《Python数据可视化编程实战》

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()

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()

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()

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()

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()

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()

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()

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()

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()

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()

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()

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()

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()

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()

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()

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()

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()

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()

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()

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()

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()

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()

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()