Breaking examples due to resample refactor · Issue #12448 · pandas-dev/pandas (original) (raw)

While using master a bit, I discovered some more cases where the new resample API breaks things:

Plotting. .plot is a dedicated groupby/resample method (which adds each group individually to the plot), while I think it is a very common idiom to quickly resample your timeseries and plot it with (old API) eg s.resample('D').plot().
Example with master:

In [1]: s = pd.Series(np.random.randn(60), index=date_range('2016-01-01', periods=60, freq='1min'))  
In [3]: s.resample('15min').plot()  
Out[3]:  
2016-01-01 00:00:00    Axes(0.125,0.1;0.775x0.8)  
2016-01-01 00:15:00    Axes(0.125,0.1;0.775x0.8)  
2016-01-01 00:30:00    Axes(0.125,0.1;0.775x0.8)  
2016-01-01 00:45:00    Axes(0.125,0.1;0.775x0.8)  
Freq: 15T, dtype: object

while previously it would just have given you one continuous line.
This one can be solved I think by special casing plot for resample (not have it a special groupby-like method, but let it warn and pass the the resample().mean() result to Series.plot() like the 'deprecated_valids')

When you previously called a method on the resample result that is also a valid Resampler method now. Eg s.resample(freq).min() would previously have given you the "minimum daily average" while now it will give you the "minimum per day".
This one is more difficult/impossible to solve I think? As you could detect that case if you know it is old code, but cannot distinguish it from perfectly valid code with the new API. If we can't solve it, I think it deserves some mention in the whatsnew explanation.
Using resample on a groupby object (xref Resampling converts int to float, but only in group by #12202). Using the example of that issue, with 0.17.1 you get:

In [1]: df = pd.DataFrame({'date': pd.date_range(start='2016-01-01', periods=4,  
freq='W'),  
...:                'group': [1, 1, 2, 2],  
...:                'val': [5, 6, 7, 8]})  
In [2]: df.set_index('date', inplace=True)  
In [3]: df  
Out[3]:  
      group  val  
date  
2016-01-03      1    5  
2016-01-10      1    6  
2016-01-17      2    7  
2016-01-24      2    8  
In [4]: df.groupby('group').resample('1D', fill_method='ffill')  
Out[4]:  
                val  
group date  
1     2016-01-03    5  
  2016-01-04    5  
  2016-01-05    5  
  2016-01-06    5  
  2016-01-07    5  
  2016-01-08    5  
  2016-01-09    5  
  2016-01-10    6  
2     2016-01-17    7  
  2016-01-18    7  
  2016-01-19    7  
  2016-01-20    7  
  2016-01-21    7  
  2016-01-22    7  
  2016-01-23    7  
  2016-01-24    8  
In [5]: pd.__version__  
Out[5]: u'0.17.1'

while with master you get:

In [29]: df.groupby('group').resample('1D', fill_method='ffill')  
Out[29]: <pandas.core.groupby.DataFrameGroupBy object at 0x0000000009BA73C8>

which will give you different results/error with further operations on that. Also, this case does not raise any FutureWarning (which should, as the user should adapt the code to groupby().resample('D').ffill())