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.
