Add columns dataframe python plus Multiplication by a number from an array

Question

How can I multiply an array to the columns of a dataframe and then sum these columns to a new column in a dataframe?

I tried it with the code below but somehow get wrong numbers:

                  AAPL  Portfolio         ACN
Date                                           
2017-01-03  116.150002  1860.880008  116.459999
2017-01-04  116.019997  1862.079960  116.739998
2017-01-05  116.610001  1852.799992  114.989998
2017-01-06  117.910004  1873.680056  116.300003
...

How it should look like is the following:

                  AAPL  Portfolio         ACN
Date                                          
2017-01-03  116.150002  1046.900003 116.459999
2017-01-04  116.019997  1047.779978 116.739998
2017-01-05  116.610001  1041.389994 114.989998
2017-01-06  117.910004  1053.140031 116.300003
...

The code looks like the following. Might be that I am thinking too complicated and therefore the code makes no sense:

import pandas_datareader.data as pdr
import pandas as pd
import datetime

start = datetime.datetime(2017, 1, 1)
end = datetime.datetime(2017, 3, 17)

ticker_list = ["AAPL","ACN"]
position_size = [4,5]

for i in range(0,len(ticker_list)):
    #print(i)
    DataInitial = pdr.DataReader(ticker_list[i], 'yahoo', start, end)
    ClosingPrices[ticker_list[i]] = DataInitial[['Close']]
    ClosingPrices['Portfolio'] = ClosingPrices['Portfolio'] + ClosingPrices[ticker_list[i]]*position_size[i] 
print(ClosingPrices)

What I want is actually:

2017-01-03: 116.150002*4+116.150002*5
2017-01-03: 116.019997*4+116.739998*5

etc...

@DYZ, as stated above I want to multiply the position to the dedicated prices and then add the column per day together. Is it clearer? — MCM
– MCM, Commented Mar 19, 2017 at 18:08
This is still not a question. SO is a Q&A site. Unless you ask a clear question, you won't get an answer. — DYZ
– DYZ, Commented Mar 19, 2017 at 18:09

jezrael · Accepted Answer · 2017-03-19 18:29:02Z

1

If need:

2017-01-03: 116.150002*4+116.150002*5
2017-01-03: 116.019997*4+116.739998*5

then use concat of multiple columns by value from dict and last sum all columns together:

ticker_list = ["AAPL","ACN"]
position_size = [4,5]
d = dict(zip(ticker_list,position_size))

print (pd.concat([ClosingPrices[col] * d[col] for col in ticker_list], axis=1))
                  AAPL         ACN
Date                              
2017-01-03  400.000000  500.000000
2017-01-04  464.079988  583.699990
2017-01-05  466.440004  574.949990
2017-01-06  471.640016  581.500015

ClosingPrices['Portfolio'] = pd.concat([ClosingPrices[col] * d[col] for col in ticker_list], 
                                        axis=1).sum(axis=1)
print (ClosingPrices)
                  AAPL    Portfolio         ACN
Date                                           
2017-01-03  100.000000   900.000000  100.000000 <-for testing values was changed to 100
2017-01-04  116.019997  1047.779978  116.739998
2017-01-05  116.610001  1041.389994  114.989998
2017-01-06  117.910004  1053.140031  116.300003

answered Mar 19, 2017 at 18:29

jezrael

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1 Comment

MCM Over a year ago

Thanks a lot this is what I was looking for.

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