Like a Python list but better.
Array is a functional mutable sequence inheriting from Python's built-in list. Array provides 100+ higher-order methods and more functionality to the built-in list, making operations on sequences simpler and one-liners neater with no third party packages required.
Array provides a combination of python built-ins, features found in NumPy arrays, and higher-order methods common to functional languages without the weird semantics of the builtins, still preserving the same functionality and the dynamic nature of the built-in list.
funct.Array is available on PyPi and can be installed with pip
$ pip install funct
Arrays can be created either with multiple arguments or by providing a sequence as an argument.
>>> from funct import Array >>> Array(1, 2, 3) Array(1, 2, 3) >>> Array([1, 2, 3]) Array(1, 2, 3)
An Array can also be initialized with the static
zerosmethod or the
padmethod.
Python built-in sequences (including nested ones) lists, tuples and ranges are converted to Arrays on instantiation. However, other iterables e.g. generators and numpy ndarrays are converted to Arrays only if the argument consists of a single iterable. The elements can be converted to Arrays by calling the
toArraymethod. ```python
Array(np.zeros(3)) Array(0.0, 0.0, 0.0) Array(np.zeros(3), np.zeros(3)) Array(array([0., 0., 0.]), array([0., 0., 0.]) Array(np.zeros(3), np.zeros(3)).toArray() Array(Array(0.0, 0.0, 0.0), Array(0.0, 0.0, 0.0)) ```
Arrays provide static methods
arange,
linspaceand
logspacefor creating linearly or logarithmically spaced Arrays.
Chaining multiple functions with Arrays result in cleaner code without multiple nested functions, e.g. ```python a.zip(b).map(func1).filter(func2).forall(func3)
all(map(func3, filter(func2, map(func1, zip(a, b))))) ``
wherea
&b
are Arrays andfunc1
,func2
&func3` some functions.
# In traditional python the multiplication could be implemented using list comprehensions as follows >>> nums = [1, 2, 3, 4, 5] >>> [a * 10 for a in nums] [10, 20, 30, 40, 50]With Arrays multiplication simplifies to
>>> from funct import Array >>> nums = Array(nums) >>> nums.mul(10) Array(10, 20, 30, 40, 50)
# Traditional python >>> nums2 = [11, 12, 13, 14, 15] >>> [a * b for a, b in zip(nums, nums2)] [11, 24, 39, 56, 75]With Arrays
>>> nums.mul(nums2) Array(11, 24, 39, 56, 75)
Same syntax applies for all mathematical operators;
add,
pow,
mod,
gt,
lt, etc.
# Traditional python >>> n = 2 >>> nums1 = [1, 2, 3, 4, 5] >>> [x for x in nums if x > n] [3, 4, 5]With Arrays
>>> nums[nums > n] Array(3, 4, 5)
>>> nums1 = Array(1, 2, 3, 4, 5) >>> nums2 = Array(5, 4, 3, 2, 1) >>> nums1.zip(nums2).map(max) Array(5, 4, 3, 4, 5)
>>> arr = Array(1, 2, "a", "b") >>> arr.groupBy(type)[:, 1] # group by type and select the 2nd element of the tuples Array(Array(1, 2), Array('a', 'b'))
Arrays also support parallel and concurrent execution. Functions applied to Arrays can be parallelized with the
parmapand
parstarmapmethods. The same methods can be run asynchronously with the
asyncmapand
asyncstarmapmethods. ```python
Array(1, 2, 3).parmap(someheavyfunc) Array(1, 2, 3).asyncmap(someotherfunc) ```
Array indexing is a combination of standard Python sequence indexing and numpy-style indexing. Array supports - Standard Python indexing (single element indexing, slicing) - Index arrays - Boolean masking - Multidimensional indexing
>>> a = Array(1, 2, 3) >>> a[0] 1 >>> a[:2] Array(1, 2)
>>> a = Array('a', 'b', 'c', 'd') >>> a[[1, 3]] Array('b', 'd')
>>> a = Array(1, 2, 3, 4) >>> a[[True, False, False, True]] Array(1, 4)
>>> a = Array((1, 2), (3, 4), (5, 6)) >>> a[:, 0] Array(1, 3, 5)
Note that when indexing 'ragged' nested Arrays multidimensional indexing may raise an
IndexError, since Array does not care whether all the nested Arrays are the same size, as opposed to numpy ndarrays.
addand
mulmethods, not with the
+and
*operators to avoid confusion and to retain the behaviour of the built-in list.
arr.abs_). However, methods for adding elements to Arrays (
append,
extend,
insert, etc.) are inplace by default. (Note: To be changed. In the next release the operations are inplace if
inplace=Trueis passed to the methods.)
==(
__eq__) Returns element-wise comparison.
bool(
__bool__) Returns whether all elements evaluate to True.
__hash__.