datafunc 0.0.4

Python functions for various dict, list, and other data structures.

datafunc

Python package of various functions for dict, list, and other data structures.

Changelog

• 11/17/2020 - 0.0.4 : Add missing type annotation. Generate docs.
• 11/17/2020 - 0.0.3 : Added duplicate() and add_sibling() methods. Added some missing type declarations.
• 11/15/2020 - 0.0.2 : Type annotations & minor refactors.
• 11/15/2020 - 0.0.1 : Initial methods.

Reference

flatten

flatten(var: object) -> Data

Traverse a dict-like object and return a new one with all the same values but only one layer deep.

Arguments:

• var: Dict-like variable to flatten.

Returns:

A mo-dots dict-like Data object.

iterable

iterable(var: Any) -> Boolean

Determine whether or not the input variable is iterable.

Arguments:

• var: Any

Returns:

Boolean

listlike

listlike(var: Any) -> Boolean

Determine if the input variable is list-like (Not a str, not dict-like, but is iterable)

Arguments:

• var: Any

Returns:

Boolean

mo_dotian

mo_dotian(var: Any) -> Boolean

Determine whether or not the input var is a mo-dots type.

Arguments:

• var: Any

Returns:

Boolean

apply_if

apply_if(func_to_apply: Callable, var: Any, condition: Callable, else_func: Callable = do_nothing) -> Any

Apply func_to_apply() to var if condiction() else apply else_func()

Arguments:

• func_to_apply: Callable to pass var to if condition(var) return true
• var: Variable to test against condition and return through func_to_apply() or else_func()
• condition: Callable to test var against. Should return a Boolean.
• else_func: Callable to return var through if condition(var) returns False.

Returns:

func_to_apply(var) if condition(var) returns True, otherwise else_func(var)

dictlike

dictlike(var: Any) -> Boolean

Determine whether or not var is dict-like (Can contain dict-like items).

Arguments:

• var: Any variable to check

Returns:

Boolean

nestable

nestable(var: Any) -> Boolean

Will return True if input var is either list-like or dict-like.

Arguments:

• var: Any input variable.

Returns:

Boolean

jsonify_nestable_vals

jsonify_nestable_vals(obj: object) -> Data

Convert any nestable (Dict-like or list-like) to a dict-like mo-dots Data object of obj's values as JSON strings.

Arguments:

• obj: Any nestable variable.

Returns:

A dict-like mo-dots Data object of obj's values as JSON strings.

compare

compare(d1: object, d2: object) -> Data

Compare dict-like variable d1 to dict-like variable d2 and return a dict-like mo-dots Data object of what's been added, removed, modified, or remained equal in d2

Arguments:

• d1: Dict-like variable as the base variable.
• d2: Dict-like variable to compare/contrast to d1

Returns:

Dict-like mo-dots Data object of differences between d1 and d2.

function_of

function_of(func: Callable, func_names: Tuple) -> Boolean

Determine whether or not a function's (func) name exists in tuple of strings (func_names).

Arguments:

• func: The callable function to test.
• func_names: Tuple of function names as strings ("func1", "func2", "func3,)

Returns:

Boolean True (func is of func_names) or False (func is not of func_names)

basevals

basevals(var: object, *attrs) -> Any

This method receives a dict and list of attributes to return the innermost value of the given dict-like var. This function seems stupid and I don't recall what it was for.

vivify

vivify(var: object, *attrs: str)

Adds the last attr variable passed to the dict-like "var" in the hierarchy mentioned via the prior *attrs For ex: vivify(animals, "cat", "leg","fingers", 4) is equivalent to animals["cat"]["leg"]["fingers"]=4 This method creates necessary objects until it reaches the final depth This behaviour is also known as autovivification and plenty of implementation are around This implementation addresses the corner case of replacing existing primitives https://gist.github.com/hrldcpr/2012250#gistcomment-1779319

duplicate

duplicate(data: object) -> object

Convenience method for copy.deepcopy()

Arguments:

• data: Any dict, mo-dots, or dotty object.

Returns:

A deep copy of the data.

add_sibling

add_sibling(data: object, node_path: List, new_key: str, new_data: Any, _i: int = 0)

Traversal-safe method to add a siblings data node.

Arguments:

• data: The data object you're traversing.
• node_path: List of path segments pointing to the node you're creating a sibling of. Same as node_path of traverse()
• new_key: The sibling key to create.
• new_data: The new data to be stored at the key.
• _i: Depth of node_path iterator.