python教程分享python必备库Matplotlib画图神器

pip3 install matplotlib -i https://pypi.tuna.tsinghua.edu.cn/simple

conda install matplotlib

import numpy as np   from matplotlib import pyplot as plt      x = np.arange(1,11)   y =  2  * x +  5   plt.title("matplotlib demo")   plt.xlabel("x axis caption")   plt.ylabel("y axis caption")   plt.plot(x,y)   plt.show()

fig, ax = plt.subplots()  # create a figure containing a single axes.  ax.plot([1, 2, 3, 4], [1, 4, 2, 3]);  # plot some data on the axes.

fig, ax = plt.subplots(figsize=(5, 2.7))    t = np.arange(0.0, 5.0, 0.01)  s = np.cos(2 * np.pi * t)  line, = ax.plot(t, s, lw=2)    ax.annotate('local max', xy=(2, 1), xytext=(3, 1.5),              arrowprops=dict(facecolor='black', shrink=0.05))    ax.set_ylim(-2, 2);

import matplotlib.pyplot as plt  import numpy as np  n = 5  menmeans = (20, 35, 30, 35, -27)  womenmeans = (25, 32, 34, 20, -25)  menstd = (2, 3, 4, 1, 2)  womenstd = (3, 5, 2, 3, 3)  ind = np.arange(n)    # the x locations for the groups  width = 0.35       # the width of the bars: can also be len(x) sequence  fig, ax = plt.subplots()  p1 = ax.bar(ind, menmeans, width, yerr=menstd, label='men')  p2 = ax.bar(ind, womenmeans, width,              bottom=menmeans, yerr=womenstd, label='women')  ax.axhline(0, color='grey', linescores')  ax.set_title('scores by group and gender')  ax.set_xticks(ind, labels=['g1', 'g2', 'g3', 'g4', 'g5'])  ax.legend()  # label with label_type 'center' instead of the default 'edge'  ax.bar_label(p1, label_type='center')  ax.bar_label(p2, label_type='center')  ax.bar_label(p2)  plt.show()

import numpy as np  import matplotlib.pyplot as plt      fig, (ax1, ax2) = plt.subplots(2, 1)  # make a little extra space between the subplots  fig.subplots_adjust(hspace=0.5)    dt = 0.01  t = np.arange(0, 30, dt)    # fixing random state for reproducibility  np.random.seed(19680801)      nse1 = np.random.randn(len(t))                 # white noise 1  nse2 = np.random.randn(len(t))                 # white noise 2  r = np.exp(-t / 0.05)    cnse1 = np.convolve(nse1, r, mode='same') * dt   # colored noise 1  cnse2 = np.convolve(nse2, r, mode='same') * dt   # colored noise 2    # two signals with a coherent part and a random part  s1 = 0.01 * np.sin(2 * np.pi * 10 * t) + cnse1  s2 = 0.01 * np.sin(2 * np.pi * 10 * t) + cnse2    ax1.plot(t, s1, t, s2)  ax1.set_xlim(0, 5)  ax1.set_xlabel('time')  ax1.set_ylabel('s1 and s2')  ax1.grid(true)    cxy, f = ax2.csd(s1, s2, 256, 1. / dt)  ax2.set_ylabel('csd (db)')  plt.show()