python stackplot_python高级统计图

堆积图柱形图

import matplotlib as mpl

import matplotlib.pyplot as plt

import numpy as np

x = range(1,6)

y = np.random.randint(1,14,5)

y1 = np.random.randint(1,10,5)

plt.bar(x,y,align='center',color='gray',tick_label=[chr(i) for i in range(65,70)],label='样本A')

plt.bar(x,y1,align='center',bottom=y,color='b',label='样本B')

plt.xlabel('value')

plt.ylabel('sample No.')

plt.show()

python高级统计图

堆积条形图

plt.barh(x,y,align='center',color='c',tick_label=[chr(i) for i in range(65,70)],label='样本')

plt.barh(x,y1,align='center',left=y,color='b',label='样本B')

plt.show()

python高级统计图

分组条形图

bar_width = 0.35

tick_label = [chr(i) for i in range(65,70)]

x = np.arange(5)

y = np.random.randint(0,10,5)

y1 = np.random.randint(0,8,5)

plt.bar(x,y,bar_width,color='c',align='center',label='样本A',alpha=0.5)

plt.bar(x+bar_width,y1,bar_width,color='r',align='center',label='样本B',alpha=0.5)

plt.xticks(x+bar_width/2,tick_label)

plt.show()

python高级统计图

分组水平条形图

plt.barh(x,y,bar_width,color='c',align='center',label='A',alpha=.5)

plt.barh(x+bar_width,y1,bar_width,color='b',align='center',label='B',alpha=.5)

plt.yticks(x+bar_width/2,tick_label)

plt.legend()

plt.show()vv

python高级统计图

hatck参数设置填充样式

plt.bar(x,y,align='center',color='c',tick_label=tick_label,hatch='///')

plt.show()

python高级统计图

plt.bar(x,y,align='center',color='c',tick_label=tick_label,hatch='***')

plt.show()

python高级统计图

plt.bar(x,y,align='center',color='c',tick_label=tick_label,hatch='---')

plt.show()

python高级统计图

stackplot()堆积折线图

x = np.arange(1,6,1)

y = np.random.randint(0,10,5)

y1 = np.random.randint(1,8,5)

y2 = np.random.randint(0,9,5)

labels = [chr(i) for i in range(65,70)]

plt.stackplot(x,y,y1,y2,labels=labels)

plt.legend()

plt.show()

python高级统计图

broken_barh()间断条形图

plt.broken_barh(np.random.rand(3,2),(10,8))

plt.broken_barh(np.random.rand(4,2),(20,8),facecolors=('r','b','y','c'))

plt.xlim(-1,2)

plt.xlabel('action time')

plt.xticks(np.arange(0,1,10))

plt.yticks([15,25],['Opera House A','Opera House B'])

plt.grid(ls='-',lw=1,color='gray')

plt.title('Action time in diffrent Opera House')

plt.show()

python高级统计图

step()阶梯图

x = np.linspace(1,10,10)

y = np.sin(x)

plt.step(x,y,color='c',where='pre',lw=2) # where in 'pre' or 'post'

plt.xlim(0,11)

plt.xticks(np.arange(1,11,1))

plt.ylim(-1.2,1.2)

plt.show()

python高级统计图

堆积直方图

# stacked = True

score1 = np.random.randint(0,100,100)

score2 = np.random.randint(0,100,100)

x = [score1,score2]

labels = ['sampleA','sampleB']

bins = range(0,101,10)

plt.hist(x,bins=bins,histtype='bar',rwidth=1,stacked=True,label=labels)

plt.xlabel('Test Score')

plt.ylabel('Students Num')

plt.title('Test Score Histgram in different classies')

plt.legend(loc='upper left')

plt.show()

python高级统计图

# stacked = False

score1 = np.random.randint(0,100,100)

score2 = np.random.randint(0,100,100)

x = [score1,score2]

labels = ['sampleA','sampleB']

bins = range(0,101,10)

plt.hist(x,bins=bins,histtype='bar',rwidth=1,stacked=False,label=labels)

plt.xlabel('Test Score')

plt.ylabel('Students Num')

plt.title('Test Score Histgram in different classies')

plt.legend(loc='upper left')

plt.show()

python高级统计图

# histtype = 'stepfilled'

score1 = np.random.randint(0,100,100)

score2 = np.random.randint(0,100,100)

x = [score1,score2]

labels = ['sampleA','sampleB']

bins = range(0,101,10)

plt.hist(x,bins=bins,histtype='stepfilled',rwidth=1,label=labels,alpha=0.5)

plt.xlabel('Test Score')

plt.ylabel('Students Num')

plt.title('Test Score Histgram in different Samples')

plt.legend(loc='upper left')

plt.show()

python高级统计图

分列式饼图

labels = [chr(i) for i in range(65,70)]

values = list(np.random.randint(0,25,4))

values.append(100 - sum(values))

values.sort(reverse=False)

explode = list(np.repeat(0.1,5))

plt.pie(values,explode=explode,labels=labels,autopct='%3.2f%%',

startangle=45,shadow=True)

#values 饼片的百分比

#explode 饼片边缘偏离半径的百分比

#labels 饼片标签文本

#autopct 饼片文本标签内容对应的数值百分比样式

#startangle 第一个饼片逆时针旋转的角度

#shadow 饼片阴影

#colors 饼片颜色

plt.title('Different samples select percent rate')

plt.show()

python高级统计图

非分列式饼图

plt.pie(values,labels=labels,autopct='%3.2f%%',

startangle=45,pctdistance=.7,labeldistance=1.2,

shadow=True)

plt.show()

python高级统计图

绘制内嵌环形饼图

elements = [chr(i) for i in range(65,70)]

weight1 = list(np.random.randint(0,50,5))

weight2 = list(np.random.randint(10,60,5))

colormapList = ['r','b','c','y']

wedges1,texts1,autotexts1 = plt.pie(weight1,autopct='%3.1f%%',radius=1,pctdistance=.85,

colors=colormapList,textprops=dict(color='w'),

wedgeprops=dict(width=0.3,edgecolor='w'))

wedges2,text2,autotexts2 = plt.pie(weight2,autopct='%3.1f%%',radius=.7,pctdistance=.75,

colors=colormapList,textprops=dict(color='w'),

wedgeprops=dict(width=.3,edgecolor='w'))

plt.legend(wedges1,

elements,

fontsize=12,title='Content table',

loc='center left',

bbox_to_anchor=(.91,0,.3,1))

plt.setp(autotexts1,size=15,weight='bold')

plt.setp(autotexts2,size=15,weight='bold')

plt.setp(texts1,size=12)

plt.title('Content list in different product')

plt.show()

python高级统计图

多分组箱线图

sampleA = np.random.randn(5000)

sampleB = np.random.randn(5000)

samples = [sampleA,sampleB]

labels = ['SampleA','SampleB']

colors = ['#1b9e77','#d95f02']

bplot = plt.boxplot(samples,

whis=1.6,

widths=.35,

sym='o',

labels=labels,

patch_artist=True)

#sampls 输入数据

#whis 四分位间距的倍数，用来确定箱须范围值

#widths 箱的宽度

#sym 离群值的标记样式

#labels 赝本标签

#patch_artist 是否给箱子提爱妮佳颜色

for patch,color in zip(bplot['boxes'],colors):

patch.set_facecolor(color)

plt.ylabel('random number')

plt.title('Samples capacity of resisting disturbarice')

plt.grid(axis='y',ls=':',lw=1,color='gray',alpha=.4)

plt.show()

python高级统计图

水平箱线图

x = np.random.randn(1000)

plt.boxplot(x,vert=False)

plt.xlabel('random values')

plt.yticks([1],['SampleA'],rotation=90)

plt.title("SampleA's boxplot")

plt.grid(axis='x',ls=':',lw=1,color='gray',alpha=.4)

plt.show()

python高级统计图

不绘制离群值箱线图

plt.boxplot(x,vert=False,showfliers=False)

plt.xlabel('values')

plt.yticks([1],['SampleA boxplot'],rotation=90)

plt.title('Boxplto')

plt.grid(axis='x',ls=':',lw=1,color='gray',alpha=.4)

plt.show()

python高级统计图

误差棒图

x = np.linspace(.1,.6,10)

y = np.exp(x)

error = .05+.15*x

lower_error = error

upper_error = .3*error

error_limit = [lower_error,upper_error]

plt.errorbar(x,y,yerr=error_limit,fmt=':o',

ecolor='y',elinewidth=4,ms=5,mfc='c',mec='r',

capthick=1,capsize=2)

#x,y 数据点位置

#yerr 但一直的非对称形式的误差范围

#fmt 数据点的标记样式和数据点演示的连接线样式

#ecolor 乌茶邦的线条颜色

#elinewidth 乌茶邦的线条粗细

#ms 数据点的大小

#mfc 数据点的标记颜色

#mec 数据点的标记边缘颜色

#capthick 误差棒的边界横杠宽度

#capsize 误差棒边界横杠的大小

plt.xlim(0,.7)

plt.show()

python高级统计图

误差棒柱形图

x = list(range(1,6))

y = list(np.random.randint(10,100,5))

std_err = list(np.random.randint(0,10,5))

error_attri = dict(elinewidth=2,ecolor='black',capsize=3)

plt.bar(x,y,color='c',

width=.6,

align='center',

yerr=std_err,

error_kw=error_attri,

tick_label=[chr(i) for i in range(65,70)])

plt.xlabel('Samples')

plt.ylabel('values')

plt.title('Error Bar')

plt.grid(True, axis='y',ls=':',color='gray',alpha=.2)

plt.show()

python高级统计图

带误差棒的水平条形图

plt.barh(x,y,

bar_width,

color=['r','b','c','y'],

align='center',

xerr=std_err,

tick_label=[chr(i) for i in range(65,70)])

plt.xlabel('values')

plt.ylabel('SampleA')

plt.title('Error Barh')

plt.show()

python高级统计图

带误差的分组柱形图

x = np.arange(5)

y1 = np.random.randint(0,100,5)

y2 = np.random.randint(10,50,5)

std_err1 = np.random.randint(0,10,5)

std_err2 = np.random.randint(0,10,5)

error_attri = dict(elinewidth=2, ecolor='black', capsize=3)

bar_width=.4

tick_label=[chr(i) for i in range(65,70)]

plt.bar(x,y1,bar_width,color='c',

align='center',yerr=std_err1,

error_kw=error_attri,label='A')

plt.bar(x+bar_width,y2,bar_width,

color='y',align='center',

yerr=std_err2,error_kw=error_attri,label='B')

plt.xlabel('Samples')

plt.ylabel('values')

plt.title('Error Bar by group')

plt.grid(True,axis='y',ls=':',color='gray',alpha=.2)

plt.legend()

plt.show()

python高级统计图

带误差棒的堆积柱形图

plt.bar(x,y1,bar_width,color='c',

align='center',yerr=std_err1,

label='SampleA',error_kw=error_attri)

plt.bar(x,y2,bar_width,bottom=y1,color='y',

align='center',yerr=std_err2,

label='SampleB',error_kw=error_attri)

plt.xlabel('Sampls')

plt.ylabel('values')

plt.title('Error Bar by group')

plt.grid(True,axis='y',ls=':',color='gray',alpha=.2)

plt.xticks(x,tick_label)

plt.legend()

plt.show()

python高级统计图