一、堆积图
1、堆积柱状图
如果将函数bar()中的参数bottom的取值设定为列表y、列表y1代表另一个数，函数bar(x,y1,bottom=y,color="r")就会输出堆积柱状图
代码示例：
import matplotlib.pyplot as plt x = [1,2,3,4,5] y = [6,10,4,5,1] y1 = [2,6,3,8,5] plt.bar(x,y,align="center",color="#66c2a5",tick_label=["A","B","C","D","E"],label="title_A") plt.bar(x,y1,align="center",color="#8da0cb",tick_label=["A","B","C","D","E"],label="title_B") plt.legend() plt.show()图像输出：
2、堆积条状图
如果将函数barh()中的参数left的取值设定为列表y，列表y1代表另一数据，函数bar(x,y1,left=y,color="r")
代码示例：
import matplotlib.pyplot as plt x = [1,2,3,4,5] y = [6,10,4,5,1] y1 = [2,6,3,8,5] plt.barh(x,y,align="center",color="#66c2a5",tick_label=["A","B","C","D","E"],label="title_A") plt.barh(x,y1,align="center",left=y,color="#8da0cb",tick_label=["A","B","C","D","E"],label="title_B") plt.legend() plt.show()图像输出：
二、分块图
1、多数据并列柱状图
代码示例：
import matplotlib.pyplot as plt import numpy as np x = np.arange(5) y = [6,10,4,5,1] y1 = [2,6,3,8,5] bar_width = 0.35 tick_label = ["A","B","C","D","E"] plt.bar(x,y,align="center",color="c",width=bar_width,label="title_A",alpha=0.5) plt.bar(x+bar_width,y1,align="center",color="b",width=bar_width,label="title_B",alpha=0.5) plt.xticks(x+bar_width/2,tick_label) plt.legend() plt.show()输出图像：
2、多数据平行条状图
代码示例：
import matplotlib.pyplot as plt import numpy as np x = np.arange(5) y = [6,10,4,5,1] y1 = [2,6,3,8,5] bar_width = 0.35 tick_label = ["A","B","C","D","E"] plt.barh(x,y,bar_width,align="center",color="c",label="title_A",alpha=0.5) plt.barh(x+bar_width,y1,bar_width,align="center",color="b",label="title_B",alpha=0.5) plt.yticks(x+bar_width/2,tick_label) plt.legend() plt.show()输出图像：
三、堆积折线图、间断条形图和阶梯图
1、函数stackplot() —— 绘制堆积折线图
代码示例：
import matplotlib.pyplot as plt import numpy as np x = np.arange(0,5,1) y = [0,4,3,5,6] y1 = [1,3,4,2,7] y2 = [3,4,1,6,5] labels = ["Blue","Brown","Green"] colors = ["#8da0cb","#fc8d62","#66c2a5"] plt.stackplot(x,y,y1,y2,labels=labels,colors=colors) plt.legend(loc="upper left") plt.show()图像输出：
2、函数broken_barh() —— 绘制间断条形图
代码示例：
import matplotlib.pyplot as plt import numpy as np plt.broken_barh([(30,100),(180,50),(260,70)],(20,8),facecolors="#1f78b4") plt.broken_barh([(60,90),(190,520),(230,30),(280,60)],(10,8),facecolors=("#7fc97f","#beaed4","#fdc086","#ffff99")) plt.xlim(0,360) plt.ylim(5,35) plt.xticks(np.arange(0,361,60)) plt.yticks([15,25],["A","B"]) plt.grid(ls="-",lw=1,color="gray") plt.show()图像输出：
3、函数step() —— 绘制阶梯图
参数说明：
where：默认参数为“pre”， 参数值“pre”表示x轴上的每个数据点对应y轴上的数值向左侧绘制水平线直到x轴上的此数据点的左侧相邻数据点为止， 也就是说，x轴上的相邻数据点的取值是按照左开右闭区间进行选取的。 除了可以取“pre”，还可以取“post”，“post”指的是x轴上的相邻数据点的取值按照左闭右开的数据点选取， 然后用对应的y轴上的数据向右绘制水平线直到x轴上的数据点右侧相邻数据点为止。代码示例：
import matplotlib.pyplot as plt import numpy as np x = np.linspace(1,10,10) y = np.sin(x) plt.step(x,y,color="#8dd3c7",where="pre",lw=2) plt.xlim(0,11) plt.xticks(np.arange(1,11,1)) plt.ylim(-1.2,1.2) plt.show()图像输出：
四、直方图
1、hist()
hist(x,bins=bins,color="b",histtype="bar",label="score",rwidth=10)参数说明：
x：连续型数据的输入值 bins：用于确定柱体的个数或是柱体边缘的范围 color：柱体的颜色 histtype：柱体类型 label：图例内容 rwidth：柱体宽度2、堆积直方图
代码实例：
import matplotlib.pyplot as plt import numpy as np x1 = np.random.randint(0,100,100) x2 = np.random.randint(0,100,100) x = [x1,x2] colors = ["#8dd3c7","#bebada"] labels = ["A","B"] bins = range(0,101,10) plt.hist(x,bins=bins,color=colors,histtype="bar",rwidth=10,stacked=True,label=labels,edgecolor = 'k') plt.legend(loc="upper left") plt.show()堆积图像输出：
对比：不堆积并列（即：stacked=False）效果图输出 ：
3、直方图与阶梯图结合对比
代码实例：
import matplotlib.pyplot as plt import numpy as np x1 = np.random.randint(0,100,100) x2 = np.random.randint(0,100,100) x = [x1,x2] colors = ["#8dd3c7","#bebada"] labels = ["A","B"] bins = range(0,101,10) plt.hist(x,bins=bins,color=colors,histtype="stepfilled",rwidth=10,stacked=True,label=labels,edgecolor = 'k') plt.legend(loc="upper right") plt.show()图像输出：
五、饼图
1、分列式饼图
代码实现：
import matplotlib.pyplot as plt labels = ["A","B","C","D"] nums = [0.35,0.15,0.2,0.3] colors = ["#377eb8","#4daf4a","#984ea3","#ff7f00"] explode = (0.1,0.1,0.1,0.1) plt.pie(nums,explode=explode,labels=labels,autopct="%3.1f%%",startangle=45,shadow=True,colors=colors) plt.show()图像输出：
2、非分裂式饼图
代码示例：
import matplotlib.pyplot as plt labels = ["A","B","C","D"] nums = [0.35,0.15,0.2,0.3] colors = ["#377eb8","#4daf4a","#984ea3","#ff7f00"] plt.pie(nums,labels=labels,autopct="%3.1f%%",startangle=45,colors=colors,pctdistance=0.7) plt.show()图像输出：
3、内嵌环形饼图
代码示例：
import matplotlib.pyplot as plt labels = ["A","B","C","D","E"] nums1 = [40,15,20,10,15] nums2 = [30,25,15,20,10] colors = ["#e41a1c","#377eb8","#4daf4a","#984ea3","#ff7f00"] w1, t1, a1 = plt.pie(nums1,autopct="%3.1f%%",radius=1,pctdistance=0.85,colors=colors,textprops=dict(color="w"),wedgeprops=dict(width=0.3,edgecolor="w")) w2, t2, a2 = plt.pie(nums2,autopct="%3.1f%%",radius=0.7,pctdistance=0.75,colors=colors,textprops=dict(color="w"),wedgeprops=dict(width=0.3,edgecolor="w")) plt.legend(nums1,labels,fontsize=12,bbox_to_anchor=(0.91,0,0.3,1)) plt.setp(a1,size=15,weight="bold") plt.setp(a2,size=15,weight="bold") plt.setp(t1,size=12) plt.show()图像输出：
六、箱线图
plt.boxplot(testList,whis=whis,widths=width,sym="o",labels=labels,patch_artist=True)参数说明：
testList:绘制箱线图的输入数据 whis：四分位间距的倍数，用来确定箱须包含数据的范围大小 widths：设置箱体的宽度 sym：离群值的标记样式 labels：绘制每一个数据集的刻度标签 patch_artist：是否给箱体添加颜色代码实例：
import matplotlib.pyplot as plt import numpy as np t1 = np.random.randn(5000) t2 = np.random.randn(5000) testList = [t1,t2] labels = ["A","B"] colors = ["#1b9e77","#d95f02"] whis = 1.6 width = 0.35 bplot = plt.boxplot(testList,whis=whis,widths=width,sym="o",labels=labels,patch_artist=True) for patch,color in zip(bplot["boxes"],colors): patch.set_facecolor(color) plt.grid(axis="y",ls=":",lw=1,color="gray",alpha=0.4) plt.show()图像输出：
2、水平方向箱线图
代码示例：
import matplotlib.pyplot as plt import numpy as np x = np.random.randn(1000) plt.boxplot(x,vert=False) plt.grid(axis="x",ls=":",lw=1,color="gray",alpha=0.4) plt.show()图像输出：
3、没有离群值的水平箱线图
代码示例：
import matplotlib.pyplot as plt import numpy as np x = np.random.randn(1000) plt.boxplot(x,vert=False,showfliers=False) plt.grid(axis="x",ls=":",lw=1,color="gray",alpha=0.4) plt.show()图像输出：
七、误差棒图
1、定量数据的误差范围
代码示例：
import matplotlib.pyplot as plt import numpy as np x = np.linspace(0.1,0.6,10) y = np.exp(x) err = 0.05 + 0.15 * x low_err = err upp_err = err * 0.3 err_lim = [low_err,upp_err] plt.errorbar(x,y,yerr=err_lim,fmt=":o",ecolor="y",elinewidth=4,ms=5,mfc='r',capthick=1,capsize=2) plt.xlim(0,0.7) plt.show()图像输出：
参数说明：
x,y : 数据点的位置 yerr:单一数值的非对称形式误差范围 //误差范围非对称形式，而且数据点下方的误差范围大于数据点上方的误差范围，xerr类似 fmt：数据点标记样式和数据点标记的连接线样式 ecolor：误差棒的线条颜色 elinewidth：误差棒的线条粗细 ms:数据点的大小 mfc：数据点的标记颜色 mec：数据点的标记边沿颜色 capthick：误差棒边界横杠的厚度 capsize：误差棒边界横杠的大小2、带误差棒的柱状图
代码实例：
import matplotlib.pyplot as plt import numpy as np x = np.arange(5) y = [100,68,79,91,82] err = [7,2,6,10,5] err_att = dict(elinewidth=2,ecolor="black",casize=3) plt.bar(x,y,color="c",width=0.6,align="center",yerr=err,error_kw=err_att,tick_label=["A","B","C","D","E"]) plt.grid(True,axis="y",ls=":",color="gray",alpha=0.2) plt.show()图像输出：
3、带误差棒的条形图
代码实例：
import matplotlib.pyplot as plt import numpy as np x = np.arange(5) y = [1200,2400,1800,2200,1600] err = [150,100,180,130,100] bar_width = 0.6 colors = ["#377eb8","#4daf4a","#984ea3","#ff7f00","#e41a1c"] plt.barh(x,y,bar_width,color=colors,align="center",xerr=err,tick_label=["A","B","C","D","E"]) plt.grid(True,axis="x",ls=":",color="gray",alpha=0.2) plt.show()图像输出：
4、带误差棒的多数据并列柱状图
代码实例：
import matplotlib.pyplot as plt import numpy as np x = np.arange(5) y1 = [100,68,79,91,82] y2 = [120,75,70,78,85] err1 = [7,2,6,10,5] err2 = [5,1,4,8,9] err_att = dict(elinewidth=2,ecolor="black",capize=3) bar_width = 0.4 tick_label=["A","B","C","D","E"] plt.bar(x,y1,bar_width,color="#87ceeb",align="center",yerr=err1,error_kw=err_att,label="NO.1") plt.bar(x+bar_width,y2,bar_width,color="#cd5c5c",align="center",yerr=err2,error_kw=err_att,label="NO.2") plt.xticks(x+bar_width/2,tick_label) plt.grid(True,axis="y",ls=":",color="gray",alpha=0.2) plt.legend() plt.show()图像输出：
5、带误差棒的堆积柱状图
代码实例：
import matplotlib.pyplot as plt import numpy as np x = np.arange(5) y1 = [1200,2400,1800,2200,1600] y2 = [1050,2100,1300,1600,1340] err1 = [150,100,180,130,80] err2 = [120,110,170,150,120] err_att = dict(elinewidth=2,ecolor="black",capize=0) bar_width = 0.6 tick_label=["A","B","C","D","E"] plt.bar(x,y1,bar_width,color="#6495ed",align="center",yerr=err1,error_kw=err_att,label="NO.1") plt.bar(x,y2,bar_width,color="#ffa500",align="center",yerr=err2,error_kw=err_att,label="NO.2") plt.xticks(x,tick_label) plt.grid(True,axis="y",ls=":",color="gray",alpha=0.2) plt.legend() plt.show()图像输出：
转载于:https://www.cnblogs.com/circleyuan/p/10350166.html
相关资源：数据结构—成绩单生成器