pyrio 1.3.2

Functional-style Streams library for processing collections. Supports querying files (json, toml, yaml, xml, csv, tsv) - as well as creating and updating them. Provides easy integration with itertools

PYRIO

Functional-style Streams API library

Facilitates processing of collections and iterables using fluent APIs.
Gives access to files of various types (json, toml, yaml, xml, csv and tsv) for reading and executing complex queries
Provides easy integration with itertools
(NB: Commonly used itertools 'recipes' are included as part of the main APIs)

How to use

Creating streams

• stream from iterable

Stream([1, 2, 3])

• from variadic arguments

Stream.of(1, 2, 3)

• empty stream

Stream.empty()

• infinite ordered stream

Stream.iterate(0, lambda x: x + 1)

• infinite unordered stream

import random

Stream.generate(lambda: random.random())

• infinite stream with given value

Stream.constant(42)

• concat
  (concatenate new streams/iterables with the current one)

Stream.of(1, 2, 3).concat(Stream.of(4, 5)).to_list()
Stream([1, 2, 3]).concat([5, 6]).to_list()

• prepend
  (prepend new stream/iterable to the current one)

Stream([2, 3, 4]).prepend(0, 1).to_list()
Stream.of(3, 4, 5).prepend(Stream.of([0, 1], 2)).to_list()

---

Intermediate operations

• filter

Stream([1, 2, 3]).filter(lambda x: x % 2 == 0)

• map

Stream([1, 2, 3]).map(str).to_list()
Stream([1, 2, 3]).map(lambda x: x + 5).to_list()

• filter_map
  (filter out all None or falsy values (if falsy=True) and applies mapper function to the elements of the stream)

Stream.of(None, "foo", "", "bar", 0, []).filter_map(str.upper, falsy=True).to_list()

["FOO", "BAR"]

• flat_map
  (map each element of the stream and yields the elements of the produced iterators)

Stream([[1, 2], [3, 4], [5]]).flat_map(lambda x: Stream(x)).to_list()

[1, 2, 3, 4, 5]

• flatten

Stream([[1, 2], [3, 4], [5]]).flatten().to_list()

[1, 2, 3, 4, 5]

• reduce
  (returns Optional)

Stream([1, 2, 3]).reduce(lambda acc, val: acc + val, identity=3).get()

• peek
  (perform the provided operation on each element of the stream without consuming it)

(Stream([1, 2, 3, 4])
    .filter(lambda x: x > 2)
    .peek(lambda x: print(f"{x} ", end=""))
    .map(lambda x: x * 20)
    .to_list())

• view
  (provides access to a selected part of the stream)

Stream([1, 2, 3, 4, 5, 6, 7, 8, 9]).view(start=1, stop=-3, step=2).to_list()

[2, 4, 6]

• distinct
  (returns a stream with the distinct elements of the current one)

Stream([1, 1, 2, 2, 2, 3]).distinct().to_list()

• skip
  (discards the first n elements of the stream and returns a new stream with the remaining ones)

Stream.iterate(0, lambda x: x + 1).skip(5).limit(5).to_list()

• limit / head
  (returns a stream with the first n elements, or fewer if the underlying iterator ends sooner)

Stream([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]).limit(3).to_tuple()
Stream([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]).head(3).to_tuple()

• tail
  (returns a stream with the last n elements, or fewer if the underlying iterator ends sooner)

Stream([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]).tail(3).to_tuple()

• take_while
  (returns a stream that yields elements based on a predicate)

Stream.of(1, 2, 3, 4, 5, 6, 7, 2, 3).take_while(lambda x: x < 5).to_list()

[1, 2, 3, 4]

• drop_while
  (returns a stream that skips elements based on a predicate and yields the remaining ones)

Stream.of(1, 2, 3, 5, 6, 7, 2).drop_while(lambda x: x < 5).to_list()

[5, 6, 7, 2]

• sorted
  (sorts the elements of the current stream according to natural order or based on the given comparator;
  if 'reverse' flag is True, the elements are sorted in descending order)

(Stream.of((3, 30), (2, 30), (2, 20), (1, 20), (1, 10))
    .sorted(lambda x: (x[0], x[1]), reverse=True)
    .to_list())

[(3, 30), (2, 30), (2, 20), (1, 20), (1, 10)]

---

Terminal operations

Collectors

• collecting result into list, tuple, set

Stream([1, 2, 3]).to_list()
Stream([1, 2, 3]).to_tuple()
Stream([1, 2, 3]).to_set()

• into dict

class Foo:
    def __init__(self, name, num):
        self.name = name
        self.num = num
        
Stream([Foo("fizz", 1), Foo("buzz", 2)]).to_dict(lambda x: (x.name, x.num))

{"fizz": 1, "buzz": 2}

In the case of a collision (duplicate keys) the 'merger' functions indicates which entry should be kept

collection = [Foo("fizz", 1), Foo("fizz", 2), Foo("buzz", 2)]
Stream(collection).to_dict(collector=lambda x: (x.name, x.num), merger=lambda old, new: old)

{"fizz": 1, "buzz": 2}

to_dict method also supports creating dictionaries from dict Item objects

first_dict = {"x": 1, "y": 2}
second_dict = {"p": 33, "q": 44, "r": None}
Stream(first_dict).concat(Stream(second_dict)).to_dict(lambda x: Item(x.key, x.value or 0)) 

{"x": 1, "y": 2, "p": 33, "q": 44, "r": 0}

e.g. you could combine streams of dicts by writing:

Stream(first_dict).concat(Stream(second_dict)).to_dict(lambda x: x) 

• alternative for working with collectors is using the collect method

Stream([1, 2, 3]).collect(tuple)
Stream.of(1, 2, 3).collect(list)
Stream.of(1, 1, 2, 2, 2, 3).collect(set)
Stream.of(1, 2, 3, 4).collect(dict, lambda x: (str(x), x * 10))

• grouping

Stream("AAAABBBCCD").group_by(collector=lambda key, grouper: (key, len(grouper)))

{"A": 4, "B": 3, "C": 2, "D": 1}

coll = [Foo("fizz", 1), Foo("fizz", 2), Foo("fizz", 3), Foo("buzz", 2), Foo("buzz", 3), Foo("buzz", 4), Foo("buzz", 5)]
Stream(coll).group_by(
    classifier=lambda obj: obj.name,
    collector=lambda key, grouper: (key, [(obj.name, obj.num) for obj in list(grouper)]))

{
  "fizz": [("fizz", 1), ("fizz", 2), ("fizz", 3)],
  "buzz": [("buzz", 2), ("buzz", 3), ("buzz", 4), ("buzz", 5)],
}

Other terminal operations

• for_each

Stream([1, 2, 3, 4]).for_each(lambda x: print(f"{'#' * x} ", end=""))

• count
  (returns the count of elements in the stream)

Stream([1, 2, 3, 4]).filter(lambda x: x % 2 == 0).count()

• sum

Stream.of(1, 2, 3, 4).sum() 

• find_first
  (search for an element of the stream that satisfies a predicate, returns an Optional with the first found value, if any, or None)

Stream.of(1, 2, 3, 4).filter(lambda x: x % 2 == 0).find_first().get()

• find_any
  (search for an element of the stream that satisfies a predicate, returns an Optional with some of the found values, if any, or None)

Stream.of(1, 2, 3, 4).filter(lambda x: x % 2 == 0).find_any().get()

• any_match
  (returns whether any elements of the stream match the given predicate)

Stream.of(1, 2, 3, 4).any_match(lambda x: x > 2)

• all_match
  (returns whether all elements of the stream match the given predicate)

Stream.of(1, 2, 3, 4).all_match(lambda x: x > 2)

• none_match
  (returns whether no elements of the stream match the given predicate)

Stream.of(1, 2, 3, 4).none_match(lambda x: x < 0)

• min
  (returns Optional with the minimum element of the stream)

Stream.of(2, 1, 3, 4).min().get()

• max
  (returns Optional with the maximum element of the stream)

Stream.of(2, 1, 3, 4).max().get()

• compare_with
  (compares linearly the contents of two streams based on a given comparator)

fizz = Foo("fizz", 1)
buzz = Foo("buzz", 2)
Stream([buzz, fizz]).compare_with(Stream([fizz, buzz]), lambda x, y: x.num == y.num)

• quantify
  (count how many of the elements are Truthy or evaluate to True based on a given predicate)

Stream([2, 3, 4, 5, 6]).quantify(predicate=lambda x: x % 2 == 0)

---

Itertools integration

Invoke use method by passing the itertools function and it's arguments as **kwargs

import itertools
import operator

Stream([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]).use(itertools.islice, start=3, stop=8)
Stream.of(1, 2, 3, 4, 5).use(itertools.accumulate, func=operator.mul).to_list()
Stream(range(3)).use(itertools.permutations, r=3).to_list()

Itertools 'recipes'

Invoke the 'recipes' described here as stream methods and pass required key-word arguments

Stream([1, 2, 3]).ncycles(count=2).to_list()
Stream.of(2, 3, 4).take_nth(10, default=66).get()
Stream(["ABC", "D", "EF"]).round_robin().to_list()

---

FileStreams

Querying files

• working with json, toml, yaml, xml files
  NB: FileStream reads data as series of Item objects from underlying dict_items view

FileStream("path/to/file").map(lambda x: f"{x.key}=>{x.value}").to_tuple()

(
  "abc=>xyz", 
  "qwerty=>42",
)

from operator import attrgetter
from pyrio import Item

(FileStream("path/to/file")
    .filter(lambda x: "a" in x.key)
    .map(lambda x: Item(x.key, sum(x.value) * 10))
    .sorted(attrgetter("value"), reverse=True)
    .map(lambda x: f"{str(x.value)}::{x.key}")
    .to_list()) 

["230::xza", "110::abba", "30::a"]

• querying csv and tsv files
  (each row is read as a dict with keys taken from the header)

FileStream("path/to/file").map(lambda x: f"fizz: {x['fizz']}, buzz: {x['buzz']}").to_tuple() 

(
  "fizz: 42, buzz: 45",
  "fizz: aaa, buzz: bbb",
)

from operator import itemgetter

FileStream("path/to/file").map(itemgetter('fizz', 'buzz')).to_tuple()

(('42', '45'), ('aaa', 'bbb'))

You could query the nested dicts by creating streams out of them

(FileStream("path/to/file")
    .map(lambda x: (Stream(x).to_dict(lambda y: Item(y.key, y.value or "Unknown"))))
    .save())

• reading a file with process() method
  • use extra f_open_options (for the underlying open file function)
  • f_read_options (to be passed to the corresponding library function that is loading the file content e.g. tomllib, json)

from decimal import Decimal

(FileStream.process(
    file_path="path/to/file.json", 
    f_open_options={"encoding": "utf-8"}, 
    f_read_options={"parse_float": Decimal})
 .map(lambda x:x.value).to_list())

['foo', True, Decimal('1.22'), Decimal('5.456367654369698986')]

To include the root tag when loading an .xml file pass 'include_root=True'

FileStream.process("path/to/custom_root.xml", include_root=True).map(
    lambda x: f"root={x.key}: inner_records={str(x.value)}"
).to_list()

["root=custom-root: inner_records={'abc': 'xyz', 'qwerty': '42'}"]

---

Saving to a file

• write the contents of a FileStream by passing a file_path to the save() method

in_memory_dict = Stream(json_dict).filter(lambda x: len(x.key) < 6).to_tuple()
FileStream("path/to/file.json").prepend(in_memory_dict).save("./tests/resources/updated.json")

If no path is given, the source file for the FileStream will be updated

FileStream("path/to/file.json").concat(in_memory_dict).save()

NB: if while updating the file something goes wrong, the original content will be restored/preserved

• handle null values
  (pass null_handler function to replace null values)

FileStream("path/to/test.toml").save(null_handler=lambda x: Item(x.key, x.value or "N/A"))

NB: useful for writing .toml files which don't allow None values

• passing advanced file open and write options
  similarly to the process method you could provide
  • f_open_options (for the underlying open function)
  • f_write_options (passed to the corresponding library that will 'dump' the contents of the stream e.g. tomli-w, pyyaml)

FileStream("path/to/file.json").concat(in_memory_dict).save(
    file_path="merged.xml",
    f_open_options={"encoding": "utf-8"},
    f_write_options={"indent": 4},
)

To add custom root tag when saving an .xml file pass 'xml_root="my-custom-root"'

FileStream("path/to/file.json").concat(in_memory_dict).save(
    file_path="path/to/custom.xml",
    f_open_options={"encoding": "utf-8"},
    f_write_options={"indent": 4},
    xml_root="my-custom-root",
)

---

• how far can we actually push it?

(
    FileStream("path/to/file.csv")
    .concat(
        FileStream("path/to/other/file.json")
        .filter(
            lambda x: (
                Stream(x.value)
                .find_first(lambda y: y.key == "name" and y.value != "Snake")
                .or_else_get(lambda: None)
            )
            is not None
        )
        .map(lambda x: x.value)
    )
    .map(lambda x: (Stream(x).to_dict(lambda y: Item(y.key, y.value or "N/A"))))
    .save("path/to/third/file.tsv")
)

or how hideous can it get?