python - rolling mean in Pandas with fixed time window (instead of fixed nb. of observations) -

i have dataframe 2 columns , 3 level index structure. columns price , volume, , indexes trader - stock - day.

i compute rolling mean of price , volume on last 50 days each trader - stock combination in data.

this came far.

test=test.set_index(['date','trader', 'stock'])

test=test.unstack().unstack()

test=test.resample("1d")

test=test.fillna(0)

test[[col+'_norm' col in test.columns]]=test.apply(lambda x: pd.rolling_mean(x,50,50))

test.stack().stack().reset_index().set_index(['trader', 'stock','date']).sort_index().head()

that is, unstack dataset twice have time axis left, , can compute 50 days rolling mean of variables because 50 observations correspond 50 days (after having resampled data).

the problem dont know how create right names rolling mean variables

test[[col+'_norm' col in test.columns]]

typeerror: can concatenate tuple (not "str") tuple

any ideas wrong here? algorithm correct these rolling means? many thanks!

the result of pd.rolling_mean (with modified column names) can concatenated original dataframe:

means = pd.rolling_mean(test, 50, 50) means.columns = [('{}_norm'.format(col[0]),)+col[1:] col in means.columns] test = pd.concat([test, means], axis=1) 

---

import numpy np import pandas pd  n = 10 test = pd.dataframe(np.random.randint(4, size=(n, 3)),                     columns=['trader', 'stock', 'foo'],                     index=pd.date_range('2000-1-1', periods=n)) test.index.names = ['date'] test = test.set_index(['trader', 'stock'], append=true)  test = test.unstack().unstack()  test = test.resample("1d")  test = test.fillna(0)  means = pd.rolling_mean(test, 50, 50) means.columns = [('{}_norm'.format(col[0]),)+col[1:] col in means.columns] test = pd.concat([test, means], axis=1)  test = test.stack().stack() test = test.reorder_levels(['trader', 'stock', 'date']) test = test.sort_index() print(test.head()) 

yields

                         foo  foo_norm trader stock date                      0      0     2000-01-01    0       nan              2000-01-02    0       nan              2000-01-03    0       nan              2000-01-04    0       nan              2000-01-05    0       nan ...