"""
Demo of the histogram (hist) function with a few features.
In addition to the basic histogram, this demo shows a few optional features:
* Setting the number of data bins
* The ``normed`` flag, which normalizes bin heights so that the integral of
the histogram is 1. The resulting histogram is a probability density.
* Setting the face color of the bars
* Setting the opacity (alpha value).
x : (n,) array or sequence of (n,) arrays
這個參數是指定每個bin(箱子)分布的數據,對應x軸
bins : integer or array_like, optional
這個參數指定bin(箱子)的個數,也就是總共有幾條條狀圖
normed : boolean, optional
If True, the first element of the return tuple will be the counts normalized to form a probability density, i.e.,n/(len(x)`dbin)
這個參數指定密度,也就是每個條狀圖的占比例比,默認為1
color : color or array_like of colors or None, optional
這個指定條狀圖的顏色
我們繪制一個10000個數據的分布條狀圖,共50份,以統計10000分的分布情況
"""
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 = 7
# the histogram of the data
n, bins, patches = plt.hist(x, num_bins, normed=1, facecolor='blue', alpha=0.9)
# 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()
plt.plot(n)
plt.title('n values')
plt.show()
plt.plot(bins)
plt.title('bins values')
plt.show()
