code: def binary() -> SelectorType:
      """
      Select all binary columns.
  
      See Also
      --------
      by_dtype : Select all columns matching the given dtype(s).
      string : Select all string columns (optionally including categoricals).
  
      Examples
      --------
      >>> import polars.selectors as cs
      >>> df = pl.DataFrame({"a": [b"hello"], "b": ["world"], "c": [b"!"], "d": [":)"]})
      >>> df
      shape: (1, 4)
      ┌───────────────┬───────┬───────────────┬─────┐
      │ a             ┆ b     ┆ c             ┆ d   │
      │ ---           ┆ ---   ┆ ---           ┆ --- │
      │ binary        ┆ str   ┆ binary        ┆ str │
      ╞═══════════════╪═══════╪═══════════════╪═════╡
      │ [binary data] ┆ world ┆ [binary data] ┆ :)  │
      └───────────────┴───────┴───────────────┴─────┘
  
      Select binary columns and export as a dict:
  
      >>> df.select(cs.binary()).to_dict(as_series=False)
      {'a': [b'hello'], 'c': [b'!']}
  
      Select all columns *except* for those that are binary:
  
      >>> df.select(~cs.binary()).to_dict(as_series=False)
      {'b': ['world'], 'd': [':)']}
  
      """
      return _selector_proxy_(F.col(Binary), name="binary")
  
imports: from __future__ import annotations
  import re
  from datetime import timezone
  from functools import reduce
  from operator import or_
  from typing import TYPE_CHECKING, Any, Collection, Literal, Mapping, overload
  from polars import functions
  from polars.datatypes import FLOAT_DTYPES, INTEGER_DTYPES, NUMERIC_DTYPES, SIGNED_INTEGER_DTYPES, TEMPORAL_DTYPES, UNSIGNED_INTEGER_DTYPES, Binary, Boolean, Categorical, Date, Datetime, Decimal, Duration, Object, String, Time, is_polars_dtype
  from polars.expr import Expr
  from polars.utils.deprecation import deprecate_nonkeyword_arguments
  from polars.utils.various import is_column
label_desc: Use image node_id 10 for calling a global function with example usage: binary() and returns: _selector_proxy_