简介

Kevin2li大约 5 分钟

简介

Matplotlib是Python中最常用的可视化工具之一，可以非常方便地创建海量类型地2D图表和一些基本的3D图表，可根据数据集的特点选择不同的绘图方式。Matplotlib是一个开源的Python 2D绘图库，可以轻松地将数据图形化，并且提供多种格式的输出。

官网：https://matplotlib.org/open in new window
用户手册：https://matplotlib.org/stable/users/indexopen in new window

教程

认识图表

基本配置

import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt

plt.rcParams['font.sans-serif'] = ['SimHei'] # 设置中文字体
plt.rcParams['axes.unicode_minus'] = False  # 修复减号显示异常

plt.style.available # 查看所有style列表
plt.style.use('seaborn-whitegrid') # 设置风格

创建图表

# 创建一行一列的简单图表
fig, ax = plt.subplots()

# or
fig = plt.figure()
ax = fig.add_axes([0, 0, 1, 1])

输出：

# 创建2行2列的图表,并指定图片大小和dpi
fig, axs = plt.subplots(2, 2, layout="constrained", figsize=(12,6), dpi=1000)
axs[0, 0].set_title("Sub1")
axs[0, 1].set_title("Sub2")
axs[1, 0].set_title("Sub3")
axs[1, 1].set_title("Sub4")
fig.suptitle("Big Title")

输出：

标题设置

ax.set_title()

其中，fontdict默认参数为：

{
    'fontsize': rcParams['axes.titlesize'],
    'fontweight': rcParams['axes.titleweight'],
    'color': rcParams['axes.titlecolor'],
    'verticalalignment': 'baseline',
    'horizontalalignment': loc
}

举例：

fig, ax = plt.subplots()
ax.set_title("Demo Figure", loc='center', y=1, pad=10, fontdict={'color':'red', 'fontsize': 20})

输出：

fig.suptitle()

设置整张图片的标题

图例设置

ax.legend()

常用参数：

labels: 图例名称
loc: 图例位置
bbox_to_anchor: 图例位置

nrow：图例占多少行
ncol: 图例占多少列
frameon: 是否显示图例边框

举例：

x = np.linspace(0, 2 * np.pi, 200)
y1 = np.sin(x)
y2 = y1 + 1
fig, ax = plt.subplots()
ax.plot(x, y1)
ax.plot(x, y2)
ax.legend(labels=["y1", "y2"], loc="upper right", ncol=2)
ax.set_title("Legend Demo")
plt.show()

输出：

坐标轴设置

标签设置

ax.set_xlabel

ax.set_ylabel

示例：

np.random.seed(23)
y = np.random.randint(0,20,(10,))
fig, ax = plt.subplots()
ax.plot(y)
ax.set_xlabel("x", loc='center', labelpad=2, fontdict={"color":"blue", 'fontsize': 20})
ax.set_ylabel("y", loc='center',labelpad=5, fontdict={"color":"red", 'fontsize': 20})

输出：

范围设置

ax.set_xlim
ax.set_ylim
ax.axis

刻度设置

ax.set_xticks

ax.set_yticks

ax.tick_params

示例：

fig, ax = plt.subplots()
data1, data2 = np.random.randint(0, 10, (10, 2)).T
ax.plot(data1, label="1")
ax.plot(data2, label="2")
ax.set( title="Demo", xticks=range(10), yticks=range(10),
)
ax.legend(bbox_to_anchor=(1., 0.67))
ax.grid(True)
ax.tick_params(axis='x', direction='in', labelrotation=15, length=6, width=2, colors='black', grid_color='b', grid_alpha=0.2)

输出：

尺度设置

ax.set_yscale

示例：

np.random.seed(19680801)
y = np.random.normal(loc=0.5, scale=0.4, size=1000)
y = y[(y > 0) & (y < 1)]
y.sort()
x = np.arange(len(y))

# plot with various axes scales
fig, axs = plt.subplots(2,2, layout="constrained")

# linear
axs[0,0].plot(x, y)
axs[0,0].set_yscale('linear')
axs[0,0].set_title('linear')
axs[0,0].grid(True)

# log
axs[0,1].plot(x, y)
axs[0,1].set_yscale('log')
axs[0,1].set_title('log')
axs[0,1].grid(True)

# symmetric log
axs[1,0].plot(x, y - y.mean())
axs[1,0].set_yscale('symlog', linthresh=0.01)
axs[1,0].set_title('symlog')
axs[1,0].grid(True)

# logit
axs[1,1].plot(x, y)
axs[1,1].set_yscale('logit')
axs[1,1].set_title('logit')
axs[1,1].grid(True)

输出：

多坐标轴设置

ax.twinx
ax.twiny

举例：

fig, ax = plt.subplots()
data1 = np.random.randn(30)
data2 = 10*np.random.randn(30)
l1, = ax.plot(data1, "r-")

ax2 = ax.twinx() 
l2, = ax2.plot(data2, "g-")
ax2.legend([l1, l2], ["y1(left)", "y2(right)"])

输出：

ax.secondary_xaxis
ax.secondary_yaxis

网格设置

ax.grid

示例：

np.random.seed(23)
y = np.random.randint(0,20,(10,))
fig, ax = plt.subplots()
ax.plot(y)
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_yticks(range(0,20,5))
ax.set_yticks(range(0,20,1),minor=True)
ax.grid(axis='y',which='major')

输出：

ax.tick_params

设置网格线颜色、粗细、样式等
详见："坐标轴设置>刻度设置"一节

外边框设置(spine)

# 隐藏上边框和右边框
ax.spines.right.set_visible(False)
ax.spines.top.set_visible(False)

# 隐藏所有边框
for pos in ['top','right','bottom','left']:
    ax.spines[pos].set_visible(False)

样式设置

color: 颜色
linewidth或lw: 粗细
linestyle或ls: 风格

:::info

参考：https://matplotlib.org/stable/gallery/lines_bars_and_markers/linestyles.htmlopen in new window :::

marker: 标记形状

:::info

参考：https://matplotlib.org/stable/gallery/lines_bars_and_markers/marker_reference.htmlopen in new window :::

文本/注解设置

ax.text

举例：

np.random.seed(23)
fig, ax = plt.subplots()
data = np.random.randint(0, 10, (7,))
ax.plot(data, "b-")
ax.text(4, 5.5, "I'm here!", fontdict={"color": "red"})
ax.text(1.8, 7, r'$\mu=115,\ \sigma=15$', fontdict={"color": "green"}) # 支持LaTex语法

输出：

ax.annotate

参考：https://matplotlib.org/stable/tutorials/text/annotations.htmlopen in new window

举例：

np.random.seed(23)
fig, ax = plt.subplots()
data = np.random.randint(0, 10, (7,))
ax.plot(data, "b-")
ax.annotate("I'm here!", [4,6], [5,5], arrowprops={"arrowstyle":"->","facecolor":'black'})

输出：

:::info{title="箭头样式"} :::

颜色设置

参考：https://matplotlib.org/stable/tutorials/colors/colormaps.htmlopen in new window

CB91_Blue   = '#2CBDFE'
CB91_Green  = '#47DBCD'
CB91_Pink   = '#F3A0F2'
CB91_Purple = '#9D2EC5'
CB91_Violet = '#661D98'
CB91_Amber  = '#F5B14C'
color_list = [CB91_Blue, CB91_Pink, CB91_Green, CB91_Amber, CB91_Purple, CB91_Violet]
# color_list=["#4E79A7","#A0CBE8","#F28E2B","#FFBE7D","#59A14F","#8CD17D","#B6992D","#F1CE63","#499894","#86BCB6","#E15759","#E19D9A"]

plt.rcParams['axes.prop_cycle'] = plt.cycler(color=color_list)

字体设置

msyh = matplotlib.font_manager.FontProperties(fname="C:/Windows/Fonts/msyh.ttc")        # 微软雅黑
simfang = matplotlib.font_manager.FontProperties(fname="C:/Windows/Fonts/simfang.ttf")  # 仿宋
simsun = matplotlib.font_manager.FontProperties(fname="C:/Windows/Fonts/simsun.ttc")    # 宋体
simhei = matplotlib.font_manager.FontProperties(fname="C:/Windows/Fonts/simhei.ttf")    # 黑体
simkai = matplotlib.font_manager.FontProperties(fname="C:/Windows/Fonts/simkai.ttf")    # 楷体

# 在需要设置中文的地方设置fontproperties属性
ax.set_title("This is a Chinese Demo\n这是一个中文示例", fontproperties=msyh)

子图布局设置

plt.GridSpec

fig = plt.figure(constrained_layout=True)

gs = plt.GridSpec(3, 3, figure=fig)
ax1 = fig.add_subplot(gs[0, :])
ax2 = fig.add_subplot(gs[1, :-1])
ax3 = fig.add_subplot(gs[1:, -1])
ax4 = fig.add_subplot(gs[-1, 0])
ax5 = fig.add_subplot(gs[-1, -2])

ax1.set_title("Sub1")
ax2.set_title("Sub2")
ax3.set_title("Sub3")
ax4.set_title("Sub4")
ax5.set_title("Sub5")

fig.suptitle("GridSpec")

输出：

plt.subplot_mosaic

x = [['A panel', 'A panel', 'B panel'],
     ['C panel', 'C panel', 'B panel']]
fig, axs = plt.subplot_mosaic(x, layout="constrained")
axs['A panel'].set_title("A panel")
axs['B panel'].set_title("B panel")
axs['C panel'].set_title("C panel")
fig.suptitle("Big Title")

输出：

主题设置

所有样式预览：https://matplotlib.org/stable/gallery/style_sheets/style_sheets_reference.htmlopen in new window

plt.style.available # 查看所有style列表
plt.style.use('seaborn-whitegrid') # 设置风格

图片保存

# 输出为矢量图，不管放大或缩小，图形皆不会失真
plt.savefig("test.svg", format="svg")

# 输出为常规的png格式
plt.savefig("test.png", format="png")

# 输出为常规的jpg格式
plt.savefig("test.jpg", format="jpg")

# 图例超出图片外使用
fig.savefig("test.png", bbox_inches="tight")

经典图表绘制

条形图

x = ['Mon', 'Tues', 'Wed', 'Thur', 'Fri', 'Sat', 'Sun']
data = np.random.randint(30, 100, (7,))
colors=["#4E79A7","#A0CBE8","#F28E2B","#FFBE7D","#59A14F","#8CD17D","#B6992D","#F1CE63","#499894","#86BCB6","#E15759","#E19D9A"]
fig, ax = plt.subplots(layout="constrained")
handle = ax.bar(x, data, color=colors)
ax.bar_label(handle, data, padding=2)
ax.set_title("Test Bar Graph")

输出：

折线图

t = np.arange(0.0, 2.0, 0.05)
s = 1 + np.sin(2 * np.pi * t)

fig, ax = plt.subplots()
ax.plot(t, s, color="blue", ls=(0,(5,10)), lw=1, marker='^')
ax.set(xlabel='time (s)', ylabel='voltage (mV)', title='About as simple as it gets, folks')
ax.grid()

输出：

饼图

labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'
sizes = [15, 30, 45, 10]
explode = (0, 0.1, 0, 0)  # only "explode" the 2nd slice (i.e. 'Hogs')

fig1, ax = plt.subplots()
ax.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%', shadow=True, startangle=90)
ax.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.

输出：

散点图

ax.scatter

举例：

np.random.seed(23)
x = np.random.rand(20)
y = np.random.rand(20)
z = np.random.randint(50, 400, (20,))
fig, ax = plt.subplots()
ax.scatter(x,y,s=z,c=z, marker='o')

输出：

简介

# 简介

# 教程

# 认识图表

# 基本配置

# 创建图表

# 标题设置

# 图例设置

# 坐标轴设置

# 标签设置

# 范围设置

# 刻度设置

# 尺度设置

# 多坐标轴设置

# 网格设置

# 外边框设置(spine)

# 样式设置

# 文本/注解设置

# 颜色设置

# 字体设置

# 子图布局设置

# 主题设置

# 图片保存

# 经典图表绘制

# 条形图

# 折线图

# 饼图

# 散点图

# 参考

简介

教程