patches : list or list of lists

    Silent list of individual patches used to create the histogram or list of such list if multiple input datasets.

import numpy as np
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt

mu, sigma = 100, 15
x = mu + sigma*np.random.randn(10000)

# the histogram of the data
n, bins, patches = plt.hist(x, 50, normed=1, facecolor='green', alpha=0.75)

# add a 'best fit' line
y = mlab.normpdf( bins, mu, sigma)
l = plt.plot(bins, y, 'r--', linewidth=1)

plt.xlabel('Smarts')
plt.ylabel('Probability')
plt.title(r'$\mathrm{Histogram\ of\ IQ:}\ \mu=100,\ \sigma=15$')
plt.axis([40, 160, 0, 0.03])
plt.grid(True)

plt.show()

• plt.hist(x,3)

  n, bins, patches = plt.hist(x, 3, normed=1, facecolor='green', alpha=0.75)
  

  

print("n {} bins {} patches {}".format(n,bins,patches))
>> n [0.00206872 0.02048887 0.00254824] bins [ 39.56574291  79.39713333 119.22852376 159.05991418] patches <a list of 3 Patch objects>