数据可视化之一matplotlib

数据可视化之matplotlib
matplotlib是python专门用于绘图的库，其官方指导网站为https://matplotlib.org/，里面涉及多种绘图的示例以及指导。这里只介绍一部分个人用到的一些方法。包括subplot，colormap。

1.contour、pcolor、plot、scatter、imshow、matshow
以上各种方法都可以绘制三维数据。用法基本相同，具体参数看官方文档。

    for i in range(16):        plt.subplot(4,4,i+1)        plt.pcolor(conv2[0,:,:,i])

    fig, axes = plt.subplots(4, 4, figsize=(6, 6),                         subplot_kw={'xticks': [], 'yticks': []})    fig.subplots_adjust(left=0.02, bottom=0.06, right=0.95, top=0.94, wspace=0.05)    for i,ax in zip(range(16), axes.flat):        ax.imshow(conv2[0,:,:,i])    plt.show()

2.subplot 控制绘图间距
方法1：subplot(nrows, ncols, plot_number)
方法2：matplotlib.pyplot.subplots(nrows=1, ncols=1, sharex=False, sharey=False, squeeze=True, subplot_kw=None, gridspec_kw=None, **fig_kw)

import matplotlib.pyplot as pltimport numpy as npmethods = [None, 'none', 'nearest', 'bilinear', 'bicubic', 'spline16',           'spline36', 'hanning', 'hamming', 'hermite', 'kaiser', 'quadric',           'catrom', 'gaussian', 'bessel', 'mitchell', 'sinc', 'lanczos']# Fixing random state for reproducibilitynp.random.seed(19680801)grid = np.random.rand(4, 4)fig, axes = plt.subplots(3, 6, figsize=(12, 6),                         subplot_kw={'xticks': [], 'yticks': []})fig.subplots_adjust(hspace=0.3, wspace=0.05)#控制间距#fig.subplots_adjust(left=0.02, bottom=0.06, right=0.95, top=0.94, wspace=0.05)for ax, interp_method in zip(axes.flat, methods):    ax.imshow(grid, interpolation=interp_method, cmap='viridis')    ax.set_title(interp_method)plt.show()

3.多个figure绘图叠加

fig, ax = plt.subplots()cmap = mpl.colors.ListedColormap(['blue','red'])c = plt.pcolor(z,cmap = cmap,vmin = 0,vmax = 30)plt.axis('tight')fig.colorbar(c,ticks = [15])plt.scatter(xid,yid,color = 'green')for i in range(len(xid)):    plt.text(xid[i]+10,yid[i]+10,str(cid[i]),ha = 'right')plt.show()