pandas 对熊猫数据框进行子集化的最佳方法
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Best way to subset a pandas dataframe
提问by Pierre-Eric Garcia
Hey I'm new to Pandas and I just came across df.query().
嘿，我是 Pandas 的新手，刚遇到df.query().
Why people would use df.query()when you can directly filter your Dataframes using brackets notation ? The official pandas tutorial also seems to prefer the latter approach.
df.query()当您可以使用括号表示法直接过滤数据框时，为什么人们会使用？pandas 官方教程似乎也更喜欢后一种方法。
With brackets notation :
用括号表示法：
df[df['age'] <= 21]
With pandas query method :
使用Pandas查询方法：
df.query('age <= 21')
Besides some of the stylistic or flexibility differences that have been mentioned, is one canonically preferred - namely for performance of operations on large dataframes?
除了已经提到的一些风格或灵活性差异之外，还有一个是规范的首选 - 即对大型数据帧的操作性能？
采纳答案by MaxU
Consider the following sample DF:
考虑以下示例 DF：
In [307]: df
Out[307]:
sex age name
0 M 40 Max
1 F 35 Anna
2 M 29 Joe
3 F 18 Maria
4 F 23 Natalie
There are quite a few good reasons to prefer .query()method.
有很多很好的理由选择.query()方法。
it might be much shorter and cleaner compared to boolean indexing:
In [308]: df.query("20 <= age <= 30 and sex=='F'") Out[308]: sex age name 4 F 23 Natalie In [309]: df[(df['age']>=20) & (df['age']<=30) & (df['sex']=='F')] Out[309]: sex age name 4 F 23 Natalieyou can prepare conditions (queries) programmatically:
In [315]: conditions = {'name':'Joe', 'sex':'M'} In [316]: q = ' and '.join(['{}=="{}"'.format(k,v) for k,v in conditions.items()]) In [317]: q Out[317]: 'name=="Joe" and sex=="M"' In [318]: df.query(q) Out[318]: sex age name 2 M 29 Joe
与布尔索引相比，它可能更短更干净：
In [308]: df.query("20 <= age <= 30 and sex=='F'") Out[308]: sex age name 4 F 23 Natalie In [309]: df[(df['age']>=20) & (df['age']<=30) & (df['sex']=='F')] Out[309]: sex age name 4 F 23 Natalie您可以以编程方式准备条件（查询）：
In [315]: conditions = {'name':'Joe', 'sex':'M'} In [316]: q = ' and '.join(['{}=="{}"'.format(k,v) for k,v in conditions.items()]) In [317]: q Out[317]: 'name=="Joe" and sex=="M"' In [318]: df.query(q) Out[318]: sex age name 2 M 29 Joe
PS there are also some disadvantages:
PS也有一些缺点：
- we can't use
.query()method for columns containing spaces or columns that consist only from digits - not all functions can be applied or in some cases we have to use
engine='python'instead of defaultengine='numexpr'(which is faster)
- 我们不能
.query()对包含空格的列或仅由数字组成的列使用方法 - 并非所有功能都可以应用，或者在某些情况下我们必须使用
engine='python'而不是默认engine='numexpr'（更快）
NOTE: Jeff (one of the main Pandas contributors and a member of Pandas core team) once said:
注意：Jeff（Pandas 的主要贡献者之一，也是 Pandas 核心团队的成员）曾经说过：
Note that in reality .query is just a nice-to-have interface, in fact it has very specific guarantees, meaning its meant to parse like a query language, and not a fully general interface.
请注意，实际上 .query 只是一个很好的接口，实际上它有非常具体的保证，这意味着它的目的是像查询语言一样解析，而不是一个完全通用的接口。
回答by Tai
Some other interesting usages in the documentation.
文档中其他一些有趣的用法。
Reuseable
可重复使用
A use case for query() is when you have a collection of DataFrame objects that have a subset of column names (or index levels/names) in common.You can pass the same query to both frames without having to specify which frame you're interested in querying -- (Source)
query() 的一个用例是当您有一组 DataFrame 对象时，这些对象具有共同的列名（或索引级别/名称）的子集。您可以将相同的查询传递给两个框架，而无需指定您对查询哪个框架感兴趣—— （来源）
Example:
例子：
dfA = pd.DataFrame([[1,2,3], [4,5,6]], columns=["X", "Y", "Z"])
dfB = pd.DataFrame([[1,3,3], [4,1,6]], columns=["X", "Y", "Z"])
q = "(X > 3) & (Y < 10)"
print(dfA.query(q))
print(dfB.query(q))
X Y Z
1 4 5 6
X Y Z
1 4 1 6
More flexible syntax
更灵活的语法
df.query('a < b and b < c') # understand a bit more English
Support inoperator and not in(alternative to isin)
支持in运算符和not in（替代isin）
df.query('a in [3, 4, 5]') # select rows whose value of column a is in [2, 3, 4]
Special usage of == and != (similar to in/not in)
== 和 != 的特殊用法（类似于in/ not in）
df.query('a == [1, 3, 5]') # select whose value of column a is in [1, 3, 5]
# equivalent to df.query('a in [1, 3, 5]')
