lost attribute after adding column to pandas DataFrame

Question

I like to add attributes to pandas DataFrame columns, for example to manage labels or units.

df = pd.DataFrame([[1, 2], [5, 6]], columns=['A', 'B'])
df['A'].units = 'm/s'

Calling the units of column (with df['A'].units) returns m/s.

However, the attribute gets lost after any DataFrame to Series operation, such as adding a new column:

df['C'] = [3, 8]
df['A'].units

AttributeError: 'Series' object has no attribute 'units'

Is there an approach to keep the attributes or an alternative to add columns?

Yes! Create your own class with the DataFrame as an attribute of your class. Manage all column attributes in your class. — piRSquared
– piRSquared, Commented Dec 23, 2016 at 17:15
@piRSquared , could you give an explicit example? I would like to return the dataframe when the object is called itself... — Joost Döbken
– Joost Döbken, Commented Jan 12, 2017 at 8:13

Zero · Accepted Answer · 2016-12-23 17:25:04Z

1

_metadata, is not part of public API. Not a stable way of doing it, still, for now

In [8]: df = pd.DataFrame([[1, 2], [5, 6]], columns=['A', 'B'])

In [9]: df['A']._metadata
Out[9]: ['name']

In [10]: df['A']._metadata.append({'units': 'm/s'})

In [11]: df['C'] = [3, 8]

In [12]: df['A']._metadata
Out[12]: ['name', {'units': 'm/s'}]

answered Dec 23, 2016 at 17:25

Zero

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