第Python中的數(shù)據(jù)可視化matplotlib與繪圖庫模塊fig=plt.figure()

#1,1,1表示一張畫布切割成1行1列共一張圖的第1個；2,2,1表示一張畫布切割成2行2列共4張圖的第一個（左上角）

ax1=fig.add_subplot(1,1,1)

ax1.bar(classes_index,student_amounts,align='center',color='darkblue')

ax1.xaxis.set_ticks_position('bottom')

ax1.yaxis.set_ticks_position('left')

plt.xticks(classes_index,

classes,

rotation=0,

fontsize=13,

fontproperties=font)

plt.xlabel('班級',fontproperties=font,fontsize=15)

plt.ylabel('學(xué)生人數(shù)',fontproperties=font,fontsize=15)

plt.title('班級-學(xué)生人數(shù)',fontproperties=font,fontsize=20)

#保存圖片，bbox_inches='tight'去掉圖形四周的空白

#plt.savefig('classes_students.png',dpi=400,bbox_inches='tight')

plt.show()

二、直方圖

importnumpyasnp

importmatplotlib.pyplotasplt

frommatplotlib.font_managerimportFontProperties

font=FontProperties(fname=r'c:\windows\fonts\simsun.ttc')

#修改背景為條紋

plt.style.use('ggplot')

mu1,mu2,sigma=50,100,10

#構(gòu)造均值為50的符合正態(tài)分布的數(shù)據(jù)

x1=mu1+sigma*np.random.randn(10000)

print(x1)

#[59.0085594943.1627214148.77109774...57.9464585954.70312714

#58.94125528]

#構(gòu)造均值為100的符合正態(tài)分布的數(shù)據(jù)

x2=mu2+sigma*np.random.randn(10000)

print(x2)

#[115.1991551182.09208214110.88092454...95.0872103104.21549068

#133.36025251]

fig=plt.figure()

ax1=fig.add_subplot(121)

#bins=50表示每個變量的值分成50份，即會有50根柱子

ax1.hist(x1,bins=50,color='darkgreen')

ax2=fig.add_subplot(122)

ax2.hist(x2,bins=50,color='orange')

fig.suptitle('兩個正態(tài)分布',fontproperties=font,fontweight='bold',fontsize=15)

ax1.set_title('綠色的正態(tài)分布',fontproperties=font)

ax2.set_title('橙色的正態(tài)分布',fontproperties=font)

plt.show()

三、折線圖

importnumpyasnp

fromnumpy.randomimportrandn

importmatplotlib.pyplotasplt

frommatplotlib.font_managerimportFontProperties

font=FontProperties(fname=r'c:\windows\fonts\simsun.ttc')

#修改背景為條紋

plt.style.use('ggplot')

np.random.seed(1)

#使用numpy的累加和，保證數(shù)據(jù)取值范圍不會在（0，1）內(nèi)波動

plot_data1=randn(40).cumsum()

print(plot_data1)

#[1.624345361.012588950.4844172-0.588551420.2768562-2.02468249

#-0.27987073-1.04107763-0.72203853-0.971408910.49069903-1.56944168

#-1.89185888-2.27591324-1.1421438-2.24203506-2.41446327-3.29232169

#-3.25010794-2.66729273-3.76791191-2.6231882-1.72159748-1.21910314

#-0.31824719-1.00197505-1.12486527-2.06063471-2.32852279-1.79816732

#-2.48982807-2.8865816-3.5737543-4.41895994-5.09020607-5.10287067

#-6.22025102-5.98576532-4.32596314-3.58391898]

plot_data2=randn(40).cumsum()

plot_data3=randn(40).cumsum()

plot_data4=randn(40).cumsum()

plt.plot(plot_data1,marker='o',color='red',line,label='紅實線')

plt.plot(plot_data2,marker='x',color='orange',line,label='橙虛線')

plt.plot(plot_data3,marker='*',color='yellow',line,label='黃點線')

plt.plot(plot_data4,marker='s',color='green',line,label='綠點圖')

#loc='best'給label自動選擇最好的位置

plt.legend(loc='best',prop=font)

plt.show()

四、散點圖+直線圖

importnumpyasnp

fromnumpy.randomimportrandn

importmatplotlib.pyplotasplt

frommatplotlib.font_managerimportFontProperties

font=FontProperties(fname=r'c:\windows\fonts\simsun.ttc')

#修改背景為條紋

plt.style.use('ggplot')

x=np.arange(1,20,1)

print(x)

#[12345678910111213141516171819]

#擬合一條水平散點線

np.random.seed(1)

y_linear=x+10*np.random.randn(19)

#print(y_linear)

#[17.24345364-4.11756414-2.28171752-6.7296862213.65407629

#-17.0153869724.448117640.3879309912.190390967.50629625

#25.62107937-8.601407099.7758279610.1594564526.33769442

#5.0010873315.275717929.2214158219.42213747]

#擬合一條x2的散點線

y_quad=x**2+10*np.random.randn(19)

print(y_quad)

#[6.82815214-7.0061917720.447237125.0159072130.02494339

#45.0085594942.1627214162.7710977471.6423056697.3211192

#126.30355467137.08339248165.03246473189.128273216.54794359

#249.28753869288.87335401312.82689651363.34415698]

#s是散點大小

fig=plt.figure()

ax1=fig.add_subplot(121)

plt.scatter(x,y_linear,s=30,color='r',label='藍(lán)點')

plt.scatter(x,y_quad,s=100,color='b',label='紅點')

ax2=fig.add_subplot(122)

plt.plot(x,y_linear,color='r')

plt.plot(x,y_quad,color='b')

#限制x軸和y軸的范圍取值

plt.xlim(min(x)-1,max(x)+1)

plt.ylim(min(y_quad)-10,max(y_quad)+10)

fig.suptitle('散點圖+直線圖',fontproperties=font,fontsize=20)

ax1.set_title('散點圖',fontproperties=font)

ax1.legend(prop=font)

ax2.set_title('直線圖',fon