沉淀再出发:用python画各种图表
一、前言
最近需要用python来做一些统计和画图，因此做一些笔记。
二、python画各种图表
2.1、使用turtle来画图
1 import turtle as t #turtle库是python的内部库，直接import使用即可 2 import time 3 4 def draw_diamond(turt): 5 for i in range(1,3): 6 turt.forward(100) #向前走100步 7 turt.right(45) #海龟头向右转45度 8 turt.forward(100) #继续向前走100步 9 turt.right(135) #海龟头再向右转135度 10 11 12 def draw_art(): 13 window = t.Screen() #创建画布 14 window.bgcolor("green") #设置画布颜色 15 brad = t.Turtle() #创建一个Turtle的实例 16 brad.shape('turtle') #形状是一个海归turtle，也可以是圆圈circle，箭头(默认)等等 17 18 brad.color("red") #海龟的颜色是红色red，橙色orange等 19 brad.speed('fast') #海龟画图的速度是快速fast，或者slow等 20 21 for i in range(1,37): #循环36次 22 draw_diamond(brad) #海龟画一个形状/花瓣，也就是菱形 23 brad.right(10) #后海龟头向右旋转10度 24 25 brad.right(90) #当图形画完一圈后，把海龟头向右转90度 26 brad.forward(300) #画一根长线/海龟往前走300步 27 28 window.exitonclick() #点击屏幕退出 29 30 draw_art() #调用函数开始画图 31 32 33 34 t.color("red", "yellow") 35 t.speed(10) 36 t.begin_fill() 37 for _ in range(50): 38 t.forward(200) 39 t.left(170) 40 end_fill() 41 time.sleep(1)
2.2、画坐标系
1 import sys 2 import math 3 import random 4 import matplotlib.pyplot as plt 5 import pylab as pl 6 import numpy as np 7 8 pl.mpl.rcParams['font.sans-serif'] = ['FangSong'] # 指定默认字体 9 pl.mpl.rcParams['axes.unicode_minus'] = False # 解决保存图像是负号'-'显示为方块的问题 10 11 x = range(10) # 横轴的数据 12 y = [i*i for i in x] # 纵轴的数据 13 y1 = [i*i+123 for i in x] # 纵轴的数据 14 pl.title('title zyr') 15 pl.plot(x, y, '1m:', label=u'compare') # 加上label参数添加图例 16 pl.plot(x, y1, '>r--', label=u'set other') # 加上label参数添加图例 17 pl.xlabel(u"横轴的数据") 18 pl.ylabel(u"纵轴的数据") 19 pl.legend() # 让图例生效 20 pl.show() # 显示绘制出的图
import sys import math import random import matplotlib.pyplot as plt import pylab as pl import numpy as np pl.mpl.rcParams['font.sans-serif'] = ['FangSong'] # 指定默认字体 pl.mpl.rcParams['axes.unicode_minus'] = False # 解决保存图像是负号'-'显示为方块的问题 x = list(range(10))+[100] y = [i*i for i in x] pl.plot(x, y, 'ob-', label=u'y=x^2') pl.xlim(-1, 11) # 限定横轴的范围 pl.ylim(-1, 110) # 限定纵轴的范围 pl.show() # 显示绘制出的图
1 颜色（color 简写为 c）： 2 # 蓝色： 'b' (blue) 3 # 绿色： 'g' (green) 4 # 红色： 'r' (red) 5 # 蓝绿色(墨绿色)： 'c' (cyan) 6 # 红紫色(洋红)： 'm' (magenta) 7 # 黄色： 'y' (yellow) 8 # 黑色： 'k' (black) 9 # 白色： 'w' (white) 10 11 # 线型（linestyle 简写为 ls）： 12 # 实线： '-' 13 # 虚线： '--' 14 # 虚点线： '-.' 15 # 点线： ':' 16 # 点： '.' 17 18 # 点型（标记marker）： 19 # 像素： ',' 20 # 圆形： 'o' 21 # 上三角： '^' 22 # 下三角： 'v' 23 # 左三角： '<' 24 # 右三角： '>' 25 # 方形： 's' 26 # 加号： '+' 27 # 叉形： 'x' 28 # 棱形： 'D' 29 # 细棱形： 'd' 30 # 三脚架朝下： '1'（像'丫'） 31 # 三脚架朝上： '2' 32 # 三脚架朝左： '3' 33 # 三脚架朝右： '4' 34 # 六角形： 'h' 35 # 旋转六角形： 'H' 36 # 五角形： 'p' 37 # 垂直线： '|' 38 # 水平线： '_'
2.3、直方图
1 import numpy as np 2 import matplotlib.pyplot as plt 3 np.random.seed(19680801) 4 mu1, sigma1 = 100, 15 5 mu2, sigma2 = 80, 15 6 x1 = mu1 + sigma1 * np.random.randn(10000) 7 x2 = mu2 + sigma2 * np.random.randn(10000) 8 # the histogram of the data 9 # 50：将数据分成50组 10 # facecolor：颜色；alpha：透明度 11 # density：是密度而不是具体数值 12 n1, bins1, patches1 = plt.hist(x1, 50, density=True, facecolor='g', alpha=1) 13 n2, bins2, patches2 = plt.hist(x2, 50, density=True, facecolor='r', alpha=0.2) 14 # n：概率值；bins：具体数值；patches：直方图对象。 15 plt.xlabel('Smarts') 16 plt.ylabel('Probability') 17 plt.title('Histogram of IQ') 18 plt.text(110, .025, r'$mu=100, sigma=15$') 19 plt.text(50, .025, r'$mu=80, sigma=15$') 20 # 设置x，y轴的具体范围 21 plt.axis([40, 160, 0, 0.03]) 22 plt.grid(True) 23 plt.show()
2.4、扇形图/饼图
1 import matplotlib.pyplot as plt 2 import time 3 from pylab import mpl 4 import numpy as np 5 import matplotlib.animation as animation 6 import time 7 8 mpl.rcParams['font.sans-serif'] = ['FangSong'] # 指定默认字体 9 mpl.rcParams['axes.unicode_minus'] = False # 解决保存图像是负号'-'显示为方块的问题 10 11 data = {'8516464': 106, '8085460': 704, '7593813': 491, '8709362': 24, '8707829': 6, '8684658': 23, '8679301': 11, 12 '8665923': 29, '8660909': 23, '8652968': 31, '8631727': 31, '8622935': 24, '8620593': 18, '8521737': 33, 13 '8605441': 49, '8495205': 82, '8477276': 57,'8474489': 71, '8456502': 50, '8446529': 68, '8433830': 136, 14 '8254158': 103, '8176029': 88, '8081724': 58, '7922592': 185, '7850099': 62,'7617723': 61, '7615562': 90, 15 '7615052': 57, '7604151': 102, '7511294': 59,'6951654': 27, '6946388': 142, '6945373': 159, '6937716': 347, 16 '7460176': 64, '7246377': 87, '7240621': 145, '7204707': 645, '7028401': 671} 17 source_data = sorted(data.items(), key=lambda x: x[1], reverse=True) 18 print(source_data) 19 labels = [source_data[i][0][:4] for i in range(len(source_data))] # 设置标签 20 fracs = [source_data[i][1] for i in range(len(source_data))] 21 explode = [x * 0.01 for x in range(len(source_data))] # 与labels一一对应，数值越大离中心区越远 22 plt.axes(aspect=1) # 设置X轴 Y轴比例 23 # labeldistance标签离中心距离 pctdistance百分百数据离中心区距离 autopct 百分比的格式 shadow阴影 24 plt.pie(x=fracs, labels=labels, explode=explode, autopct='%3.1f %%', 25 shadow=False, labeldistance=1.1, startangle=0, pctdistance=0.8, center=(-1, 0)) 26 # 控制位置：bbox_to_anchor数组中，前者控制左右移动，后者控制上下。ncol控制 图例所列的列数。默认值为1。fancybox 圆边 27 plt.legend(loc=7, bbox_to_anchor=(1.2, 0.80), ncol=3, fancybox=True, shadow=True, fontsize=8) 28 plt.show()
1 import matplotlib.pyplot as plt 2 labels = 'Frogs', 'Hogs', 'Dogs', 'Logs' 3 sizes = [15, 30, 45, 10] 4 # 设置分离的距离，0表示不分离 5 explode = (0, 0.1, 0, 0) 6 plt.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%', 7 shadow=True, startangle=90) 8 # Equal aspect ratio 保证画出的图是正圆形 9 plt.axis('equal') 10 plt.show()
1 import numpy as np 2 import matplotlib.pyplot as plt 3 # 设置每环的宽度 4 size = 0.3 5 vals = np.array([[60., 32.], [37., 40.], [29., 10.]]) 6 # 通过get_cmap随机获取颜色 7 cmap = plt.get_cmap("tab20c") 8 outer_colors = cmap(np.arange(3)*4) 9 inner_colors = cmap(np.array([1, 2, 5, 6, 9, 10])) 10 print(vals.sum(axis=1)) 11 # [92. 77. 39.] 12 plt.pie(vals.sum(axis=1), radius=1, colors=outer_colors, 13 wedgeprops=dict(width=size, edgecolor='w')) 14 print(vals.flatten()) 15 # [60. 32. 37. 40. 29. 10.] 16 plt.pie(vals.flatten(), radius=1-size, colors=inner_colors, 17 wedgeprops=dict(width=size, edgecolor='w')) 18 # equal 使得为正圆 19 plt.axis('equal') 20 plt.show()
1 import numpy as np 2 import matplotlib.pyplot as plt 3 np.random.seed(19680801) 4 N = 10 5 theta = np.linspace(0.0, 2 * np.pi, N, endpoint=False) 6 radii = 10 * np.random.rand(N) 7 width = np.pi / 4 * np.random.rand(N) 8 ax = plt.subplot(111, projection='polar') 9 bars = ax.bar(theta, radii, width=width, bottom=0.0) 10 # left表示从哪开始， 11 # radii表示从中心点向边缘绘制的长度（半径） 12 # width表示末端的弧长 13 # 自定义颜色和不透明度 14 for r, bar in zip(radii, bars): 15 bar.set_facecolor(plt.cm.viridis(r / 10.)) 16 bar.set_alpha(0.5) 17 plt.show()
2.5、动图
1 import matplotlib.pyplot as plt 2 import time 3 from pylab import mpl 4 import numpy as np 5 import matplotlib.animation as animation 6 import time 7 8 mpl.rcParams['font.sans-serif'] = ['FangSong'] # 指定默认字体 9 mpl.rcParams['axes.unicode_minus'] = False # 解决保存图像是负号'-'显示为方块的问题 10 11 12 # Fixing random state for reproducibility 13 np.random.seed(196) 14 # 初始数据绘图 15 dis = np.zeros(40) 16 dis2 = dis 17 fig, ax = plt.subplots() 18 line, = ax.plot(dis) 19 ax.set_ylim(-1, 1) 20 plt.grid(True) 21 ax.set_ylabel("distance: m") 22 ax.set_xlabel("time") 23 24 def update(frame): 25 global dis 26 global dis2 27 global line 28 # 读入模拟 29 a = np.random.rand() * 2 - 1 30 time.sleep(np.random.rand() / 10) 31 # 绘图数据生成 32 dis[0:-1] = dis2[1:] 33 dis[-1] = a 34 dis2 = dis 35 # 绘图 36 line.set_ydata(dis) 37 # 颜色设置 38 plt.setp(line, 'color', 'c', 'linewidth', 2.0) 39 ani = animation.FuncAnimation(fig, update, frames=None, interval=100) 40 plt.show()
2.6、画其他图形
1 import matplotlib.pyplot as plt 2 plt.rcdefaults() 3 4 import numpy as np 5 import matplotlib.pyplot as plt 6 import matplotlib.path as mpath 7 import matplotlib.lines as mlines 8 import matplotlib.patches as mpatches 9 from matplotlib.collections import PatchCollection 10 11 12 def label(xy, text): 13 y = xy[1] - 0.15 # shift y-value for label so that it's below the artist 14 plt.text(xy[0], y, text, ha="center", family='sans-serif', size=14) 15 16 17 fig, ax = plt.subplots() 18 # create 3x3 grid to plot the artists 19 grid = np.mgrid[0.2:0.8:3j, 0.2:0.8:3j].reshape(2, -1).T 20 21 patches = [] 22 23 # add a circle 24 circle = mpatches.Circle(grid[0], 0.1, ec="none") 25 patches.append(circle) 26 label(grid[0], "Circle") 27 28 # add a rectangle 29 rect = mpatches.Rectangle(grid[1] - [0.025, 0.05], 0.05, 0.1, ec="none") 30 patches.append(rect) 31 label(grid[1], "Rectangle") 32 33 # add a wedge 34 wedge = mpatches.Wedge(grid[2], 0.1, 30, 270, ec="none") 35 patches.append(wedge) 36 label(grid[2], "Wedge") 37 38 # add a Polygon 39 polygon = mpatches.RegularPolygon(grid[3], 5, 0.1) 40 patches.append(polygon) 41 label(grid[3], "Polygon") 42 43 # add an ellipse 44 ellipse = mpatches.Ellipse(grid[4], 0.2, 0.1) 45 patches.append(ellipse) 46 label(grid[4], "Ellipse") 47 48 # add an arrow 49 arrow = mpatches.Arrow(grid[5, 0] - 0.05, grid[5, 1] - 0.05, 0.1, 0.1, width=0.1) 50 patches.append(arrow) 51 label(grid[5], "Arrow") 52 53 # add a path patch 54 Path = mpath.Path 55 path_data = [ 56 (Path.MOVETO, [0.018, -0.11]), 57 (Path.CURVE4, [-0.031, -0.051]), 58 (Path.CURVE4, [-0.115, 0.073]), 59 (Path.CURVE4, [-0.03 , 0.073]), 60 (Path.LINETO, [-0.011, 0.039]), 61 (Path.CURVE4, [0.043, 0.121]), 62 (Path.CURVE4, [0.075, -0.005]), 63 (Path.CURVE4, [0.035, -0.027]), 64 (Path.CLOSEPOLY, [0.018, -0.11]) 65 ] 66 codes, verts = zip(*path_data) 67 path = mpath.Path(verts + grid[6], codes) 68 patch = mpatches.PathPatch(path) 69 patches.append(patch) 70 label(grid[6], "PathPatch") 71 72 # add a fancy box 73 fancybox = mpatches.FancyBboxPatch( 74 grid[7] - [0.025, 0.05], 0.05, 0.1, 75 boxstyle=mpatches.BoxStyle("Round", pad=0.02)) 76 patches.append(fancybox) 77 label(grid[7], "FancyBboxPatch") 78 79 # add a line 80 x, y = np.array([[-0.06, 0.0, 0.1], [0.05, -0.05, 0.05]]) 81 line = mlines.Line2D(x + grid[8, 0], y + grid[8, 1], lw=5., alpha=0.3) 82 label(grid[8], "Line2D") 83 84 colors = np.linspace(0, 1, len(patches)) 85 collection = PatchCollection(patches, cmap=plt.cm.hsv, alpha=0.3) 86 collection.set_array(np.array(colors)) 87 ax.add_collection(collection) 88 ax.add_line(line) 89 90 plt.subplots_adjust(left=0, right=1, bottom=0, top=1) 91 plt.axis('equal') 92 plt.axis('off') 93 94 plt.show()
2.7、画点图
1 from numpy import *; 2 import numpy as np 3 import matplotlib.pyplot as plt 4 5 N = 50 6 x = np.random.rand(N) 7 y = np.random.rand(N) 8 colors = np.random.rand(N) 9 area = np.pi * (15 * np.random.rand(N))**2 10 plt.scatter(x, y, s=area, c=colors, alpha=0.5, marker=(9, 3, 30)) 11 plt.show()
2.8、画数学曲线
1 #coding:utf-8 2 import numpy as np 3 import matplotlib.pyplot as plt 4 x=np.linspace(0,10,1000) 5 y=np.sin(x) 6 z=np.cos(x**2) 7 #控制图形的长和宽单位为英寸， 8 # 调用figure创建一个绘图对象，并且使它成为当前的绘图对象。 9 plt.figure(figsize=(8,4)) 10 #$可以让字体变得跟好看 11 #给所绘制的曲线一个名字，此名字在图示(legend)中显示。 12 # 只要在字符串前后添加"$"符号，matplotlib就会使用其内嵌的latex引擎绘制的数学公式。 13 #color : 指定曲线的颜色 14 #linewidth : 指定曲线的宽度 15 plt.plot(x,y,label="$sin(x)$",color="red",linewidth=2) 16 #b-- 曲线的颜色和线型 17 plt.plot(x,z,"b--",label="$cos(x^2)$") 18 #设置X轴的文字 19 plt.xlabel("Time(s)") 20 #设置Y轴的文字 21 plt.ylabel("Volt") 22 #设置图表的标题 23 plt.title("PyPlot First Example") 24 #设置Y轴的范围 25 plt.ylim(-1.2,1.2) 26 #显示图示 27 plt.legend() 28 #显示出我们创建的所有绘图对象。 29 plt.show()
1 #coding:utf-8 2 import numpy as np 3 import matplotlib.pyplot as plt 4 x=np.arange(0,5,0.1) 5 ## plot返回一个列表，通过line,获取其第一个元素 6 line,=plt.plot(x,x*x) 7 # 调用Line2D对象的set_*方法设置属性值 是否抗锯齿 8 line.set_antialiased(False) 9 # 同时绘制sin和cos两条曲线，lines是一个有两个Line2D对象的列表 10 lines = plt.plot(x, np.sin(x), x, np.cos(x)) 11 ## 调用setp函数同时配置多个Line2D对象的多个属性值 12 plt.setp(lines, color="r", linewidth=2.0) 13 plt.show()
1 import numpy as np 2 import matplotlib.pyplot as plt 3 x = np.arange(0., 5., 0.2) 4 # 红色破折号, 蓝色方块 ，绿色三角块 5 plt.plot(x, x, 'r--', x, x**2, 'bs', x, x**3, 'g^') 6 plt.show()
2.9.画多个子图
1 import matplotlib.pyplot as plt 2 ''' 3 subplot(numRows, numCols, plotNum) 4 numRows行 * numCols列个子区域 5 如果numRows，numCols和plotNum这三个数都小于10的话，可以把它们缩写为一个整数， 6 例如subplot(323)和subplot(3,2,3)是相同的 7 ''' 8 for idx, color in enumerate("rgbyckbyc"): 9 plt.subplot(330+idx+1, facecolor=color) 10 plt.show() 11 plt.subplot(221) # 第一行的左图 12 plt.subplot(222) # 第一行的右图 13 #第二行全占 14 plt.subplot(212) # 第二整行 15 plt.show() 16 17 plt.subplot(331, facecolor='r') 18 plt.show()
2.10、标记刻度
1 import matplotlib.pyplot as pl 2 from matplotlib.ticker import MultipleLocator, FuncFormatter 3 import numpy as np 4 x = np.arange(0, 4*np.pi, 0.01) 5 y = np.sin(x) 6 pl.figure(figsize=(8,4)) 7 pl.plot(x, y) 8 ax = pl.gca() 9 10 def pi_formatter(x, pos): 11 """ 12 比较罗嗦地将数值转换为以pi/4为单位的刻度文本 13 """ 14 m = np.round(x / (np.pi/4)) 15 n = 4 16 if m%2==0: m, n = m/2, n/2 17 if m%2==0: m, n = m/2, n/2 18 if m == 0: 19 return "0" 20 if m == 1 and n == 1: 21 return "$pi$" 22 if n == 1: 23 return r"$%d pi$" % m 24 if m == 1: 25 return r"$frac{pi}{%d}$" % n 26 return r"$frac{%d pi}{%d}$" % (m,n) 27 # 设置两个坐标轴的范围 28 pl.ylim(-1.5,1.5) 29 pl.xlim(0, np.max(x)) 30 # 设置图的底边距 31 pl.subplots_adjust(bottom = 0.15) 32 pl.grid() #开启网格 33 # 主刻度为pi/4 34 ax.xaxis.set_major_locator( MultipleLocator(np.pi/4) ) 35 # 主刻度文本用pi_formatter函数计算 36 ax.xaxis.set_major_formatter( FuncFormatter( pi_formatter ) ) 37 # 副刻度为pi/20 38 ax.xaxis.set_minor_locator( MultipleLocator(np.pi/20) ) 39 # 设置刻度文本的大小 40 for tick in ax.xaxis.get_major_ticks(): 41 tick.label1.set_fontsize(16) 42 pl.show()
2.11、三维图
1 import matplotlib.pyplot as plt 2 from mpl_toolkits.mplot3d import Axes3D 3 import numpy as np 4 5 def fun(x,y): 6 #return np.power(x,2)+np.power(y,2) 7 return 2*(x*0.8+y*0.1)*(x*0.2+y*0.9)*(x*0.3+y*0.7)*(x*0.3+y*0.7)*(x*0.4+y*0.7)*(x*0.4+y*0.7) 8 9 def fun2(xx,yy): 10 return xx 11 12 fig1=plt.figure() 13 ax=Axes3D(fig1) 14 X=np.arange(0,1,0.01) 15 Y=np.arange(0,1,0.01) 16 17 XX=np.arange(0,1,0.01) 18 YY=np.arange(1,0,-0.01) 19 20 ZZ=np.arange(0,1,0.01) 21 22 ZZ,ZZ=np.meshgrid(ZZ,ZZ) 23 24 #ZZ=fun2(XX,YY) 25 X,Y=np.meshgrid(X,Y) 26 Z=fun(X,Y) 27 plt.title("This is main title") 28 ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=plt.cm.coolwarm) 29 30 ax.plot_surface(XX, YY, ZZ, rstride=1, cstride=1, cmap=plt.cm.coolwarm) 31 32 ax.set_xlabel(u'θ1', color='r') 33 ax.set_ylabel(u'θ2', color='g') 34 ax.set_zlabel('z label', color='b') 35 plt.show()
1 import numpy as np 2 import matplotlib.pyplot as plt 3 from mpl_toolkits.mplot3d import Axes3D 4 data = np.random.randint(0, 255, size=[40, 40, 40]) 5 x, y, z = data[0], data[1], data[2] 6 ax = plt.subplot(111, projection='3d') # 创建一个三维的绘图工程 7 # 将数据点分成三部分画，在颜色上有区分度 8 ax.scatter(x[:10], y[:10], z[:10], c='y') # 绘制数据点 9 ax.scatter(x[10:20], y[10:20], z[10:20], c='r') 10 ax.scatter(x[30:40], y[30:40], z[30:40], c='g') 11 ax.set_zlabel('Z') # 坐标轴 12 ax.set_ylabel('Y') 13 ax.set_xlabel('X') 14 plt.show()
1 from matplotlib import pyplot as plt 2 import numpy as np 3 from mpl_toolkits.mplot3d import Axes3D 4 fig = plt.figure() 5 ax = Axes3D(fig) 6 X = np.arange(-4, 4, 0.25) 7 Y = np.arange(-4, 4, 0.25) 8 X, Y = np.meshgrid(X, Y) 9 R = np.sqrt(X**2 + Y**2) 10 Z = np.sin(R) 11 # 具体函数方法可用 help(function) 查看，如：help(ax.plot_surface) 12 ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap='rainbow') 13 plt.show()
2.12、画3d柱状图
1 #-*- coding:utf-8 -*- 2 import numpy as np 3 import matplotlib as mpl 4 import matplotlib.pyplot as plt 5 import random 6 import matplotlib.dates as mdates 7 8 from mpl_toolkits.mplot3d import Axes3D 9 #generate random numbers 10 Query_times1=range(0,50) 11 Query_times2=range(0,30) 12 list_random1=random.sample(Query_times1,25) 13 list_random2=random.sample(Query_times2,25) 14 mpl.rcParams['font.size'] = 8 15 16 fig = plt.figure() 17 ax = fig.add_subplot(111, projection='3d') 18 19 xs = np.arange(1,26) 20 ys =list_random1 21 ys2=list_random2 22 ys3=list_random1 23 ys4=list_random2 24 ys5=list_random1 25 ys6=list_random2 26 ys7=list_random1 27 ys8=list_random2 28 29 30 z1=3 31 total_width, n = 0.8, 2 32 width = total_width / n 33 color =plt.cm.Set2((np.arange(plt.cm.Set2.N))) 34 p1=ax.bar(xs, ys, z1, zdir='y', color='#FF0080', alpha=0.8,width=width,label='h=3,DA') 35 p2=ax.bar(xs + width, ys2, z1, zdir='y', color='CYAN', alpha=0.8,width=width,label='h=3,DGA') 36 37 z2=4 38 total_width, n = 0.8, 2 39 width = total_width / n 40 color =plt.cm.Set2((np.arange(plt.cm.Set2.N))) 41 p3=ax.bar(xs, ys3, z2, zdir='y', color='b', alpha=0.8,width=width,label='h=4,DA') 42 p4=ax.bar(xs + width, ys4, z2, zdir='y', color='#9AFF02', alpha=0.8,width=width,label='h=4,DGA') 43 44 z3=5 45 total_width, n = 0.8, 2 46 width = total_width / n 47 color =plt.cm.Set2((np.arange(plt.cm.Set2.N))) 48 p5=ax.bar(xs, ys5, z3, zdir='y', color='#FF8000', alpha=0.8,width=width,label='h=5,DA') 49 p6=ax.bar(xs + width, ys6, z3, zdir='y', color='violet', alpha=0.8,width=width,label='h=5,DGA') 50 51 z4=6 52 total_width, n = 0.8, 2 53 width = total_width / n 54 color =plt.cm.Set2((np.arange(plt.cm.Set2.N))) 55 p7=ax.bar(xs, ys7, z4, zdir='y', color='r', alpha=0.8,width=width,label='h=6,DA') 56 p8=ax.bar(xs + width, ys8, z4, zdir='y', color='#0072E3', alpha=0.8,width=width,label='h=6,DGA') 57 58 # ax.xaxis.set_major_locator(mpl.ticker.FixedLocator(xs)) 59 # ax.yaxis.set_major_locator(mpl.ticker.FixedLocator(ys)) 60 61 ax.set_xlabel('k-') 62 ax.set_ylabel('Spatial hierarchy (h)') 63 ax.set_zlabel('Count') 64 # plt.legend(loc='upper left') 65 #plt.legend(loc='upper left', bbox_to_anchor=(0.0,0.6),ncol=1,fancybox=True,shadow=False)#Control the position of the legend 66 plt.show()
2.13、柱状图、并列柱状图
1 import numpy as np 2 import matplotlib.animation as animation 3 import time 4 5 mpl.rcParams['font.sans-serif'] = ['FangSong'] # 指定默认字体 6 mpl.rcParams['axes.unicode_minus'] = False # 解决保存图像是负号'-'显示为方块的问题 7 8 source_data = {'mock_verify': 369, 'mock_notify': 192, 'mock_sale': 517} # 设置原始数据 9 10 for a, b in source_data.items(): 11 plt.text(a, b + 0.05, '%.0f' % b, ha='center', va='bottom', fontsize=11) # ha 文字指定在柱体中间， va指定文字位置 fontsize指定文字体大小 12 13 # 设置X轴Y轴数据，两者都可以是list或者tuple 14 x_axis = tuple(source_data.keys()) 15 y_axis = tuple(source_data.values()) 16 plt.bar(x_axis, y_axis, color='rg') # 如果不指定color，所有的柱体都会是一个颜色 17 18 plt.xlabel(u"渠道名") # 指定x轴描述信息 19 plt.ylabel(u"访问量") # 指定y轴描述信息 20 plt.title("渠道访问量统计表") # 指定图表描述信息 21 plt.ylim(0, 600) # 指定Y轴的高度 22 # plt.savefig('{}.png'.format(time.strftime('%Y%m%d%H%M%S'))) # 保存为图片 23 plt.show()
1 import numpy as np 2 import matplotlib.pyplot as plt 3 x = [1,2] #横坐标 4 y = [3,4] #第一个纵坐标 5 y1 = [5,6] #第二个纵坐标 6 x = np.arange(len(x)) #首先用第一个的长度作为横坐标 7 width = 0.05 #设置柱与柱之间的宽度 8 fig,ax = plt.subplots() 9 ax.bar(x,y,width,alpha = 0.9) 10 ax.bar(x+width,y1,width,alpha = 0.9,color= 'red') 11 ax.set_xticks(x +width/2)#将坐标设置在指定位置 12 ax.set_xticklabels(x)#将横坐标替换成 13 plt.show()
1 # -*- coding: utf-8 -*- 2 import matplotlib.pyplot as plt 3 4 name_list = ['Monday','Tuesday','Friday','Sunday'] 5 num_list = [1.5,0.6,7.8,6] 6 num_list1 = [1,2,3,1] 7 x =list(range(len(num_list))) 8 total_width, n = 0.8, 2 9 width = total_width / n 10 11 plt.bar(x, num_list, width=width, label='boy',fc = 'y') 12 for i in range(len(x)): 13 x[i] = x[i] + width 14 plt.bar(x, num_list1, width=width, label='girl',tick_label = name_list,fc = 'r') 15 plt.legend() 16 plt.show()
1 import numpy as np 2 import matplotlib.pyplot as plt 3 size = 5 4 a = np.random.random(size) 5 b = np.random.random(size) 6 c = np.random.random(size) 7 x = np.arange(size) 8 # 有多少个类型，只需更改n即可 9 total_width, n = 0.8, 3 10 width = total_width / n 11 # 重新拟定x的坐标 12 x = x - (total_width - width) / 2 13 # 这里使用的是偏移 14 plt.bar(x, a, width=width, label='a') 15 plt.bar(x + width, b, width=width, label='b') 16 plt.bar(x + 2 * width, c, width=width, label='c') 17 plt.legend() 18 plt.show()
2.13、堆叠柱状图
1 # -*- coding: utf-8 -*- 2 import matplotlib.pyplot as plt 3 4 name_list = ['Monday','Tuesday','Friday','Sunday'] 5 num_list = [1.5,0.6,7.8,6] 6 num_list1 = [1,2,3,1] 7 plt.bar(range(len(num_list)), num_list, label='boy',fc = 'y') 8 plt.bar(range(len(num_list)), num_list1, bottom=num_list, label='girl',tick_label = name_list,fc = 'r') 9 plt.legend() 10 plt.show()
1 import numpy as np 2 import matplotlib.pyplot as plt 3 size = 5 4 a = np.random.random(size) 5 b = np.random.random(size) 6 c = np.random.random(size) 7 x = np.arange(size) 8 # 这里使用的是偏移 9 plt.bar(x, a, width=0.5, label='a',fc='r') 10 plt.bar(x, b, bottom=a, width=0.5, label='b', fc='g') 11 plt.bar(x, c, bottom=a+b, width=0.5, label='c', fc='b') 12 plt.ylim(0, 2.5) 13 plt.legend() 14 plt.grid(True) 15 plt.show()
2.14、横排柱状图
1 # -*- coding: utf-8 -*- 2 import matplotlib.pyplot as plt 3 4 name_list = ['Monday','Tuesday','Friday','Sunday'] 5 num_list = [1.5,0.6,7.8,6] 6 plt.barh(range(len(num_list)), num_list,tick_label = name_list) 7 plt.show()
三、总结
使用python中的库，我们可以按照自己的想法来画图，但是需要注意一些细节上的东西，比如尺寸和刻度，比如颜色，字体，以及相应对比的数据，特别是用于数据分析上面的对比，我们需要重点掌握。