Python系列 之 matplotlib库 基础知识

Python系列 之 matplotlib库 基础知识学习

• Pyplot
• • 简单示例
  • 中文显示问题
  • • 注册全局字体
    • font_manager.FontProperties注册字体
• Figure
• • Figure的组成部分
  • 创建Figure对象
• Axes
• • 简单的例子
  • 设置样式
  • • linewidths
    • linestyles
    • markersizes
  • Axes对象的一些方法
  • • Axes.axis方法
    • Axes.cla和Axes.clear方法
    • Axes.set_axis_off和Axes.set_axis_on方法
    • Axis labels, title, 和 legend的 *** 作
    • Axes.set_facecolor和Axes.get_facecolor方法
    • Axes.grid方法
    • Axes.invert_xaxis和Axes.invert_yaxis方法
    • Ticks and tick labels
    • 绘图示例

matplotlib属于第三方库需要另外进行安装才可以使用

pip install -i https://pypi.tuna.tsinghua.edu.cn/simple matplotlib

Matplotlib将数据绘制在Figure对象上,Figure对象应包含一个或多个Axes对象,Axes对象包含一组X-Y坐标或者更多维度坐标的区域
pyplot是matplotlib的绘图接口；对Figure对象进行管理

官方教程

# import
import matplotlib.pyplot as plt

Pyplot

matplotlib.pyplot是使 matplotlib 可以像MATLAB一样工作的函数集合；每个pyplot函数都会对图形进行一些更改：例如，创建图形、在图形中创建绘图区域、在绘图区域中绘制一些线、用标签装饰绘图等；
pyplot的API调用通常不如面向对象的 API 灵活;pyplot大多数的函数调用也可以用Axes对象的方法调用实现

简单示例

import matplotlib.pyplot as plt
import numpy as np
x = np.arange(0, 5, 0.1)
y = np.sin(x)
plt.plot(x, y)
plt.show()

中文显示问题 注册全局字体

import matplotlib.pyplot as plt
# 注册全局字体
plt.rcParams['font.sans-serif'] = ['simsun']
# plt.rcParams['font.family'] = ['simsun']

font_manager.FontProperties注册字体

# 注册字体
from matplotlib.font_manager import FontProperties
font_simsun = FontProperties(fname="./fonts/simsun.ttc")
# plt.xlabel("X 轴", fontproperties=font_simsun)

Figure

Figure就像一张空白的纸张可以在上画上图表的内容

Figure的组成部分

创建Figure对象

fig = plt.figure() # 创建一个空白的Figure对象 不包含Axes对象
fig, ax = plt.subplots() # 创建包含一个Axes对象的Figure对象
fig, ax = plt.subplots(2, 3) # 创建包含一组Axes对象的Figure对象 一组 2*3 的Axes对象 2行3列
plt.show()

Axes

Axes对象的属性和方法定义了大多部分的绘制方法以及绘制样式
Axes对象基于在Figure对象上的绘制区域，通常包括两个（在3D情况下为三个）Axis对象

简单的例子

Axes对象的plot方法

import matplotlib.pyplot as plt
import numpy as np
fit, ax = plt.subplots()
# ax.plot方法绘制
# 将y与x绘制为线/标记
# Axes.plot(*args, scalex=True, scaley=True, data=None, **kwargs)
# plot([x], y, [fmt], *, data=None, **kwargs)
# plot([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs)
# 参数fmt是定义颜色、标记和线型等基本格式的便捷方法
# fmt = '[marker][line][color]'
# 可以将Line2D特性用作关键字参数
# Line2D属性URL：https://matplotlib.org/stable/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D
x = np.arange(10)
# 红色圆形标记虚线连接
ax.plot(x, 'ro--',label='Data_1')
ax.plot(x,x**2,label='Data_2', color='red',marker='o',linestyle='dashed')
# 设置X-Y坐标轴标签
ax.set_xlabel("X Label")
ax.set_ylabel("Y Label")
# 设置title
ax.set_title("Title")
# 显示legend 图例
ax.legend()
plt.show()

设置样式

• Colors
• Linewidths
• linestyles
• markersizes

linewidths

# Linewidths
fig, ax = plt.subplots()
x = np.arange(0,10,0.1)
for width in range(1,5):
    ax.plot(x,x+width,linewidth=width)
    ax.text(x=10,y=10+width,s=f"width is {width}")
plt.show()

linestyles

linestyle	description
‘-’ or ‘solid’	solid line
‘–’ or ‘dashed’	dashed line
‘-.’ or ‘dashdot’	dash-dotted line
‘:’ or ‘dotted’	dotted line
‘none’, ‘None’, ’ ', or ‘’	draw nothing

#linestyles
linestyle = {'-':'solid', '--' : 'dashed','-.' : 'dashdot',
             ':' : 'dotted','none':"None"}

fig, ax = plt.subplots()
x = np.arange(0,10,2)
y = np.ones(5)
dy = 0.0
for style, val in linestyle.items():
    ax.plot(x,y+dy,linestyle=val)
    ax.annotate(val,(8.0,1+dy))
    dy+=5
    
plt.show()

markersizes

marker	description
‘.’	point
‘,’	pixel
‘o’	circle
‘v’	triangle_down
‘^’	triangle_up
‘<’	triangle_left
‘>’	triangle_right
‘1’	tri_down
‘2’	tri_up
‘3’	tri_left
‘4’	tri_right
‘8’	octagon
‘s’	square
‘p’	pentagon
‘*’	star
‘h’	hexagon1
‘H’	hexagon2
‘+’	plus
‘x’	x
‘D’	diamond
d	thin_diamond
‘|’	vline
‘_’	hline
‘P’	plus_filled
‘X’	x_filled
0	tickleft
1	tickright
2	tickup
3	tickdown
4	caretleft
5	caretright
6	caretup
7	caretdown
8	caretleftbase
9	caretrightbase
10	caretupbase
11	caretdownbase
‘None’	nothing
None	nothing
’ ’	nothing
‘’	nothing

Line2D.markers中存放了所有marker的取值

from matplotlib.lines import Line2D
print(Line2D.markers)
# {'.': 'point', ',': 'pixel', 'o': 'circle', 'v': 'triangle_down', 
#  '^': 'triangle_up', '<': 'triangle_left', '>': 'triangle_right',
#  '1': 'tri_down', '2': 'tri_up', '3': 'tri_left', '4': 'tri_right',
#  '8': 'octagon', 's': 'square', 'p': 'pentagon', '*': 'star',
#  'h': 'hexagon1', 'H': 'hexagon2', '+': 'plus', 'x': 'x', 'D': 'diamond',
#  'd': 'thin_diamond', '|': 'vline', '_': 'hline', 'P': 'plus_filled',
# 'X': 'x_filled', 0: 'tickleft', 1: 'tickright', 2: 'tickup', 3: 'tickdown',
#  4: 'caretleft', 5: 'caretright', 6: 'caretup', 7: 'caretdown',
#  8: 'caretleftbase', 9: 'caretrightbase', 10: 'caretupbase',
#  11: 'caretdownbase', 'None': 'nothing', None: 'nothing', ' ': 'nothing', '': 'nothing'}

markers = Line2D.markers
fig, ax = plt.subplots(1,2,figsize=(20,20))
x = np.arange(0,10,2)
y = np.ones(5)
dy = 0.0
for marker in list(markers.keys())[:10]:
    ax[0].plot(x,y+dy,marker=marker,markersize=10,linestyle=":",color="red")
    ax[0].text(4,5+dy,f"{repr(marker)}={markers[marker]}",fontsize=15)
    #隐去坐标轴 axis 
    ax[0].set_axis_off()    
    dy+=10
for marker in list(markers.keys())[10:20]:
    ax[1].plot(x,y+dy,marker=marker,markersize=10,linestyle=":",color="blue")
    ax[1].text(4,5+dy,f"{repr(marker)}={markers[marker]}",fontsize=15)
    #隐去坐标轴 axis 
    ax[1].set_axis_off()    
    dy+=10
    
plt.show()

Axes对象的一些方法 Axes.axis方法

获取或设置一些轴属性

参数	说明
[xmin, xmax, ymin, ymax]	设置X-Y轴的最小最大刻度
True	显示X-Y轴以及X-Ylabel
False	与True相反
‘on’	等于True
‘off’	等于False
‘equal’	Set equal scaling (i.e., make circles circular) by changing axis limits. This is the same as ax.set_aspect(‘equal’, adjustable=‘datalim’). Explicit data limits may not be respected in this case.
‘scaled’	Set equal scaling (i.e., make circles circular) by changing dimensions of the plot box. This is the same as ax.set_aspect('equal', adjustable='box', anchor='C').Additionally, further autoscaling will be disabled.
‘tight’	Set limits just large enough to show all data, thendisable further autoscaling.
‘auto’	Automatic scaling (fill plot box with data).
‘image’	‘scaled’ with axis limits equal to data limits.
‘square’	Square plot; similar to ‘scaled’, but initially forcing xmax-xmin == ymax-ymin.

import matplotlib.pyplot as plt
# axis方法 快速获取或设置一些轴的属性
# axis(*args, emit=True, **kwargs)
# Call signatures:
# xmin, xmax, ymin, ymax = axis()
# xmin, xmax, ymin, ymax = axis([xmin, xmax, ymin, ymax])
# xmin, xmax, ymin, ymax = axis(option)
# xmin, xmax, ymin, ymax = axis(**kwargs)
# Parameters：
# [xmin, xmax, ymin, ymax] 设置X-Y坐标的最小最大刻度
# bool or Str
# bool 显示或隐藏X-Y轴以及X-Ylabe
# Str in ['on','off','equal', 'scaled', 'tight','auto','image','square']
# Returns  xmin, xmax, ymin, ymax  : float
fig, ax = plt.subplots()
ax.plot([0,1,2,3,4,5,6],[2,1,2,3,4,5,8])
# ax.axis([0,5,0,5])
print(ax.axis('equal'))
ax.grid()
plt.show()
# print(help(ax.axis))

Axes.cla和Axes.clear方法

清除 Axes区域

import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.plot([10,11,10,11,12,13])
# ax.cla()
ax.clear()
plt.show()

Axes.set_axis_off和Axes.set_axis_on方法

隐藏或显示axis信息
包括 axis lines,ticks,ticklabels, grid and axis labels.

import matplotlib.pyplot as plt
fig, (ax0,ax1) = plt.subplots(ncols=2)
ax0.plot([10,11,10,11,12,13],label="data-1")
ax0.set_xlabel("X Label")
ax0.grid()
ax0.set_axis_off()
ax0.legend()
ax1.plot([10,11,10,11,12,13],label="data-1")
ax1.set_xlabel("X Label")
ax1.set_axis_on()
plt.show()

Axis labels, title, 和 legend的 *** 作

设置axis的 labels, title, 和 legend

方法	说明
Axes.set_xlabel	Set the label for the x-axis.
Axes.get_xlabel	Get the xlabel text string.
Axes.set_ylabel	Set the label for the y-axis.
Axes.get_ylabel	Get the ylabel text string.
Axes.set_title	Set a title for the Axes.
Axes.get_title	Get an Axes title.
Axes.legend	Place a legend on the Axes.
Axes.get_legend	Return the Legend instance, or None if no legend is defined.
Axes.get_legend_handles_labels	Return handles and labels for legend

import matplotlib.pyplot as plt
fig, ax = plt.subplots()
# Axes.set_xlabel(xlabel, fontdict=None, labelpad=None, *, loc=None, **kwargs)
# Parameters:
# xlabel : str 
# loc : {'left', 'center', 'right'}
# **kwargs : Text properties
fontdict = {"":"",}
ax.set_xlabel(xlabel="X Label",loc="right")
print("get_xlabel:", ax.get_xlabel())
ax.set_ylabel("Y Label")
print("get_ylabel:", ax.get_ylabel())
# Axes.set_title(label, fontdict=None, loc=None, pad=None, *, y=None, **kwargs)
ax.set_title("this is title")
print("get_title:",ax.get_title())
ax.plot([0,10,20,30],label="data")

# Axes.legend(*args, **kwargs)
# legend(handles, labels)
# legend(handles=handles)
# legend(labels)
ax.legend()
# get_legend_handles_labels 返回 handles 和 labels
handle,labels = ax.get_legend_handles_labels()
print(handle,labels)
plt.show()

Axes.set_facecolor和Axes.get_facecolor方法

import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.set_facecolor('gray')
ax.grid()
print(ax.get_facecolor())
plt.show()

Axes.grid方法

设置Axes的网格线条样式

# Axes.grid(visible=None, which='major', axis='both', **kwargs)
# Parameters：
# visible : bool or None
# which : {'major', 'minor', 'both'}
# axis : {'both', 'x', 'y'}
# **kwargs : Line2D properties
import matplotlib.pyplot as plt
x = [1,2,3,4,5,6]
fig, (ax0,ax1,ax2) = plt.subplots(ncols=3)
ax0.plot(x)
ax0.grid(False)
ax1.plot(x)
ax1.grid(which='major')
ax2.plot(x)
# 网格 红色 虚线 线宽 2
ax2.grid(**{'color':'r','ls':':','lw':2})
plt.show()

Axes.invert_xaxis和Axes.invert_yaxis方法

X-Y轴反转 *** 作

# Axes.invert_xaxis 反转x轴
# Axes.invert_yaxis 反转y轴
import matplotlib.pyplot as plt
fig, (ax,ax0,ax1) = plt.subplots(ncols=3)
ax.plot([0,1,2,3,4,5])
ax.set_title("Normal")
ax0.plot([0,1,2,3,4,5])
ax0.invert_xaxis()
ax0.set_title('Axes.invert_xaxis')
ax1.plot([0,1,2,3,4,5])
ax1.invert_yaxis()
ax1.set_title('Axes.invert_yaxis')
plt.show()

Ticks and tick labels

import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.plot([10,20,30,40,50],[10,20,30,40,50])
# Axes.set_xticks(ticks, labels=None, *, minor=False, **kwargs)
ax.set_xticks(ticks=range(10,100,50),labels=["A","B"])
# Axes.get_xticks
# Axes.set_xticklabels(labels, *, fontdict=None, minor=False, **kwargs)
ax.set_xticklabels(["C","D"])
# Axes.get_xticklabels
print(ax.get_xticklabels())
# Axes.set_yticks
# Axes.get_yticks
# Axes.set_yticklabels
# Axes.get_yticklabels
plt.show()

绘图示例

折线图：

import matplotlib.pyplot as plt
import numpy as np
data = {"male":{"2012":69660,"2013":70063,"2014":70522,"2015":70857,"2016":71307,"2017":71650,"2018":71864,"2019":72039,"2020":72357,"2021":72311},
        "female":{"2012":66262,"2013":66663,"2014":67124,"2015":67469,"2016":67925,"2017":68361,"2018":68677,"2019":68969,"2020":68855,"2021":68949}}
male = data["male"]
female = data["female"]
malex = list(male.keys())
maley = list(male.values())

fig, ax = plt.subplots()
ax.plot(maley,"ro:",label="male")

femalex = list(female.keys())
femaley = list(female.values())
ax.plot(femaley,"go--",label="female")

ax.set_xticks(np.arange(len(femalex)),malex)
ax.set_title("男女人数统计",size=20)
ax.set_xlabel("年份")
ax.set_ylabel("数量(万)")

ax.grid()
ax.legend()
plt.show()

柱形图：

import matplotlib.pyplot as plt
import numpy as np
data = {"male":{"2012":69660,"2013":70063,"2014":70522,"2015":70857,"2016":71307,"2017":71650,"2018":71864,"2019":72039,"2020":72357,"2021":72311},
        "female":{"2012":66262,"2013":66663,"2014":67124,"2015":67469,"2016":67925,"2017":68361,"2018":68677,"2019":68969,"2020":68855,"2021":68949}}
male = data["male"]
female = data["female"]
malex = list(male.keys())
maley = np.array(list(male.values()))

fig, (ax,ax0)= plt.subplots(figsize=(10,12),nrows=2)
width = 0.38
rects1 = ax.bar(np.arange(len(malex))-width/2,maley,width,label="male")

femalex = list(female.keys())
femaley = np.array(list(female.values()))
rects2 = ax.bar(np.arange(len(femaley))+width/2, femaley,width,label="female")

ax.bar_label(rects1,padding=3)
ax.bar_label(rects2, padding=3)
ax.set_xticks(np.arange(len(malex)),malex)
ax.set_title("男女人数统计",size=20)
ax.set_ylabel("数量(万)",size=18)
ax.set_yscale("log")
ax.grid()
ax.legend()


diff = maley-femaley
rect_plot = ax0.plot(np.arange(len(diff)),diff,'ro-.', label=" ")

rect_bar = ax0.bar(np.arange(len(diff)),diff, label=" ")
ax0.set_title("男女人数差值",size=20)
ax0.set_xlabel("年份",size=18)
ax0.set_ylabel("数量(万)",size=18)

ax0.bar_label(rect_bar,padding=3)
ax0.set_xticks(np.arange(len(diff)),malex)
ax0.set_yscale("log")
ax0.legend()
plt.subplots_adjust(hspace=0.2)

plt.show()

以上就是对 matplotlib库 基础知识的学习，
后续有时间再进行详细整理其他知识

参考：
matplotlib API