python - How to convert DatetimeIndexResampler to DataFrame?

Question

I want to build a matrix from series but before that I have to resample those series. However, to avoid processing the whole matrix twice with replace(np.nan, 0.0) I want to append the dataframes to a collecting dataframe and then remove NaN values in one pass.

So instead of

user_activities = user.groupby(["DOC_ACC_DT", "DOC_ACTV_CD"]).agg("sum")["SUM_DOC_CNT"].unstack().resample("1D").replace(np.nan, 0)
df = df.append(user_activities[activity].rename(user_id))

I want

user_activities = user.groupby(["DOC_ACC_DT", "DOC_ACTV_CD"]).agg("sum")["SUM_DOC_CNT"].unstack().resample("1D")
df = df.append(user_activities[activity].rename(user_id))

but that is not working because user_activities is not a dataframe after resample().

The error suggests that I try apply() but that method expects a parameter:

/usr/local/lib/python2.7/dist-packages/pandas/core/groupby.pyc in _make_wrapper(self, name)
    507                    "using the 'apply' method".format(kind, name,
    508                                                      type(self).__name__))
--> 509             raise AttributeError(msg)
    510 
    511         # need to setup the selection

AttributeError: Cannot access callable attribute 'rename' of 'SeriesGroupBy' objects, try using the 'apply' method

How can I solve this issue?

Answer 1

The interface to .resample has changed in Pandas 0.18.0 to be more groupby-like and hence more flexible ie resample no longer returns a DataFrame: it's now "lazyly evaluated" at the moment of the aggregation or interpolation.

I suggest reading resample API changes http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#resample-api

See also:

• http://pandas.pydata.org/pandas-docs/stable/timeseries.html#resampling

• http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.resample.html

for upscaling

df.resample("1D").interpolate()

for downscaling

using mean

df.resample("1D").mean()

using OHLC

ie open high low close values or first maximal minimal last values

df.resample("1D").ohlc()