fccoelho fccoelho - 4 months ago 57x
Python Question

Counting daily events on Pandas Time series

Hi I have a time series and would like to count how many events I have per day(i.e. rows in the table within a day). The command I'd like to use is:

ts.resample('D', how='count')

but "count" is not a valid aggregation function for time series, I suppose.

just to clarify, here is a sample of the dataframe:

0 2008-02-22 03:43:00
1 2008-02-22 03:43:00
2 2010-08-05 06:48:00
3 2006-02-07 06:40:00
4 2005-06-06 05:04:00
5 2008-04-17 02:11:00
6 2012-05-12 06:46:00
7 2004-05-17 08:42:00
8 2004-08-02 05:02:00
9 2008-03-26 03:53:00
Name: Data_Hora, dtype: datetime64[ns]

and this is the error I am getting:


TypeError Traceback (most recent call last)
<ipython-input-42-86643e21ce18> in <module>()
----> 1 ts.resample('D').count()

/usr/local/lib/python2.7/dist-packages/pandas/core/generic.pyc in resample(self, rule, how, axis, fill_method, closed, label, convention, kind, loffset, limit, base)
255 def resample(self, rule, how=None, axis=0, fill_method=None,
256 closed=None, label=None, convention='start',
--> 257 kind=None, loffset=None, limit=None, base=0):
258 """
259 Convenience method for frequency conversion and resampling of regular

/usr/local/lib/python2.7/dist-packages/pandas/tseries/resample.pyc in resample(self, obj)
98 return obj
99 else: # pragma: no cover
--> 100 raise TypeError('Only valid with DatetimeIndex or PeriodIndex')
102 rs_axis = rs._get_axis(self.axis)

TypeError: Only valid with DatetimeIndex or PeriodIndex

That can be fixed by turning the datetime column into an index with set_index. However after I do that, I still get the following error:

DataError: No numeric types to aggregate

because my Dataframe does not have a numeric column.

But I just want to count rows!! The simple "select count(*) group by ... " from SQL.


In order to get this to work, after removing the rows in which the index was NaT:

df2 = df[df.index!=pd.NaT]

I had to add a column of ones:

df2['n'] = 1

and then count only that column:

df2.n.resample('D', how="sum")

then I could visualize the data with:

plot(df2.n.resample('D', how="sum"))