Question

我的数据长度超过3000。
下面是制作20天价值的代码（股票市场的成交量）
花了超过2分钟。

有什么好方法可以减少运行时间。

import pandas as pd
import numpy as np
from pandas.io.data import DataReader
import matplotlib.pylab as plt
data = DataReader('047040.KS','yahoo',start='2010')
  data['vr']=0
  data['Volume Ratio']=0
  data['acend']=0
 data['vr'] = np.sign(data['Close']-data['Open']) 
 data['vr'] = np.where(data['vr']==0,0.5,data['vr']) 
 data['vr'] = np.where(data['vr']<0,0,data['vr']) 
 data['acend'] = np.multiply(data['Volume'],data['vr']) 

 for i in range(len(data['Open'])):
     if i<19:
         data['Volume Ratio'][i]=0
     else:
         data['Volume Ratio'][i] = ((sum(data['acend'][i-19:i]))/((sum(data['Volume'][i-19:i])-sum(data['acend'][i-19:i]))))*100

Answer 1

考虑使用条件行选择和rolling.sum()：

data.loc[data.index[:20], 'Volume Ratio'] = 0
data.loc[data.index[20:], 'Volume Ratio'] = (data.loc[:20:, 'acend'].rolling(window=20).sum() / (data.loc[:20:, 'Volume'].rolling(window=20).sum() - data.loc[:20:, 'acend'].rolling(window=20).sum()) * 100

或简化 - .rolling.sum()会为前20个值创建np.nan，因此只需使用.fillna(0)：

data['new_col'] = data['acend'].rolling(window=20).sum().div(data['Volume'].rolling(window=20).sum().subtract(data['acend'].rolling(window=20).sum()).mul(100).fillna(0)

如何更有效地计算轧制率

1 个答案: