threadx - Create elegant data transformation pipelines. It lets you thread values through a sequence of operations with a sense of clarity and simplicity that feels natural. And it all revolves around two key elements:

• thread: Passes the result of each step as the input to the next.
• x: A smart placeholder that knows exactly where to inject the previous result, whether in a method call, item lookup, or even unpacking.

Here’s what it looks like in action:

from threadx import thread, x

thread('./data.log', 
       read_file, 
       x.splitlines, 
       (map, x.strip, x), 
       (map, json.loads, x), 
       (map, x['time'], x), 
       sum)

What’s happening here? The file content is being read, split, stripped, converted to JSON, and the execution-time summed—all in a linear and readable way. No intermediary variables, no nesting, just the data flowing from one step to the next.

The data.log file (generated by inspector) contains entries like this:

{"time": 12000, "fn": "foo", ...}
{"time": 12345, "fn": "bar", ...}

What Makes threadx Interesting?

• Readable Flow: Instead of diving into layers of nested calls, you write each transformation as a clear, sequential step.
• The x Factor: x acts as a placeholder for where the output of the previous step goes. It’s surprisingly flexible, supporting method calls, attribute/item lookups, and more.
• No Extra Variables: Avoid the noise of intermediate variables or lambda functions. Your transformations stay clean and minimal.

Table of Contents

• Install
• Usage
  • Pass result as first argument
  • Pass x as nth argument
  • Unpacking arguments
  • Method call
  • Attribute lookup
  • Getting Item And Slicing
  • Debugging
  • Fewer lambdas
  • Build data transformation pipeline
• Why I Built This

Install

pip install threadx 

Usage

Import

from threadx import thread, x, stop

Pass result as first argument

thread allows you to pass the result of the previous step automatically as the first argument in each new function:

thread([1, 2, 3],  # => [1, 2, 3]
       sum,        # => 6
       str)        # => '6'

Or, be explicit about it:

thread([1, 2, 3],
       (sum, x),
       (str, x))

Pass x as nth argument

Want to pass the result into a different argument position? No problem:

thread(10, 
       (range, x, 20, 3),  # same as (range, 20, 3)
       list)               # => [10, 13, 16, 19]

thread(20, 
       (range, 10, x, 3),
       list)               # => [10, 13, 16, 19]

thread(3, 
       (range, 10, 20, x),
       list)               # => [10, 13, 16, 19]

Unpacking arguments

Unpacking works as usual

thread([10, 20], 
       (range, *x, 3),     # unpack to (range, 10, 20, 3)
       list)               # => [10, 13, 16, 19]

Method call

Use x.method_name for method calls, just like magic.

thread(['a', 'b'], 
       (x.index, 'a'))      # => 0

thread(['a', 'b'], 
       (x.count, 'b'))      # => 1

Attribute lookup

Use x.attribute_name to lookup class and instance attributes.

thread({'a': 1, 'b': 2},
       x.keys, 
       list)                # => ['a', 'b']

Getting Item And Slicing

data = {'a': {'b': [1, 2, 3, 4]}}

thread(data, 
       x['a'], 
       x['b'][0])                   # => 1

thread(data, 
       x['a']['b'][:2])             # => [1, 2]

Debugging

Easily inspect intermediate results using stop. Usefull for debugging.

thread(data, 
       x['a'], 
       x['b'], 
       stop,                    # => [1, 2, 3, 4], Stop and return for inspection
       sum,                     # This won’t be executed
       str)

Fewer lambdas

Remove verbose lambdas in simple cases.

data = [[1, 2, 3, 4], [10, 20, 30, 40]]

# Normal way:
thread(data, 
       (map, lambda i: i[0], x), 
       list)                                   # => [1, 10]
# or
thread(data, 
       (map, x[0], x), 
       list)                                   # => [1, 10]


# Normal way:
thread(range(12), 
       (filter, lambda i: i % 2 == 0, x), 
       list)                                   # => [0, 2, 4, 6, 8, 10]
# or
thread(range(12), 
       (filter, x % 2 == 0, x), 
       list)                                   # => [0, 2, 4, 6, 8, 10]

Build data transformation pipeline

# make a tuple or list
pipeline = (read_file, 
            x.splitlines, 
            (map, x.strip, x), 
            (map, json.loads, x), 
            (map, x['time'], x), 
            sum)

thread('./data.log', *pipeline)  # works jsut like any other function.

Why I Built This

After spending a few years working with Clojure, I found myself missing its threading macros when I returned to Python (for a side project). Sure, Python has some tools for chaining operations, but nothing quite as elegant or powerful as what I was used to.