bodo.pandas.BodoDataFrame.query

BodoDataFrame.query(expr, *, parser="pandas", engine=None, local_dict=None, global_dict=None, resolvers=None, level=0, inplace=False)

Query the columns (select the rows) of the BodoDataFrame with a boolean expression.

Parameters

expr: str: The query string to evaluate.

local_dict dict | None, default None: A dictionary of local variables, taken from stack locals by default.

global_dict dict | None, default None: A dictionary of global variables, taken from stack globals by default.

resolvers: list-like of dict-like | None, default None: An additional list of maps between names and their respective values to use for variable lookup while parsing expr. The provided resolvers will be added to the resolvers used internally to inject the BodoDataFrame.index and BodoDataFrame.columns variables that refer to their respective BodoDataFrame instance attributes.

level: int, default 0: The number of prior stack frames to traverse and add to the current scope. Most users will not need to change this parameter (unless passing an unsupported argument; see below).

Setting the parser, engine, or inplace parameters to a value other than the default will trigger a fallback to pandas.DataFrame.query. In such a case, if using variable references in expr, either the level parameter must be incremented or a local_dict/global_dict must be provided explicitly.

Returns

BodoDataFrame

Notes

For more details on supported operations, functions, and formatting in the query string expr, see the pandas documentation at pandas.DataFrame.query.

Examples

import bodo.pandas as bd

bdf = bd.DataFrame(
    {
        "A": [1, 3, 5, 7, 9],
        "B B": [0.6, 3.2, 0.19, 0.18, 7.4],
        "C&C": ["DD", "EEE", "F", "EE", "E"]
    }
)

bdf_queried1 = bdf.query("ceil(`B B`) == A or (A > `B B` and `C&C`.str.contains('EE'))")
print(bdf_queried1)

Output:

   A   B B C&C
0  1   0.6  DD
1  7  0.18  EE

---

list_var = [3.2, 0.18, 2, 10.1, 5]
bdf_queried2 = bdf.query("A in @list_var & not (`B B` == @list_var | `C&C`.str.len() > @length)", local_dict={"length": 1})
print(bdf_queried2)

Output:

   A   B B C&C
0  5  0.19   F