The functions available from pydash can be used in two styles.
The first is by using the module directly or importing from it:
>>> import pydash
# Arrays >>> pydash.flatten([1, 2, [3, [4, 5, [6, 7]]]]) [1, 2, 3, [4, 5, [6, 7]]]
>>> pydash.flatten_deep([1, 2, [3, [4, 5, [6, 7]]]]) [1, 2, 3, 4, 5, 6, 7]
# Collections >>> pydash.map([{'name': 'moe', 'age': 40}, {'name': 'larry', 'age': 50}], 'name') ['moe', 'larry']
# Functions >>> curried = pydash.curry(lambda a, b, c: a + b + c) >>> curried(1, 2)(3) 6
# Objects >>> pydash.omit({'name': 'moe', 'age': 40}, 'age') {'name': 'moe'}
# Utilities >>> pydash.times(3, lambda index: index) [0, 1, 2]
# Chaining >>> pydash.chain([1, 2, 3, 4]).without(2, 3).reject(lambda x: x > 1).value() [1]
The second style is to use the py_ or _ instances (they are the same object as two different aliases):
>>> from pydash import py
# Method calling which is equivalent to pydash.flatten(...) >>> py.flatten([1, 2, [3, [4, 5, [6, 7]]]]) [1, 2, 3, [4, 5, [6, 7]]]
# Method chaining which is equivalent to pydash.chain(...) >>> py([1, 2, 3, 4]).without(2, 3).reject(lambda x: x > 1).value() [1]
# Late method chaining >>> py().without(2, 3).reject(lambda x: x > 1)([1, 2, 3, 4]) [1]
For further details consult API Reference <api>.