Speed difference between bracket notation and dot notation for accessing columns in pandas

Question

Let's have a small dataframe: df = pd.DataFrame({'CID': [1,2,3,4,12345, 6]})

When I search for membership the speed is vastly different based on whether I ask to search in df.CID or in df['CID'].

In[25]:%timeit 12345 in df.CID
Out[25]:89.8 µs ± 254 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)

In[26]:%timeit 12345 in df['CID']
Out[26]:42.3 µs ± 334 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)

In[27]:type( df.CID)
Out[27]: pandas.core.series.Series

In[28]:type( df['CID'])
Out[28]: pandas.core.series.Series

Why is that?

Answer 1

df['CID'] delegates to NDFrame.__getitem__ and it is more obvious you are performing an indexing operation.

On the other hand, df.CID delegates to NDFrame.__getattr__, which has to do some additional heavy lifting, mainly to determine whether 'CID' is an attribute, a function, or a column you're calling using the attribute access (a convenience, but not recommended for production code).

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Now, why is it not recommended? Consider,

df = pd.DataFrame({'A': [1, 2, 3]})
df.A

0    1
1    2
2    3
Name: A, dtype: int64

There are no issues referring to column "A" as df.A, because it does not conflict with any attribute or function namings in pandas. However, consider the pop function (just as an example).

df.pop
# <bound method NDFrame.pop of ...>

df.pop is a bound method of df. Now, I'd like to create a column called "pop" for various reasons.

df['pop'] = [4, 5, 6]
df
   A  pop
0  1    4
1  2    5
2  3    6

Great, but,

df.pop
# <bound method NDFrame.pop of ...>

I cannot use the attribute notation to access this column. However...

df['pop']

0    4
1    5
2    6
Name: pop, dtype: int64

Bracket notation still works. That's why this is better.