I create DataFrame object in Jupyter notebook:
data = {'state':['Ohio','Ohio','Ohio','Nevada','Nevada'],
'year':[2000, 2001, 2002, 2000, 2001],
'pop':[1.5, 2.0, 3.6, 2.4, 2.9]}
frame = DataFrame(data)
When I'm extracting column 'year', it's ok:
In [30]: frame.year
Out[30]: 0 2000
1 2001
2 2002
3 2000
4 2001
Name: year, dtype: int64
But when I'm extracting column 'pop' (frame.pop), result is:
Out[31]:
<bound method NDFrame.pop of pop state year
0 1.5 Ohio 2000
1 2.0 Ohio 2001
2 3.6 Ohio 2002
3 2.4 Nevada 2000
4 2.9 Nevada 2001>
Why the result is not the same as for "frame.year"?
pop
is a dataframe level function. The takeaway here is try to avoid using the .
accessor to access columns. There are many dataframe attributes and functions that might clash with your column names, in which case the .
will invoke those instead of your columns.
You want to use the [..]
dict accessor instead:
frame['pop']
0 1.5
1 2.0
2 3.6
3 2.4
4 2.9
Name: pop, dtype: float64
If you want to use pop
, you can. This is how:
frame.pop('pop')
0 1.5
1 2.0
2 3.6
3 2.4
4 2.9
Name: pop, dtype: float64
frame
state year
0 Ohio 2000
1 Ohio 2001
2 Ohio 2002
3 Nevada 2000
4 Nevada 2001
Do note that this modifies the original dataframe, so don't do it unless you're trying to remove columns.
The way I am using ...eval
frame.eval('pop')
Out[108]:
0 1.5
1 2.0
2 3.6
3 2.4
4 2.9
dtype: float64
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