A student of mine ran into a confusing problem which ended up being due to an asymmetry in DataFrame.__setattr__ and DataFrame.__getattr__ when an attribute and a column have the same name. Here is a short example session:
import pandas as pd
print(pd.__version__) # '0.14.1'
data = pd.DataFrame({'x':[1, 2, 3]})
# try to create a new column, making a common mistake
data.y = 2 * data.x
# oops! That didn't create a column.
# we need to do it this way instead
data['y'] = 2 * data.x
# update the attribute, and it updates the column, not the attribute
data.y = 0
print(data['y']) # [0, 0, 0]
# print the attribute, and it prints the attribute, not the column
print(data.y) # [2, 4, 6]
print(data['y']) # [0, 0, 0]
The confusion derived from the fact that in this situation, data.__getattr__('y') refers to the attribute, while data.__setattr__('y', val) refers to the column.
I understand that using attributes to access columns is not recommended, but the asymmetry in this corner case led to a lot of confusion! It would be better if __getattr__ and __setattr__ would always refer to the same object.
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