[英]Fastest way to access pandas column
I am confused by the difference in performance between the various ways to access a pandas column.我对访问 Pandas 列的各种方法之间的性能差异感到困惑。
In [1]: df = pd.DataFrame([[1,1,1],[2,2,2]],columns=['a','b','c'])
In [2]: %timeit df['a']
The slowest run took 75.37 times longer than the fastest. This could
mean that an intermediate result is being cached.
100000 loops, best of 3: 3.12 µs per loop
In [3]: %timeit df.a
The slowest run took 5.14 times longer than the fastest. This could
mean that an intermediate result is being cached.
100000 loops, best of 3: 6.59 µs per loop
In [4]: %timeit df.loc[:,'a']
10000 loops, best of 3: 55 µs per loop
I understand that the last variant is slower because it enables the values to be set, not just accessed.我知道最后一个变体速度较慢,因为它可以设置值,而不仅仅是访问值。 But why is
df.a
slower than df['a']
?但是为什么
df.a
比df['a']
慢? This seems true regardless of the intermediate results being cached.无论中间结果被缓存如何,这似乎都是正确的。
Here is a link that explains what is a difference between a .
这是一个链接,解释了
.
access and []
access.访问和
[]
访问。
Also look into the behavior of these operators in the documentation还要查看文档中这些运算符的行为
getitem (for []
) and getattr (for .
) methods. getitem (对于
[]
)和getattr (对于.
)方法。
.
seems to access the column through a function call, thereby taking less time than a []
which is accessed as a dictionary key-value似乎通过函数调用访问列,因此比作为字典键值访问的
[]
花费的时间更少
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