关注分类变量各分类的比例，用饼图  关注变量的频率分布，用直方图  关注变量的变化趋势，用折线图  关注两个变量的相关，用散点图  展示一个变量的集中趋势和离散趋势，用箱图

# -*- coding: utf-8 -*-  # @File    : pylot_demo.py # @Date    : 2018-05-14  import numpy as np import matplotlib.pyplot as plt

def plot_pie1():  labels = "Forgs", "Hogs", "Dogs", "Logs"   sizes = [15, 30, 45, 10]   explode = (0, 0.1, 0, 0)   plt.pie(sizes, explode, labels, autopct='%1.1f%%', shadow=False, startangle=90)  plt.savefig("pie1", dpi=600)  plt.show()

def plot_pie2():  labels = "Forgs", "Hogs", "Dogs", "Logs"   sizes = [15, 30, 45, 10]   explode = (0, 0.1, 0, 0)   plt.pie(sizes, explode, labels, autopct='%1.1f%%', shadow=False, startangle=90)   plt.axis("equal")  plt.savefig("pie2", dpi=600)  plt.show()

def plot_hist():  np.random.seed(0)  mu, sigma = 100, 20  # 均值和标准差  a = np.random.normal(mu, sigma, size=100)   # bins直方图的个数  plt.hist(a, 20, normed=1, histtype="stepfilled", facecolor="b", alpha=0.75)  plt.title("histogram")  plt.savefig("hist", dpi=600)   plt.show()

def plot_polar():  N = 20  theta = np.linspace(0.0, 2*np.pi, N, endpoint=False)  radii = 20 * np.random.randn(N)  width = np.pi / 4 * np.random.randn(N)   ax = plt.subplot(111, projection="polar")  bars = ax.bar(theta, radii, width=width, bottom=0.0)   for r, bar in zip(radii, bars):  bar.set_facecolor(plt.cm.viridis(r/10.))  bar.set_alpha(0.5)   plt.savefig("polar", dpi=600)   plt.show()

def plot_scatter():  fig, ax = plt.subplots()  ax.plot(10*np.random.randn(100), 10*np.random.randn(100), "o")  ax.set_title("simple scatter")  plt.savefig("scatter", dpi=600)   plt.show()