Quickstart¶

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]