Why does subclassing a DataFrame mutate the origin

Question

I am ignoring the warnings and trying to subclass a pandas DataFrame. My reasons for doing so are as follows:

• I want to retain all the existing methods of DataFrame.
• I want to set a few additional attributes at class instantiation, which will later be used to define additional methods that I can call on the subclass.

Here's a snippet:

class SubFrame(pd.DataFrame):

    def __init__(self, *args, **kwargs):
        freq = kwargs.pop('freq', None)
        ddof = kwargs.pop('ddof', None)
        super(SubFrame, self).__init__(*args, **kwargs)
        self.freq = freq
        self.ddof = ddof
        self.index.freq = pd.tseries.frequencies.to_offset(self.freq)

    @property
    def _constructor(self):
        return SubFrame

Here's a use example. Say I have the DataFrame

print(df)
               col0     col1     col2
2014-07-31  0.28393  1.84587 -1.37899
2014-08-31  5.71914  2.19755  3.97959
2014-09-30 -3.16015 -7.47063 -1.40869
2014-10-31  5.08850  1.14998  2.43273
2014-11-30  1.89474 -1.08953  2.67830

where the index has no frequency

print(df.index)
DatetimeIndex(['2014-07-31', '2014-08-31', '2014-09-30', '2014-10-31',
               '2014-11-30'],
              dtype='datetime64[ns]', freq=None)

Using SubFrame allows me to specify that frequency in one step:

sf = SubFrame(df, freq='M')
print(sf.index)
DatetimeIndex(['2014-07-31', '2014-08-31', '2014-09-30', '2014-10-31',
               '2014-11-30'],
              dtype='datetime64[ns]', freq='M')

The issue is, this modifies df:

print(df.index.freq)
<MonthEnd>

What's going on here, and how can I avoid this?

Moreover, I profess to using copied code that I don't understand all that well. What is happening within __init__ above? Is it necessary to use args/kwargs with pop here? (Why can't I just specify params as usual?)

Answer 1

I'll add to the warnings. Not that I want to discourage you, I actually applaud your efforts.

However, this won't the last of your questions as to what is going on.

That said, once you run:

super(SubFrame, self).__init__(*args, **kwargs)

self is a bone-fide dataframe. You created it by passing another dataframe to the constructor.

Try this as an experiment

d1 = pd.DataFrame(1, list('AB'), list('XY'))
d2 = pd.DataFrame(d1)

d2.index.name = 'IDX'

d1

     X  Y
IDX      
A    1  1
B    1  1

So the observed behavior is consistent, in that when you construct one dataframe by passing another dataframe to the constructor, you end up pointing to the same objects.

To answer your question, subclassing isn't what is allowing the mutating of the original object... its the way pandas constructs a dataframe from a passed dataframe.

Avoid this by instantiating with a copy

d2 = pd.DataFrame(d1.copy())

---

What's going on in the __init__

You want to pass on all the args and kwargs to pd.DataFrame.__init__ with the exception of the specific kwargs that are intended for your subclass. In this case, freq and ddof. pop is a convenient way to grab the values and delete the key from kwargs before passing it on to pd.DataFrame.__init__

---

How I'd implement pipe

def add_freq(df, freq):
    df = df.copy()
    df.index.freq = pd.tseries.frequencies.to_offset(freq)
    return df

df = pd.DataFrame(dict(A=[1, 2]), pd.to_datetime(['2017-03-31', '2017-04-30']))

df.pipe(add_freq, 'M')