Python:Pandas系列-为什么使用loc? [英] Python: Pandas Series - Why use loc?

问题描述

为什么我们对熊猫数据框使用"loc"?似乎下面的代码在使用或不使用loc的情况下都以模拟速度编译并运行

Why do we use 'loc' for pandas dataframes? it seems the following code with or without using loc both compile anr run at a simulular speed

%timeit df_user1 = df.loc[df.user_id=='5561']

100 loops, best of 3: 11.9 ms per loop

或

%timeit df_user1_noloc = df[df.user_id=='5561']

100 loops, best of 3: 12 ms per loop

那为什么要使用loc?

So why use loc?

编辑:该问题已被标记为重复问题.但是，尽管 pandas iloc vs ix vs loc的解释?确实提到了*

This has been flagged as a duplicate question. But although pandas iloc vs ix vs loc explanation? does mention that *

您可以仅通过使用数据框的列来进行列检索 getitem :

you can do column retrieval just by using the data frame's getitem:

*

df['time']    # equivalent to df.loc[:, 'time']

它没有说明我们为什么使用loc，尽管它解释了loc的许多功能，但我的具体问题是为什么不完全省略loc"?为此，我在下面接受了非常详细的答案.

it does not say why we use loc, although it does explain lots of features of loc, my specific question is 'why not just omit loc altogether'? for which i have accepted a very detailed answer below.

另外，其他帖子的答案(我认为不是答案)在讨论中非常隐蔽，任何搜索我正在寻找的人的人都将很难找到信息，并且可以更好地为您提供信息我的问题的答案.

Also that other post the answer (which i do not think is an answer) is very hidden in the discussion and any person searching for what i was looking for would find it hard to locate the information and would be much better served by the answer provided to my question.

推荐答案

• 显式优于隐式.

  • Explicit is better than implicit.

    df[boolean_mask]选择其中boolean_mask为True的行，但是在某些情况下您可能不希望它出现:df具有布尔值的列标签:

    df[boolean_mask] selects rows where boolean_mask is True, but there is a corner case when you might not want it to: when df has boolean-valued column labels:

    In [229]: df = pd.DataFrame({True:[1,2,3],False:[3,4,5]}); df
    Out[229]: 
       False  True 
    0      3      1
    1      4      2
    2      5      3
    

    您可能要使用df[[True]]选择True列.相反，它会引发一个ValueError:

    You might want to use df[[True]] to select the True column. Instead it raises a ValueError:

    In [230]: df[[True]]
    ValueError: Item wrong length 1 instead of 3.
    

    相对于使用loc:

    In [231]: df.loc[[True]]
    Out[231]: 
       False  True 
    0      3      1
    

    相反，即使df2的结构与上面的df1几乎相同，以下内容也不会引起ValueError的出现:

    In contrast, the following does not raise ValueError even though the structure of df2 is almost the same as df1 above:

    In [258]: df2 = pd.DataFrame({'A':[1,2,3],'B':[3,4,5]}); df2
    Out[258]: 
       A  B
    0  1  3
    1  2  4
    2  3  5
    
    In [259]: df2[['B']]
    Out[259]: 
       B
    0  3
    1  4
    2  5
    

    因此，df[boolean_mask]并不总是与df.loc[boolean_mask]相同.即使这可以说是不太可能的用例，我还是建议始终使用df.loc[boolean_mask]而不是df[boolean_mask]，因为df.loc语法的含义是明确的.使用df.loc[indexer]，您会自动知道df.loc正在选择行.相反，不清楚df[indexer]是否在不了解indexer和df的详细信息的情况下选择行或列(或提高ValueError).

    Thus, df[boolean_mask] does not always behave the same as df.loc[boolean_mask]. Even though this is arguably an unlikely use case, I would recommend always using df.loc[boolean_mask] instead of df[boolean_mask] because the meaning of df.loc's syntax is explicit. With df.loc[indexer] you know automatically that df.loc is selecting rows. In contrast, it is not clear if df[indexer] will select rows or columns (or raise ValueError) without knowing details about indexer and df.

    df.loc[row_indexer, column_index]可以选择和行. df[indexer]只能根据indexer中的值类型和df所具有的列值类型来选择行或列(同样，它们是否为布尔值?).

    df.loc[row_indexer, column_index] can select rows and columns. df[indexer] can only select rows or columns depending on the type of values in indexer and the type of column values df has (again, are they boolean?).

    In [237]: df2.loc[[True,False,True], 'B']
    Out[237]: 
    0    3
    2    5
    Name: B, dtype: int64
    

  • 将切片传递到df.loc时，端点包括在范围内.将切片传递给df[...]时，该切片将被解释为半开间隔:

  • When a slice is passed to df.loc the end-points are included in the range. When a slice is passed to df[...], the slice is interpreted as a half-open interval:

    In [239]: df2.loc[1:2]
    Out[239]: 
       A  B
    1  2  4
    2  3  5
    
    In [271]: df2[1:2]
    Out[271]: 
       A  B
    1  2  4
    

  这篇关于Python:Pandas系列-为什么使用loc?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！