关于python中plt.hist参数的使用详解

如下所示:

matplotlib.pyplot.hist(

x, bins=10, range=None, normed=False,

weights=None, cumulative=False, bottom=None,

histtype=u'bar', align=u'mid', orientation=u'vertical',

rwidth=None, log=False, color=None, label=None, stacked=False,

hold=None, **kwargs)

x : (n,) array or sequence of (n,) arrays

这个参数是指定每个bin(箱子)分布的数据,对应x轴

bins : integer or array_like, optional

这个参数指定bin(箱子)的个数,也就是总共有几条条状图

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)

这个参数指定密度,也就是每个条状图的占比例比,默认为1

color : color or array_like of colors or None, optional

这个指定条状图的颜色

我们绘制一个10000个数据的分布条状图,共50份,以统计10000分的分布情况

"""

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).

"""

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 = 50

# 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()

以上这篇关于python中plt.hist参数的使用详解就是小编分享给大家的全部内容了,希望能给大家一个参考,也希望大家多多支持。

以上是 关于python中plt.hist参数的使用详解 的全部内容, 来源链接: utcz.com/z/323483.html

回到顶部