布尔索引的Pandas，loc和non loc [英] Pandas, loc vs non loc for boolean indexing

问题描述

我所做的所有研究都指向使用loc作为通过col(s)值过滤数据框的方法，今天我正在阅读

All the research I do point to using loc as the way to filter a dataframe by a col(s) value(s), today I was reading this and I discovered by the examples I tested, that loc isn't isn't really needed when filtering cols by it's values:

EX:

df = pd.DataFrame(np.arange(0, 20, 0.5).reshape(8, 5), columns=['a', 'b', 'c', 'd', 'e'])    

df.loc[df['a'] >= 15]

      a     b     c     d     e
6  15.0  15.5  16.0  16.5  17.0
7  17.5  18.0  18.5  19.0  19.5

df[df['a'] >= 15]

      a     b     c     d     e
6  15.0  15.5  16.0  16.5  17.0
7  17.5  18.0  18.5  19.0  19.5

注意:我确实知道执行loc或iloc会通过索引和位置返回行.我不是基于此功能进行比较.

Note: I do know that doing loc or iloc return the rows by it's index and and the position. I'm not comparing based on this functionality.

但是在过滤时，执行"where"子句与不使用loc有什么区别?如果有的话.为什么我遇到的有关该主题的所有示例都使用loc?

But when filtering, doing "where" clauses what's the difference between using or not using loc? If any. And why do all the examples I come across regarding this subject use loc?

推荐答案

根据文档，

As per the docs, loc accepts a boolean array for selecting rows, and in your case

>>> df['a'] >= 15
>>> 
0    False
1    False
2    False
3    False
4    False
5    False
6     True
7     True
Name: a, dtype: bool

被视为布尔数组.

根据pandas的作者韦斯·麦金尼(Wes McKinney)的说法，您可以在此处省略loc并发出df[df['a'] >= 15]的事实是一种特殊情况的方便.

The fact that you can omit loc here and issue df[df['a'] >= 15] is a special case convenience according to Wes McKinney, the author of pandas.

直接从他的书中引用用于数据分析的Python ，第144，df[val]用于...

Quoting directly from his book, Python for Data Analysis, p. 144, df[val] is used to...

从DataFrame中选择单列或列序列； 特殊情况 便利:布尔数组(过滤器行)，切片(切片行)或布尔DataFrame (根据某些条件设置值)

Select single column or sequence of columns from the DataFrame; special case conveniences: boolean array (filter rows), slice (slice rows), or boolean DataFrame (set values based on some criterion)

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