Python Question
Calculate stock returns in pandas DataFrame
I would like to calculate the annual performance (as change in market value) of these two firms whose data is stored in the dataframe below.
df = pd.DataFrame({'tic' : ['AAPL', 'AAPL', 'AAPL', 'GOOGL','GOOGL','GOOGL'],
'mktvalue' : [20,25,30,50,55,60],
'fyear' : [2014,2015,2016,2014,2015,2016]})
I have have seen a similar with solution a lambda function, but until now I couldn't adapt it to my data. I had a solution like this in mind to calculate the performance based on the year:
df['performance'] = df.fyear.apply(lambda x: (df.mktvalue[(df['fyear'] == 2014)]) /
(df.mktvalue[(df['fyear'] == 2013)]) if x == 2014
else (df.mktvalue[(df['fyear'] == 2013)]) /
(df.mktvalue[(df['fyear'] == 2013)])
One of my major problems was that in the implementation when calling
(df.mktvalue[(df['fyear'] == 2013)])
I received all of the market values of 2013 instead of only the corresponding one to this firm.
I would appreciate any help!
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Answer Source
is that what you want?
In [129]: df['performance'] = df.groupby('tic').mktvalue.pct_change().fillna(0)
In [130]: df
Out[130]:
fyear mktvalue tic performance
0 2014 20 AAPL 0.000000
1 2015 25 AAPL 0.250000
2 2016 30 AAPL 0.200000
3 2014 50 GOOGL 0.000000
4 2015 55 GOOGL 0.100000
5 2016 60 GOOGL 0.090909
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