Python学习之Matplotlib (一)

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Matplotlib

Matplotlib 是一个 Python 的 2D绘图库，能够生成出版质量级别的图形

一、简介

import numpy as np  import matplotlib.pyplot as plt    # 初始化x轴变量  x1 = np.linspace(-2,2,1000) # 返回一个数组x1，数组元素从1递增到5，一共1000个元素  x2 = np.arange(-2,2,0.001) # 返回一个数组x2，数组元素从1递增到5，步长为0.001    # 设置y轴变量  y1 = 1 + np.sin(np.pi*x1)  y2 = 1 + np.cos(np.pi*x2)    # 图1  plt.subplot(1,2,1) # 作用同matlab中的subplot  plt.plot(x1, y1, label='Sinusoidal function', color='red', linewidth=1.0, linestyle='--')   plt.title('fig 1') # 标题  plt.xlabel('x1') # 设置x轴变量名  plt.ylabel('y1') # 设置y轴变量名    # 图2  plt.subplot(1,2,2)  plt.plot(x2, y2, label='cosine function', color='green', linewidth=1.0, linestyle='-')  plt.legend() # 设置图例  plt.xlim((-3,3)) # 设置x轴取值范围  plt.ylim((0,4)) # 设置y轴取值范围  plt.title('fig 2')  plt.xlabel('x2')  plt.ylabel('y2')plt.tight_layout() # 自动调整子图之间的距离

二、直方图

Barchart Demo matplotlib.pyplot.bar

import numpy as npimport matplotlib.pyplot as pltn_groups = 5 precision1 = (0.82, 0.91, 0.87, 0.79, 0.93) # 期望值std_p1 = (0.04, 0.03, 0.04, 0.09, 0.06) # 方差precision2 = (0.85, 0.78, 0.94, 0.89, 0.85)std_p2 = (0.07, 0.05, 0.09, 0.03, 0.03)precision3 = (0.82, 0.88, 0.74, 0.81, 0.95)std_p3 = (0.04, 0.07, 0.08, 0.05, 0.08)fig, ax = plt.subplots()index = np.arange(n_groups)bar_width = 0.25 # 设置直方图宽度opacity = 0.4error_config = {'ecolor': '0.3'}rects1 = ax.bar(index, precision1, bar_width,                alpha=opacity, color='b',                yerr=std_p1, error_kw=error_config,                label='precision1')rects2 = ax.bar(index + bar_width, precision2, bar_width,                alpha=opacity, color='g',                yerr=std_p2, error_kw=error_config,                label='precision2')rects3 = ax.bar(index + 2*bar_width, precision3, bar_width,                alpha=opacity, color='r',                yerr=std_p3, error_kw=error_config,                label='precision3')ax.set_xlabel('time(s)')ax.set_ylabel('Precision')ax.set_title('precision changed by time')ax.set_xticks(index + bar_width)ax.set_xticklabels(('1:00', '3:00', '5:00', '7:00', '9:00'))ax.legend()fig.tight_layout()plt.show()

三、填充

Fill plot demo matplotlib.pyplot.fill

import numpy as npimport matplotlib.pyplot as pltx = np.linspace(0,2*np.pi,100)y = np.sin(x) - 0.5plt.fill(x,y,'g')plt.show()

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