How Python uses matplotlib to draw a stack-histogram tutorial

This article mainly introduces Python based on Matplotlib to draw a stack histogram method, involving Python using matplotlib for graphic drawing of the relevant operation skills, the need for friends can refer to the following

In this paper, we describe the method of Python-based matplotlib to draw a stack histogram. Share to everyone for your reference, as follows:

Usually we do only a set of data histogram statistics, so we just draw a histogram directly can be.

But sometimes we also draw a histogram of multiple sets of data (for example, when I was a freshman to a university city inner ring time distribution), freshman to senior with different colors of the histogram, displayed in a picture, this will be very intuitive.

#!/usr/bin/env python#-*-coding:utf-8-*-#http://www.jb51.net/article/100363.htm# numpy Array intorduction#http:// Matplotlib.org/examples/statistics/histogram_demo_multihist.htmlimport NumPy as Npimport Pylab as Pimport  Matplotlibd1=np.array ([18.46,19.15,18.13, 18.30, 18.07, 18.24, 18.26, 17.14, 18.44, 18.06, 17.44, 16.57, 16.34, 17.21 ]) d1=d1//1+ (D1-D1//1)/0.6d2=np.array ([19.33, 19.06, 18.10, 17.55, 19.55, 19.13, 18.54, 18.30, 18.36, 19.59, 20.01, 1 9.17, 19.30, 18.54, 18.35, 20.04]) d2=d2//1+ (D2-D2//1)/0.6d3=np.array ([20.52, 20.41, 19.20, 19.04, 19.09, 19.01, 17.49, 19 .20.01, 20.11]) d3=d3//1+ (D3-D3//1)/0.6d4=np.array ([22.02, 21.03,21.06, 20.46, 19.46, 20.15, 19.49, 19.43, 19.5 1, 19.39, 19.33, 19.18, 19.13, 19.22, 18.46, 19.07, 18.57, 18.45, 19.17, 18.41, 18.30]) d4=d4//1+ (D4-D4//1)/0.6x= ([ D1,D2,D3,D4]) p.figure () #normed is False is GOODN, bins, patches = p.hist (x, V, [16.5, 22.5],normed=0, histtype= ' Barstacke d ', color=[' blue ', ' green ', ' red', ' yellow '], label=[', ', ', ', ']) print type (x) p.legend () #legend should is signed after set down The Informationp.show ()

For example, it is clear that the blue histogram (freshman) runs fastest and the yellow (senior) histogram runs the slowest.