stream4py 0.1.0

Stream4Py: Functional, chainable streams for Python with lazy evaluation

Stream4Py

A Python library inspired by Java Streams, Haskell lists, and Linux pipes, providing a powerful, lazy-evaluated Stream class for functional-style data manipulation. Stream4Py makes it easy to work with iterables, files, subprocess output, and general data pipelines.

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Features

• Lazy and eager evaluation methods.
• Chainable operations like map, filter, flat_map, unique, enumerate, flatten, sections.
• File parsing and subprocess piping with from_io and pipe.
• Collection helpers like to_list, to_dict, to_set, and cache.
• Built-in itertools utilities (islice, zip_longest, accumulate, etc.).
• Inspired by Java Streams, Haskell functional programming, and Python itertools.

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Installation

Install via pip:

pip install stream4py

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Quick Start

from stream4py import Stream

# Create a stream
s = Stream([1, 2, 3, 4, 5])

# Lazy operations
result = (
    s.filter(lambda x: x % 2 == 0)
     .map(lambda x: x * 10)
     .unique()
)

# Convert to list (triggers evaluation)
print(result.to_list())  # [20, 40]

# Stream lines from a file
lines = Stream.from_io(open("file.txt"))
lines.filter(lambda x: "error" in x).for_each(print)

# Subprocess streaming
Stream.subprocess_run(("seq", "100")).pipe(("grep", "1")).for_each(print)

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Quick Reference

Method	Type	Description	Example
map(func)	Lazy	Apply a function to each item	Stream([1,2,3]).map(lambda x: x*2)
filter(predicate)	Lazy	Keep items satisfying a predicate	Stream([1,2,3]).filter(lambda x: x>1)
filterfalse(predicate)	Lazy	Keep items for which predicate is False	Stream([1,2,3]).filterfalse(lambda x: x>1)
flat_map(func)	Lazy	Map then flatten iterables	Stream([1,2]).flat_map(lambda x: (x,x*10))
flatten()	Lazy	Flatten nested iterables	Stream([[1,2],[3]]).flatten()
unique(key=None)	Lazy	Keep only unique items	Stream([1,2,2]).unique()
type_is(cls)	Lazy	Keep items of a specific type	Stream([1,'a']).type_is(int)
enumerate(start=0)	Lazy	Enumerate items	Stream(['a','b']).enumerate(1)
peek(func)	Lazy	Apply function without changing items	Stream([1,2]).peek(print)
islice(start, stop, step)	Lazy	Slice like itertools.islice	Stream([1,2,3]).islice(1,3)
batched(size)	Lazy	Yield items in batches	Stream(range(5)).batched(2)
drop(n)	Lazy	Drop first n items	Stream([1,2,3]).drop(1)
take(n)	Lazy	Take first n items	Stream([1,2,3]).take(2)
dropwhile(predicate)	Lazy	Drop items while predicate is true	Stream([1,2,3]).dropwhile(lambda x: x<2)
takewhile(predicate)	Lazy	Take items while predicate is true	Stream([1,2,3]).takewhile(lambda x: x<3)
reverse()	Lazy	Reverse the items	Stream([1,2,3]).reverse()
zip(*iterables)	Lazy	Zip with other iterables	Stream([1,2]).zip(['a','b'])
zip_longest(*iterables)	Lazy	Zip with padding	Stream([1]).zip_longest([2,3])
accumulate(func=None, initial=None)	Lazy	Cumulative sums or function	Stream([1,2,3]).accumulate()
subprocess_run(command)	Lazy	Run a subprocess and stream output	Stream.subprocess_run(('ls',))
pipe(command)	Lazy	Pipe stream to subprocess	Stream(['foo']).pipe(('grep','f'))
sum(start=0)	Eager	Sum all items	Stream([1,2,3]).sum()
min(key=None, default=None)	Eager	Minimum value	Stream([1,2,3]).min()
max(key=None, default=None)	Eager	Maximum value	Stream([1,2,3]).max()
sorted(key=None, reverse=False)	Eager	Sort items	Stream([3,1,2]).sorted()
first(default=None)	Eager	First item	Stream([1,2]).first()
find(func)	Eager	Find first item matching function	Stream([1,2,3]).find(lambda x: x>1)
group_by(key)	Eager	Group items by key	Stream([1,2,3,4]).group_by(lambda x: x%2)
for_each(func)	Eager	Apply function to all items	Stream([1,2]).for_each(print)
cache()	Eager	Cache stream items	Stream(range(3)).cache()
to_list()	Eager	Collect as list	Stream([1,2]).to_list()
to_tuple()	Eager	Collect as tuple	Stream([1,2]).to_tuple()
to_set()	Eager	Collect as set	Stream([1,2]).to_set()
to_dict()	Eager	Collect as dict (from tuples)	Stream([(1,'a')]).to_dict()
collect(func)	Eager	Apply function to iterable	Stream([1,2]).collect(sum)
from_io(io)	Lazy	Stream lines from file or binary IO	Stream.from_io(open('file.txt'))
sections(predicate)	Lazy	Split into sections based on predicate	Stream([1,1,2]).sections(lambda x:x==2)
range(start, stop, step=1)	Lazy	Stream over a range	Stream.range(1,5)

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Contributing

Contributions are welcome! Please open an issue or pull request with improvements or bug fixes.

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License

stream4py is distributed under the terms of the MIT license.