《Python数据可视化编程实战》

《Python数据可视化编程实战》 

绘制并定制化图表

3.1 柱状图、线形图、堆积柱状图

from matplotlib.pyplot import *   x = [1,2,3,4,5,6] y = [3,4,6,7,3,2]   #create new figure figure()   #线 subplot(2,3,1) plot(x,y)   #柱状图 subplot(2,3,2) bar(x,y)   #水平柱状图 subplot(2,3,3) barh(x,y)   #叠加柱状图 subplot(2,3,4) bar(x,y)   y1=[2,3,4,5,6,7] bar(x,y1,bottom=y,color='r')   #箱线图 subplot(2,3,5) boxplot(x) #散点图 subplot(2,3,6) scatter(x,y) show()

 

3.2 箱线图和直方图

from matplotlib.pyplot import *   figure() dataset = [1,3,5,7,8,3,4,5,6,7,1,2,34,3,4,4,5,6,3,2,2,3,4,5,6,7,4,3]   subplot(1,2,1)   boxplot(dataset, vert=False)   subplot(1,2,2) #直方图 hist(dataset)   show()

 

 

3.3 正弦余弦及图标

from  matplotlib.pyplot import * import numpy as np   x = np.linspace(-np.pi, np.pi, 256, endpoint=True)   y = np.cos(x) y1= np.sin(x)   plot(x,y) plot(x,y1)   #图表名称 title("Functions $\sin$ and $\cos$")   #x,y轴坐标范围 xlim(-3,3) ylim(-1,1)   #坐标上刻度 xticks([-np.pi, -np.pi/2,0,np.pi/2,np.pi],        [r'$-\pi$', r'$-\pi/2$', r'$0$', r'$+\pi/2$',r'$+\pi$']) yticks([-1, 0, 1],        [r'$-1$',r'$0$',r'$+1$' ]) #网格 grid() show()

 

3.4 设置图表的线型、属性和格式化字符串

from  matplotlib.pyplot import * import numpy as np   x = np.linspace(-np.pi, np.pi, 256, endpoint=True)   y = np.cos(x) y1= np.sin(x)   #线段颜色，线条风格，线条宽度，线条标记，标记的边缘颜色，标记边缘宽度，标记内颜色，标记大小 plot([1,2],c='r',ls='-',lw=2, marker='D', mec='g',mew=2, mfc='b',ms=30) plot(x,y1)   #图表名称 title("Functions $\sin$ and $\cos$")   #x,y轴坐标范围 xlim(-3,3) ylim(-1,4)   #坐标上刻度 xticks([-np.pi, -np.pi/2,0,np.pi/2,np.pi],        [r'$-\pi$', r'$-\pi/2$', r'$0$', r'$+\pi/2$',r'$+\pi$']) yticks([-1, 0, 1],        [r'$-1$',r'$0$',r'$+1$' ])   grid()   show()

 

3.5 设置刻度、时间刻度标签、网格

import matplotlib.pyplot as mpl from pylab import * import datetime import numpy as np   fig = figure()   ax = gca()   # 时间区间 start = datetime.datetime(2017,11,11) stop = datetime.datetime(2017,11,30) delta = datetime.timedelta(days =1)   dates = mpl.dates.drange(start,stop,delta)   values = np.random.rand(len(dates))   ax.plot_date(dates, values, ls='-')   date_format = mpl.dates.DateFormatter('%Y-%m-%d')   ax.xaxis.set_major_formatter(date_format)   fig.autofmt_xdate()   show()

 

3.6 添加图例和注释

from matplotlib.pyplot import * import numpy as np   x1 = np.random.normal(30, 2,100) plot(x1, label='plot')   #图例 #图标的起始位置，宽度，高度 归一化坐标 #loc 可选，为了图标不覆盖图 #ncol 图例个数 #图例平铺 #坐标轴和图例边界之间的间距 legend(bbox_to_anchor=(0., 1.02, 1., .102),loc = 4,        ncol=1, mode="expand",borderaxespad=0.1)   #注解 # Import data 注释 #（55,30） 要关注的点 #xycoords = ‘data’ 注释和数据使用相同坐标系 #xytest 注释的位置 #arrowprops注释用的箭头 annotate("Import data", (55,30), xycoords='data',                xytext=(5,35),                arrowprops=dict(arrowstyle='->'))   show()

 

3.7 直方图、饼图

直方图

import matplotlib.pyplot as plt   import numpy as np   mu=100 sigma = 15 x = np.random.normal(mu, sigma, 10000)   ax = plt.gca()   ax.hist(x,bins=30, color='g')   ax.set_xlabel('v') ax.set_ylabel('f')   ax.set_title(r'$\mathrm{Histogram:}\ \mu=%d,\ \sigma=%d$' % (mu,sigma))   plt.show()

 

 

饼图

from pylab import *   figure(1, figsize=(6,6)) ax = axes([0.1,0.1,0.8,0.8])   labels ='spring','summer','autumn','winter' x=[15,30,45,10] #explode=(0.1,0.2,0.1,0.1) explode=(0.1,0,0,0) pie(x, explode=explode, labels=labels, autopct='%1.1f%%', startangle=67)   title('rainy days by season') show()

 

3.8 设置坐标轴

import matplotlib.pyplot as plt   import numpy as np   x = np.linspace(-np.pi, np.pi, 500, endpoint=True) y = np.sin(x)   plt.plot(x,y)   ax = plt.gca() #top bottom left right 四条线段框成的   #上下边界颜色 ax.spines['right'].set_color('none') ax.spines['top'].set_color('r')   #坐标轴位置 ax.spines['bottom'].set_position(('data', 0)) ax.spines['left'].set_position(('data', 0))   #坐标轴上刻度位置 ax.xaxis.set_ticks_position('bottom') ax.yaxis.set_ticks_position('left')   plt.grid() plt.show()

 

3.9 误差条形图

import matplotlib.pyplot as plt   import numpy as np   x = np.arange(0,10,1)   y = np.log(x)   xe = 0.1 * np.abs(np.random.randn(len(y)))   plt.bar(x,y,yerr=xe,width=0.4,align='center',         ecolor='r',color='cyan',label='experimert')   plt.xlabel('x') plt.ylabel('y') plt.title('measurements') plt.legend(loc='upper left')  # 这种图例用法更直接   plt.show()

 

3.10 带填充区域的图表

import matplotlib.pyplot as plt from matplotlib.pyplot import * import numpy as np   x = np.arange(0,2,0.01)   y1 = np.sin(2*np.pi*x) y2=1.2*np.sin(4*np.pi*x)   fig = figure() ax = gca()   ax.plot(x,y1,x,y2,color='b')   ax.fill_between(x,y1,y2,where = y2>y1, facecolor='g',interpolate=True) ax.fill_between(x,y1,y2,where = y2<y1, facecolor='darkblue',interpolate=True)   ax.set_title('filled between')   show()

 

3.11 散点图

import matplotlib.pyplot as plt   import numpy as np   x = np.random.randn(1000)   y1 = np.random.randn(len(x))   y2 = 1.8 + np.exp(x)   ax1 = plt.subplot(1,2,1) ax1.scatter(x,y1,color='r',alpha=.3,edgecolors='white',label='no correl') plt.xlabel('no correlation') plt.grid(True) plt.legend()   ax1 = plt.subplot(1,2,2) #alpha透明度 edgecolors边缘颜色 label图例（结合legend使用） plt.scatter(x,y2,color='g',alpha=.3,edgecolors='gray',label='correl') plt.xlabel('correlation') plt.grid(True) plt.legend()   plt.show()

 

第四章 更多图表和定制化

4.4 向图表添加数据表

from matplotlib.pyplot import * import matplotlib.pyplot as plt import numpy as np   plt.figure() ax = plt.gca() y = np.random.randn(9)   col_labels = ['c1','c2','c3'] row_labels = ['r1','r2','r3'] table_vals = [[11,12,13],[21,22,23],[31,32,33]] row_colors = ['r','g','b']   my_table = plt.table(cellText=table_vals,                      colWidths=[0.1]*3,                      rowLabels=row_labels,                      colLabels=col_labels,                      rowColours=row_colors,                      loc='upper right')   plt.plot(y) plt,show()

 

 

4.5 使用subplots

from matplotlib.pyplot import * import matplotlib.pyplot as plt import numpy as np   plt.figure(0) #子图的分割规划 a1 = plt.subplot2grid((3,3),(0,0),colspan=3) a2 = plt.subplot2grid((3,3),(1,0),colspan=2) a3 = plt.subplot2grid((3,3),(1,2),colspan=1) a4 = plt.subplot2grid((3,3),(2,0),colspan=1) a5 = plt.subplot2grid((3,3),(2,1),colspan=2)   all_axex = plt.gcf().axes for ax in all_axex:     for ticklabel in ax.get_xticklabels() + ax.get_yticklabels():         ticklabel.set_fontsize(10)   plt.suptitle("Demo") plt.show()

 

 

4.6 定制化网格

grid();

color、linestyle 、linewidth等参数可设

4.7 创建等高线图

基于矩阵

等高线标签

等高线疏密

import matplotlib.pyplot as plt import numpy as np import matplotlib as mpl   def process_signals(x,y):     return (1-(x**2 + y**2))*np.exp(-y**3/3)   x = np.arange(-1.5, 1.5, 0.1) y = np.arange(-1.5,1.5,0.1)   X,Y = np.meshgrid(x,y) Z = process_signals(X,Y) N = np.arange(-1, 1.5, 0.3) #作为等值线的间隔   CS = plt.contour(Z, N, linewidths = 2,cmap = mpl.cm.jet) plt.clabel(CS, inline=True, fmt='%1.1f', fontsize=10) #等值线标签 plt.colorbar(CS) plt.show()

 

4.8 填充图表底层区域

from matplotlib.pyplot import * import matplotlib.pyplot as plt import numpy as np from math import sqrt   t = range(1000) y = [sqrt(i) for i in t]   plt.plot(t,y,color='r',lw=2) plt.fill_between(t,y,color='y') plt.show()

 

第五章 3D可视化图表

在选择3D之前最好慎重考虑，因为3D可视化比2D更加让人感到迷惑。

5.2 3D柱状图

import matplotlib.pyplot as plt import numpy as np import matplotlib as mpl import random import matplotlib.dates as mdates   from mpl_toolkits.mplot3d import Axes3D   mpl.rcParams['font.size'] =10   fig = plt.figure() ax = fig.add_subplot(111,projection='3d')   for z in [2015,2016,2017]:     xs = range(1,13)     ys = 1000 * np.random.rand(12)     color = plt.cm.Set2(random.choice(range(plt.cm.Set2.N)))     ax.bar(xs,ys,zs=z,zdir='y',color=color,alpha=0.8)   ax.xaxis.set_major_locator(mpl.ticker.FixedLocator(xs)) ax.yaxis.set_major_locator(mpl.ticker.FixedLocator(ys))   ax.set_xlabel('M') ax.set_ylabel('Y') ax.set_zlabel('Sales')   plt.show()

 

5.3 曲面图

import matplotlib.pyplot as plt import numpy as np import matplotlib as mpl import random from mpl_toolkits.mplot3d import Axes3D from matplotlib import cm fig = plt.figure() ax = fig.add_subplot(111,projection='3d') n_angles = 36 n_radii = 8 radii = np.linspace(0.125, 1.0, n_radii) angles = np.linspace(0, 2*np.pi, n_angles, endpoint=False) angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)   x = np.append(0, (radii*np.cos(angles)).flatten()) y = np.append(0, (radii*np.sin(angles)).flatten()) z = np.sin(-x*y)   ax.plot_trisurf(x,y,z,cmap=cm.jet, lw=0.2) plt.show()

 

5.4 3D直方图

import matplotlib.pyplot as plt import numpy as np import matplotlib as mpl import random from mpl_toolkits.mplot3d import Axes3D   mpl.rcParams['font.size'] =10   fig = plt.figure() ax = fig.add_subplot(111,projection='3d') samples = 25 x = np.random.normal(5,1,samples)   #x上正态分布 y = np.random.normal(3, .5, samples) #y上正态分布   #xy平面上，按照10*10的网格划分，落在网格内个数hist，x划分边界、y划分边界 hist, xedges, yedges = np.histogram2d(x,y,bins=10) elements = (len(xedges)-1)*(len(yedges)-1) xpos,ypos = np.meshgrid(xedges[:-1]+.25,yedges[:-1]+.25)   xpos = xpos.flatten() #多维数组变为一维数组 ypos = ypos.flatten() zpos = np.zeros(elements)   dx = .1 * np.ones_like(zpos) #zpos一致的全1数组 dy = dx.copy() dz = hist.flatten()   #每个立体以（xpos,ypos,zpos)为左下角，以（xpos+dx,ypos+dy,zpos+dz）为右上角 ax.bar3d(xpos,ypos,zpos,dx,dy,dz,color='b',alpha=0.4)   plt.show()

 

第六章 用图像和地图绘制图表

6.3 绘制带图像的图表

6.4 图像图表显示

第七章 使用正确的图表理解数据

为什么要以这种方式展示数据？

7.2 对数图

import matplotlib.pyplot as plt import numpy as np   x = np.linspace(1,10) y = [10**e1 for e1 in x] z = [2*e2 for e2 in x]   fig = plt.figure(figsize=(10, 8)) ax1 = fig.add_subplot(2,2,1) ax1.plot(x, y, color='b') ax1.set_yscale('log') #两个坐标轴和主次刻度打开网格显示 plt.grid(b=True, which='both', axis='both')   ax2 = fig.add_subplot(2,2,2) ax2.plot(x,y,color='r') ax2.set_yscale('linear') plt.grid(b=True, which='both', axis='both')   ax3 = fig.add_subplot(2,2,3) ax3.plot(x,z,color='g') ax3.set_yscale('log') plt.grid(b=True, which='both', axis='both')   ax4 = fig.add_subplot(2,2,4) ax4.plot(x,z,color='magenta') ax4.set_yscale('linear') plt.grid(b=True, which='both', axis='both')   plt.show()

 

7.3 创建火柴杆图

import matplotlib.pyplot as plt import numpy as np   x = np.linspace(1,10) y = np.sin(x+1) + np.cos(x**2)   bottom = -0.1 hold = False label = "delta"   markerline, stemlines, baseline = plt.stem(x, y, bottom=bottom,label=label, hold=hold)   plt.setp(markerline, color='r', marker= 'o') plt.setp(stemlines,color='b', linestyle=':') plt.setp(baseline, color='g',lw=1, linestyle='-')   plt.legend()   plt.show()

 

7.4 矢量图

7.5 使用颜色表

颜色要注意观察者会对颜色和颜色要表达的信息做一定的假设。不要做不相关的颜色映射，比如将财务数据映射到表示温度的颜色上去。

如果数据没有与红绿有强关联时，尽可能不要使用红绿两种颜色。

import matplotlib.pyplot as plt import numpy as np import matplotlib as mpl   red_yellow_green = ['#d73027','#f46d43','#fdae61'] sample_size = 1000 fig,ax = plt.subplots(1)   for i in range(3):     y = np.random.normal(size=sample_size).cumsum()     x = np.arange(sample_size)     ax.scatter(x, y, label=str(i), lw=0.1, edgecolors='grey',facecolor=red_yellow_green[i])      plt.legend() plt.show()

 

7.7 使用散点图和直方图

7.8 两个变量间的互相关图形

7.9 自相关的重要性

第八章 更多的matplotlib知识

8.6 使用文本和字体属性

函数：

test: 在指定位置添加文本

xlabel:x轴标签

ylabel:y轴标签

title:设置坐标轴的标题

suptitle:为图表添加一个居中的标题

figtest:在图表任意位置添加文本，归一化坐标

 

属性：

family:字体类型

size/fontsize:字体大小

style/fontstyle:字体风格

variant:字体变体形式

weight/fontweight:粗细

stretch/fontstretch:拉伸

fontproperties:

 

8.7 用LaTeX渲染文本

LaTeX 是一个用于生成科学技术文档的高质量的排版系统，已经是事实上的科学排版或出版物的标准。

帮助文档：http://latex-project.org/

import matplotlib.pyplot as plt import numpy as np   t = np.arange(0.0, 1.0+0.01, 0.01) s = np.cos(4 * np.pi *t) * np.sin(np.pi*t/4) + 2   #plt.rc('text', usetex=True)  #未安装Latex plt.rc('font', **{'family':'sans-serif','sans-serif':['Helvetica'],'size':16})   plt.plot(t, s, alpha=0.55)   plt.annotate(r'$\cos(4 \times \pi \times {t}) \times \sin(\pi \times \frac{t}{4}) + 2$',xy=(.9, 2.2), xytext=(.5, 2.6),color='r', arrowprops={'arrowstyle':'->'})   plt.text(.01, 2.7, r'$\alpha, \beta, \gamma, \Gamma, \pi, \Pi, \phi, \varphi, \Phi$')   plt.xlabel(r'time (s)') plt.ylabel(r'y values(W)')   plt.title(r"Hello python visualization.") plt.subplots_adjust(top=0.8)   plt.show()

 

 

 结语：本篇文档是基于书籍《Python数据可视化编程实战》学习总结。

来源：https://www.cnblogs.com/caiyishuai/p/9516069.html