matplotlib笔记速查表-(基本图例,绘画属性,常用视图)

已于 2025-03-02 20:54:24 修改
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文章标签： matplotlib 笔记 数据分析 数据可视化 python pandas 阿里云
于 2025-03-02 19:17:13 首次发布
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基本图形绘制

import numpy as np
import pandas as pd

from matplotlib import pyplot as plt

x = np.linspace(-5,5,51)
y = x**2

# 样式：'-', '--', '-.', ':', '.', ',', , o, ^, v, <, >, s, +, x, D, d, 1, 2, 3, 4, h, H, p, |, _
# 颜色：b(蓝色)，g(绿色)，r(红色)，c(青色)，m(品红)，y(黄色)，k(黑色)，w(白色)

fig = plt.figure(figsize=(5,3),facecolor='white')
plt.plot(x,y,c='r',ls='--')
plt.plot(x,y,'b--')
plt.grid()
fig.savefig(fname='matplotlib/img/1.png')

绘图属性设置

Pyplot函数APi方法描述
text()mpl.axes.Axes.text()在Axes对象的任意位置添加文字
xlabel()mpl.axes.Axes.set_xlabel()为X轴添加标签
ylabel()mpl.axes.Axes.set_ylabel()为Y轴添加标签
title()mpl.axes.Axes.set_title()为Axes对象添加标题
legend()mpl.axes.Axes.legend()为Axes对象添加图例
annnotate()mpl.axes.Axes.annotate()为Axes对象添加注释(箭头可选)
suptitle()mpl.figure.Figure.suptitle()为Figure对象添加中心化的标题

subplot多图布局

import numpy as np
import pandas as pd

from matplotlib import pyplot as plt

fig = plt.figure(figsize=(6,4))
x = np.linspace(-np.pi,np.pi,30)
y = np.sin(x)
plt.subplot(221)
plt.plot(x,y,c='red',linestyle='--')
plt.subplot(222)
plt.plot(x,y,c='pink',linestyle=':')
plt.subplot(223)
plt.plot(x,y,c='green',linestyle='dashed')
plt.subplot(224)
plt.plot(x,y,c='yellow',linestyle='-.')

fig.savefig('matplotlib/img/2.png')

图例

loc, ncol

import numpy as np

from matplotlib import pyplot as plt

x = np.linspace(0,2**np.pi)

ax = plt.subplot()
ax.plot(x,np.sin(x),label='sin')
ax.plot(x,np.cos(x),label='cos')

# help(ax.legend)
ax.legend(
    fontsize=18,
    loc='center',
    ncol=2,
    bbox_to_anchor=[0,1,1,0.2]
)

plt.savefig('matplotlib/img/3.png')

线条属性

短名全名功能示例值
ccolor线条/标记颜色‘red’, ‘#FF0000’, ‘g’
markermarker数据点标记样式‘o’, ‘s’, ‘^’, ‘x’
lslinestyle线条样式‘-’, ‘–’, ‘:’, ‘None’
lwlinewidth线条宽度2.0, 3.5
labellabel图例标签‘Data Series 1’
mfcmarkerfacecolor标记内部填充颜色‘blue’, ‘none’
msmarkersize标记大小8, 10.5
mecmarkeredgecolor标记边缘颜色‘black’, ‘#333333’
mewmarkeredgewidth标记边缘宽度1.0, 2.5
alphaalpha透明度（0: 透明, 1: 不透明）0.5
  • 线条属性
    • 线条：color、linestyle、linewidth、alpha
    • 标记点：marker、markerfacecolor、markersize、markeredgecolor、markeredgewidth
    • 图例：label
import numpy as np

from matplotlib import pyplot as plt

plt.figure(figsize=(6,4))
ax = plt.subplot()

x = np.linspace(0,2*np.pi,100)
line = plt.Line2D(
	x,
    np.sin(x),
    color='pink',
    marker='o',
    linestyle='--',
    linewidth=2,
    label='sin',
    markerfacecolor='pink',
    markersize=2,
    markeredgecolor='black',
    markeredgewidth=2,
    alpha=0.5,
    )
ax.add_line(line)
ax.legend(
    loc='upper left'
)
plt.savefig('matplotlib/img/4.png')

坐标轴刻度

plt.xticks

  • ticks=np.arange(0,11,1)：刻度值列表
  • labels=[‘min’,‘0’,‘max’]：数值标签
import numpy as np

from matplotlib import pyplot as plt

plt.figure(figsize=(6,4))
ax = plt.subplot()

x = np.linspace(0,10)
y = np.sin(x)

line = plt.Line2D(
    x,
    y,
    color='red'
)
ax.add_line(line)
plt.xticks(
    np.arange(0,11,1),
    fontsize=20,
    color='red'
)
plt.yticks(
    [-1,0,1],
    labels=['min','0','max'],
    fontsize=20,
    color='blue',
    ha='right',
    # from matplotlib.text import Text 可以通过Text看到提示
)

plt.savefig('matplotlib/img/5.png')

坐标轴范围和配置

  • xlim
  • ylim
  • axis
plt.figure(figsize=(5,3))

x = np.linspace(0,2*np.pi)
y = np.sin(x)
plt.plot(x,y,c='r')

#设置坐标轴范围:axis([xmin, xmax, ymin, ymax])
# plt.axis([-2,8,-2,2])

#option
#off: 不显示坐标轴
#equal: 让x轴和y轴刻度距离相等
#*scaled*: 自动缩放坐标轴和图片匹配
#tight: 紧凑型自动适配图片
#square: 让画布呈现正方形,x轴和y轴宽高一致
plt.axis('square')
plt.show()

标题和网格

  • title
  • grid
import numpy as np

from matplotlib import pyplot as plt

ax = plt.subplot()
x = np.linspace(0,10)
y = np.sin(x)
plt.plot(x,y)

plt.title(
    'title',
    loc='center',
    fontsize=20,
)
plt.suptitle(
    'subtitle',
    y=1.1,
    fontsize=26,
)
ax.grid(ls='--',lw=0.5,color='grey',axis='x')
ax.axis('equal')
plt.savefig('matplotlib/img/6.png',bbox_inches='tight')

标签

  • xlabel
import numpy as np

from matplotlib import pyplot as plt
# from matplotlib.text import Text  # 方便看到提示

plt.figure(figsize=(8,4))

ax = plt.subplot()
x = np.linspace(0,10)
y = np.sin(x)
plt.plot(x,y)
# Text(rotation=45,horizontalalignment='right')
plt.xlabel('y=sin(x)',fontsize=20,rotation=45,color='red')
plt.ylabel('y=sin(x)',fontsize=20,color='green',horizontalalignment='right')
plt.title('sin')
plt.axis('equal')

plt.savefig('matplotlib/img/7.png', bbox_inches='tight')

文字

  • x：x坐标
  • y：y坐标
  • s：文字
import numpy as np

from matplotlib import pyplot as plt
from matplotlib.text import Text

ax = plt.subplot()
x = np.linspace(1,10,10)
y = np.array([60,30,20,90,40,60,50,80,70,30])
plt.plot(x,y,ls='--',marker='o')

Text(color='r')

for a,b in zip(x,y):
    ax.text(
        a-0.2,
        b,
        b,
        fontsize=12,
        color='r',
        ha='center',
        va='center'
)

plt.savefig(
    'matplotlib/img/8.png',
    bbox_inches='tight'
)

常用视图

  • bar柱状图：width，bottom
  • barh条形图
  • hist直方图：bins, density
  • boxplot箱型图：notch, sym
  • scatter散点图
  • hexbin六边形图
  • pie饼图：autopct=‘%.1f%%’,pctdistance,labeldistance,textprops,explode
  • stackplot面积图
  • imshow热力图：cmap
  • 极坐标图、雷达图：polar=True

柱形图

  • width
  • bottom
import numpy as np

from matplotlib import pyplot as plt

plt.figure(figsize=(5,3))
x, y = np.arange(2014,2021),np.array([1962035,2838693,2317447,2335002,2438570,1675591,3568120])
ax = plt.subplot()
ax.bar(
    x,
    y
)
plt.title('sale')
plt.xlabel('year')
plt.ylabel('salery',rotation=0,horizontalalignment='right')

for a,b in zip(x,y):
    ax.text(
        a,
        b + 0.2,
        '{:.2f}M'.format(b/1000000),
        ha='center'
    )

plt.savefig(
    'matplotlib/img/9.png',
    bbox_inches='tight'
)

簇型柱状图

import numpy as np

from matplotlib import pyplot as plt

ax = plt.subplot()
x, y1, y2, y3 = np.arange(2014,2021),np.array([634704,1218844,1013322,1068521,419352,1190076,695421]),np.array([534917,746554,904058,12269,526985,117510,1433961]),np.array([792414,873295,400067,1254212,1492233,368005,138738])
i = 0.2
ax.bar(x-i,y1,label='y1',width=i)
ax.bar(x,y2,label='y2',width=i)
ax.bar(x+i,y3,label='y3',width=i)

ax.legend(loc='upper right')

plt.savefig('matplotlib/img/10.png', bbox_inches='tight')

堆叠柱状图

import numpy as np
from matplotlib import pyplot as plt

plt.figure(figsize=(5,3))
x, y1, y2, y3 = np.arange(2014,2021),np.array([634704,1218844,1013322,1068521,419352,1190076,695421]),np.array([534917,746554,904058,12269,526985,117510,1433961]),np.array([792414,873295,400067,1254212,1492233,368005,138738])
plt.bar(x,y1,label='y1')
plt.bar(x,y2,bottom=y1,label='y2')
plt.bar(x,y3,bottom=y1+y2,label='y3')

plt.title('bar')
plt.legend()

plt.savefig('matplotlib/img/11.png',bbox_inches='tight')

直方图

  • density：归一化
  • bins：组
import numpy as np
import pandas as pd

from matplotlib import pyplot as plt

x = np.random.randint(0,10,100)

#统计每个数出现的次数
print(pd.Series(x).value_counts().sort_index())

#直方图
plt.xticks(range(10))
# bins: 组数
# plt.hist(x,bins=5)
plt.hist(x,bins=[0,3,6,9,10],facecolor='blue',alpha=0.4,edgecolor='k',density=True)
plt.savefig('matplotlib/img/12.png', bbox_inches='tight')

箱型图

  • data：一维或二维列表
  • labels：字符串或者列表，也可以通过xticks设置
data = np.random.normal(size=(500,4))
labels = ['A','B','C','D']
plt.boxplot(data,
            labels=labels,
            notch=True,# 箱型图样式
            sym='r*' # 颜色+marker
           )
plt.show()

散点图

x = range(1,7,1)
y = range(10,70,10)
plt.scatter(x,y)

饼图

  • autopct='%.1f%%'：显示百分比(1位)
  • pctdistance：百分比文字的位置
  • labels：标签
  • testprops：传入字典，文字样式
  • explode：传入列表，分裂效果
  • shadow：是否有阴影
from matplotlib import pyplot as plt

plt.figure(figsize=(10,10))
x = [12932,9622,9505,7898,7675,7315,5164,5132,4885,4468]
citys = ['Guangdong', 'Shandong', 'Hubei', 'Jiangsu', 'Zhejiang', 'Hebei', 'Guangxi', 'Shanghai', 'Beijing', 'Sichuan']
plt.pie(x,
        autopct='%.1f%%', # 显示百分比
        pctdistance=0.8, # 百分比文字的位置
        labels=citys, # 标签
        labeldistance=1, # 标签的位置
        #shadow=True,
        textprops={'fontsize':14,'color':'blue'}, # 文字样式
        explode=[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1] #分裂效果
    )
plt.legend()
plt.savefig('matplotlib/img/13.png', bbox_inches='tight')

面积图

from matplotlib import pyplot as plt

x = [2014,2015,2016,2017,2018,2019,2020]
y = [1962035,2838693,2317447,2335002,2438570,1675591,3568120]
plt.figure(figsize=(5,3))
plt.stackplot(x,y)
plt.plot(x,y)
plt.savefig('matplotlib/img/14.png', bbox_inches='tight')

热力图

  • data：二维数组
  • cmap：颜色映射
import pandas as pd

from matplotlib import pyplot as plt

df = pd.DataFrame({
    'Province': ['Guangdong', 'Guangxi', 'Hunan', 'Hubei', 'Jiangxi', 'Sichuan', 'Fujian', 'Jiangsu', 'Henan', 'Hebei', 'Shandong', 'Shanxi'],
    'A': [9275, 9849, 5413, 7425, 1159, 8869, 4189, 5818, 9914, 1374, 7908, 8703],
    'B': [8498, 8413, 7855, 9862, 1334, 8405, 2610, 7852, 8509, 7141, 4466, 2270],
    'C': [5019, 9222, 2973, 3341, 1927, 8244, 2293, 3670, 3010, 9549, 3310, 5979],
    'D': [4468, 6162, 1597, 7923, 9884, 5410, 8297, 8593, 8881, 9895, 7333, 9614],
    'E': [3637, 3401, 1078, 4348, 8205, 8609, 2708, 1827, 1950, 7170, 7452, 9307],
    'F': [5997, 4045, 4316, 3878, 5933, 3332, 2305, 5824, 7763, 3065, 4683, 2951],
    'G': [8307, 1983, 6213, 3065, 6321, 8103, 9252, 4199, 2435, 7211, 2780, 6253]
})

y = df.Province
data = df.drop(columns='Province').values
x = df.drop(columns='Province').columns
plt.figure(figsize=(8,6))

# 热力图
plt.imshow(data,cmap='Blues') # print(plt.colormaps())

# 修改刻度
plt.xticks(range(len(x)),x)
plt.yticks(range(len(y)),y)

# 添加文字
for i in range(len(x)):
    for j in range(len(y)):
        plt.text(
            x=i,
            y=j,
            s=data[j,i],
            ha='center',
            va='center',
            fontsize=10
        )
#颜色条
plt.colorbar()
	
plt.savefig('matplotlib/img/15.png', bbox_inches='tight')

极坐标图

  • 设置projection或者polar好像都是一样的效果
import numpy as np

from matplotlib import pyplot as plt

N = 8
x = np.linspace(0,2*np.pi,N,endpoint=False)
height = np.random.randint(3,15,size=N)

width = 2*np.pi / N
color = np.random.rand(8,3) # 随机颜色
#画图
# polar:极坐标
axes = plt.subplot(111,projection='polar')
axes.bar(x=x,height=height,width=width,bottom=0,color=color)
plt.savefig('matplotlib/img/16.png', bbox_inches='tight')

雷达图

plt.figure(figsize=(5,8))

x = np.linspace(0,2*np.pi,6,endpoint=False)
y = [80,60,90,70,70,100]

# 首尾相连
x = np.concatenate((x,[x[0]]))
y = np.concatenate((y,[y[0]]))
axes = plt.subplot(111,polar=True)
axes.plot(x,y,'o-',lw=2) # 连线
axes.fill(x,y,alpha=0.3) # 填充

#显示刻度
axes.set_rgrids([20,40,60,80,100],fontsize=12)
plt.show()
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