1 Matplotlib简介
- 自从numpy和pandas数据分析的广泛应用，人们发现虽然可以对大量的数据进行快速方便的，各种各样的操作，但是对结果查看不够直观，因此，Matplotlib应运而生，在数据分析与机器学习中，我们经常要用到大量的可视化操作。一张制作精美的数据图片，可以展示大量的信息，一图顶千言。而在可视化中，Matplotlib算得上是最常用的工具。Matplotlib 是 python 最著名的绘图库，它提供了一整套 API，十分适合绘制图表，或修改图表的一些属性，如字体、标签、范围等。
- numpy，pandas，Matplotlib被成为数据分析不可或缺的的三剑客。
- Matplotlib 是一个 Python 的 2D 绘图库，它交互式环境生成 出版质量级别的图形。通过 Matplotlib这个标准类库，开发者 只需要几行代码就可以实现生成绘图，折线图、散点图、柱状图、饼图、直方图、组合图等数据分析可视化图表。
- 安装方式：
安装了python之后，使用bash命令即可（建议使用anaconda，方便不同python环境的切换）：
pip install matplotlib -i https://pypi.tuna.tsinghua.edu.cn/simple
2 基础知识
2.1 图形绘制
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0,2*np.pi)
y = np.sin(x)
plt.figure(figsize=(9,6))
plt.plot(x,y)
plt.grid(linestyle = "--",
color = 'green',
alpha = 0.75)
plt.axis([-1,10,-1.5,1.5])
plt.xlim([-1,10])
plt.ylim([-1.5,1.5])
2.2 坐标轴刻度、标签、标题
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0,2*np.pi)
y = np.sin(x)
plt.plot(x,y)
plt.xticks(np.arange(0,7,np.pi/2))
plt.yticks([-1,0,1])
_ = plt.yticks(ticks = [-1,0,1],labels=['min',' 0 ','max'],fontsize = 20,ha = 'right')
font= {'family':'serif','style':'italic','weight':'normal','color':'red','size':16}
_ = plt.xticks(ticks = np.arange(0,7,np.pi/2),
labels = ['0',r'$\frac{\pi}{2}$',r'$\pi$',r'$\frac{3\pi} {2}$',r'$2\pi$'],
fontsize = 20,
fontweight = 'normal',
color = 'red')
plt.ylabel('y = sin(x)',
rotation = 0,
horizontalalignment = 'right',
fontstyle = 'normal',
fontsize = 20)
from matplotlib.font_manager import FontManager
fm = FontManager()
mat_fonts = set(f.name for f in fm.ttflist)
plt.rcParams['font.sans-serif'] = 'STZhongsong'
plt.title('正弦波')
2.3 图例
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0,2*np.pi)
y = np.sin(x)
plt.figure(figsize=(9,6))
plt.plot(x,y)
plt.plot(x,np.cos(x))
plt.legend(['Sin','Cos'],fontsize = 18,loc = 'center',ncol = 2,bbox_to_anchor = [0,1.05,1,0.2])
2.4 脊柱移动
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-np.pi,np.pi,50)
plt.rcParams['axes.unicode_minus'] = False
plt.figure(figsize=(9,6))
plt.plot(x,np.sin(x),x,np.cos(x))
ax = plt.gca()
ax.spines['right'].set_color('white')
ax.spines['top'].set_color('#FFFFFF')
ax.spines['bottom'].set_position(('data',0))
ax.spines['left'].set_position(('data',0))
plt.yticks([-1,0,1],labels=['-1','0','1'],fontsize = 18)
_ = plt.xticks([-np.pi,-np.pi/2,np.pi/2,np.pi],
labels=[r'$-\pi$',r'$-\frac{\pi}{2}$',r'$\frac{\pi}{2}$',r'$\pi$'],
fontsize = 18)
2.5 图片保存
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0,2*np.pi)
y = np.sin(x)
plt.figure(linewidth = 4)
plt.plot(x,y,color = 'red')
plt.plot(x,np.cos(x),color = 'k')
ax = plt.gca()
ax.set_facecolor('lightgreen')
plt.legend(['Sin','Cos'],fontsize = 18,loc = 'center',ncol = 2,bbox_to_anchor = [0,1.05,1,0.2])
plt.savefig('./基础5.png',
dpi = 100,
facecolor = 'violet',
edgecolor = 'lightgreen',
bbox_inches = 'tight')
3 风格和样式
3.1 颜色、线形、点形、线宽、透明度
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0,2*np.pi,20)
y1 = np.sin(x)
y2 = np.cos(x)
plt.plot(x,y1,color = 'indigo',ls = '-.',marker = 'p')
plt.plot(x,y2,color = '#FF00EE',ls = '--',marker = 'o')
plt.plot(x,y1 + y2,color = (0.2,0.7,0.2),marker = '*',ls = ':')
plt.plot(x,y1 + 2*y2,linewidth = 3,alpha = 0.7,color = 'orange')
plt.plot(x,2*y1 - y2,'bo--')
3.2 更多属性设置
import numpy as np
import matplotlib.pyplot as plt
def f(x):
return np.exp(-x)*np.cos(2*np.pi*x)
x = np.linspace(0,5,50)
plt.figure(figsize=(9,6))
plt.plot(x,f(x),color = 'purple',
marker = 'o',
ls = '--',
lw = 2,
alpha = 0.6,
markerfacecolor = 'red',
markersize = 10,
markeredgecolor = 'green',
markeredgewidth = 3)
_ = plt.xticks(size = 18)
_ = plt.yticks(size = 18)
4 多图布局
4.1 子视图
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-np.pi,np.pi,50)
y = np.sin(x)
plt.figure(figsize=(9,6))
ax = plt.subplot(221)
ax.plot(x,y,color = 'red')
ax.set_facecolor('green')
ax = plt.subplot(2,2,2)
line, = ax.plot(x,-y)
line.set_marker('*')
line.set_markerfacecolor('red')
line.set_markeredgecolor('green')
line.set_markersize(10)
ax = plt.subplot(2,1,2)
plt.sca(ax)
x = np.linspace(-np.pi,np.pi,200)
plt.plot(x,np.sin(x*x),color = 'red')
4.2 嵌套
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-np.pi,np.pi,25)
y = np.sin(x)
fig = plt.figure(figsize=(9,6))
plt.plot(x,y)
ax = plt.axes([0.2,0.55,0.3,0.3])
ax.plot(x,y,color = 'g')
ax = fig.add_axes([0.55,0.2,0.3,0.3])
ax.plot(x,y,color = 'r')
4.3 多图布局分格显示
4.3.1 均匀布局
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0,2*np.pi)
fig, ((ax11,ax12,ax13), (ax21,ax22,ax23),(ax31,ax32,ax33)) = plt.subplots(3, 3)
fig.set_figwidth(9)
fig.set_figheight(6)
ax11.plot(x,np.sin(x))
ax12.plot(x,np.cos(x))
ax13.plot(x,np.tanh(x))
ax21.plot(x,np.tan(x))
ax22.plot(x,np.cosh(x))
ax23.plot(x,np.sinh(x))
ax31.plot(x,np.sin(x) + np.cos(x))
ax32.plot(x,np.sin(x*x) + np.cos(x*x))
ax33.plot(x,np.sin(x)*np.cos(x))
plt.tight_layout()
plt.show()
4.3.2 不均匀布局
- 方式一
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0,2*np.pi,200)
fig = plt.figure(figsize=(12,9))
ax1 = plt.subplot(3,1,1)
ax1.plot(x,np.sin(10*x))
ax1.set_title('ax1_title')
ax2 = plt.subplot(3,3,(4,5))
ax2.set_facecolor('green')
ax2.plot(x,np.cos(x),color = 'red')
ax3 = plt.subplot(3,3,(6,9))
ax3.plot(x,np.sin(x) + np.cos(x))
ax4 = plt.subplot(3,3,7)
ax4.plot([1,3],[2,4])
ax5 = plt.subplot(3,3,8)
ax5.scatter([1,2,3], [0,2, 4])
ax5.set_xlabel('ax5_x',fontsize = 12)
ax5.set_ylabel('ax5_y',fontsize = 12)
plt.show()
- 方式二
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0,2*np.pi,100)
plt.figure(figsize=(12,9))
ax1 = plt.subplot2grid(shape = (3, 3),
loc = (0, 0),
colspan=3)
ax1.set_title('ax1_title')
ax2 = plt.subplot2grid((3, 3), (1, 0), colspan=2)
ax2.set_facecolor('green')
ax2.plot(x,np.cos(x),color = 'red')
ax3 = plt.subplot2grid((3, 3), (1, 2), rowspan=2)
ax3.plot(x,np.sin(x) + np.cos(x))
ax4 = plt.subplot2grid((3, 3), (2, 0))
ax4.plot([1,3],[2,4])
ax5 = plt.subplot2grid((3, 3), (2, 1))
ax5.scatter([1,2,3], [0,2, 4])
ax5.set_xlabel('ax5_x',fontsize = 12)
ax5.set_ylabel('ax5_y',fontsize = 12)
- 方式三
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
x = np.linspace(0,2*np.pi,200)
fig = plt.figure(figsize=(12,9))
gs = gridspec.GridSpec(3, 3)
ax1 = fig.add_subplot(gs[0,:])
ax1.plot(x,np.sin(10*x))
ax1.set_title('ax1_title')
ax2 = plt.subplot(gs[1, :2])
ax2.set_facecolor('green')
ax2.plot(x,np.cos(x),color = 'red')
ax3 = plt.subplot(gs[1:, 2])
ax3.plot(x,np.sin(x) + np.cos(x))
ax4 = plt.subplot(gs[-1, 0])
ax4.plot([1,3],[2,4])
ax5 = plt.subplot(gs[-1, -2])
ax5.scatter([1,2,3], [0,2, 4])
ax5.set_xlabel('ax5_x',fontsize = 12)
ax5.set_ylabel('ax5_y',fontsize = 12)
plt.show()
4.4 双轴显示
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-np.pi,np.pi,100)
data1 = np.exp(x)
data2 = np.sin(x)
plt.figure(figsize=(9,6))
plt.rcParams['font.size'] = 16
ax1 = plt.gca()
ax1.set_xlabel('time (s)')
ax1.set_ylabel('exp', color='red')
ax1.plot(data1, color='red')
ax1.tick_params(axis='y', labelcolor='red')
ax2 = ax1.twinx()
ax2.set_ylabel('sin', color='blue')
ax2.plot(data2, color='blue')
ax2.tick_params(axis='y', labelcolor='blue')
plt.tight_layout()
5 文本、注释、箭头
- 常用函数如下：
; 5.1 文本
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontManager
fm = FontManager()
mat_fonts = set(f.name for f in fm.ttflist)
font = {'fontsize': 20, 'family': 'STCaiyun', 'color': 'red', 'weight': 'bold'}
x = np.linspace(0.0, 5.0, 100)
y = np.cos(2*np.pi*x) * np.exp(-x)
plt.figure(figsize=(9,6))
plt.plot(x, y, 'k')
plt.title('exponential decay',fontdict=font)
plt.suptitle('指数衰减',y = 1.05,fontdict = font,fontsize = 30)
plt.text(x = 2, y = 0.65,
s = r'$\cos(2 \pi t) \exp(-t)$')
plt.xlabel('time (s)')
plt.ylabel('voltage (mV)')
plt.show()
5.2 箭头
import matplotlib.pyplot as plt
import numpy as np
loc = np.random.randint(0,10,size = (10,2))
plt.figure(figsize=(10, 10))
plt.plot(loc[:,0], loc[:,1], 'g*', ms=20)
plt.grid(True)
way = np.arange(10)
np.random.shuffle(way)
for i in range(0, len(way)-1):
start = loc[way[i]]
end = loc[way[i+1]]
plt.arrow(start[0], start[1], end[0]-start[0], end[1]-start[1],
head_width=0.2, lw=2,
length_includes_head = True)
plt.text(start[0],start[1],s = i,fontsize = 18,color = 'red')
if i == len(way) - 2:
plt.text(end[0],end[1],s = i + 1,fontsize = 18,color = 'red')
5.3 注释
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
fig,ax = plt.subplots()
x = np.arange(0.0,5.0,0.01)
y = np.cos(2*np.pi*x)
line, = ax.plot(x,y,lw=2)
ax.annotate('local max',
xy=(2, 1),
xytext=(3, 1.5),
arrowprops=dict(facecolor='black', shrink=0.05))
ax.annotate('local min',
xy = (2.5,-1),
xytext = (4,-1.8),
arrowprops = dict(facecolor = 'black', width = 2,
headwidth = 10,
headlength = 10,
shrink = 0.1))
ax.annotate('median',
xy = (2.25,0),
xytext = (0.5,-1.8),
arrowprops = dict(arrowstyle = '-|>'),
fontsize = 20)
ax.set_ylim(-2, 2)
5.4 注释箭头连接形状
import matplotlib.pyplot as plt
def annotate_con_style(ax, connectionstyle):
x1, y1 = 3,2
x2, y2 = 8,6
ax.plot([x1, x2], [y1, y2], ".")
ax.annotate(" ",
xy=(x1, y1),
xytext=(x2, y2),
arrowprops=dict(arrowstyle='->', color='red',
shrinkA = 5,shrinkB = 5,
connectionstyle=connectionstyle))
ax.text(0.05, 0.95, connectionstyle.replace(",", "\n"),
transform=ax.transAxes,
ha="left", va="top")
fig, axs = plt.subplots(3, 5, figsize=(9,6))
annotate_con_style(axs[0, 0], "angle3,angleA=90,angleB=0")
annotate_con_style(axs[1, 0], "angle3,angleA=0,angleB=90")
annotate_con_style(axs[2, 0], "angle3,angleA = 0,angleB=150")
annotate_con_style(axs[0, 1], "arc3,rad=0.")
annotate_con_style(axs[1, 1], "arc3,rad=0.3")
annotate_con_style(axs[2, 1], "arc3,rad=-0.3")
annotate_con_style(axs[0, 2], "angle,angleA=-90,angleB=180,rad=0")
annotate_con_style(axs[1, 2], "angle,angleA=-90,angleB=180,rad=5")
annotate_con_style(axs[2, 2], "angle,angleA=-90,angleB=10,rad=5")
annotate_con_style(axs[0, 3], "arc,angleA=-90,angleB=0,armA=30,armB=30,rad=0")
annotate_con_style(axs[1, 3], "arc,angleA=-90,angleB=0,armA=30,armB=30,rad=5")
annotate_con_style(axs[2, 3], "arc,angleA=-90,angleB=0,armA=0,armB=40,rad=0")
annotate_con_style(axs[0, 4], "bar,fraction=0.3")
annotate_con_style(axs[1, 4], "bar,fraction=-0.3")
annotate_con_style(axs[2, 4], "bar,angle=180,fraction=-0.2")
for ax in axs.flat:
ax.set(xlim=(0, 10), ylim=(0, 10),xticks = [],yticks = [],aspect=1)
fig.tight_layout(pad=0.2)
6 常用视图
6.1 折线图
import numpy as np
import matplotlib.pyplot as plt
x = np.random.randint(0,10,size = 15)
print(x)
plt.figure(figsize=(9,6))
plt.plot(x,marker = '*',color = 'r')
plt.plot(x.cumsum(),marker = 'o')
fig,axs = plt.subplots(2,1)
fig.set_figwidth(9)
fig.set_figheight(6)
axs[0].plot(x,marker = '*',color = 'red')
axs[1].plot(x.cumsum(),marker = 'o')
6.2 柱状图
- 堆叠柱状图
import numpy as np
import matplotlib.pyplot as plt
labels = ['G1', 'G2', 'G3', 'G4', 'G5','G6']
men_means = np.random.randint(20,35,size = 6)
women_means = np.random.randint(20,35,size = 6)
men_std = np.random.randint(1,7,size = 6)
women_std = np.random.randint(1,7,size = 6)
width = 0.35
plt.bar(labels,
men_means,
width,
yerr=4,
label='Men')
plt.bar(labels, women_means, width, yerr=2, bottom=men_means, label='Women')
plt.ylabel('Scores')
plt.title('Scores by group and gender')
plt.legend()
- 分组带标签柱状图
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
labels = ['G1', 'G2', 'G3', 'G4', 'G5','G6']
men_means = np.random.randint(20,35,size = 6)
women_means = np.random.randint(20,35,size = 6)
x = np.arange(len(men_means))
width = 0.35
plt.figure(figsize=(9,6))
rects1 = plt.bar(x - width/2, men_means, width)
rects2 = plt.bar(x + width/2, women_means, width)
plt.ylabel('Scores')
plt.title('Scores by group and gender')
plt.xticks(x,labels)
plt.legend(['Men','Women'])
def set_label(rects):
for rect in rects:
height = rect.get_height()
plt.text(x = rect.get_x() + rect.get_width()/2,
y = height + 0.5,
s = height,
ha = 'center')
set_label(rects1)
set_label(rects2)
plt.tight_layout()
6.3 极坐标图
- 极坐标线形图
import numpy as np
import matplotlib.pyplot as plt
r = np.arange(0, 4*np.pi, 0.01)
y = np.linspace(0,2,len(r))
ax = plt.subplot(111,projection = 'polar',facecolor = 'lightgreen')
ax.plot(r, y,color = 'red')
ax.set_rmax(3)
ax.set_rticks([0.5, 1, 1.5, 2])
ax.set_rlabel_position(-22.5)
ax.grid(True)
ax.set_title("A line plot on a polar axis", va='center',ha = 'center',pad = 30)
- 极坐标柱状图
import numpy as np
import matplotlib.pyplot as plt
N = 8
theta = np.linspace(0.0, 2 * np.pi, N, endpoint=False)
radii = np.random.randint(3,15,size = N)
width = np.pi / 4
colors = np.random.rand(8,3)
ax = plt.subplot(111, projection='polar')
ax.bar(theta, radii, width=width, bottom=0.0,color = colors)
6.4 直方图
import numpy as np
import matplotlib.pyplot as plt
mu = 100
sigma = 15
x = np.random.normal(loc = mu,scale = 15,size = 10000)
fig, ax = plt.subplots()
n, bins, patches = ax.hist(x, 200, density=True)
y = ((1 / (np.sqrt(2 * np.pi) * sigma)) *
np.exp(-0.5 * (1 / sigma * (bins - mu))**2))
plt.plot(bins, y, '--')
plt.xlabel('Smarts')
plt.ylabel('Probability density')
plt.title(r'Histogram of IQ: $\mu=100$, $\sigma=15$')
fig.tight_layout()
plt.savefig('./直方图.png')
6.5 箱型图
import numpy as np
import matplotlib.pyplot as plt
data=np.random.normal(size=(500,4))
lables = ['A','B','C','D']
plt.boxplot(data,1,'gD',labels=lables)
6.6 散点图
import numpy as np
import matplotlib.pyplot as plt
data = np.random.randn(100,2)
s = np.random.randint(100,300,size = 100)
color = np.random.randn(100)
plt.scatter(data[:,0],
data[:,1],
s = s,
c = color,
alpha = 0.5)
6.7 饼图
6.7.1 一般饼图
import numpy as np
import matplotlib.pyplot as plt
matplotlib.rcParams['font.sans-serif']='Kaiti SC'
labels =["五星","四星","三星","二星","一星"]
percent = [95,261,105,30,9]
fig=plt.figure(figsize=(5,5), dpi=150)
explode = (0, 0.1, 0, 0, 0)
plt.pie(x = percent,
explode=explode,
labels=labels,
autopct='%0.1f%%',
shadow=True)
plt.savefig("./饼图.jpg")
6.7.2 嵌套饼图
import pandas as pd
import matplotlib.pyplot as plt
food = pd.read_excel('./food.xlsx')
inner = food.groupby(by = 'type')['花费'].sum()
outer = food['花费']
plt.rcParams['font.family'] = 'Kaiti SC'
plt.rcParams['font.size'] = 18
fig=plt.figure(figsize=(8,8))
plt.pie(x = inner,
radius=0.6,
wedgeprops=dict(linewidth=3,width=0.6,edgecolor='w'),
labels = inner.index,
labeldistance=0.4)
plt.pie(x = outer,
radius=1,
wedgeprops=dict(linewidth=3,width=0.3,edgecolor='k'),
labels = food['食材'],
labeldistance=1.2)
plt.legend(inner.index,bbox_to_anchor = (0.9,0.6,0.4,0.4),title = '食物占比')
plt.tight_layout()
plt.savefig('./嵌套饼图.png',dpi = 200)
6.7.3 甜甜圈
import numpy as np
import matplotlib.pyplot as plt
plt.figure(figsize=(6,6))
recipe = ["225g flour",
"90g sugar",
"1 egg",
"60g butter",
"100ml milk",
"1/2package of yeast"]
data = [225, 90, 50, 60, 100, 5]
wedges, texts = plt.pie(data,startangle=40)
bbox_props = dict(boxstyle="square,pad=0.3", fc="w", ec="k", lw=0.72)
kw = dict(arrowprops=dict(arrowstyle="-"),
bbox=bbox_props,va="center")
for i, p in enumerate(wedges):
ang = (p.theta2 - p.theta1)/2. + p.theta1
y = np.sin(np.deg2rad(ang))
x = np.cos(np.deg2rad(ang))
ha = {-1: "right", 1: "left"}[int(np.sign(x))]
connectionstyle = "angle,angleA=0,angleB={}".format(ang)
kw["arrowprops"].update({"connectionstyle": connectionstyle})
plt.annotate(recipe[i], xy=(x, y), xytext=(1.35*np.sign(x), 1.4*y),
ha=ha,**kw,fontsize = 18,weight = 'bold')
plt.title("Matplotlib bakery: A donut",fontsize = 18,pad = 25)
plt.tight_layout()
6.8 热力图
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
vegetables = ["cucumber", "tomato", "lettuce", "asparagus","potato", "wheat", "barley"]
farmers = list('ABCDEFG')
harvest = np.random.rand(7,7)*5
plt.rcParams['font.size'] = 18
plt.rcParams['font.weight'] = 'heavy'
plt.figure(figsize=(9,9))
im = plt.imshow(harvest)
plt.xticks(np.arange(len(farmers)),farmers,rotation = 45,ha = 'right')
plt.yticks(np.arange(len(vegetables)),vegetables)
for i in range(len(vegetables)):
for j in range(len(farmers)):
text = plt.text(j, i, round(harvest[i, j],1),
ha="center", va="center", color='r')
plt.title("Harvest of local farmers (in tons/year)",pad = 20)
fig.tight_layout()
plt.savefig('./热力图.png')
6.9 面积图
import matplotlib.pyplot as plt
plt.figure(figsize=(9,6))
days = [1,2,3,4,5]
sleeping =[7,8,6,11,7]
eating = [2,3,4,3,2]
working =[7,8,7,2,2]
playing = [8,5,7,8,13]
plt.stackplot(days,sleeping,eating,working,playing)
plt.xlabel('x')
plt.ylabel('y')
plt.title('Stack Plot',fontsize = 18)
plt.legend(['Sleeping','Eating','Working','Playing'],fontsize = 18)
6.10 蜘蛛图
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams['font.family'] = 'Kaiti SC'
labels=np.array(["个人能力","IQ","服务意识","团队精神","解决问题能力","持续学习"])
stats=[83, 61, 95, 67, 76, 88]
angles=np.linspace(0, 2*np.pi, len(labels), endpoint=False)
stats=np.concatenate((stats,[stats[0]]))
angles=np.concatenate((angles,[angles[0]]))
fig = plt.figure(figsize=(9,9))
ax = fig.add_subplot(111, polar=True)
ax.plot(angles, stats, 'o-', linewidth=2)
ax.fill(angles, stats, alpha=0.25)
ax.set_thetagrids(angles*180/np.pi,
labels,
fontsize = 18)
ax.set_rgrids([20,40,60,80],fontsize = 18)
7 3D绘图
7.1 三维折线图散点图
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d.axes3d import Axes3D
x = np.linspace(0,60,300)
y = np.sin(x)
z = np.cos(x)
fig = plt.figure(figsize=(9,6))
ax3 = Axes3D(fig)
ax3.plot(x,y,z)
ax3.scatter(np.random.rand(50)*60,np.random.rand(50),np.random.rand(50),
color = 'red',s = 100)
7.2 三维柱状图
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d.axes3d import Axes3D
month = np.arange(1,5)
fig = plt.figure(figsize=(9,6))
ax3 = Axes3D(fig)
for m in month:
ax3.bar(np.arange(4),
np.random.randint(1,10,size = 4),
zs = m ,
zdir = 'x',
alpha = 0.7,
width = 0.5)
ax3.set_xlabel('X',fontsize = 18,color = 'red')
ax3.set_ylabel('Y',fontsize = 18,color = 'red')
ax3.set_zlabel('Z',fontsize = 18,color = 'green')
Original: https://blog.csdn.net/qq_34516746/article/details/124391585
Author: jaydenStyle
Title: Matplotlib数据可视化（3）Python数据分析
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