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Top level ``eval`` module.
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é )Ú annotationsN)Ú TYPE_CHECKING)Úfind_stack_level)Úvalidate_bool_kwarg©Úis_extension_array_dtypeÚis_string_dtype)ÚENGINES)ÚPARSERSÚExpr)Útokenize_string)Ú ensure_scope)ÚNDFrame)Ú pprint_thing)ÚBinOpc ó¾ ddl m} ddlm} | |rdnd} | t vr.t t j « « }t d| d| « | dk( r |s t d« | S ) a?
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Make sure a valid engine is passed.
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Parameters
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----------
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engine : str
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String to validate.
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Raises
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------
|
KeyError
|
* If an invalid engine is passed.
|
ImportError
|
* If numexpr was requested but doesn't exist.
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|
Returns
|
-------
|
str
|
Engine name.
|
r )ÚNUMEXPR_INSTALLED)Ú USE_NUMEXPRÚnumexprÚpythonzInvalid engine 'z ' passed, valid engines are z~'numexpr' is not installed or an unsupported version. Cannot use engine='numexpr' for query/eval if 'numexpr' is not installed) Ú pandas.core.computation.checkr Ú#pandas.core.computation.expressionsr r
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ÚlistÚkeysÚKeyErrorÚ ImportError)Úenginer r Ú valid_enginess úOH:\Change_password\venv_build\Lib\site-packages\pandas/core/computation/eval.pyÚ _check_enginer ! sv õ* @Ý?à ~Ù )¨xà WÑ Ü W\\^Ó, ÜØ vhÐ&BÀ=À/Ð Ró
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Make sure a valid parser is passed.
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parser : str
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zInvalid parser 'z ' passed, valid parsers are N)r r r )Úparsers r Ú _check_parserr$ N s6 ð WÑ ÜØ vhÐ&BÄ7Ç<Á<Ã>ÐBRÐ Só
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Make sure an expression is not an empty string
|
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Parameters
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expr : object
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An object that can be converted to a string
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ValueError)Úexprs r Ú_check_expressionr2 l s ñ ÜÐ9Ó:Ð:ð r! c ó2 t | « }t |« |S )a&
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Convert an object to an expression.
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This function converts an object to an expression (a unicode string) and
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checks to make sure it isn't empty after conversion. This is used to
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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
|
-------
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str
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The string representation of an object.
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|
Raises
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------
|
ValueError
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* If the expression is empty.
|
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|f< n| ||< |s|| if}n|D ] }||v s| ||< n ||| ifz }d} d} ð |du r|r|S | S yc c} w # t@ $ r}t d« |d}~ww xY w# tD tF f$ r}t d« |d}~ww xY w)a'
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Evaluate a Python expression as a string using various backends.
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The following arithmetic operations are supported: ``+``, ``-``, ``*``,
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``/``, ``**``, ``%``, ``//`` (python engine only) along with the following
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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
|
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