401 lines
13 KiB
Python
401 lines
13 KiB
Python
|
"""
|
||
|
|
Top level ``eval`` module.
|
||
|
|
"""
|
||
|
|
|
||
|
|
import tokenize
|
||
|
|
from typing import Optional
|
||
|
|
import warnings
|
||
|
|
|
||
|
|
from pandas._libs.lib import no_default
|
||
|
|
from pandas.util._validators import validate_bool_kwarg
|
||
|
|
|
||
|
|
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.io.formats.printing import pprint_thing
|
||
|
|
|
||
|
|
|
||
|
|
def _check_engine(engine: Optional[str]) -> 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
|
||
|
|
|
||
|
|
if engine is None:
|
||
|
|
engine = "numexpr" if NUMEXPR_INSTALLED 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,
|
||
|
|
parser="pandas",
|
||
|
|
engine: Optional[str] = None,
|
||
|
|
truediv=no_default,
|
||
|
|
local_dict=None,
|
||
|
|
global_dict=None,
|
||
|
|
resolvers=(),
|
||
|
|
level=0,
|
||
|
|
target=None,
|
||
|
|
inplace=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.
|
||
|
|
|
||
|
|
truediv : bool, optional
|
||
|
|
Whether to use true division, like in Python >= 3.
|
||
|
|
|
||
|
|
.. deprecated:: 1.0.0
|
||
|
|
|
||
|
|
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")
|
||
|
|
|
||
|
|
if truediv is not no_default:
|
||
|
|
warnings.warn(
|
||
|
|
(
|
||
|
|
"The `truediv` parameter in pd.eval is deprecated and "
|
||
|
|
"will be removed in a future version."
|
||
|
|
),
|
||
|
|
FutureWarning,
|
||
|
|
stacklevel=2,
|
||
|
|
)
|
||
|
|
|
||
|
|
if isinstance(expr, str):
|
||
|
|
_check_expression(expr)
|
||
|
|
exprs = [e.strip() for e in expr.splitlines() if e.strip() != ""]
|
||
|
|
else:
|
||
|
|
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)
|
||
|
|
|
||
|
|
# 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"
|
||
|
|
)
|
||
|
|
elif 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.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:
|
||
|
|
with warnings.catch_warnings(record=True):
|
||
|
|
# TODO: Filter the warnings we actually care about here.
|
||
|
|
target[assigner] = ret
|
||
|
|
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
|