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"""
Top level ``eval`` module.
"""
from __future__ import annotations
import tokenize
from typing import TYPE_CHECKING
import warnings
from pandas.util._exceptions import find_stack_level
from pandas.util._validators import validate_bool_kwarg
from pandas.core.dtypes.common import is_extension_array_dtype
from pandas.core.computation.engines import ENGINES
from pandas.core.computation.expr import (
PARSERS,
Expr,
)
from pandas.core.computation.parsing import tokenize_string
from pandas.core.computation.scope import ensure_scope
from pandas.core.generic import NDFrame
from pandas.io.formats.printing import pprint_thing
if TYPE_CHECKING:
from pandas.core.computation.ops import BinOp
def _check_engine(engine: str | None) -> str:
"""
Make sure a valid engine is passed.
Parameters
----------
engine : str
String to validate.
Raises
------
KeyError
* If an invalid engine is passed.
ImportError
* If numexpr was requested but doesn't exist.
Returns
-------
str
Engine name.
"""
from pandas.core.computation.check import NUMEXPR_INSTALLED
from pandas.core.computation.expressions import USE_NUMEXPR
if engine is None:
engine = "numexpr" if USE_NUMEXPR else "python"
if engine not in ENGINES:
valid_engines = list(ENGINES.keys())
raise KeyError(
f"Invalid engine '{engine}' passed, valid engines are {valid_engines}"
)
# TODO: validate this in a more general way (thinking of future engines
# that won't necessarily be import-able)
# Could potentially be done on engine instantiation
if engine == "numexpr" and not NUMEXPR_INSTALLED:
raise ImportError(
"'numexpr' is not installed or an unsupported version. Cannot use "
"engine='numexpr' for query/eval if 'numexpr' is not installed"
)
return engine
def _check_parser(parser: str):
"""
Make sure a valid parser is passed.
Parameters
----------
parser : str
Raises
------
KeyError
* If an invalid parser is passed
"""
if parser not in PARSERS:
raise KeyError(
f"Invalid parser '{parser}' passed, valid parsers are {PARSERS.keys()}"
)
def _check_resolvers(resolvers):
if resolvers is not None:
for resolver in resolvers:
if not hasattr(resolver, "__getitem__"):
name = type(resolver).__name__
raise TypeError(
f"Resolver of type '{name}' does not "
"implement the __getitem__ method"
)
def _check_expression(expr):
"""
Make sure an expression is not an empty string
Parameters
----------
expr : object
An object that can be converted to a string
Raises
------
ValueError
* If expr is an empty string
"""
if not expr:
raise ValueError("expr cannot be an empty string")
def _convert_expression(expr) -> str:
"""
Convert an object to an expression.
This function converts an object to an expression (a unicode string) and
checks to make sure it isn't empty after conversion. This is used to
convert operators to their string representation for recursive calls to
:func:`~pandas.eval`.
Parameters
----------
expr : object
The object to be converted to a string.
Returns
-------
str
The string representation of an object.
Raises
------
ValueError
* If the expression is empty.
"""
s = pprint_thing(expr)
_check_expression(s)
return s
def _check_for_locals(expr: str, stack_level: int, parser: str):
at_top_of_stack = stack_level == 0
not_pandas_parser = parser != "pandas"
if not_pandas_parser:
msg = "The '@' prefix is only supported by the pandas parser"
elif at_top_of_stack:
msg = (
"The '@' prefix is not allowed in top-level eval calls.\n"
"please refer to your variables by name without the '@' prefix."
)
if at_top_of_stack or not_pandas_parser:
for toknum, tokval in tokenize_string(expr):
if toknum == tokenize.OP and tokval == "@":
raise SyntaxError(msg)
def eval(
expr: str | BinOp, # we leave BinOp out of the docstr bc it isn't for users
parser: str = "pandas",
engine: str | None = None,
local_dict=None,
global_dict=None,
resolvers=(),
level: int = 0,
target=None,
inplace: bool = False,
):
"""
Evaluate a Python expression as a string using various backends.
The following arithmetic operations are supported: ``+``, ``-``, ``*``,
``/``, ``**``, ``%``, ``//`` (python engine only) along with the following
boolean operations: ``|`` (or), ``&`` (and), and ``~`` (not).
Additionally, the ``'pandas'`` parser allows the use of :keyword:`and`,
:keyword:`or`, and :keyword:`not` with the same semantics as the
corresponding bitwise operators. :class:`~pandas.Series` and
:class:`~pandas.DataFrame` objects are supported and behave as they would
with plain ol' Python evaluation.
Parameters
----------
expr : str
The expression to evaluate. This string cannot contain any Python
`statements
<https://docs.python.org/3/reference/simple_stmts.html#simple-statements>`__,
only Python `expressions
<https://docs.python.org/3/reference/simple_stmts.html#expression-statements>`__.
parser : {'pandas', 'python'}, default 'pandas'
The parser to use to construct the syntax tree from the expression. The
default of ``'pandas'`` parses code slightly different than standard
Python. Alternatively, you can parse an expression using the
``'python'`` parser to retain strict Python semantics. See the
:ref:`enhancing performance <enhancingperf.eval>` documentation for
more details.
engine : {'python', 'numexpr'}, default 'numexpr'
The engine used to evaluate the expression. Supported engines are
- None : tries to use ``numexpr``, falls back to ``python``
- ``'numexpr'`` : This default engine evaluates pandas objects using
numexpr for large speed ups in complex expressions with large frames.
- ``'python'`` : Performs operations as if you had ``eval``'d in top
level python. This engine is generally not that useful.
More backends may be available in the future.
local_dict : dict or None, optional
A dictionary of local variables, taken from locals() by default.
global_dict : dict or None, optional
A dictionary of global variables, taken from globals() by default.
resolvers : list of dict-like or None, optional
A list of objects implementing the ``__getitem__`` special method that
you can use to inject an additional collection of namespaces to use for
variable lookup. For example, this is used in the
:meth:`~DataFrame.query` method to inject the
``DataFrame.index`` and ``DataFrame.columns``
variables that refer to their respective :class:`~pandas.DataFrame`
instance attributes.
level : int, optional
The number of prior stack frames to traverse and add to the current
scope. Most users will **not** need to change this parameter.
target : object, optional, default None
This is the target object for assignment. It is used when there is
variable assignment in the expression. If so, then `target` must
support item assignment with string keys, and if a copy is being
returned, it must also support `.copy()`.
inplace : bool, default False
If `target` is provided, and the expression mutates `target`, whether
to modify `target` inplace. Otherwise, return a copy of `target` with
the mutation.
Returns
-------
ndarray, numeric scalar, DataFrame, Series, or None
The completion value of evaluating the given code or None if ``inplace=True``.
Raises
------
ValueError
There are many instances where such an error can be raised:
- `target=None`, but the expression is multiline.
- The expression is multiline, but not all them have item assignment.
An example of such an arrangement is this:
a = b + 1
a + 2
Here, there are expressions on different lines, making it multiline,
but the last line has no variable assigned to the output of `a + 2`.
- `inplace=True`, but the expression is missing item assignment.
- Item assignment is provided, but the `target` does not support
string item assignment.
- Item assignment is provided and `inplace=False`, but the `target`
does not support the `.copy()` method
See Also
--------
DataFrame.query : Evaluates a boolean expression to query the columns
of a frame.
DataFrame.eval : Evaluate a string describing operations on
DataFrame columns.
Notes
-----
The ``dtype`` of any objects involved in an arithmetic ``%`` operation are
recursively cast to ``float64``.
See the :ref:`enhancing performance <enhancingperf.eval>` documentation for
more details.
Examples
--------
>>> df = pd.DataFrame({"animal": ["dog", "pig"], "age": [10, 20]})
>>> df
animal age
0 dog 10
1 pig 20
We can add a new column using ``pd.eval``:
>>> pd.eval("double_age = df.age * 2", target=df)
animal age double_age
0 dog 10 20
1 pig 20 40
"""
inplace = validate_bool_kwarg(inplace, "inplace")
exprs: list[str | BinOp]
if isinstance(expr, str):
_check_expression(expr)
exprs = [e.strip() for e in expr.splitlines() if e.strip() != ""]
else:
# ops.BinOp; for internal compat, not intended to be passed by users
exprs = [expr]
multi_line = len(exprs) > 1
if multi_line and target is None:
raise ValueError(
"multi-line expressions are only valid in the "
"context of data, use DataFrame.eval"
)
engine = _check_engine(engine)
_check_parser(parser)
_check_resolvers(resolvers)
ret = None
first_expr = True
target_modified = False
for expr in exprs:
expr = _convert_expression(expr)
_check_for_locals(expr, level, parser)
# get our (possibly passed-in) scope
env = ensure_scope(
level + 1,
global_dict=global_dict,
local_dict=local_dict,
resolvers=resolvers,
target=target,
)
parsed_expr = Expr(expr, engine=engine, parser=parser, env=env)
if engine == "numexpr" and (
is_extension_array_dtype(parsed_expr.terms.return_type)
or getattr(parsed_expr.terms, "operand_types", None) is not None
and any(
is_extension_array_dtype(elem)
for elem in parsed_expr.terms.operand_types
)
):
warnings.warn(
"Engine has switched to 'python' because numexpr does not support "
"extension array dtypes. Please set your engine to python manually.",
RuntimeWarning,
stacklevel=find_stack_level(),
)
engine = "python"
# construct the engine and evaluate the parsed expression
eng = ENGINES[engine]
eng_inst = eng(parsed_expr)
ret = eng_inst.evaluate()
if parsed_expr.assigner is None:
if multi_line:
raise ValueError(
"Multi-line expressions are only valid "
"if all expressions contain an assignment"
)
if inplace:
raise ValueError("Cannot operate inplace if there is no assignment")
# assign if needed
assigner = parsed_expr.assigner
if env.target is not None and assigner is not None:
target_modified = True
# if returning a copy, copy only on the first assignment
if not inplace and first_expr:
try:
target = env.target
if isinstance(target, NDFrame):
target = target.copy(deep=None)
else:
target = target.copy()
except AttributeError as err:
raise ValueError("Cannot return a copy of the target") from err
else:
target = env.target
# TypeError is most commonly raised (e.g. int, list), but you
# get IndexError if you try to do this assignment on np.ndarray.
# we will ignore numpy warnings here; e.g. if trying
# to use a non-numeric indexer
try:
if inplace and isinstance(target, NDFrame):
target.loc[:, assigner] = ret
else:
target[assigner] = ret # pyright: ignore[reportGeneralTypeIssues]
except (TypeError, IndexError) as err:
raise ValueError("Cannot assign expression output to target") from err
if not resolvers:
resolvers = ({assigner: ret},)
else:
# existing resolver needs updated to handle
# case of mutating existing column in copy
for resolver in resolvers:
if assigner in resolver:
resolver[assigner] = ret
break
else:
resolvers += ({assigner: ret},)
ret = None
first_expr = False
# We want to exclude `inplace=None` as being False.
if inplace is False:
return target if target_modified else ret
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