tinystream 0.1.15

Yet another python streams library

tinystream / python-streams

This is a simple and lightweight Streams API inspired by Java Streams with support for type hinting.

This package is released as tinystream at pypi.

Basic API

from tinystream import Stream

stream = Stream([1, 2, 3, 4, 5])  # Stream.of_many(*), Stream.of_dict()

stream \
    .map(lambda x: x + 1) \       # flatmap(), peek(), map_key(), map_kwargs(), map_keys()
    .filter(lambda x: x > 2) \    # filter_key(), filter_type()
    .sorted(reverse=True) \       # sort()
    .reverse() \
    .limit(2) \
    .concat([4]) \
    .sum()                        # reduce(), max(), min(), collect(), count(), find()

Aggregators

Aggregators like sum(), count(), max() will collect() the data and end the stream. collect() also caches the data and can be called multiple times, since it returns only a list.

Built-in Optional support

Some aggregators like sum(), max() are optional:

assert Stream((1, 2, 3, 4, 5)).sum().present

Get next value as optional:

assert Stream((1, 2, 3, 4, 5)).next().present

Create custom optional:

from tinystream import Opt

assert Opt(None).absent

Map optional:

assert Opt("String").map(str.lower).len == 6

Get default value:

assert Opt(None).get(6) == 6
assert Opt(None).get(lambda: 6) == 6
assert Opt(None).if_absent(lambda: 3).present

Filter value:

assert Opt(0).filter(lambda x: x > 0).absent

You can also access optional index elements of the stream, but this will collect() and end the stream.

assert Stream([])[2].absent

Type hinting

You can typehint datatypes like:

from dataclasses import dataclass

@dataclass
class Node:
    name: str
    parent: "Node" = None

parent = Node(name="B")
child = Node(name="A", parent=parent)

for lambdas:

stream = Stream([child])
assert stream.map(lambda x: x.parent).type(Node).next().get().name == "B"

This is not necessary when you pass a mapping function:

def map_parent(n: Node):
    return n.parent

assert stream.map(map_parent).next().get().name == "B"

More features

Typed dictionaries

Dictionaries are streamed as tuple(key, value)

children = {"a": Node(name="Child")} 
stream = Stream.of_dict(children)
for item in stream:
    # item[0] is known as str
    # item[1] is known as Node

This is the same like (but without known types):

stream = Stream(children)

Filter by existing key

items_with_name = Stream([child]).filter_key("name")

Filter by type

nodes_only = Stream([child]).filter_type(Node)

Map object name attribute

names = Stream([child]).map_key("name")

Deep mapping of name attributes

list = [
   {"node": Node(name="Node A")},
   {"node": Node(name="Node B")},
   {"node": Node(name="Node C")},
   {"node": Node(name="Node D")},
]
names = Stream(list).map_keys("node", "name")

Collected join

all_names = Stream([child]).map_key("name").join(", ")

Map kwargs

list = [
   {"name": "Node A"},
   {"name": "Node B"},
]
# Short cut for map(lambda x: Node(**x))
nodes = Stream(list).map_kwargs(Node)

Stream many

many = Stream.of_many([1, 2, 3], (4, 5, 6))
many = many.concat([7, 8, 9])

End of stream

stream = Stream(["a", "b", "c"]).on_end(lambda: print("Finished"))
char = stream.next().get()
if char == "a":
    stream.end()

Examples

A given data structure like:

data = {
   "ranges": [
      {"days": 3},
      {"weeks": 1},
   ]
}

Without tinystream:

if "ranges" in data:
    range_data: timedelta
    for range_data in map(lambda x: timedelta(**x), data["ranges"]):
        pass

With tinystream:

for range_data in Opt(data).map_key("ranges").stream().map_kwargs(timedelta):
    pass

Comparison with other libraries

There are a couple of other implementation to fulfill similar requirements.

• https://github.com/vxgmichel/aiostream
• https://github.com/python-streamz/streamz
• https://pypi.org/project/fluentpy
• https://github.com/ramsteak/streams
• https://github.com/alemazzo/Python-Java-Stream (outdated)
• https://github.com/JaviOverflow/python-streams (outdated)
• https://github.com/9seconds/streams/ (outdated)
• https://github.com/tolsac/streampy (outdated)
• Apache Spark

Run the tests

PYTHONPATH="." pytest --cov=tinystream -n 4 tests/

Release update

1. Update version in setup.py
2. Package library

   python setup.py sdist
   

3. Publish library

   twine upload dist/tinystream-[version].tar.gz
   

References

• https://github.com/MichaelKim0407/tutorial-pip-package
• https://packaging.python.org/en/latest/guides/making-a-pypi-friendly-readme/