At Quantego.com we love working with Pandas Dataframes. We use them to store and analyze results from simulation runs. On top of our data matrix and a multi-level index we also need to accommodate custom plotting functions and attributes from the previous simulation run.
Subclassing pandas.DataFrame for this task was a no-brainer. The new version 0.16.1 (to be released in the next days) includes some fixes to make working with subclasses of complex data-frames (DF) easier. Here an example of what can be done. First define two new classes for pandas.Series (single col DF) and pandas.DataFrame . You can define new functions or attributes, as needed.
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 |
class CustomSeries(pandas.Series): @property def _constructor(self): return CustomSeries def custom_series_function(self): return 'OK' class CustomDataFrame(pandas.DataFrame): "My custom dataframe" def __init__(self, *args, **kw): super(CustomDataFrame, self).__init__(*args, **kw) @property def _constructor(self): return CustomDataFrame _constructor_sliced = CustomSeries def custom_frame_function(self): return 'OK' |
Notice _constructor and _constructor_sliced . They make sure you get the correct class back, when slicing the DF.
Via self you have convenient access to all Pandas functions and can even roll your own.