使用來自另一個數據幀的行更新數據幀

Question

如果您能幫助我，請先謝謝您。 我要完成的工作是使用同一日期的另一個數據框（indexed_orders）更新一個填充有零的數據框，該數據框具有日期時間索引（我的交易數據框）。 我的代碼如下：

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的哪些輸出：

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

並輸出以下用於交易：

            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     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

我想更新我的idexed_orders中顯示的日期的交易數據框，在正確的“符號”（即交易中的AAPL，IBM，GOOG和XOM名稱）下方的列中插入“股份”的數量。 當indexed_orders中的“訂單”列指定為“賣出”時，我還希望“份額”的值為負。 換句話說，我正在嘗試提出更新交易數據框架的代碼，例如：打印交易

            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

我認為需要使用嵌套的布爾語句進行某種迭代，但是我肯定很難弄清楚。 特別是，我很難提出一種方法來遍歷行並基於索引的日期時間進行更新。

任何幫助將不勝感激。

Answer 1

首先，您可以使用“ Order列對份額更改進行簽名。 然后，您可以按Date和Symbol進行分組，並按合計順序進行匯總。 這會給你一個Series的訂單的所有獨特的天， Symbols在那些日子里交易。 最后，使用unstack將Series轉換為表格格式。

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
'''