matplotlib从入门到精通教程

1、、导入1.导入matplotlib库简写为pitimport matpl otiib .pyplot as p It二、基本图表2.用plot方法画出x=(0,10)间sin的图像x = np.linsp ace(0, 10, 30)p It .pl ot(x, np.sin( x);3.用点加线的方式画出x=(0,10)间sin的图像间sin的点图像P It .pl ot(x, np.sin( x), -o);4.用 scatter 方法画出 x=(0,10)p lt.scatter(x, np .sin(x);5.用饼图的面积及颜色展示一组4维数据rng = np .ra ndom.Ra

2、ndomState(0)x = rn g.ra ndn (100)y = rn g.ra ndn (100)colors = rn g.ra nd(100)sizes = 1000 * rng.ra nd(100)p lt.scatter(x, y, c=colors, s=sizes, alp ha=0.3, cma p=viridis)plt.colorbar(); # 展示色阶6.绘制一组误差为 0.8的数据的误差条图x = np.linsp ace(0, 10, 50)dy = 0.8y = np.sin(x) + dy * np .ra ndom .randn(50)P lt.eiT

3、orbar(x, y, yerr=dy, fmt=.k)7.绘制一个柱状图 x = 123,4,5,6,7,8y = 3,1,4,5,8,9,7,2label=A,B,C,D,E,F,G,HP lt.bar(x,y,tick_label = label);8. 绘制一个水平方向柱状图P lt.barh(x,y,tick_label = label);9. 绘制1000个随机值的直方图data = np.ran dom.ra ndn (1000) plt.hist(data);10. 设置直方图分30个bins，并设置为频率分布p lt.hist(data, bin s=30,histt yp

4、e=ste pfilled, den sity=True) p lt.show();11. 在一张图中绘制3组不同的直方图，并设置透明度x1 = np.ran dom. normal 0.8, 1000) x2 = np.ran dom. normal(-2, 1, 1000) x3 = np.ran dom. normal(3, 2, 1000) kwargs = dict(a Ip ha=0.3, bin s=40, den sity = True) plt.hist(x1, *kwargs);plt.hist(x2, *kwargs);plt.hist(x3, *kwargs);12.

5、绘制一张二维直方图mean = 0, 0cov = 1, 1, 1,2x, y = np.ran dom.multivariate_ no rmal(mea n, cov, 10000).T plt.hist2d(x, y, bin s=30);13. 绘制一张设置网格大小为 30的六角形直方图P lt.hexb in(x, y, gridsize=30);二、自定义图表兀素14. 绘制x=(0,10)间sin的图像，设置线性为虚线x = np .lins pace(0,10,100)plt. plot(x,n p.sin(x),-);15设置y轴显示范围为(-1.5,1.5)x = np .

6、lins pace(0,10,100) pl t. pl ot(x, np .sin(x) plt.ylim(-1.5, 1.5);16.设置 x,y 轴标签 variable x, value yx = np .lins pace(0.05, 10, 100)y = np .sin(x)pit. pl ot(x, y, label=sin(x) p It.xlabelCvariable x);plt.ylabel(value y);17.设置图表标题三角函数”x = np .lins pace(0.05, 10, 100) y = np .sin(x) plt. pl ot(x, y, la

7、bel=sin(x) plt.title(三角函数);18. 显示网格x = np .lins pace(0.05, 10, 100) y = np .sin(x) plt .pl ot(x, y) plt.gridO19. 绘制平行于x轴y=0.8的水平参考线x = np .lins pace(0.05, 10, 100) y = np .sin(x) plt .pl ot(x, y) p lt.axhline(y=0.8, ls=-, c=r)20. 绘制垂直于x轴x6的参考区域，以及y轴yv0.2 and y-0.2的参考区x = np .lins pace(0.05, 10, 100)

8、 y = np .sin(x) plt .pl ot(x, y)plt.axvspan(xmin=4, xmax=6, facecolor=r, alpha=0.3) # 垂直 x 轴plt.axhspan(ymin=-0.2, ymax=0.2, facecolor=y, alpha=0.3);# 垂直 y 轴21. 添加注释文字sin(x)x = np .lins pace(0.05, 10, 100) y = np .sin(x) plt .pl ot(x, y) p lt.text(3.2, 0, sin(x), weight=bold, color=r);22. 用箭头标出第一个峰值

9、x = np .lins pace(0.05, 10, 100)y = np .sin(x) plt .pl ot(x, y)p lt.annotate(maximum,xy=(n p.pi/2, 1),xytext=(np.p i/2+1, 1), weight-bold.color=r,arrow prop s=dict(arrowstyIe=-, connectionstyle=arc3, color=r);四、自定义图例23.在一张图里绘制 sin,cos的图形，并展示图例 x = np.linsp ace(0, 10, 1000) fig, ax = p lt.sub pl ots(

10、) ax.pl ot(x, np.sin( x), label=s in)ax.pl ot(x, np .cos(x), -, label=cos)ax.l ege nd();24.调整图例在左上角展示，且不显示边框 ax.l ege nd(loc= upper left, frame on=False); fig25.调整图例在画面下方居中展示，且分成2列 ax.l ege nd(frame on=False, loc=lower cen ter, n col=2) fig26.绘制的图像，并只显示前2者的图例 y = np.sin( x:, np.n ewaxis + np.pi * np

11、.aran ge(0, 2, 0.5) lines = p It .pl ot(x, y) # lines是plt.Line2D 类型的实例的列表 plt.lege nd(li nes:2, first, seco nd);#第二个方法 #p It. plot(x, y:, 0, label=first)#p It. plot(x, y:, 1, label=seco nd)#plt.pl ot(x, y:, 2:)#p It .l ege nd(framea Ip ha=1, frame on=True);27.将图例分不同的区域展示 fig, ax = p lt.sub pl ots()

12、lines =styles =-, -, -., :x = np.linsp ace(0, 10, 1000) for i in ran ge(4):lines += ax.pl ot(x, np.sin(x - i * np.pi / 2),stylesi, color=black)ax.axis(equal)#设置第一组标签ax.lege nd(lin es:2, li ne A, li ne B,loc= upper right, frame on=False)#创建第二组标签from mat pl otiib.lege nd import Lege ndleg = Lege nd(ax

13、, li nes2:, li ne C, li ne D, loc=lower right, frame on=False) ax.add_artist(leg);五、自定义色阶28.展示色阶x = np.linsp ace(0, 10, 1000)I = np.sin(x) * np. cos(x:, np.n ewaxis) p It.imshow(l) p lt.colorbar();29.改变配色为gray p lt.imshow(l, cma p=gray);30.将色阶分成6个离散值显示 p lt.imshow(l, cmap=p lt.cm.get_cma p(Blues, 6)

14、 p lt.colorbar()plt.clim(-1, 1);六、多子图31.在一个1010的画布中，(0.65,0.65)的位置创建一个0.20.2的子图ax1 = P lt.axesOax2 = plt.axes(0.65, 0.65, 0.2, 0.2)32.在2个子图中，显示sin(x)和cos(x)的图像 fig = p It.figureOax1 = fig.add_axes(0.1, 0.5, 0.8, 0.4, ylim=(-1.2, 1.2)ax2 = fig.add_axes(0.1, 0.1,0.8, 0.4, ylim=(-1.2, 1.2) x = np.linsp

15、 ace(0, 10) ax1. plot(np.sin( x);ax2. plot(np .cos(x);33.用for创建6个子图，并且在图中标识出对应的子图坐标for i in ran ge(1,7):plt.sub plot(2, 3, i)p lt.text(0.5, 0.5, str(2, 3, i),fo ntsize=18, ha=ce nter) #方法二# fig - p It.figureO# fig.sub pl ots_adjust(hs pace=0.4, wsp ace=0.4)# for i in ran ge(1,7):# ax - fig.add_sub p

16、lot(2, 3, i)# ax.text(0.5, 0.5, str(2, 3, i),fo ntsize=18, ha=ce nter)34.设置相同行和列共享x,y轴 fig, ax = p lt.sub pl ots(2, 3, sharex=col, sharey=row)35.用的方式取出每个子图，并添加子图座标文字for i in ran ge(2):for j in ran ge(3):axi, j.text(0.5, 0.5, str(i, j),fo ntsize=18, ha-ce nter)fig36. 组合绘制大小不同的子图，样式如下Image Namegrid -

17、plt.GridS pec(2, 3, ws pace=0.4, hsp ace=0.3) plt.subplot(grid0, 0)plt.sub plot(grid0, 1:)plt.subplot(grid1, :2)plt.sub plot(grid1,2);37. 显示一组二维数据的频度分布，并分别在x,y轴上，显示该维度的数据的频度分布 mean = 0, 0cov = 1, 1, 1,2x, y = np.ran dom.multivariate_ no rmal(mea n, cov, 3000).T # Set up the axes with grids pecfig =

18、p lt.figure(figsize=(6, 6)grid = p It.GridS pec(4, 4, hsp ace=0.2, wsp ace=0.2)main_ax = fig.add_sub pl ot(gnd:-1, 1:)y_hist = fig.add_sub pl ot(gnd:-1,0, xticklabels=, sharey=ma in_ax) x_hist = fig.add_sub pl ot(gnd-1, 1:, yticklabels=, sharex=ma in_ax) # scatter points on the main axes main _ax.sc

19、atter(x, y,s=3,a Ip ha=0.2) # histogram on the attached axes x_hist.hist(x, 40, histty pe=ste pfilled, orie ntati on= vertical) x_hist.i nvert_yaxis() y_hist.hist(y, 40, histty pe=ste pfilled,orie ntati on=horiz on tal) y_hist.i nvert_xaxis()七、三维图像38. 创建一个三维画布 from mp l_toolkits import mpl ot3dfig =

20、 p It.figureOax = p lt.axes( projecti on=3d)39. 绘制一个三维螺旋线 ax = P lt.axes( projecti on=3d)# Data for a three-dime nsion al li ne zli ne = np .li nsp ace(0, 15, 1000) xli ne = np .si n( zli ne)yli ne = np .cos(zli ne)ax.pl ot3D(xli ne, yli ne, zli ne);40. 绘制一组三维点 ax = p lt.axes( projecti on=3d)zdata =

21、 15 * np.ran dom.ra ndom(100)xdata = np.sin( zdata) + 0.1 * np .ra ndom .randn (100)ydata = np. cos(zdata) + 0.1 * np.ran dom.ra ndn (100) ax.scatter3D(xdata, ydata, zdata, c=zdata, cmap=Gree ns);八、宝可梦数据集可视化41.展示前5个宝可梦的Defense,Attack,HP的堆积条形图 P okem on = dfName:5hp = dfHP :5attack = dfAttack:5defe n

22、se = dfDefe nse:5ind = x for x, _ in enu merate( pokem on) plt.figure(figsize=(10,10)p lt.bar(i nd, defe nse, width=0.8, label=Defe nse, color=blue, bottom=attack+h p)p lt.bar(i nd, attack, width=0.8, label=Attack, color=gold, bottom=h p)plt.bar(i nd, h p, width=0.8, label=H p, color=red) p lt.xtick

23、s(i nd, p okem on)plt.ylabel(Value)plt.xlabel( Pokemo n)p It .l ege nd(loc=upper right)plt.title(5 P okemon Defe nse & Attack & Hp)P lt.show()42.展示前5个宝可梦的Attack,HP的簇状条形图N = 5 pokemon_h p = dfHP :5 pokemon_attack = dfAttack:5 ind = np.aran ge(N)width = 0.35p lt.bar(i nd, p okem on_hp, width, label=HP

24、)p lt.bar(i nd + width, p okem on _attack, width,label=Attack) plt.ylabel(Values)plt.title( P okemon Hp & Attack) plt.xticks(i nd + width / 2, (dfName:5),rotatio n=45) p It .l ege nd(loc=best)p lt.show() 43.展示前5个宝可梦的Defense,Attack,HP的堆积图x = dfName:4y1 = df HP :4y2 = dfAttack:4y3 = dfDefe nse:4 label

25、s = HP , Attack, Defe nse fig, ax = p lt.sub pl ots()ax.stack plot(x, y1, y2, y3)ax.l ege nd(loc=upper left, labels=labels) plt.xticks(rotatio n=90)p lt.show() 44.公用x轴，展示前5个宝可梦的 Defense,Attack,HP的折线图 x = dfName:5y1 = dfH P:5y2 = dfAttack:5y3 = dfDefe nse:5 # Create two sub pl ots shari ng y axisfig,

26、 (ax1, ax2,ax3) = p lt.sub pl ots(3, sharey=True) ax1. plot(x, y1, ko-)ax1.set(title=3 sub pl ots, ylabel=H P) ax2. plot(x, y2, r.-)ax2.set(xlabel= Pokemo n, ylabel=Attack)ax3. plot(x, y3,:)ax3.set(xlabel=P okem on, ylabel=Defe nse) p lt.show() 45.展示前15个宝可梦的Attack,HP的折线图 plt. ploMdfTH P :15, -r,label= HP)plt .p lot(dfAttack:15, :g,label=Attack)p lt.lege nd();46.用scatter的x,y,c属性，展示所有宝可梦的Defense,Attack,HP数据x = dfAttack y = dfDefe nse colors = df HP p lt.scatter(x, y, c=colors, alp ha=0