Calculating cumulative returns with pandas dataframe

Question

I have this dataframe

Poloniex_DOGE_BTC   Poloniex_XMR_BTC    Daily_rets  perc_ret
172 0.006085    -0.000839   0.003309    0
173 0.006229    0.002111    0.005135         


        

Answer 1

If performance is important, use numpy.cumprod:

np.cumprod(1 + df['Daily_rets'].values) - 1

Timings:

#7k rows
df = pd.concat([df] * 1000, ignore_index=True)

In [191]: %timeit np.cumprod(1 + df['Daily_rets'].values) - 1
41 µs ± 282 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)

In [192]: %timeit (1 + df.Daily_rets).cumprod() - 1
554 µs ± 3.63 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)