Matplotlib绘图

Matplotlib基本概念

Matplotlib：基于对象的思维构建的视觉符号。

每一个Axes（坐标轴）对象包含一个或者多个Axis(轴)对象，比如X轴、Y轴。

一个Figure（画像）是由一堆坐标轴对象组成的。

换句话说，Marker(标记)/Line（线）表示针对一个或多个Axis(轴)绘制的数据集。一个Axes对象(有效地)是一个Figure(画像)的子图

连续图

绘制连续图（连线图、折线图）。

需求说明:对GDP数据按照年进行绘制。

默认方式

hfgdp = [0.2,0.23,0.3,0.36,0.42,0.47,0.53,0.58,0.65,0.74,0.86,0.94,1,1.14,1.2]
plt.plot(hfgdp)
plt.show()

添加轴说明

这里指定X轴为年份、Y轴位GDP值，标题为GDP data

hfgdp = [0.2,0.23,0.3,0.36,0.42,0.47,0.53,0.58,0.65,0.74,0.86,0.94,1,1.14,1.2]
years = [str(i) for i in range(2008,2008+len(hfgdp))]
fig,ax = plt.subplots()
ax.plot(years, hfgdp)
ax.set(xlabel='Years',ylabel='GDP',title='GDP data')
plt.show()

展示离散点

指定离散值以红点的形式展示。 

hfgdp = [0.2,0.23,0.3,0.36,0.42,0.47,0.53,0.58,0.65,0.74,0.86,0.94,1,1.14,1.2]
years = [str(i) for i in range(2008,2008+len(hfgdp))]
fig,ax = plt.subplots()
ax.plot(years, hfgdp ,"ro")
ax.set(xlabel='Years',ylabel='GDP',title='GDP data')
plt.show()

 标记marker的样式见下，一般常用的是“o”即圆点。

参数	说明
'-'	实线
'--'	虚线
'-.'	点划线
':'	冒号
'.'	点
','	像素
'o'	圆圈
'v'	下尖号
'^'	上尖号
'<'	左尖号
'>'	右尖号
'1'	下三角
'2'	上三角
'3'	左三角
'4'	右三角
's'	方块
'p'	五边形
'*'	星号
'h'	六边形
'H'	六边形
'+'	加号
'x'	字母x
'D'	钻石
'd'	细钻石
'|'	竖线
'_'	横线

参数	说明
'b'	蓝色
'g'	绿色
'r'	红色
'c'	青色
'm'	洋红
'y'	黄色
'b'	黑色
'w'	白色

指定输出格式

可以自定义颜色及线的粗细程度。

hfgdp = [0.2,0.23,0.3,0.36,0.42,0.47,0.53,0.58,0.65,0.74,0.86,0.94,1,1.14,1.2]
njgdp = [0.392,0.431,0.523,0.624,0.733,0.823,0.911,1.02,1.083,1.192,1.31,1.401,1.483,1.642,1.694]
years = [str(i) for i in range(2008,2008+len(hfgdp))]
fig,ax = plt.subplots()
ax.plot(years,hfgdp,color="#009150",linewidth=3.5,linestyle=":")
ax.plot(years,njgdp,color="#33036b",linewidth=1.5,linestyle="-.")
ax.set(xlabel='Years',ylabel='GDP',title='GDP data')
plt.show()

饼图

计算程序语言的占比的饼图。

labels =  'Python','C', 'Java', 'C++', 'C#', 'others'
rate = [14.26,13.06,11.19,8.66,5.92]
explode=(0,0.1,0,0,0.2,0)
rate.append(100-sum(rate))
fig,ax = plt.subplots()
ax.pie(rate,labels=labels,explode=explode,autopct='%1.0f%%',shadow=True,startangle=0)
plt.title("Program Language rate 2022 Mar")
plt.show()

柱状图

普通柱状图

通过柱状图展示GDP的明细数据。

hfgdp = [0.2,0.23,0.3,0.36,0.42,0.47,0.53,0.58,0.65,0.74,0.86,0.94,1,1.14,1.2]
years = [str(i) for i in range(2008,2008+len(hfgdp))]
fig,ax = plt.subplots()
plt.bar (years,hfgdp,color="#009150",linewidth=3.5,linestyle=":")
ax.set(xlabel='Years',ylabel='GDP',title='GDP data')
plt.show()

两分类堆叠柱状图

housedata=pd.read_csv('data/sndHsPr.csv')
data = housedata.pivot_table(values='price',index='dist',columns=['school'],aggfunc=np.mean)
data.plot(kind='bar',stacked=True)
plt.show()

多分类堆叠柱状图

housedata=pd.read_csv('data/sndHsPr.csv')
data = housedata.pivot_table(values='price',index='dist',columns=['school','subway'],aggfunc=np.mean)
data.plot(kind='bar',stacked=True)
plt.show()

直方图

housedata=pd.read_csv('data/sndHsPr.csv')
plt.hist(housedata['price'],bins=40)
plt.show()

概率密度图

import seaborn as sns
housedata=pd.read_csv('data/sndHsPr.csv')
price=housedata['price']
plt.hist(price,bins=40,density=True)
sns.kdeplot(price)
plt.show()
sns.displot(data=rate,kde=True)
plt.show()

散点图

housedata=pd.read_csv('data/sndHsPr.csv')
housedata.plot.scatter(x='area',y='price')
plt.show()

双轴图

通过展示GDP和GDP增长率，以双轴的形式显示。

hfgdp = [0.2,0.23,0.3,0.36,0.42,0.47,0.53,0.58,0.65,0.74,0.86,0.94,1,1.14,1.2]
hfgdpcr = [0.154,0.166,0.164,0.152,0.124,0.113,0.098,0.103,0.099,0.085,0.085,0.075,0.043,0.092,0.035]
#plt.plot(gdp)
#plt.xticks(rotation=270)
years = [str(i) for i in range(2008,2008+len(hfgdp))]
fig,ax = plt.subplots()
plt.bar (years,hfgdp,color="#009150",width=0.8)
ax.set(xlabel='Years',ylabel='GDP',title='GDP data')
plt.xticks(rotation=90) #控制x轴里值的显示方式，水平或者垂直及任意角度
axr=ax.twinx()
axr.plot(years,hfgdpcr,color="#001024",linewidth=2.5,linestyle="-.")axr.set(ylabel='GDP increase rate',title='GDP data')
plt.show()

面积图

idx = [ 1, 2, 3, 4, 5, 6, 7, 8, 9]
y1 = [23, 42, 33, 43, 8, 44, 43, 18, 21]
y2 = [9, 31, 25, 14, 17, 17, 42, 22, 28]
y3 = [18, 29, 19, 22, 18, 16, 13, 32, 21]
plt.stackplot(idx,
y1, y2, y3)
plt.title('Stack Plot Example')
plt.show()

热力图

普通热力图

import seaborn as snsimport pandas as pddf = pd.read_csv("data/automobile.csv")sns.set(rc={'figure.figsize':(16,10)})cm = df.columns.tolist()xcorr = df.corr()#cmap是设置热图的颜色cmap = sns.diverging_palette(220, 10, as_cmap=True)sns.heatmap(data=df.corr(),annot=True,linewidths=.5,center=0,cbar=True,#mask=mask,cmap=cmap, #"PiYG",fmt='0.2f')#plt.savefig("data/mobile.png")plt.show()

仅显示左下角热力图

import seaborn as snsimport pandas as pddf = pd.read_csv("data/automobile.csv")sns.set(rc={'figure.figsize':(16,10)})cm = df.columns.tolist()xcorr = df.corr()#设置右上三角不绘制#mask为 和相关系数矩阵xcorr一样大的 全0(False)矩阵mask = np.zeros_like(xcorr, dtype=np.bool_)# 将mask右上三角(列号》=行号)设置为Truemask[np.triu_indices_from(mask)] = True#cmap是设置热图的颜色cmap = sns.diverging_palette(220, 10, as_cmap=True)sns.heatmap(data=df.corr(),annot=True,linewidths=.5,center=0,cbar=True,mask=mask,cmap=cmap, #"PiYG",fmt='0.2f')plt.savefig("data/mobile.png")plt.show() 

成对图

import seaborn as sns
import matplotlib.pyplot as plt
iris= sns.load_dataset('iris')
sns.pairplot(iris, hue='species')
plt.show()