Docs url : https://matplotlib.org/3.1.1/contents.html

matplotlib.pyplot.hist(x, bins=None, range=None, density=False, weights=None, 
                       cumulative=False, bottom=None, histtype='bar', align='mid', 
                       orientation='vertical', rwidth=None, log=False, color=None, 
                       label=None, stacked=False, data=None, **kwargs)

Parameters:

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

Input values, this takes either a single array or a sequence of arrays which are not required to be of the same length.

bins int or sequence or str, default: rcParams["hist.bins"] (default: 10)

If bins is an integer, it defines the number of equal-width bins in the range.

If bins is a sequence, it defines the bin edges, including the left edge of the first bin and the right edge of the last bin; in this case, bins may be unequally spaced. All but the last (righthand-most) bin is half-open. In other words, if bins is:

[1, 2, 3, 4]

then the first bin is [1, 2) (including 1, but excluding 2) and the second [2, 3). The last bin, however, is [3, 4], which includes 4.

If bins is a string, it is one of the binning strategies supported by numpy.histogram_bin_edges: 'auto', 'fd', 'doane', 'scott', 'stone', 'rice', 'sturges', or 'sqrt'.

range tuple or None, default: None

The lower and upper range of the bins. Lower and upper outliers are ignored. If not provided, range is (x.min(), x.max()). Range has no effect if bins is a sequence.

If bins is a sequence or range is specified, autoscaling is based on the specified bin range instead of the range of x.

density bool, default: False(old attribute: normed)

If True, draw and return a probability density: each bin will display the bin's raw count divided by the total number of counts and the bin width (density = counts / (sum(counts) * np.diff(bins))), so that the area under the histogram integrates to 1 (np.sum(density * np.diff(bins)) == 1).

If stacked is also True, the sum of the histograms is normalized to 1.

weights (n,) array-like or None, default: None

An array of weights, of the same shape as x. Each value in x only contributes its associated weight towards the bin count (instead of 1). If density is True, the weights are normalized, so that the integral of the density over the range remains 1.

This parameter can be used to draw a histogram of data that has already been binned, e.g. using numpy.histogram (by treating each bin as a single point with a weight equal to its count)

counts, bins = np.histogram(data)
plt.hist(bins[:-1], bins, weights=counts)

(or you may alternatively use bar()).

cumulative bool or -1, default: False

If True, then a histogram is computed where each bin gives the counts in that bin plus all bins for smaller values. The last bin gives the total number of datapoints.

If density is also True then the histogram is normalized such that the last bin equals 1.

If cumulative is a number less than 0 (e.g., -1), the direction of accumulation is reversed. In this case, if density is also True, then the histogram is normalized such that the first bin equals 1.

**bottom ** array-like, scalar, or None, default: None

Location of the bottom of each bin, ie. bins are drawn from bottom to bottom + hist(x, bins) If a scalar, the bottom of each bin is shifted by the same amount. If an array, each bin is shifted independently and the length of bottom must match the number of bins. If None, defaults to 0.

**histtype ** {'bar', 'barstacked', 'step', 'stepfilled'}, default: 'bar'

The type of histogram to draw.

• 'bar' is a traditional bar-type histogram. If multiple data are given the bars are arranged side by side.

• 'barstacked' is a bar-type histogram where multiple data are stacked on top of each other.

• 'step' generates a lineplot that is by default unfilled.

• 'stepfilled' generates a lineplot that is by default filled.

  align {'left', 'mid', 'right'}, default: 'mid'

The horizontal alignment of the histogram bars.

• 'left': bars are centered on the left bin edges.

• 'mid': bars are centered between the bin edges.

• 'right': bars are centered on the right bin edges.

  orientation {'vertical', 'horizontal'}, default: 'vertical'

If 'horizontal', barh will be used for bar-type histograms and the bottom kwarg will be the left edges.

rwidth float or None, default: None

The relative width of the bars as a fraction of the bin width. If None, automatically compute the width.

Ignored if histtype is 'step' or 'stepfilled'.

**log ** bool, default: False

If True, the histogram axis will be set to a log scale.

color color or array-like of colors or None, default: None

Color or sequence of colors, one per dataset. Default (None) uses the standard line color sequence.

label str or None, default: None

String, or sequence of strings to match multiple datasets. Bar charts yield multiple patches per dataset, but only the first gets the label, so that legend will work as expected.

**stacked ** bool, default: False

If True, multiple data are stacked on top of each other If False multiple data are arranged side by side if histtype is 'bar' or on top of each other if histtype is 'step'

(1)annotate语法说明 ：

annotate(s='str' ,xy=(x,y) ,xytext=(l1,l2) ,..)

s 为注释文本内容

xy 为被注释的坐标点

xytext 为注释文字的坐标位置

xycoords 参数如下:

figure points points from the lower left of the figure 点在图左下方

figure pixels pixels from the lower left of the figure 图左下角的像素

figure fraction fraction of figure from lower left 左下角数字部分

axes points points from lower left corner of axes 从左下角点的坐标

axes pixels pixels from lower left corner of axes 从左下角的像素坐标

axes fraction fraction of axes from lower left 左下角部分

data

use the coordinate system of the object being annotated(default) 使用的坐标系统被注释的对象（默认）

polar(theta,r) if not native ‘data’ coordinates t

extcoords 设置注释文字偏移量

arrowprops #箭头参数,参数类型为字典dict

width the width of the arrow in points 点箭头的宽度

headwidth the width of the base of the arrow head in points 在点的箭头底座的宽度

headlength the length of the arrow head in points 点箭头的长度

shrink fraction of total length to ‘shrink’ from both ends 总长度为分数“缩水”从两端

facecolor 箭头颜色

bbox给标题增加外框 ，常用参数如下：

boxstyle方框外形

facecolor(简写fc)背景颜色

edgecolor(简写ec)边框线条颜色

edgewidth边框线条大小

语法说明

text(x,y,string,fontsize=15,verticalalignment="top",horizontalalignment="right")

x,y : 表示坐标值上的值

string : 表示说明文字

fontsize : 表示字体大小

verticalalignment ：垂直对齐方式 ，参数：[ ‘center’ | ‘top’ | ‘bottom’ | ‘baseline’ ]

horizontalalignment ：水平对齐方式 ，参数：[ ‘center’ | ‘right’ | ‘left’ ]

xycoords选择指定的坐标轴系统:

figure points points from the lower left of the figure 点在图左下方

figure pixels pixels from the lower left of the figure 图左下角的像素

figure fraction fraction of figure from lower left 左下角数字部分

axes points points from lower left corner of axes 从左下角点的坐标

axes pixels pixels from lower left corner of axes 从左下角的像素坐标

axes fraction fraction of axes from lower left 左下角部分

data use the coordinate system of the object being annotated(default) 使用的坐标系统被注释的对象（默认）

polar(theta,r) if not native ‘data’ coordinates t

arrowprops #箭头参数,参数类型为字典dict

width the width of the arrow in points 点箭头的宽度

headwidth the width of the base of the arrow head in points 在点的箭头底座的宽度

headlength the length of the arrow head in points 点箭头的长度

shrink fraction of total length to ‘shrink’ from both ends 总长度为分数“缩水”从两端

facecolor 箭头颜色

bbox给标题增加外框 ，常用参数如下：

boxstyle方框外形

facecolor(简写fc)背景颜色

edgecolor(简写ec)边框线条颜色

edgewidth边框线条大小

1.matplotlib默认字体不支持中文显示，图表中的中文会出现乱码

2.动态配置matplotlib是一个比较麻烦的做法，这里不再提，网上有很多教程

比如可以这样

import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif']=['SimHei']

3.一劳永逸的做法是修改matplotlib的配置文件matplotlibrc

想查看当前工作的matplotlibrc文件是哪个，你可以使用下面的方式查看：

# python环境下
>>> import matplotlib
>>> matplotlib.matplotlib_fname()

使用sublime打开该配置文件，查找到

# font.sans-serif     :  DejaVu Sans, Bitstream Vera Sans, ,,,

配置文件中语句基本上全部被注释掉了，要启动配置需要去掉前面的#；这里是默认的字体列表，当然，不支持中文

所以需要写上支持中文的字体，但不能随便添加，有个前提：必须是matplotlib字体库中存在的或本机安装的字体

matplotlib的字体库路径如下： D:\Anaconda\Lib\site-packages\matplotlib\mpl-data\fonts\ttf

我的是基于Anaconda的，只要你找到了matplotlibrc，在它的同级目录下就是fonts文件夹

进入ttf文件夹后可以看到所有的字体文件均为.ttf格式，这意味着本机安装的字体只有是.ttf格式的才能使用，像win10里面的.ttc格式的字体是无法使用的

我从win7上考了个msyh.ttf即微软雅黑，放到该目录下，并右键安装

然后找到C:\Users\admin\.matplotlib文件夹下的fontList.*文件，只要前缀一致都删掉，缓存文件，删了没什么影响(如果怕，剪切一下也行)，然后运行含有matplotlib的程序，等待新生成的缓存文件

缓存文件出来后，打开查看要使用的字体信息

 {
   "fname": "D:\\Anaconda\\lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\MSYH.ttf",
   "name": "Microsoft YaHei",
   "style": "normal",
   "variant": "normal",
   "weight": 400,
   "stretch": "normal",
   "size": "scalable",
   "_class": "FontEntry"
 },

这里的msyh.ttf对应的name为Microsoft YaHei，这就是我们要在matplotlibrc文件中要写入的字体名，修改font.sans-serif如下即可：

font.sans-serif     : Microsoft YaHei, DejaVu Sans, Bitstream Vera Sans, Computer Modern Sans Serif, Lucida Grande, Verdana, Geneva, Lucid, Arial, Helvetica, Avant Garde, sans-serif

重启程序或者使用jupyter时restart the kernel，再运行即可

ax = plt.gca()
ax.spines["top"].set_color
ax.spines["bottom"].set_position((”data", 0))
ax.spines["right"].set_visiable

plt.style.available	# 查看全部可用的样式
plt.style.library	# 查看当前样式属性	
plt.style.use(style)# 使用样式

mpl.rc_file_defaults()	# 还原样式至本次使用前
mpl.rcdefaults()	# 还原样式至初始配置