Matplotlib can be used to create histograms. A histogram shows the frequency on the vertical axis and the horizontal axis is another dimension. Usually it has bins, where every bin has a minimum and maximum value. Each bin also has a frequency between x and infinite.
Related course
Data Visualization with Python and Matplotlib
Matplotlib histogram example
Below we show the most minimal Matplotlib histogram:
import numpy as np import matplotlib.mlab as mlab import matplotlib.pyplot as plt x = [21,22,23,4,5,6,77,8,9,10,31,32,33,34,35,36,37,18,49,50,100] num_bins = 5 n, bins, patches = plt.hist(x, num_bins, facecolor='blue', alpha=0.5) plt.show() |
Output:
A complete matplotlib python histogram
Many things can be added to a histogram such as a fit line, labels and so on. The code below creates a more advanced histogram.
#!/usr/bin/env python import numpy as np import matplotlib.mlab as mlab import matplotlib.pyplot as plt # example data mu = 100 # mean of distribution sigma = 15 # standard deviation of distribution x = mu + sigma * np.random.randn(10000) num_bins = 20 # the histogram of the data n, bins, patches = plt.hist(x, num_bins, normed=1, facecolor='blue', alpha=0.5) # add a 'best fit' line y = mlab.normpdf(bins, mu, sigma) plt.plot(bins, y, 'r--') plt.xlabel('Smarts') plt.ylabel('Probability') plt.title(r'Histogram of IQ: $\mu=100$, $\sigma=15$') # Tweak spacing to prevent clipping of ylabel plt.subplots_adjust(left=0.15) plt.show() |
Output:
