iterrate over dataframe and based on the value of one column do operations in a new column with prev-CodePudding

I have small df of stock prices with their actions. I would like to calculate the adjust ownership amount of a stock after the split (i.e. if you own a 1000 share and stock have 2-1 split then your ownership become 2000 share). I would like to iterrate over "Stock Splits" column and if the value != 0 then multiply "ownership" with "Stock Splits" otherwise maintain the last quantity before the split. I tried many methods, but I am not sure where I am going wrong - i do think the logic is wrong but don't know how to fix it.

import yfinance as yf
aapl = yf.Ticker("AAPL")
hist = aapl.history(start="2014-06-01")
hist["ownership"] = 1000


    Open    High    Low Close   Volume  Dividends   Stock Splits    ownership
Date                                
2014-06-02  20.338966   20.366877   19.971301   20.168608   369350800   0.0 0.0 1000
2014-06-03  20.162511   20.492319   20.155774   20.453819   292709200   0.0 0.0 1000
2014-06-04  20.450610   20.785872   20.407940   20.687378   335482000   0.0 0.0 1000
2014-06-05  20.731655   20.833356   20.616479   20.768549   303805600   0.0 0.0 1000
2014-06-06  20.850357   20.893990   20.676150   20.711439   349938400   0.0 0.0 1000

my codes is as follow:

 hist.loc[hist['Stock Splits']==0,'ownerAdj'] = hist['ownership'].shift(1)
hist.loc[hist['Stock Splits']!=0,'ownerAdj'] = hist['ownership'].shift(1) * hist['Stock Splits']

However I am not always getting correct figures, like in the below example, in 2014-06-09 aapl had split (7 to 1) so the results should be 7000 from 2014-06-09 until the next date it has another split which is 2020-08-31 but I am getting back the 1000 after the split

Date    Open    High    Low Close   Volume  Dividends   Stock Splits    ownership   ownerAdj
0   2014-06-02  20.338964   20.366875   19.971299   20.168606   369350800   0.0 0.0 1000    NaN
1   2014-06-03  20.162515   20.492323   20.155778   20.453823   292709200   0.0 0.0 1000    1000.0
2   2014-06-04  20.450608   20.785870   20.407938   20.687376   335482000   0.0 0.0 1000    1000.0
3   2014-06-05  20.731645   20.833346   20.616470   20.768539   303805600   0.0 0.0 1000    1000.0
4   2014-06-06  20.850359   20.893992   20.676152   20.711441   349938400   0.0 0.0 1000    1000.0
5   2014-06-09  20.818268   21.083269   20.604921   21.042845   301660000   0.0 7.0 1000    7000.0
6   2014-06-10  21.274162   21.346027   21.013652   21.166365   251108000   0.0 0.0 1000    1000.0
7   2014-06-11  21.139424   21.280908   20.991204   21.078789   182724000   0.0 0.0 1000    1000.0

I tried to run loop but I am getting error:

for i, row in hist.iterrows():
    if row["Stock Splits"] == 0:
        row["ownerAdj"] = row["ownership"].shift(1)
    elif row["Stock Splits"] != 0:
        row["ownerAdj"] = row["ownership"].shift(1) * row["Stock Splits"]

 ---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-51-2d94c5e86953> in <module>
      1 for i, row in hist.iterrows():
      2     if row["Stock Splits"] == 0:
----> 3         row["adjust2"] = row["ownership"].shift(1)
      4     elif row["Stock Splits"] != 0:
      5         row["adjust2"] = row["ownership"].shift(1) * row["Stock Splits"]

AttributeError: 'numpy.float64' object has no attribute 'shift'

CodePudding user response：

You can do this vectorized

hist['ownership'] = 1000 * np.cumprod(np.maximum(hist["Stock Splits"], 1))

In parts:

# No split can be expressed as a 1.0 split (You get 1 for every 1).
# Assumes you don't have negative splits.
adj_split = np.maximum(hist["Stock Splits"], 1)  

# The multiple of the initial ownership at each day compared to the first.
cumsplit = np.cumprod(adj_split)

initial_ownership = 1000
hist["ownership"] = cumsplit * initial_ownership