thomas.mac - 1 year ago 70
Python Question

# Calculating yearly standard deviation, given monthly returns in pandas

I have a function to calculate monthly returns:

``````def monthlyreturns(df):
first = df.resample('M').first().to_period('M')
last = df.resample('M').last().to_period('M')
return ((last-first)/first) * 100
``````

and a resulting df from monthlyreturns(stocks):

``````           FOX     FOXA     MMM
Date
2012-01    5.4     3.2      -.08
2012-02    .07     1.2      -.62
...
2017-08    -.2     -4.2     2.3
``````

My question is - how can I calculate the standard deviation for ea year? My expected output would be to keep the df in the same format (with stocks in columns, and date as index) , but calculate the yearly standard deviation, given monthly returns (so there should be about 7 values for ea stock)

So far I have tried:

``````sd = pd.DataFrame()
x = -13
y = -1
for date in reversed(periods):                     #where periods is ea year
sd[date] = np.std(monthly_returns.iloc[x:y])
x -= 12
y -= 12
if x < -72:
break
``````

This works - but the dates and columns are swapped , and was wondering if there was a cleaner code to do this

``````monthly_returns.groupby(monthly_returns.index.year).std()
``````#           FOX      FOXA       MMM