Calculate percentiles/quantiles for a timeseries with resample or groupby - pandas

I have a time series of hourly values and I am trying to derive some basic statistics on a weekly/monthly basis.

If we use the following abstract dataframe, were each column is time-series:

rng = pd.date_range('1/1/2016', periods=2400, freq='H')
df = pd.DataFrame(np.random.randn(len(rng), 4), columns=list('ABCD'), index=rng)

print df[:5] returns:

                            A         B         C         D
2016-01-01 00:00:00  1.521581  0.102335  0.796271  0.317046
2016-01-01 01:00:00 -0.369221 -0.179821 -1.340149 -0.347298
2016-01-01 02:00:00  0.750247  0.698579  0.440716  0.362159
2016-01-01 03:00:00 -0.465073  1.783315  1.165954  0.142973
2016-01-01 04:00:00  1.995332  1.230331 -0.135243  1.189431

I can call:

r = df.resample('W-MON')

and then use: r.min(), r.mean(), r.max(), which all work fine. For instance print r.min()[:5] returns:

                   A         B         C         D
2016-01-04 -2.676778 -2.450659 -2.401721 -3.209390
2016-01-11 -2.710066 -2.372032 -2.864887 -2.387026
2016-01-18 -2.984805 -2.527528 -3.414003 -2.616434
2016-01-25 -2.625299 -2.947864 -2.642569 -2.262959
2016-02-01 -2.100062 -2.568878 -3.008864 -2.315566

However, if I try to calculate percentiles, using the quantile formula, i.e. r.quantile(0.95), I get one value for each column

A    0.090502
B    0.136594
C    0.058720
D    0.125131

Is there a way to combine the grouping / resampling using quantiles as arguments?

Thanks

np.random.seed(1234) rng = pd.date_range('1/1/2016', periods=2400, freq='H') df = pd.DataFrame(np.random.randn(len(rng), 4), columns=list('ABCD'), index=rng) #print (df) r = df.resample('W-MON') print (r.apply(lambda x: x.quantile(0.95))) A B C D 2016-01-04 1.540236 1.925962 1.439512 1.606239 2016-01-11 1.727545 1.520913 1.596961 1.652290 2016-01-18 1.595396 1.669630 1.763577 1.933235 2016-01-25 1.500270 1.604542 1.648790 1.778329 2016-02-01 1.608245 1.791356 1.548159 1.786005 2016-02-08 1.531625 1.408163 1.300414 1.877863 2016-02-15 1.818673 1.613632 1.498623 1.524481 2016-02-22 1.557928 1.566523 1.974486 1.727555 2016-02-29 1.530757 1.529591 1.869422 1.433620 2016-03-07 1.651609 1.452537 1.585765 1.414499 2016-03-14 1.311807 1.717968 1.410036 1.903715 2016-03-21 1.529065 1.693964 1.784480 1.708263 2016-03-28 1.636786 1.405565 1.809235 1.802555 2016-04-04 1.768068 1.564308 1.552492 1.801424 2016-04-11 1.824578 1.794437 1.649749 1.564300

g = df.groupby([pd.TimeGrouper('W-MON')]) print (g.quantile(0.95)) A B C D 2016-01-04 1.540236 1.925962 1.439512 1.606239 2016-01-11 1.727545 1.520913 1.596961 1.652290 2016-01-18 1.595396 1.669630 1.763577 1.933235 2016-01-25 1.500270 1.604542 1.648790 1.778329 2016-02-01 1.608245 1.791356 1.548159 1.786005 2016-02-08 1.531625 1.408163 1.300414 1.877863 2016-02-15 1.818673 1.613632 1.498623 1.524481 2016-02-22 1.557928 1.566523 1.974486 1.727555 2016-02-29 1.530757 1.529591 1.869422 1.433620 2016-03-07 1.651609 1.452537 1.585765 1.414499 2016-03-14 1.311807 1.717968 1.410036 1.903715 2016-03-21 1.529065 1.693964 1.784480 1.708263 2016-03-28 1.636786 1.405565 1.809235 1.802555 2016-04-04 1.768068 1.564308 1.552492 1.801424 2016-04-11 1.824578 1.794437 1.649749 1.564300

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