Pandas: Data Manipulation - eval() function

Last update on August 19 2022 21:50:50 (UTC/GMT +8 hours)

eval() function

The eval() function is used to concatenate pandas objects along a particular axis with optional set logic along the other axes.

Syntax:

pandas.eval(expr, parser='pandas', engine=None, truediv=True, local_dict=None, global_dict=None, resolvers=(), level=0, target=None, inplace=False

Arithmetic operations +, -, *, /, **, % are supported along with the the boolean operations | (or), & (and), and ~ (not).

Parameters:

Name	Description	Type / Default Value	Required / Optional
expr	The expression to evaluate. This string cannot contain any Python statements, only Python expressions.	str or unicode	Required
parser	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 documentation for more details.	string Default Value: ‘pandas’, {‘pandas’, ‘python’}	Required
engine	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.	string or None Default Value: ‘numexpr’, {‘python’, ‘numexpr’}	Required
truediv	Whether to use true division, like in Python >= 3	bool	Optional
local_dict	A dictionary of local variables, taken from locals() by default.	dict or None	Optional
global_dict	A dictionary of global variables, taken from globals() by default.	dict or None	Optional
resolvers	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 query() method to inject the DataFrame.index and DataFrame.columns variables that refer to their respective DataFrame instance attributes.	list of dict-like or None	Optional
level	The number of prior stack frames to traverse and add to the current scope. Most users will not need to change this parameter.	int	Optional
target	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().	object Default Value: None	Optional
inplace	If target is provided, and the expression mutates target, whether to modify target inplace. Otherwise, return a copy of target with the mutation.	bool Default Value: False	Required

Returns: ndarray, numeric scalar, DataFrame, Series

Raises

There are many instances where 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
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

Notes

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

Example:

Download the Pandas DataFrame Notebooks from here.

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