pandas_query.py

Sure, here is a summary of the 20 examples of using `pd.DataFrame.query()` to filter data:

1. Filter on exact string match: `df.query('name == "Alice"')`
2. Filter on multiple conditions: `df.query('age > 30 and city == "New York"')`
3. Filter on a list of values: `df.query('age in [25, 30, 35]')`
4. Filter on a range of values: `df.query('age.between(25, 35)')`
5. Filter on string contains: `df.query('name.str.contains("an")')`
6. Filter on string starts with: `df.query('name.str.startswith("A")')`
7. Filter on string ends with: `df.query('name.str.endswith("a")')`
8. Filter on regular expression: `df.query('name.str.match("^[Aa].*a$")')`
9. Filter on null values: `df.query('city.isna()')`
10. Filter on column names with spaces: `df.query('`column name` > 5')`
11. Filter on a calculated column: `df.query('salary * 1.1 > bonus')`
12. Filter on a column with spaces in the name: `df.query('`column name` == "value"')`
13. Filter on a datetime column: `df.query('date > "2022-01-01"')`
14. Filter on a timedelta column: `df.query('delta > "1 day"')`
15. Filter on a column with nested data: `df.query('col["key"] == "value"')`
16. Filter on a column that contains lists: `df.query('tags.apply(lambda x: "fruit" in x)')`
17. Filter on a column that contains dictionaries: `df.query('metadata.apply(lambda x: x.get("type", "") == "document")')`
18. Filter on a column with a window function: `df.query('rolling_mean > 30')`
19. Filter on a column with a custom function: `df.query('col.apply(lambda x: my_func(x) > 5)')`
20. Filter on a column with a SQL-like query: `df.query('SELECT * FROM df WHERE col1 = "value1" AND col2 > 10')`

These examples cover a wide range of scenarios and can be used as a starting point for customizing and building more complex queries to filter pandas dataframes.