基于matplotlib的数据可视化 - 笔记

import numpy as npimport matplotlib.pyplot as plt# 生成曲线上各个点的x,y坐标，然后用一段段直线连起来# 利用linspace函数产生一个等差数列x = np.linspace(-np.pi, np.pi, 200)cos_y = np.cos(x) / 2sin_y = np.sin(x)# 用直线连接曲线上各点plt.plot(x, cos_y)plt.plot(x, sin_y)# 显示图形plt.show()

import numpy as npimport matplotlib.pyplot as pltx = np.linspace(-np.pi, np.pi, 200)cos_y = np.cos(x) / 2sin_y = np.sin(x)# 用直线连接曲线上各点plt.plot(x, cos_y, linestyle='--', linewidth=1,color='dodgerblue')plt.plot(x, sin_y, linestyle=':', linewidth=2.5,color='orangered')plt.show()

# 获取当前坐标轴对象ax = mp.gca()# 将上、右轴设置为none / Noneax.spines['top'].set_color('none')ax.spines['right'].set_color('none')

import numpy as npimport matplotlib.pyplot as mpx = np.linspace(-np.pi, np.pi, 200) # 产生一个等差数列cos_y = np.cos(x)sin_y = np.sin(x)ax = mp.gca() ax.spines['left'].set_position(('axes',0.5))ax.spines['bottom'].set_position(('data', 0))# 将右边框和顶边框设置成无色ax.spines['right'].set_color('None')ax.spines['top'].set_color('None')# 用直线连接曲线上各点mp.plot(x, cos_y, linestyle='--', linewidth=1,color='dodgerblue')mp.plot(x, sin_y, linestyle=':', linewidth=1.2,color='orangered')# 显示图形mp.show()

raise ValueError('bottom cannot be >= top')ValueError: bottom cannot be >= top

import numpy as npimport matplotlib.pyplot as pltplt.figure('Locator')locators = [ 'plt.NullLocator()', 'plt.MaxNLocator(nbins=3,steps=[3,5,7,9])', 'plt.FixedLocator(locs=[0,2.5,5,7.5,10])', 'plt.AutoLocator()', 'plt.IndexLocator(offset=0.5, base=1.5)', 'plt.MultipleLocator()', 'plt.LinearLocator(numticks=21)', 'plt.LogLocator(base=2, subs=[1.0])']n_locators = len(locators)for i, locator in enumerate(locators): plt.subplot(n_locators, 1, i + 1) plt.xlim(0, 10) plt.ylim(-1, 1) plt.yticks(()) ax = plt.gca() ax.spines['left'].set_color('none') ax.spines['right'].set_color('none') ax.spines['top'].set_color('none') ax.spines['bottom'].set_position(('data', 0)) ax.xaxis.set_major_locator(eval(locator)) ax.xaxis.set_minor_locator(plt.MultipleLocator(0.1)) plt.plot(np.arange(11), np.zeros(11)) plt.text(5, 0.3, locator[3:], ha='center', size=10)plt.tight_layout()plt.show()

import numpy as npimport matplotlib.pyplot as plt# 生成曲线上各个点的x,y坐标，然后用一段段直线连起来x = np.linspace(-np.pi, np.pi, 200) # 产生一个等差数列cos_y = np.cos(x) / 2sin_y = np.sin(x)# 设置坐标范围plt.xlim(x.min() * 1.1, x.max() * 1.1)plt.ylim(min(cos_y.min(), sin_y.min()) * 1.1, max(cos_y.max(), sin_y.max()) * 1.1)# 设置坐标轴刻度标签plt.xticks([-np.pi, -np.pi / 2, 0, np.pi / 2, np.pi * 3 / 4, np.pi], [r', r'$\frac{\pi}{2}$', r'$\frac{3\pi}{4}$', r'$\pi$'])plt.yticks([-1, -0.5, 0.5, 1])# 将矩形坐标轴改成十字坐标轴：# 获取当前坐标轴对象ax = plt.gca() # get current axis# 将垂直坐标刻度置于左边框ax.yaxis.set_ticks_position('left')# 将左边框置于数据坐标原点ax.spines['left'].set_position(('data', 0))# 将水平坐标刻度置于底边框ax.xaxis.set_ticks_position('bottom')# 将底边框置于数据坐标原点ax.spines['bottom'].set_position(('data', 0))# 将右边框和顶边框设置成无色ax.spines['right'].set_color('None')ax.spines['top'].set_color('None')# 用直线连接曲线上各点plt.plot(x, cos_y, linestyle='--', linewidth=1, color='dodgerblue')plt.plot(x, sin_y, linestyle=':', linewidth=1.2, color='orangered')# 显示图形plt.show()

import numpy as npimport matplotlib.pyplot as plt# 生成曲线上各个点的x,y坐标，然后用一段段直线连起来x = np.linspace(-np.pi, np.pi, 200) # 产生一个等差数列cos_y = np.cos(x) / 2sin_y = np.sin(x)# 设置坐标范围plt.xlim(x.min() * 1.1, x.max() * 1.1)plt.ylim(min(cos_y.min(), sin_y.min()) * 1.1, max(cos_y.max(), sin_y.max()) * 1.1)# 设置坐标轴刻度标签plt.xticks([-np.pi, -np.pi / 2, 0, np.pi / 2, np.pi * 3 / 4, np.pi], [r', r'$\frac{\pi}{2}$', r'$\frac{3\pi}{4}$', r'$\pi$'])plt.yticks([-1, -0.5, 0.5, 1])# 将矩形坐标轴改成十字坐标轴：# 获取当前坐标轴对象ax = plt.gca() # get current axis# 将垂直坐标刻度置于左边框ax.yaxis.set_ticks_position('none')# 将左边框置于数据坐标原点ax.spines['left'].set_position(('data', 0))# 将水平坐标刻度置于底边框ax.xaxis.set_ticks_position('none')# 将底边框置于数据坐标原点ax.spines['bottom'].set_position(('data', 0))# 将右边框和顶边框设置成无色ax.spines['right'].set_color('None')ax.spines['top'].set_color('None')# 用直线连接曲线上各点plt.plot(x, cos_y, linestyle='--', linewidth=1, color='dodgerblue')plt.plot(x, sin_y, linestyle=':', linewidth=1.2, color='orangered')# 显示图形plt.show()

line, = ax.plot([1, 2, 3], label='Inline label')ax.legend()

ax.plot([1, 2, 3]) # 线1ax.plot([-1, -2, -3]) # 线2ax.legend([‘line_one', 'line_two'])

line1, = ax.plot(y1)line2, = ax.plot(y2)plt.legend((line1, line2), ('label1', 'label2'))

ax.plot(y1, label = ' 图例文本1 ')ax.plot(y2, label = ' 图例文本2 ')plt.legend()# plt.legend(loc='图例位置 如 upper left')

=============== =============Location String Location Code=============== ============='best' 0'upper right' 1'upper left' 2'lower left' 3'lower right' 4'right' 5'center left' 6'center right' 7'lower center' 8'upper center' 9'center' 10=============== =============

import numpy as npimport matplotlib.pyplot as plt# 尽管在此用range与np.linspace等效，# 但scatter散点函数要求x y的长度一致时，np.linspace的优势就表现出现了，# 可以直接选择点的个数# 同时实际用的时候np.linspace的报错率低# 如在使用range时，x * 2 就会报错。# TypeError: unsupported operand type(s) for *: 'range' and 'int'# x = range(1, 100, 2)x = np.linspace(1,100,50)y1 = x * 0.25y2 = x * 0.5y3 = x * 1y4 = x * 2d = np.linspace(1,10000,50)plt.figure('Scatter', facecolor='lightgray')plt.title('Scatter', fontsize=20)plt.xlabel('x', fontsize=14)plt.ylabel('y', fontsize=14)plt.tick_params(labelsize=10)plt.grid(linestyle=':')plt.scatter(x, y1, s=60, c=d, cmap='jet_r', alpha=0.5)plt.scatter(x, y2, s=60, c=d, cmap='jet', alpha=0.5)plt.scatter(x, y3, s=60, c=d, cmap='gray_r', alpha=0.5)plt.scatter(x, y4, s=60, c=d, cmap='gray', alpha=0.5)plt.show()

help(plt.scatter)

mport numpy as npimport matplotlib.pyplot as pltx = np.linspace(-np.pi*1.5,np.pi*1.5,200)sin_y = np.sin(x)cos_y = np.cos(x)# 计算特殊点的坐标xo = np.pi * 3 / 4sin_yo = np.sin(xo)cos_yo = np.cos(xo)plt.plot(x, sin_y, linestyle='--', linewidth=1,color='dodgerblue')plt.plot(x, cos_y, linestyle=':', linewidth=1.2,color='orangered')# 绘制特殊点# plot(x,y)绘制一条线，x值[xo,xo],y值[cos_yo,sin_yo]，# 实际上将两个坐标点(xo, cos_yo), (xo, sin_yo)连接起来plt.plot([xo, xo], [cos_yo, sin_yo], linestyle='--',color='limegreen')# 绘制plt.scatter([xo, xo], [cos_yo, sin_yo], s=60,edgecolor='limegreen', facecolor='white',zorder=10)# 显示图形plt.show()

import numpy as npimport matplotlib.pyplot as pltn = 10000# 产生均值为0标准差为1区间的n个服从正态分布的随机数x = np.random.normal(0, 1, n)y = np.random.normal(0, 1, n)# 获取颜色的分布对应值c_color = np.sqrt(x**2 + y**2)print(d)plt.figure('Scatter', facecolor='lightgray')plt.title('Scatter', fontsize=20)plt.xlabel('x', fontsize=14)plt.ylabel('y', fontsize=14)plt.tick_params(labelsize=10)plt.grid(linestyle=':')plt.scatter(x, y, s=60, c=c_color, cmap='jet_r', alpha=0.5)plt.show()

plt.annotate( '注释文本'，字符串str xy=被注释点的坐标，序列，数组，可迭代对象iterable xycoords=被注释点的坐标属性，相对位置，有每一个属性值对应位置描述 xytext=注释文本的坐标，若无，则默认xy坐标点 textcoords=注释文本坐标的属性， fontsize=字体大小， arrowprops=dict(arrowstyle=箭头形状,connectionstyle=箭头连线的风格))

import numpy as npimport matplotlib.pyplot as pltx = np.linspace(-np.pi*1.5,np.pi*1.5,200)sin_y = np.sin(x)cos_y = np.cos(x)# 计算特殊点的坐标xo = np.pi * 3 / 4sin_yo = np.sin(xo)cos_yo = np.cos(xo)plt.plot(x, sin_y, linestyle='--', linewidth=1,color='dodgerblue')plt.plot(x, cos_y, linestyle=':', linewidth=1.2,color='orangered')# 绘制特殊点# plot(x,y)绘制一条线，x值[xo,xo],y值[cos_yo,sin_yo]，# 实际上将两个坐标点(xo, cos_yo), (xo, sin_yo)连接起来plt.plot([xo, xo], [cos_yo, sin_yo], linestyle='--',color='limegreen')# 绘制plt.scatter([xo, xo], [cos_yo, sin_yo], s=60,edgecolor='limegreen', facecolor='white',zorder=10)# 添加注释plt.annotate( r'$cos(\frac{3\pi}{4})=-\frac{\sqrt{2}}{2}$', xy=(xo, cos_yo), xycoords='data', xytext=(-90, -40), textcoords='offset points', fontsize=14, arrowprops=dict( arrowstyle='->', connectionstyle='arc3, rad=0.2'))plt.annotate( r'$sin(\frac{3\pi}{4})=\frac{\sqrt{2}}{2}$', xy=(xo, sin_yo), xycoords='data', xytext=(20, 20), textcoords='offset points', fontsize=14, arrowprops=dict( arrowstyle='->', connectionstyle='arc3, rad=0'))# 显示图形plt.show()

import numpy as npimport matplotlib.pyplot as plt# 生成曲线上各个点的x,y坐标，然后用一段段直线连起来x = np.linspace(-np.pi, np.pi, 200) # 产生一个等差数列cos_y = np.cos(x) / 2sin_y = np.sin(x)# 计算特殊点的坐标xo = np.pi * 3 / 4yo_cos = np.cos(xo) / 2yo_sin = np.sin(xo)# 设置坐标范围plt.xlim(x.min() * 1.1, x.max() * 1.1)plt.ylim(min(cos_y.min(), sin_y.min()) * 1.1, max(cos_y.max(), sin_y.max()) * 1.1)# 设置坐标轴刻度标签plt.xticks([-np.pi, -np.pi / 2, 0, np.pi / 2, np.pi * 3 / 4, np.pi], [r', r'$\frac{\pi}{2}$', r'$\frac{3\pi}{4}$', r'$\pi$'])# plt.yticks([])plt.yticks([-1, -0.5, 0.5, 1])# 将矩形坐标轴改成十字坐标轴：# 获取当前坐标轴对象ax = plt.gca() # get current axis# 将垂直坐标刻度置于左边框ax.yaxis.set_ticks_position('left')# 将左边框置于数据坐标原点ax.spines['left'].set_position(('data', 0))# 将水平坐标刻度置于底边框ax.xaxis.set_ticks_position('bottom')# 将底边框置于数据坐标原点ax.spines['bottom'].set_position(('data', 0))# 将右边框和顶边框设置成无色ax.spines['right'].set_color('none')ax.spines['top'].set_color('none')# 用直线连接曲线上各点plt.plot(x, cos_y, linestyle='--', linewidth=1, color='dodgerblue', label=r'$y=\frac{1}{2}cos(x)$')plt.plot(x, sin_y, linestyle=':', linewidth=1.2, color='orangered', label=r'$y=sin(x)$')# 绘制特殊点plt.plot([xo, xo], [yo_cos, yo_sin], linestyle='--', linewidth=1, color='limegreen')plt.scatter([xo, xo], [yo_cos, yo_sin], s=60, edgecolor='limegreen', facecolor='white', zorder=3)# 添加注释plt.annotate( r'$\frac{1}{2}cos(\frac{3\pi}{4})=-\frac{\sqrt{2}}{2}$', xy=(xo, yo_cos), xycoords='data', xytext=(-90, -40), textcoords='offset points', fontsize=14, arrowprops=dict( arrowstyle='->', connectionstyle='arc3, rad=0.2'))plt.annotate( r'$sin(\frac{3\pi}{4})=\frac{\sqrt{2}}{2}$', xy=(xo, yo_sin), xycoords='data', xytext=(20, 20), textcoords='offset points', fontsize=14, arrowprops=dict( arrowstyle='->', connectionstyle='arc3, rad=0'))plt.legend(loc='upper left')# 显示图形plt.show()