Question

如果你帮我解决这个问题，请提前感谢你。我想要完成的是在相同的日期使用另一个数据帧（indexed_orders）更新一个带有日期时间索引（我的交易数据帧）的零填充的数据框。我的代码如下：

import pandas as pd
import numpy as np
import os
import csv


orders = pd.read_csv('./orders/orders.csv', parse_dates=True, sep=',', dayfirst=True) #initiate orders data frame from csv data file
indexed_orders = orders.set_index(['Date']) #set Date as index for orders
print indexed_orders

symbol_list = orders['Symbol'].tolist() #creates list of symbols
symbols = list(set(symbol_list)) #gets rid of duplicates in list


dates_list = orders['Date'].tolist() #creates list of order dates
dates_orders = list(set(dates_list)) #gets rid of duplicates in list


start_date = '2011-01-05' #establish date range
end_date = '2011-01-20'

dates = pd.date_range(start_date, end_date) #establish dates from start_date and end_date

trade = pd.DataFrame(0, index = dates, columns = symbols) #establish trade data frame
trade['Cash'] = 0 #add column for future calculations
print trade

indexed_orders的哪些输出：

Date         Symbol Order  Shares
2011-01-10   AAPL   BUY    1500
2011-01-13   AAPL  SELL    1500
2011-01-13    IBM   BUY    4000
2011-01-26   GOOG   BUY    1000
2011-02-02    XOM  SELL    4000
2011-02-10    XOM   BUY    4000
2011-03-03   GOOG  SELL    1000
2011-03-03    IBM  SELL    2200
2011-06-03    IBM  SELL    3300
2011-05-03    IBM   BUY    1500
2011-06-10   AAPL   BUY    1200
2011-08-01   GOOG   BUY      55
2011-08-01   GOOG  SELL      55
2011-12-20   AAPL  SELL    1200

并为交易输出以下内容：

            GOOG  AAPL  XOM  IBM  Cash
2011-01-05     0     0    0    0     0
2011-01-06     0     0    0    0     0
2011-01-07     0     0    0    0     0
2011-01-08     0     0    0    0     0
2011-01-09     0     0    0    0     0
2011-01-10     0     0    0    0     0
2011-01-11     0     0    0    0     0
2011-01-12     0     0    0    0     0
2011-01-13     0     0    0    0     0
2011-01-14     0     0    0    0     0
2011-01-15     0     0    0    0     0
2011-01-16     0     0    0    0     0
2011-01-17     0     0    0    0     0
2011-01-18     0     0    0    0     0
2011-01-19     0     0    0    0     0
2011-01-20     0     0    0    0     0

我想在我的idexed_orders中的日期更新我的交易数据框，插入＆＃39;股票的数量＆＃39;在正确的＆＃39;符号＆＃39;下的列中（这是交易中的AAPL，IBM，GOOG和XOM名称）。我也想要＆＃39; Shares＆＃39;当“订单”时为负面。 indexed_orders中的列指定＆＃39;卖出＆＃39;。换句话说，我试图提出更新交易数据框的代码，以便：印刷贸易

            GOOG  AAPL  XOM  IBM  Cash
2011-01-05     0     0    0    0     0
2011-01-06     0     0    0    0     0
2011-01-07     0     0    0    0     0
2011-01-08     0     0    0    0     0
2011-01-09     0     0    0    0     0
2011-01-10     0  1500    0    0     0
2011-01-11     0     0    0    0     0
2011-01-12     0     0    0    0     0
2011-01-13     0 -1500    0 4000     0
2011-01-14     0     0    0    0     0
2011-01-15     0     0    0    0     0
2011-01-16     0     0    0    0     0
2011-01-17     0     0    0    0     0
2011-01-18     0     0    0    0     0
2011-01-19     0     0    0    0     0
2011-01-20     0     0    0    0     0

我正在考虑使用嵌套的布尔语句进行某种迭代，但我肯定很难找到一个。特别是，我很难想出一种方法来整理行并根据索引的日期时间进行更新。

任何帮助都会非常感激。

Answer 1

首先，您可以使用Order列来签署共享更改。然后，您可以按Date和Symbol进行分组，并通过汇总订单进行汇总。这将为您提供所有唯一日期的Series个订单以及当天交易的Symbols。最后，使用unstack将Series转换为表格格式。

import numpy as np
import pandas as pd

df = pd.io.parsers.read_csv('temp.txt', sep = '\t')

print df

'''
        Date Symbol Order  Shares
0    1/10/11   AAPL   BUY    1500
1    1/13/11   AAPL  SELL    1500
2    1/13/11    IBM   BUY    4000
3    1/26/11   GOOG   BUY    1000
4     2/2/11    XOM  SELL    4000
5    2/10/11    XOM   BUY    4000
6     3/3/11   GOOG  SELL    1000
7     3/3/11    IBM  SELL    2200
8     6/3/11    IBM  SELL    3300
9     5/3/11    IBM   BUY    1500
10   6/10/11   AAPL   BUY    1200
11    8/1/11   GOOG   BUY      55
12    8/1/11   GOOG  SELL      55
13  12/20/11   AAPL  SELL    1200
'''

df['SharesChange'] = df.Shares * df.Order.apply(lambda o: 1 if o == 'BUY' else -1)

df = df.groupby(['Date', 'Symbol']).agg({'SharesChange' : np.sum}).unstack().fillna(0)

print df
'''
         SharesChange
Symbol           AAPL  GOOG   IBM   XOM
Date
1/10/11          1500     0     0     0
1/13/11         -1500     0  4000     0
1/26/11             0  1000     0     0
12/20/11        -1200     0     0     0
2/10/11             0     0     0  4000
2/2/11              0     0     0 -4000
3/3/11              0 -1000 -2200     0
5/3/11              0     0  1500     0
6/10/11          1200     0     0     0
6/3/11              0     0 -3300     0
8/1/11              0     0     0     0
'''

使用其他数据框中的行更新数据框

1 个答案: