Dynamically accessing a pandas dataframe column

Question

Consider this simple example

import pandas as pd

df = pd.DataFrame({\'one\' : [1,2,3],
                   \'two\' : [1,0,0]})

df 
Out[9]: 
   one  two
0            


        

Answer 1

You pass a string as the second argument. In effect, you're trying to do something like:

df.'two'

Which is invalid syntax. If you're trying to dynamically access a column, you'll need to use the index notation, [...] because the dot/attribute accessor notation doesn't work for dynamic access.

---

Dynamic access on its own is possible. For example, you can use getattr (but I don't recommend this, it's an antipattern):

In [674]: df
Out[674]: 
   one  two
0    1    1
1    2    0
2    3    0

In [675]: getattr(df, 'one')
Out[675]: 
0    1
1    2
2    3
Name: one, dtype: int64

Dynamically selecting by attribute from a groupby call can be done, something like:

In [677]: getattr(df.groupby('one'), mycol).sum() 
Out[677]: 
one
1    1
2    0
3    0
Name: two, dtype: int64

But don't do it. It is a horrid anti pattern, and much more unreadable than df.groupby('one')[mycol].sum().

Answer 2

I think you need [] for select column by column name what is general solution for selecting columns, because select by attributes have many exceptions:

• You can use this access only if the index element is a valid python identifier, e.g. s.1 is not allowed. See here for an explanation of valid identifiers.
• The attribute will not be available if it conflicts with an existing method name, e.g. s.min is not allowed.
• Similarly, the attribute will not be available if it conflicts with any of the following list: index, major_axis, minor_axis, items, labels.
• In any of these cases, standard indexing will still work, e.g. s['1'], s['min'], and s['index'] will access the corresponding element or column.

def megabug(df,mycol):
    return df.groupby('one')[mycol].sum()

print (megabug(df, 'two'))

one
1    1
2    0
3    0
Name: two, dtype: int64