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Top level ``eval`` module.
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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.
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Make sure a valid parser is passed.
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Make sure an expression is not an empty string
Parameters
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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
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str
The string representation of an object.
Raises
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ValueError
* If the expression is empty.
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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
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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