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eval(expr: 'str | BinOp', parser: 'str' = 'pandas', engine: 'str | None' = None, truediv=<no_default>, local_dict=None, global_dict=None, resolvers=(), level=0, target=None, inplace=False)

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 and , or , and not with the same semantics as the corresponding bitwise operators. ~pandas.Series and ~pandas.DataFrame objects are supported and behave as they would with plain ol' Python evaluation.

Notes

The dtype of any objects involved in an arithmetic % operation are recursively cast to float64 .

See the enhancing performance <enhancingperf.eval> documentation for more details.

Parameters

expr : str

The expression to evaluate. This string cannot contain any Python :None:None:`statements <https://docs.python.org/3/reference/simple_stmts.html#simple-statements>`, only Python :None:None:`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 enhancing performance <enhancingperf.eval> documentation for more details.

engine : {'python', 'numexpr'}, default 'numexpr'

The engine used to evaluate the expression. Supported engines are

None

None
'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

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 ~DataFrame.query method to inject the DataFrame.index and DataFrame.columns variables that refer to their respective ~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 :None:None:`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 :None:None:`target` is provided, and the expression mutates :None:None:`target`, whether to modify :None:None:`target` inplace. Otherwise, return a copy of :None:None:`target` with the mutation.

Raises

ValueError

There are many instances where such an error can be raised:

:None:None:`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 :None:None:`a + 2`.
:None:None:`inplace=True`, but the expression is missing item assignment.
Item assignment is provided, but the :None:None:`target` does not support string item assignment.
Item assignment is provided and :None:None:`inplace=False`, but the :None:None:`target` does not support the .copy() method

Returns

ndarray, numeric scalar, DataFrame, Series, or None: The completion value of evaluating the given code or None if inplace=True .

Evaluate a Python expression as a string using various backends.

Examples

This example is valid syntax, but we were not able to check execution

>>> 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 :

This example is valid syntax, but we were not able to check execution

>>> 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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