python数据可视化编程实战第二版_《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

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。

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。

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