matplotlib¶

import matplotlib.pyplot as plt
import numpy as np
x = np.arange(1, 5)
plt.plot(x, x*1.5, label='Normal')
plt.plot(x, x*3.0, label='Fast')
plt.plot(x, x/3.0, label='Slow')
plt.grid(True)
plt.title('Sample Growth of a Measure')
plt.xlabel('Samples')
plt.ylabel('Values Measured')
plt.legend(loc='upper left')
plt.show()

import matplotlib.pyplot as plt
import numpy as np
x = np.arange(0.0, 6.0, 0.01)
plt.plot(x, x**2)
plt.show()

import matplotlib.pyplot as plt
x = np.arange(1, 5)
plt.plot(x, x*1.5)
plt.plot(x, x*3.0)
plt.plot(x, x/3.0)
plt.show()

# 也可以在一个plot函数中传入多对X,Y值，在一个图中绘制多个曲线
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(1, 5)
plt.plot(x, x*1.5, x, x*3.0, x, x/3.0)
plt.show()

import matplotlib.pyplot as plt
plt.interactive(True)# enable interactive mode, in case it was not
plt.hold(False) # empty window will pop up
plt.plot([1, 2, 3])
plt.plot([2, 4, 6])

plt.hold(True) # 将hold属性改回True

# 使用grid方法为图添加网格线
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(1, 5)
plt.plot(x, x*1.5, x, x*3.0, x, x/3.0)
plt.grid(True)
plt.show()

import matplotlib.pyplot as plt
import numpy as np

fig  = plt.figure(figsize=(10,3))

x = np.linspace(0, 5, 100)

# default grid appearance
ax1 = fig.add_subplot(121)
ax1.plot(x, x**2, x, x**3, lw=2)
ax1.grid(True) # 显式网格线

# 对网格线进行设置
ax2 = fig.add_subplot(122)
ax2.plot(x, x**2, x, x**3, lw=2)
ax2.grid(color='b', alpha=0.5, linestyle='dashed', linewidth=0.5) # 用与plot同样的参数设置网格线

plt.grid(b=True, which='major', axis='both')
# which指定绘制的网格刻度类型（major、minor或者both）
# axis指定绘制哪组网格线（both、x或者y）

plt.axis([-1, 1, -10, 10])
plt.axhline(); # 添加水平线，重要参数包括：y轴位置、xmin、xmax，默认在y=0位置绘制水平线

plt.axis([-1, 1, -10, 10])
plt.axvline(0.5, 0.2, 0.8); # 添加垂直线，重要参数包括：x轴位置、ymin、ymax，默认在x=0位置绘制水平线(ymin、ymax都是比例值)

plt.axis([-1, 1, -10, 10])
plt.axhspan(-5, 2); # 绘制一条水平带（矩形），需要ymin、ymax参数指定水平带的宽度（ymin、ymax都是实际值）

plt.axis([-2, 2, -10, 10])
plt.axvspan(-1, 1); # 绘制一条垂直带（矩形），需要xmin、xmax参数指定水平带的宽度（xmin、xmax都是实际值）

import matplotlib.pyplot as plt
import numpy as np
x = np.arange(1, 5)
plt.plot(x, x*1.5, x, x*3.0, x, x/3.0)
plt.axis() # shows the current axis limits values；如果axis方法没有任何参数，则返回当前坐标轴的上下限
# (1.0, 4.0, 0.0, 12.0)
plt.axis([0, 5, -1, 13]) # set new axes limits；axis方法中有参数，设置坐标轴的上下限；参数顺序为[xmin, xmax, ymin, ymax]
plt.show()

import matplotlib.pyplot as plt
import numpy as np
x = np.arange(1, 5)
plt.plot(x, x*1.5, x, x*3.0, x, x/3.0)
plt.axis('tight') # 紧凑型坐标轴
plt.show()

import matplotlib.pyplot as plt
import numpy as np
x = np.arange(1, 5)
plt.plot(x, x*1.5, x, x*3.0, x, x/3.0)
plt.xlim([0, 5]) # ylim([ymin, ymax])
plt.ylim([-1, 13]) # xlim([xmin, xmax])
plt.show()

import matplotlib.pyplot as plt
plt.plot([1, 3, 2, 4])
plt.xlabel('This is the X axis')
plt.ylabel('This is the Y axis')
plt.show()

import matplotlib.pyplot as plt
plt.plot([1, 3, 2, 4])
plt.title('Simple plot')
plt.show()

import matplotlib.pyplot as plt
import numpy as np
x = np.arange(1, 5)
plt.plot(x, x*1.5, label='Normal') # 在plot函数中增加label参数
plt.plot(x, x*3.0, label='Fast')
plt.plot(x, x/3.0, label='Slow')
plt.legend()
plt.show()

import matplotlib.pyplot as plt
import numpy as np
x = np.arange(1, 5)
plt.plot(x, x*1.5)
plt.plot(x, x*3.0)
plt.plot(x, x/3.0)
plt.legend(['Normal', 'Fast', 'Slow']) # 在legend方法中传入字符串列表
plt.show()

import matplotlib.pyplot as plt
import numpy as np
x = np.arange(1, 5)
plt.plot(x, x*1.5, label='_nolegend_') # label参数为'_nolegend_'，则图例中不显示
plt.plot(x, x*3.0, label='Fast')
plt.plot(x, x/3.0, label='Slow')
plt.legend()
plt.show()

import matplotlib.pyplot as plt
import numpy as np
x = np.arange(1, 5)
plt.plot(x, x*1.5, label='Normal')
plt.plot(x, x*3.0, label='Fast')
plt.plot(x, x/3.0, label='Slow')
plt.legend(loc=0)
plt.show()

import matplotlib.pyplot as plt
import numpy as np
x = np.arange(1, 5)
plt.plot(x, x*1.5, label='Normal')
plt.plot(x, x*3.0, label='Fast')
plt.plot(x, x/3.0, label='Slow')
plt.legend(loc=(0,1)) # loc参数可以是2元素的元组，表示图例左下角的坐标
plt.show()

import matplotlib.pyplot as plt
import numpy as np
x = np.arange(1, 5)
plt.plot(x, x*1.5, label='Normal')
plt.plot(x, x*3.0, label='Fast')
plt.plot(x, x/3.0, label='Slow')
plt.legend(loc=(-0.1,0.9)) # 图例也可以超过图的界限
plt.show()

import matplotlib.pyplot as plt
import numpy as np
plt.plot(randn(1000).cumsum(), 'k', label='one')   # 在添加subplot时传入label参数
plt.plot(randn(1000).cumsum(), 'k--', label='two') # 要从图例中去除一个或多个元素，不传入label或传入label='_nolegend_'即可
plt.plot(randn(1000).cumsum(), 'k.', label='three')
plt.legend(loc='best') # loc='best'
plt.show()

import matplotlib.pyplot as plt
import numpy as np
x = np.arange(1, 5)
plt.plot(x, x*1.5, label='Normal')
plt.plot(x, x*3.0, label='Fast')
plt.plot(x, x/3.0, label='Slow')
plt.legend(loc=0, ncol=2) # ncol控制图例中有几列
plt.show()

# 查看matplotlib中的默认设置
import matplotlib as mpl
mpl.rcParams['figure.figsize'] # figsize以英尺为单位，matplotlib中其他参数都以点为单位

mpl.rcParams['savefig.dpi']

# 显示当前的默认配置
import matplotlib as mpl
mpl.rcParams

# 别名和HTML颜色名
import matplotlib.pyplot as plt
import numpy as np
y = np.arange(1, 3)
plt.plot(y, 'y'); # 后面加分号，则不打印plot对象
plt.plot(y+1, 'magenta'); # 没有x，则x默认为[0, 1]
plt.plot(y+2, 'cyan'); # 没有颜色参数时，plot为不同的曲线自动选择不同颜色，有颜色参数，则使用指定的颜色
plt.show()

# HTML颜色名和HTML十六进制字符串、归一化到[0, 1]的RGB元组
import matplotlib.pyplot as plt
import numpy as np
y = np.arange(1, 3)
plt.plot(y, c="red", alpha=0.5); # 设置半透明
plt.plot(y+1, color="#1155dd");  # RGB hex code for a bluish color
plt.plot(y+2, color="#15cc55");  # RGB hex code for a greenish color
plt.plot(y+3, color=(0.1843, 0.3098, 0.3098)); # 归一化到[0, 1]的RGB元组
plt.show()

# 灰度
import matplotlib.pyplot as plt
import numpy as np
y = np.arange(1, 3)
plt.plot(y, c="0.1");
plt.plot(y+1, c="0.5");
plt.plot(y+2, c="0.9");

# 透明度
import matplotlib.pyplot as plt
import numpy as np
y = np.arange(1, 3)
plt.plot(y, c="red", alpha=0.1);    # 设置透明度
plt.plot(y+1, c="red", alpha=0.5);
plt.plot(y+2, c="red", alpha=0.9);

import matplotlib.pyplot as plt
plt.subplot(111, axisbg=(0.1843, 0.3098, 0.3098));

import matplotlib.pyplot as plt
import numpy as np
y = np.arange(1, 3)
plt.plot(y, '--', y+1, '-.', y+2, ':');
plt.show()

import matplotlib.pyplot as plt
import numpy as np
y = np.arange(1, 3)
plt.plot(y, lw=1);
plt.plot(y+1, lw=2);
plt.plot(y+2, lw=3);

# 第一段线2个点的宽度，接下来的空白区5个点的宽度，第二段线5个点的宽度，空白区2个点的宽度，以此类推
plt.plot(np.linspace(-np.pi, np.pi, 256, endpoint=True), np.cos(np.linspace(-np.pi, np.pi, 256, endpoint=True)), dashes=[2, 5, 5, 2]);

import matplotlib.pyplot as plt
import numpy as np
y = np.arange(1, 3, 0.2)
plt.plot(y, '1', y+0.5, '2', y+1, '3', y+1.5, '4');
plt.show()

import matplotlib.pyplot as plt
import numpy as np
y = np.arange(1, 3, 0.2)
plt.plot(y, 'v', y+0.5, '^', y+1, '<', y+1.5, '>');
plt.show()

import matplotlib.pyplot as plt
import numpy as np
y = np.arange(1, 3, 0.2)
plt.plot(y, 's', y+0.5, 'p', y+1, 'h', y+1.5, 'H', y+2, '8');
plt.show()

import matplotlib.pyplot as plt
import numpy as np
y = np.arange(1, 3, 0.2)
plt.plot(y, '.', y+0.5, 'x', y+1, '*', y+1.5, '+', y+2, ',');
plt.show()

import matplotlib.pyplot as plt
import numpy as np
y = np.arange(1, 3, 0.2)
plt.plot(y, 'o', y+0.5, 'D', y+1, 'd');
plt.plot(y+1.5, marker="None");
plt.show()

import matplotlib.pyplot as plt
import numpy as np
y = np.arange(1, 3, 0.2)
plt.plot(y, '_', y+1, '|');
plt.show()

import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 5, 10)
plt.plot(x, x**2, 'b^:') # blue line with dots
plt.plot(x, x**3, 'go-.') # green dashed line
plt.show()

import matplotlib.pyplot as plt
import numpy as np
y = np.arange(1, 3, 0.3)
plt.plot(y, color='blue', linestyle='dashdot', linewidth=4, marker='o', markerfacecolor='red', markeredgecolor='black', markeredgewidth=3, markersize=12);
plt.show()

import matplotlib.pyplot as plt
import numpy as np
y = np.arange(1, 3)
x = np.arange(1, 3)
plt.plot(x, y, x, y+1, x, y+2, color='green'); # 为多个曲线设置同一颜色
plt.xlim([1, 2])
plt.show()

import matplotlib.pyplot as plt
import numpy as np
y = np.arange(1, 3)
plt.plot(y, 'y', y+1, 'm', y+2, 'c'); # 为多个曲线设置不同颜色
plt.show()

plt.plot(x, y, linewidth=1.5);

line, = plt.plot(x, y)
#  plt.plot(x, y)返回的是list
# line = plt.plot(x, y)，则line是list
# line, = plt.plot(x, y)，则line是matplotlib.lines.Line2D实例，所以line后面有个逗号
line.set_linewidth(1.5)

line = plt.plot(x, y)
plt.setp(line, 'linewidth', 1.5);

locs, labels = plt.xticks() # 不传入任何参数，xticks()会返回当前刻度的位置和标签

print locs
for label in labels:
    print label,

import matplotlib.pyplot as plt
x = [5, 3, 7, 2, 4, 1]
plt.plot(x);
plt.xticks(range(len(x)), ['a', 'b', 'c', 'd', 'e', 'f']); # 传入位置和标签参数，以修改坐标轴刻度
plt.yticks(range(1, 8, 2));
plt.show()

import matplotlib.pyplot as plt
import numpy as np

fig = plt.figure(figsize=(10, 4))
ax = fig.add_subplot(111)

x = np.linspace(0, 5, 100)

ax.plot(x, x**2, x, x**3, lw=2)

ax.set_xticks([1, 2, 3, 4, 5])
ax.set_xticklabels([r'$\alpha$', r'$\beta$', r'$\gamma$', r'$\delta$', r'$\epsilon$'], fontsize=18)

yticks = [0, 50, 100, 150]
ax.set_yticks(yticks)
ax.set_yticklabels(["$%.1f$" % y for y in yticks], fontsize=18); # use LaTeX formatted labels

import matplotlib.pyplot as plt
import numpy as np

fig = plt.figure(figsize=(10, 4))
ax = fig.add_subplot(111)

x = np.linspace(0, 5, 100)
      
ax.plot(x, x**2, x, np.exp(x))
ax.set_title("scientific notation")

ax.set_yticks([0, 50, 100, 150])

from matplotlib import ticker
formatter = ticker.ScalarFormatter(useMathText=True)
formatter.set_scientific(True) 
formatter.set_powerlimits((-1,1)) 
ax.yaxis.set_major_formatter(formatter) 

import matplotlib as mpl
import matplotlib.pyplot as plt

# distance between x and y axis and the numbers on the axes
mpl.rcParams['xtick.major.pad'] = 20
mpl.rcParams['ytick.major.pad'] = 20

import matplotlib.pyplot as plt
x = [5, 3, 7, 2, 4, 1]
plt.plot(x);
plt.xticks(range(len(x)), ['a', 'b', 'c', 'd', 'e', 'f']); # 传入位置和标签参数，以修改坐标轴刻度
plt.yticks(range(1, 8, 2));
plt.show()

# 例子
import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(-np.pi, np.pi, 256, endpoint=True) # 生成-Pi到Pi之间具有相同线性距离的256个点

y = np.cos(x) # 计算余弦值
y1 = np.sin(x) # 计算正弦值

plt.plot(x, y) # 绘制余弦图
plt.plot(x, y1) # 绘制正弦图

plt.title("Functions $\sin$ and $\cos$") # LaTex语法，用$\sin$、$\cos$表达式在图表上写上希腊字母

plt.xlim(-3.0, 3.0) # 设置x轴刻度的最大、最小值（当xticks的刻度-np.pi比xlim最小值-3.0更小时，以xticks为准）
plt.ylim(-1.0, 1.0) # 设置y轴刻度的最大、最小值

plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi],
          [r'$-\pi$', r'$-\pi/2$', r'$0$', r'$+\pi/2$', r'$+\pi$']);# LaTex语法，用$-\pi$等表达式在图表上写上希腊字母
plt.yticks([-1, 0, +1],
          [r'$-1$', r'$0$', r'$+1$']);# 设置x轴、y轴刻度及标签

Image('chart suggestions.png')

import matplotlib.pyplot as plt

x = [1,2,3,4]
y = [5,4,3,2]

plt.figure() # 创建一个新的图表
# 如果给方法提供一个字符串参数，例如sample charts，这个字符串就会成为窗口的后台标题
# 如果通过相同的字符串参数调用figure()方法，将会激活相应的图表，并且接下来的绘图操作都在此图表中进行

plt.subplot(231) # 把图表分割成2*3的网格，也可以用plt.subplot(2, 3, 1)这种形式调用，第一个参数是行数，第二个参数是列数，第三个参数表示标号
plt.plot(x, y)

plt.subplot(232)
plt.bar(x, y) # 柱状图

plt.subplot(233)
plt.barh(x, y) # 水平柱状图

plt.subplot(234)
plt.bar(x, y)
y1 = [7,8,5,3]
plt.bar(x, y1, bottom=y, color = 'r') # 绘制堆叠柱状图，将两个柱状图方法调用连在一起，通过设置参数bottom=y，把两个柱状图连接起来形成堆叠柱状图

plt.subplot(235)
plt.boxplot(x) # 箱线图

plt.subplot(236)
plt.scatter(x,y); # 散点图

import matplotlib.pyplot as plt

n = np.array([0,1,2,3,4,5])
xx = np.linspace(-0.7, 1, 100)
x = np.linspace(-3.3, 3.3, 101)

fig, axes = plt.subplots(1, 4, figsize=(12,3))

axes[0].scatter(xx, xx + 0.25*np.random.randn(len(xx))) # 散点图
axes[0].set_title("scatter")

axes[1].step(n, n**2, lw=2) # 阶梯图
axes[1].set_title("step")

axes[2].bar(n, n**2, align="center", width=0.5, alpha=0.5) # 条形图
axes[2].set_title("bar")

axes[3].fill_between(x, x**2, x**3, color="green", alpha=0.5); # 条带图
axes[3].set_title("fill_between");

import matplotlib.pyplot as plt
import numpy as np
y = np.random.randn(1000)
plt.hist(y); # 默认绘制10个bin
plt.show()

import matplotlib.pyplot as plt
import numpy as np
y = np.random.randn(1000)
plt.hist(y, bins=25); # 设置bins参数，绘制25个bin
plt.show()

import matplotlib.pyplot as plt
import numpy as np

n = np.random.randn(100000)

fig, axes = plt.subplots(1, 2, figsize=(12,4))

axes[0].hist(n) # 普通直方图，概率密度函数f(x)
axes[0].set_title("Default histogram")
axes[0].set_xlim((min(n), max(n)))

axes[1].hist(n, cumulative=True, bins=50)
axes[1].set_title("Cumulative detailed histogram") # 累积直方图，分布函数F(x)
axes[1].set_xlim((min(n), max(n)));

import matplotlib.pyplot as plt
import numpy as np

mu = 100
sigma = 15
x = np.random.normal(mu, sigma, 10000) # 生成正态分布数据集

ax = plt.gca() # 获取当前图表

ax.hist(x, bins=35, normed=True, color='r') # 35个bins，颜色为红色，概率密度直方图

ax.set_xlabel('Values')
ax.set_ylabel('Frequency') # 设置x、y轴标题

ax.set_title(r'$\mathrm{Histogram:}\ \mu=%d,\ \sigma=%d$' % (mu, sigma)); # 设置图表标题

import matplotlib.pyplot as plt
import numpy as np
x = np.arange(0, 4, 0.2)
y = np.exp(-x)
e1 = 0.1 * np.abs(np.random.randn(len(y)))
plt.errorbar(x, y, yerr=e1, fmt='x:'); # yerr参数，设置误差条的长度，误差条上下部分的长度均为e1的长度；fmt参数，设置线型
plt.show()

import matplotlib.pyplot as plt
import numpy as np
x = np.arange(0, 4, 0.2)
y = np.exp(-x)
e1 = 0.1 * np.abs(np.random.randn(len(y)))
plt.errorbar(x, y, yerr=e1, fmt='None'); # fmt参数为None时，只绘制误差条，不绘制点和连线
plt.show()

# ecolor参数设置误差条的颜色，如果没有该参数，则使用点的颜色；elinewidth参数设置误差条的线宽；capsize参数设置误差条的帽宽
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(0, 4, 0.2)
y = np.exp(-x)
e1 = 0.1 * np.abs(np.random.randn(len(y)))
plt.errorbar(x, y, yerr=e1, fmt='.-', ecolor='r', elinewidth=2, capsize=5); 
plt.show()

e2 = 0.1 * np.abs(np.random.randn(len(y)))
plt.errorbar(x, y, yerr=e1, xerr=e2, fmt='.-', capsize=0); # xerr绘制水平的误差条
plt.show()

plt.errorbar(x, y, yerr=[e1, e2], fmt='.-'); # 为yerr参数传入数组，绘制不对称的误差条，第一个数是负误差，第二个数是正误差
plt.show()

import matplotlib.pyplot as plt
import numpy as np

# 生成measurements的标号1~10
x = np.arange(1, 11, 1)

# 为每个measurements生成相应的values
y = np.log(x)

# 生成服从标准正态分布的误差
xe = 0.1 * np.abs(np.random.randn(len(y)))

# 绘制误差条形图
plt.bar(x,
        y,
        yerr=xe, # 在条形图中生成误差条（一个标准差、一个标准误、95%置信区间等）
        width=0.4, # 误差条宽度
        align='center', # 指定条形的位置
        ecolor='r', # 指定误差条的颜色
        color='cyan', # 指定条形的填充颜色
        label='experiment #1', # 字符串标签，用于生成legend
        hatch='-') # 指定阴影线

# give some explainations
plt.xlabel('# measurement')
plt.ylabel('Measured values')
plt.title('Measurements')
plt.legend(loc='upper left')

plt.show()

import matplotlib.pyplot as plt
plt.bar([1, 2, 3], [3, 2, 5]); # 第一个参数为条形左下角的x轴坐标，第二个参数为条形的高度；matplotlib会自动设置条形的宽度，本例中条形宽0.8
plt.show()

# width参数设置条形宽度；color参数设置条形颜色；xerr、yerr参数设置水平、垂直误差条；bottom参数设置条形底部的垂直坐标
e1 = 0.3 * np.random.rand(3)
e2 = 0.3 * np.random.rand(3)
import matplotlib.pyplot as plt
plt.bar([1, 2, 3], [3, 2, 5], width=0.5, color='r', xerr=e1, yerr=e2, bottom=1);
plt.ylim([0, 7])
plt.show()

# 例子：从字典中绘制条形图
import matplotlib.pyplot as plt
import numpy as np
dic = {'A': 40, 'B': 70, 'C': 30, 'D': 85}
for i, key in enumerate(dic): # 用for循环，每次绘制一个条形
    plt.bar(i, dic[key]);
plt.xticks(np.arange(len(dic))+0.4, dic.keys());
plt.yticks(dic.values());
plt.show()

# 例子：绘制有误差条的并列条形图
import matplotlib.pyplot as plt
import numpy as np
data1 = 10*np.random.rand(5)
data2 = 10*np.random.rand(5)
data3 = 10*np.random.rand(5)
e2 = 0.5 * np.abs(np.random.randn(len(data2)))
locs = np.arange(1, len(data1)+1)
width = 0.27
plt.bar(locs, data1, width=width);
plt.bar(locs+width, data2, yerr=e2, width=width, color='red');
plt.bar(locs+2*width, data3, width=width, color='green') ;
plt.xticks(locs + width*1.5, locs);
plt.show()

import matplotlib.pyplot as plt
plt.barh([1, 2, 3], [3, 2, 5]);
plt.show()

import matplotlib.pyplot as plt
plt.figure(figsize=(4, 4)); # 饼图最好绘制在正方形区域，可以保证饼是圆形而非椭圆形
x = [45, 35, 20] # 当各部分之和大于1时，会自动计算各部分占总体的比例
labels = ['Cats', 'Dogs', 'Fishes']
plt.pie(x, labels=labels); # labels参数可以设置各区域标签
plt.show() # 第一块饼从3点钟方向开始绘制

import matplotlib.pyplot as plt
plt.figure(figsize=(4, 4));
x = [0.1, 0.2, 0.3] # 当各部分之和小于1时，则不计算各部分占总体的比例，饼的大小是数值和1之比
labels = ['Cats', 'Dogs', 'Fishes']
plt.pie(x, labels=labels); # labels参数可以设置各区域标签
plt.show()

# labels参数设置每一块的标签；labeldistance参数设置标签距离圆心的距离（比例值）
# autopct参数设置比例值的显示格式；pctdistance参数设置比例值文字距离圆心的距离
# explode参数设置每一块顶点距圆形的长度（比例值）；colors参数设置每一块的颜色；
# shadow参数为布尔值，设置是否绘制阴影
import matplotlib.pyplot as plt
plt.figure(figsize=(4, 4));
x = [4, 9, 21, 55, 30, 18]
labels = ['Swiss', 'Austria', 'Spain', 'Italy', 'France', 'Benelux']
explode = [0.2, 0.1, 0, 0, 0.1, 0]
colors = ['r', 'k', 'b', 'm', 'c', 'g']
plt.pie(x, labels=labels, labeldistance=1.2, explode=explode, colors=colors, autopct='%1.1f%%', pctdistance=0.5, shadow=True);
plt.show()

import matplotlib.pyplot as plt

# 绘制一个正方形的figure and axes
plt.figure(1, figsize=(4, 4))
ax = plt.axes([0.1, 0.1, 0.8, 0.8])

# 圆弧按照逆时针绘制
labels = 'Spring', 'Summer', 'Autumn', 'Winter'
values = [15, 16, 16, 28]
explode =[0.1, 0.1, 0.1, 0.1] # 指定分裂序列，每一个元素表示每个圆弧间的偏移量，为半径的百分比

# 绘制饼图
plt.pie(values,
        explode=explode,
        labels=labels,
        autopct='%1.1f%%', # 格式化绘制圆弧中的标签，标签可以是一个格式化字符串或者一个可调用的对象（函数）
        startangle=67) # 指定圆弧开始绘制的角度，默认从角度0（x轴）开始绘制

plt.title('Rainy days by season');

import matplotlib.pyplot as plt
import numpy as np
x = np.random.randn(1000)
y = np.random.randn(1000)
plt.scatter(x, y);
plt.show()

import matplotlib.pyplot as plt
import numpy as np

x = np.random.randn(1000)

y1 = np.random.randn(len(x)) # y1与x不相关
y2 = 1.2 + np.exp(x) # y2与x高度相关

ax1 = plt.subplot(121)
plt.scatter(x, y1, color='indigo', alpha=0.3, edgecolors='white', label='no correl')
plt.xlabel('no correlation')
plt.grid(True)
plt.legend()

ax2 = plt.subplot(122, sharey=ax1, sharex=ax1)
plt.scatter(x, y2, color='green', alpha=0.3, edgecolors='grey', label='correl')
plt.xlabel('strong correlation')
plt.grid(True)
plt.legend()

plt.show()

# s参数设置散点的大小；c参数设置散点的颜色；marker参数设置散点的形状
import matplotlib.pyplot as plt
import numpy as np

size = 50*np.random.randn(1000)
colors = np.random.rand(1000)
plt.scatter(x, y, s=size, c=colors, marker='d');
plt.show()

import matplotlib.pyplot as plt
import numpy as np

x = np.arange(0.0, 2, 0.01)

y1 = np.sin(2*np.pi*x)
y2 = 1.2*np.sin(4*np.pi*x)

fig = plt.figure()
ax = plt.gca()

# 绘制曲线
ax.plot(x, y1, x, y2, color='black')

# 填充曲线之间的区域
ax.fill_between(x, y1, y2,
                where=y2>=y1, # where指定一个条件来填充曲线，where接受布尔值（可以是表达式）
                facecolor='darkblue',
                interpolate=True)

ax.fill_between(x, y1, y2, where=y2<=y1, facecolor='deeppink', interpolate=True)

ax.set_title('filled between');

plt.show()

import matplotlib.pyplot as plt
import numpy as np
theta = np.arange(0., 2., 1./180.)*np.pi
plt.polar(3*theta, theta/5); # theta参数是角度，r参数是距离圆心的距离
plt.polar(theta, np.cos(4*theta));
plt.polar(theta, [1.4]*len(theta));
plt.show()

# polar plot using add_axes and polar projection
import matplotlib.pyplot as plt

fig = plt.figure()
ax = fig.add_axes([0.0, 0.0, .6, .6], polar=True)
t = np.linspace(0, 2 * np.pi, 100)
ax.plot(t, t, color='blue', lw=3);

import matplotlib.pyplot as plt
import numpy as np
theta = np.arange(0., 2., 1./180.)*np.pi
r = np.abs(np.sin(5*theta) - 2.*np.cos(theta))
plt.polar(theta, r);
plt.rgrids(np.arange(0.2, 3.1, .7), angle=0); # radii参数设置网格线的位置；labels参数设置网格线的标签；angle参数设置网格线的角度（默认值22.5）
plt.thetagrids(range(45, 360, 90), frac=0.9); # angles参数设置网格线的角度，labels参数设置网格线的标签；frac参数设置网格线标签的位置（比例值）
plt.show()