Day 79 量化投资与Python——Matplotlib
量化投资与Python——Matplotlib
简介
案例
案例一：常用函数
import matplotlib.pyplot as plt import numpy as np plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签 plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号 x = np.linspace(-5, 5, 20) y1 = x y2 = x ** 2 y3 = 3 * x ** 3 + 5 * x ** 2 + 2 * x + 1 plt.plot(x, y1, 'h-r', label='y=x') plt.plot(x, y2, '*-c', label='y=x**2') plt.plot(x, y3, label='y=3x**3+5x**2+2x+1') plt.title('折线图') plt.xlabel('x轴') plt.ylabel('y轴') # plt.xlim(0, 10) # x轴的范围 # plt.ylim(0, 10) # x轴的范围 # plt.xticks(np.arange(0, 10, 3)) # 刻度、步长 # plt.yticks(np.arange(0, 10, 3)) # 刻度、步长 plt.legend() # 设置曲线图例说明 plt.show()
案例二：画布
x = np.linspace(-5, 5, 20) y1 = x y2 = x ** 2 y3 = 3 * x ** 3 + 5 * x ** 2 + 2 * x + 1 fig = plt.figure() ax1 = fig.add_subplot(2, 2, 1) ax1.plot(x, y2, 'o-r') fig.show() ax2 = fig.add_subplot(2, 2, 2) ax2.plot(x, y1, 'o-r', label='y=x') fig.show() ax3 = fig.add_subplot(2, 2, 3) ax3.plot(x, y3, 'o-r', label='y=x') fig.show()
案例四：柱状图与饼状图 （bar、pie）
# 柱状图 data = [5, 8, 13, 21] label = ['a', 'b', 'c', 'd'] plt.bar(np.arange(len(data)), data, align='center', color='red', width=0.3) # 默认 align='center' width=0.8 plt.xticks(np.arange(len(data)), labels=label) plt.show() # 饼状图 data = [5, 8, 13, 21] label = ['a', 'b', 'c', 'd'] plt.pie(data,labels=label,autopct='%.2f%%',explode=[0,0,0.1,0])# .2 表示保留两位小数 # plt.axis('equal') # 是图竖起来 plt.show()
案例五：绘制横向柱状图 barh
import matplotlib.pyplot as plt import numpy as np x = [5, 8, 13, 21] y = ['a', 'b', 'c', 'd'] plt.barh(x,y)
案例六：绘制直方图 hist
是一个特殊的柱状图，又叫做密度图
- plt.hist()的参数
+ bins
可以是一个bin数量的整数值，也可以是表示bin的一个序列。默认值为10
+ normed
如果值为True，直方图的值将进行归一化处理，形成概率密度，默认值为False
+ color
指定直方图的颜色。可以是单一颜色值或颜色的序列。如果指定了多个数据集合,例如DataFrame对象，颜色序列将会设置为相同的顺序。如果未指定，将会使用一个默认的线条颜色
+ orientation
通过设置orientation为horizontal创建水平直方图。默认值为vertical、
data = [1,2,3,1,2,3,4,6,7,4,7,5,3,5,2,3]
plt.hist(data,bins=20)
案例七：散点图 scatter
因变量随自变量而变化的大致趋势
import numpy as np x = np.array([4,5,6,1,8,4]) y = x **3 plt.scatter(x,y)
案例：绘制K线图
# mpl_finance 中有许多绘制金融相关的函数接口 # 绘制K线图：mpl_finance.candlestick_ochl 函数 import mpl_finance as fin import pandas as pd from matplotlib.dates import date2num df = pd.read_csv('./601318.csv', parse_dates=['date'], index_col=['date'])[['open', 'close', 'high', 'low']] df['time'] = date2num(df.index.to_pydatetime()) fig = plt.figure() ax = fig.add_subplot(1, 1, 1) arr = df[['time', 'open', 'close', 'high', 'low']].values fin.candlestick_ochl(ax, arr) plt.grid() fig.show()
Matplotlib 线点颜色参数，来自源码
============= =============================== character description ============= =============================== ``'.'`` point marker ``','`` pixel marker ``'o'`` circle marker ``'v'`` triangle_down marker ``'^'`` triangle_up marker ``'<'`` triangle_left marker ``'>'`` triangle_right marker ``'1'`` tri_down marker ``'2'`` tri_up marker ``'3'`` tri_left marker ``'4'`` tri_right marker ``'s'`` square marker ``'p'`` pentagon marker ``'*'`` star marker ``'h'`` hexagon1 marker ``'H'`` hexagon2 marker ``'+'`` plus marker ``'x'`` x marker ``'D'`` diamond marker ``'d'`` thin_diamond marker ``'|'`` vline marker ``'_'`` hline marker ============= =============================== **Line Styles** ============= =============================== character description ============= =============================== ``'-'`` solid line style ``'--'`` dashed line style ``'-.'`` dash-dot line style ``':'`` dotted line style ============= =============================== Example format strings:: 'b' # blue markers with default shape 'or' # red circles '-g' # green solid line '--' # dashed line with default color '^k:' # black triangle_up markers connected by a dotted line **Colors** The supported color abbreviations are the single letter codes ============= =============================== character color ============= =============================== ``'b'`` blue ``'g'`` green ``'r'`` red ``'c'`` cyan ``'m'`` magenta ``'y'`` yellow ``'k'`` black ``'w'`` white ============= ===============================
