Given a dataframe,
stock_id returns
date
2015-01-03 bac_i.us 0.000000
2015-01-03 glt.us 0.000000
2015-01-03 lpx.us 0.000000
2015-01-03 rbc.us 0.000000
2015-01-03 clnt.us 0.000000
... ... ...
2015-12-31 atu.us -0.014771
2015-12-31 jhy.us -0.008246
2015-12-31 xco.us 0.148148
2015-12-31 mik.us 0.000452
2015-12-31 apf.us 0.009147
[277359 rows x 2 columns]
In pandas, to calcuate the rolling mean of each stock,
df.to_pandas().groupby('stock_id').rolling(window=30).mean() returns
stock_id date
aaap.us 2015-11-12 NaN
2015-11-13 NaN
2015-11-14 NaN
2015-11-15 NaN
2015-11-16 NaN
... ...
zixi.us 2015-12-27 -0.002902
2015-12-28 -0.002965
2015-12-29 -0.002838
2015-12-30 -0.002584
2015-12-31 -0.003644
[277359 rows x 1 columns]
In Xorbits, since the groupby.rolling is currently under development, you can do,
df.groupby('stock_id', group_keys=True).apply(lambda x: x.rolling(window=30).mean())The result is exactly the same,
returns
stock_id date
aaap.us 2015-11-12 NaN
2015-11-13 NaN
2015-11-14 NaN
2015-11-15 NaN
2015-11-16 NaN
... ...
zixi.us 2015-12-27 -0.002902
2015-12-28 -0.002965
2015-12-29 -0.002838
2015-12-30 -0.002584
2015-12-31 -0.003644
[277359 rows x 1 columns]