pandas处理finance.yahoo股票数据 WTI CL USO OIL

1.参考

用Python做科学计算-基础篇 »matplotlib-绘制精美的图表 »快速绘图 使用pyplot模块绘图

2.数据来源

CL

USO

OIL

3.代码

 #encoding='utf-8'
 import pandas  as pd
 import numpy as np
 import matplotlib.pyplot as plt

 import matplotlib
 import sys
 import time

 print('{:<10}: {}'.format('python',sys.version))
 print('{:<10}: {}'.format('pandas',pd.__version__))
 print('{:<10}: {}'.format('numpy',np.__version__))
 print('{:<10}: {}'.format('matplotlib',matplotlib.__version__))
 # python    : 3.6.1 (v3.6.1:69c0db5, Mar 21 2017, 18:41:36) [MSC v.1900 64 bit (AMD64)]
 # pandas    : 0.20.2
 # numpy     : 1.13.0
 # matplotlib: 2.0.2

 df_cl = pd.read_csv('CL.csv',na_values=['-'],index_col='日期',parse_dates=['日期'],encoding='gbk')
 df_uso = pd.read_csv('USO.csv',na_values=['null'],index_col='Date',parse_dates=['Date'])
 df_oil = pd.read_csv('OIL.csv',na_values=['null'],index_col='Date',parse_dates=['Date'])

 # 传入由Series组成的字典
 df_merge = pd.DataFrame({'CL':df_cl['最新股价'],'USO':df_uso['Adj Close'],'OIL':df_oil['Adj Close']},columns=['CL','USO','OIL'])
 # 对不同的列填充不同的值
 # df_merge_fillna = df_merge.fillna({'CL':df_cl['最新股价'][0],'USO':df_uso['Adj Close'][0],'OIL':df_oil['Adj Close'][0]})
 df_merge_deal_na = df_merge.dropna()

 # plt.figure(1)
 ax1 = plt.subplot(211)
 ax2 = plt.subplot(212)
 # plt.sca(ax1)
 # plt.plot(df_uso['Adj Close'])只支持index 0 1 2
 # ax1.set_ylim([0,150])
 df_cl['最新股价'].plot(ax=ax1,yticks=range(0,150,10),grid=True)
 df_merge_deal_na.plot(ax=ax2)
 plt.show()
 # 需要手动关闭作图窗口才能继续，如果ctrl+c会导致作图窗口无响应
 # plt.clf()
 # 如果关闭了图表窗口，则无法使用savefig()保存图像。
 # http://hyry.dip.jp/tech/book/page/scipy/matplotlib_fast_plot.html
 ax1 = plt.subplot(211)
 ax2 = plt.subplot(212)
 df_cl['最新股价'].plot(ax=ax1,yticks=range(0,150,10),grid=True)
 df_merge_deal_na.plot(ax=ax2)
 plt.savefig('result_{}.png'.format(time.strftime('%m%d_%H%M')), dpi=200)

4.结果