Best way to subset a pandas dataframe

Question

Hey I'm new to Pandas and I just came across 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.

With brackets notation :

df[df['age'] <= 21]

With pandas query method :

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?

Answer 1

Consider the following sample 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.

• 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  Natalie
  

• you 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
  

---

PS there are also some disadvantages:

• 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 default engine='numexpr' (which is faster)

NOTE: Jeff (one of the main Pandas contributors and a member of Pandas core team) once said:

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.