Matplotlib绘制的27个常用图(附对应代码实现)
- 2019 年 11 月 22 日
- 筆記
0 共用模块
模块名称：example_utils.py，里面包括三个函数，各自功能如下：
import matplotlib.pyplot as plt # 创建画图fig和axes def setup_axes(): fig, axes = plt.subplots(ncols=3, figsize=(6.5,3)) for ax in fig.axes: ax.set(xticks=[], yticks=[]) fig.subplots_adjust(wspace=0, left=0, right=0.93) return fig, axes # 图片标题 def title(fig, text, y=0.9): fig.suptitle(text, size=14, y=y, weight='semibold', x=0.98, ha='right', bbox=dict(boxstyle='round', fc='floralwhite', ec='#8B7E66', lw=2)) # 为数据添加文本注释 def label(ax, text, y=0): ax.annotate(text, xy=(0.5, 0.00), xycoords='axes fraction', ha='center', style='italic', bbox=dict(boxstyle='round', facecolor='floralwhite', ec='#8B7E66'))
1 基本绘图
对应代码：
import numpy as np import matplotlib.pyplot as plt import example_utils x = np.linspace(0, 10, 100) fig, axes = example_utils.setup_axes() for ax in axes: ax.margins(y=0.10) # 子图1 默认plot多条线，颜色系统分配 for i in range(1, 6): axes[0].plot(x, i * x) # 子图2 展示线的不同linestyle for i, ls in enumerate(['-', '--', ':', '-.']): axes[1].plot(x, np.cos(x) + i, linestyle=ls) # 子图3 展示线的不同linestyle和marker for i, (ls, mk) in enumerate(zip(['', '-', ':'], ['o', '^', 's'])): axes[2].plot(x, np.cos(x) + i * x, linestyle=ls, marker=mk, markevery=10) # 设置标题 # example_utils.title(fig, '"ax.plot(x, y, ...)": Lines and/or markers', y=0.95) # 保存图片 fig.savefig('plot_example.png', facecolor='none') # 展示图片 plt.show()
2 散点图
对应代码：
""" 散点图的基本用法 """ import numpy as np import matplotlib.pyplot as plt import example_utils # 随机生成数据 np.random.seed(1874) x, y, z = np.random.normal(0, 1, (3, 100)) t = np.arctan2(y, x) size = 50 * np.cos(2 * t)**2 + 10 fig, axes = example_utils.setup_axes() # 子图1 axes[0].scatter(x, y, marker='o', color='darkblue', facecolor='white', s=80) example_utils.label(axes[0], 'scatter(x, y)') # 子图2 axes[1].scatter(x, y, marker='s', color='darkblue', s=size) example_utils.label(axes[1], 'scatter(x, y, s)') # 子图3 axes[2].scatter(x, y, s=size, c=z, cmap='gist_ncar') example_utils.label(axes[2], 'scatter(x, y, s, c)') # example_utils.title(fig, '"ax.scatter(...)": Colored/scaled markers', # y=0.95) fig.savefig('scatter_example.png', facecolor='none') plt.show()
3 柱状图
对应代码：
import numpy as np import matplotlib.pyplot as plt import example_utils def main(): fig, axes = example_utils.setup_axes() basic_bar(axes[0]) tornado(axes[1]) general(axes[2]) # example_utils.title(fig, '"ax.bar(...)": Plot rectangles') fig.savefig('bar_example.png', facecolor='none') plt.show() # 子图1 def basic_bar(ax): y = [1, 3, 4, 5.5, 3, 2] err = [0.2, 1, 2.5, 1, 1, 0.5] x = np.arange(len(y)) ax.bar(x, y, yerr=err, color='lightblue', ecolor='black') ax.margins(0.05) ax.set_ylim(bottom=0) example_utils.label(ax, 'bar(x, y, yerr=e)') # 子图2 def tornado(ax): y = np.arange(8) x1 = y + np.random.random(8) + 1 x2 = y + 3 * np.random.random(8) + 1 ax.barh(y, x1, color='lightblue') ax.barh(y, -x2, color='salmon') ax.margins(0.15) example_utils.label(ax, 'barh(x, y)') # 子图3 def general(ax): num = 10 left = np.random.randint(0, 10, num) bottom = np.random.randint(0, 10, num) width = np.random.random(num) + 0.5 height = np.random.random(num) + 0.5 ax.bar(left, height, width, bottom, color='salmon') ax.margins(0.15) example_utils.label(ax, 'bar(l, h, w, b)') main()
4 填充画图
对应代码：
""" fill函数的各种用法 """ import numpy as np import matplotlib.pyplot as plt import example_utils # -- 产生数据 ---------------------- def stackplot_data(): x = np.linspace(0, 10, 100) y = np.random.normal(0, 1, (5, 100)) y = y.cumsum(axis=1) y -= y.min(axis=0, keepdims=True) return x, y def sin_data(): x = np.linspace(0, 10, 100) y = np.sin(x) y2 = np.cos(x) return x, y, y2 def fill_data(): t = np.linspace(0, 2*np.pi, 100) r = np.random.normal(0, 1, 100).cumsum() r -= r.min() return r * np.cos(t), r * np.sin(t) def fill_example(ax): # fill一个多边形区域 x, y = fill_data() ax.fill(x, y, color='lightblue') ax.margins(0.1) example_utils.label(ax, 'fill') def fill_between_example(ax): # 两条线间填充 x, y1, y2 = sin_data() # fill_between的最常用法1 err = np.random.rand(x.size)**2 + 0.1 y = 0.7 * x + 2 ax.fill_between(x, y + err, y - err, color='orange') # 最常用法2:两条曲线相交区域对应不同填充色 ax.fill_between(x, y1, y2, where=y1 > y2, color='lightblue') ax.fill_between(x, y1, y2, where=y1 < y2, color='forestgreen') # 最常用法3 ax.fill_betweenx(x, -y1, where=y1 > 0, color='red', alpha=0.5) ax.fill_betweenx(x, -y1, where=y1 < 0, color='blue', alpha=0.5) ax.margins(0.15) example_utils.label(ax, 'fill_between/x') def stackplot_example(ax): # Stackplot就是多次调用 ax.fill_between x, y = stackplot_data() ax.stackplot(x, y.cumsum(axis=0), alpha=0.5) example_utils.label(ax, 'stackplot') def main(): fig, axes = example_utils.setup_axes() fill_example(axes[0]) fill_between_example(axes[1]) stackplot_example(axes[2]) # example_utils.title(fig, 'fill/fill_between/stackplot: Filled polygons', # y=0.95) fig.savefig('fill_example.png', facecolor='none') plt.show() main()
5 imshow
一个震撼的交互型3D可视化R包 – 可直接转ggplot2图为3D
对应代码：
import matplotlib.pyplot as plt import numpy as np from matplotlib.cbook import get_sample_data from mpl_toolkits import axes_grid1 import example_utils def main(): fig, axes = setup_axes() plot(axes, *load_data()) # example_utils.title(fig, '"ax.imshow(data, ...)": Colormapped or RGB arrays') fig.savefig('imshow_example.png', facecolor='none') plt.show() def plot(axes, img_data, scalar_data, ny): # 默认线性插值 axes[0].imshow(scalar_data, cmap='gist_earth', extent=[0, ny, ny, 0]) # 最近邻插值 axes[1].imshow(scalar_data, cmap='gist_earth', interpolation='nearest', extent=[0, ny, ny, 0]) # 展示RGB/RGBA数据 axes[2].imshow(img_data) def load_data(): img_data = plt.imread(get_sample_data('5.png')) ny, nx, nbands = img_data.shape scalar_data = np.load(get_sample_data('bivariate_normal.npy')) return img_data, scalar_data, ny def setup_axes(): fig = plt.figure(figsize=(6, 3)) axes = axes_grid1.ImageGrid(fig, [0, 0, .93, 1], (1, 3), axes_pad=0) for ax in axes: ax.set(xticks=[], yticks=[]) return fig, axes main()
6 pcolor
还在用PCA降维？快学学大牛最爱的t-SNE算法吧, 附Python/R代码
对应代码：
""" pcolor/pcolormesh的基本用法 记住一点：假如数据在矩形区域内建议使用imshow，这样速度更快。此例子展示imshow不能使用的场景 """ import matplotlib.pyplot as plt import numpy as np from matplotlib.cbook import get_sample_data import example_utils # 拿到数据 ... z = np.load(get_sample_data('./bivariate_normal.npy')) ny, nx = z.shape y, x = np.mgrid[:ny, :nx] y = (y - y.mean()) * (x + 10)**2 mask = (z > -0.1) & (z < 0.1) z2 = np.ma.masked_where(mask, z) fig, axes = example_utils.setup_axes() # pcolor 或 pcolormesh 都可，后者效率更高 axes[0].pcolor(x, y, z, cmap='gist_earth') example_utils.label(axes[0], 'either') # pcolor和pcolormesh的不同展示 # 使用pcolor axes[1].pcolor(x, y, z2, cmap='gist_earth', edgecolor='black') example_utils.label(axes[1], 'pcolor(x,y,z)') # 使用pcolormesh axes[2].pcolormesh(x, y, z2, cmap='gist_earth', edgecolor='black', lw=0.5, antialiased=True) example_utils.label(axes[2], 'pcolormesh(x,y,z)') #example_utils.title(fig, 'pcolor/pcolormesh: Colormapped 2D arrays') fig.savefig('pcolor_example.png', facecolor='none') plt.show()
7 contour
对应代码：
import matplotlib.pyplot as plt import numpy as np from matplotlib.cbook import get_sample_data import example_utils z = np.load(get_sample_data('bivariate_normal.npy')) fig, axes = example_utils.setup_axes() axes[0].contour(z, cmap='gist_earth') example_utils.label(axes[0], 'contour') axes[1].contourf(z, cmap='gist_earth') example_utils.label(axes[1], 'contourf') axes[2].contourf(z, cmap='gist_earth') cont = axes[2].contour(z, colors='black') axes[2].clabel(cont, fontsize=6) example_utils.label(axes[2], 'contourf + contourn + clabel') # example_utils.title(fig, '"contour, contourf, clabel": Contour/label 2D data', # y=0.96) fig.savefig('contour_example.png', facecolor='none') plt.show()
8 向量场
对应代码：
import matplotlib.pyplot as plt import numpy as np import example_utils # Generate data n = 256 x = np.linspace(-3, 3, n) y = np.linspace(-3, 3, n) xi, yi = np.meshgrid(x, y) z = (1 - xi / 2 + xi**5 + yi**3) * np.exp(-xi**2 - yi**2) dy, dx = np.gradient(z) mag = np.hypot(dx, dy) fig, axes = example_utils.setup_axes() # 单箭头 axes[0].arrow(0, 0, -0.5, 0.5, width=0.005, color='black') axes[0].axis([-1, 1, -1, 1]) example_utils.label(axes[0], 'arrow(x, y, dx, dy)') # ax.quiver ds = np.s_[::16, ::16] # Downsample our array a bit... axes[1].quiver(xi[ds], yi[ds], dx[ds], dy[ds], z[ds], cmap='gist_earth', width=0.01, scale=0.25, pivot='middle') axes[1].axis('tight') example_utils.label(axes[1], 'quiver(x, y, dx, dy)') # ax.streamplot # 宽度和颜色变化 lw = 2 * (mag - mag.min()) / mag.ptp() + 0.2 axes[2].streamplot(xi, yi, dx, dy, color=z, density=1.5, linewidth=lw, cmap='gist_earth') example_utils.label(axes[2], 'streamplot(x, y, dx, dy)') # example_utils.title(fig, '"arrow/quiver/streamplot": Vector fields', y=0.96) # fig.savefig('vector_example.png', facecolor='none') plt.show()
9 数据分布图
对应代码：
""" Matplotlib 提供许多专业的绘制统计学相关的图函数 更多统计学相关图可使用 Seaborn 库，它基于Matplotlib编写。 """ import numpy as np import matplotlib.pyplot as plt import example_utils def main(): colors = ['cyan', 'red', 'blue', 'green', 'purple'] dists = generate_data() fig, axes = example_utils.setup_axes() hist(axes[0], dists, colors) boxplot(axes[1], dists, colors) violinplot(axes[2], dists, colors) # example_utils.title(fig, 'hist/boxplot/violinplot: Statistical plotting', # y=0.9) fig.savefig('statistical_example.png', facecolor='none') plt.show() def generate_data(): means = [0, -1, 2.5, 4.3, -3.6] sigmas = [1.2, 5, 3, 1.5, 2] # 每一个分布的样本个数 nums = [150, 1000, 100, 200, 500] dists = [np.random.normal(*args) for args in zip(means, sigmas, nums)] return dists # 频率分布直方图 def hist(ax, dists, colors): ax.set_color_cycle(colors) for dist in dists: ax.hist(dist, bins=20, density=True, edgecolor='none', alpha=0.5) ax.margins(y=0.05) ax.set_ylim(bottom=0) example_utils.label(ax, 'ax.hist(dists)') # 箱型图 def boxplot(ax, dists, colors): result = ax.boxplot(dists, patch_artist=True, notch=True, vert=False) for box, color in zip(result['boxes'], colors): box.set(facecolor=color, alpha=0.5) for item in ['whiskers', 'caps', 'medians']: plt.setp(result[item], color='gray', linewidth=1.5) plt.setp(result['fliers'], markeredgecolor='gray', markeredgewidth=1.5) plt.setp(result['medians'], color='black') ax.margins(0.05) ax.set(yticks=[], ylim=[0, 6]) example_utils.label(ax, 'ax.boxplot(dists)') #小提琴图 def violinplot(ax, dists, colors): result = ax.violinplot(dists, vert=False, showmedians=True) for body, color in zip(result['bodies'], colors): body.set(facecolor=color, alpha=0.5) for item in ['cbars', 'cmaxes', 'cmins', 'cmedians']: plt.setp(result[item], edgecolor='gray', linewidth=1.5) plt.setp(result['cmedians'], edgecolor='black') ax.margins(0.05) ax.set(ylim=[0, 6]) example_utils.label(ax, 'ax.violinplot(dists)') main()
本文参考：
https://nbviewer.jupyter.org/github/matplotlib/AnatomyOfMatplotlib/blob/master/AnatomyOfMatplotlib-Part2-Plotting_Methods_Overview.ipynb
