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EntilZha/PyFunctional: Python library for creating data pipelines with chain functional programming

PyFunctional makes creating data pipelines easy by using chained functional operators. Here are a few examples of what it can do:

Chained operators: seq(1, 2, 3).map(lambda x: x * 2).reduce(lambda x, y: x + y)
Expressive and feature complete API
Read and write text, csv, json, jsonl, sqlite, gzip, bz2, and lzma/xz files
Parallelize "embarrassingly parallel" operations like map easily
Complete documentation, rigorous unit test suite, 100% test coverage, and CI which provide robustness

PyFunctional's API takes inspiration from Scala collections, Apache Spark RDDs, and Microsoft LINQ.

PyFunctional is available on pypi and can be installed by running:

# Install from command line
$ pip install pyfunctional

Then in python run: from functional import seq

PyFunctional is useful for many tasks, and can natively open several common file types. Here are a few examples of what you can do.

from functional import seq

seq(1, 2, 3, 4)\
    .map(lambda x: x * 2)\
    .filter(lambda x: x > 4)\
    .reduce(lambda x, y: x + y)
# 14

# or if you don't like backslash continuation
(seq(1, 2, 3, 4)
    .map(lambda x: x * 2)
    .filter(lambda x: x > 4)
    .reduce(lambda x, y: x + y)
)
# 14

Streams, Transformations and Actions

PyFunctional has three types of functions:

Streams: read data for use by the collections API.
Transformations: transform data from streams with functions such as map, flat_map, and filter
Actions: These cause a series of transformations to evaluate to a concrete value. to_list, reduce, and to_dict are examples of actions.

In the expression seq(1, 2, 3).map(lambda x: x * 2).reduce(lambda x, y: x + y), seq is the stream, map is the transformation, and reduce is the action.

Filtering a list of account transactions

from functional import seq
from collections import namedtuple

Transaction = namedtuple('Transaction', 'reason amount')
transactions = [
    Transaction('github', 7),
    Transaction('food', 10),
    Transaction('coffee', 5),
    Transaction('digitalocean', 5),
    Transaction('food', 5),
    Transaction('riotgames', 25),
    Transaction('food', 10),
    Transaction('amazon', 200),
    Transaction('paycheck', -1000)
]

# Using the Scala/Spark inspired APIs
food_cost = seq(transactions)\
    .filter(lambda x: x.reason == 'food')\
    .map(lambda x: x.amount).sum()

# Using the LINQ inspired APIs
food_cost = seq(transactions)\
    .where(lambda x: x.reason == 'food')\
    .select(lambda x: x.amount).sum()

# Using PyFunctional with fn
from fn import _
food_cost = seq(transactions).filter(_.reason == 'food').map(_.amount).sum()

The account transactions example could be done easily in pure python using list comprehensions. To show some of the things PyFunctional excels at, take a look at a couple of word count examples.

words = 'I dont want to believe I want to know'.split(' ')
seq(words).map(lambda word: (word, 1)).reduce_by_key(lambda x, y: x + y)
# [('dont', 1), ('I', 2), ('to', 2), ('know', 1), ('want', 2), ('believe', 1)]

In the next example we have chat logs formatted in json lines (jsonl) which contain messages and metadata. A typical jsonl file will have one valid json on each line of a file. Below are a few lines out of examples/chat_logs.jsonl.

{"message":"hello anyone there?","date":"10/09","user":"bob"}
{"message":"need some help with a program","date":"10/09","user":"bob"}
{"message":"sure thing. What do you need help with?","date":"10/09","user":"dave"}

from operator import add
import re
messages = seq.jsonl('examples/chat_logs.jsonl')

# Split words on space and normalize before doing word count
def extract_words(message):
    return re.sub('[^0-9a-z ]+', '', message.lower()).split(' ')


word_counts = messages\
    .map(lambda log: extract_words(log['message']))\
    .flatten().map(lambda word: (word, 1))\
    .reduce_by_key(add).order_by(lambda x: x[1])

Next, let's continue that example but introduce a json database of users from examples/users.json. In the previous example we showed how PyFunctional can do word counts, in the next example let's show how PyFunctional can join different data sources.

# First read the json file
users = seq.json('examples/users.json')
#[('sarah',{'date_created':'08/08','news_email':True,'email':'sarah@gmail.com'}),...]

email_domains = users.map(lambda u: u[1]['email'].split('@')[1]).distinct()
# ['yahoo.com', 'python.org', 'gmail.com']

# Join users with their messages
message_tuples = messages.group_by(lambda m: m['user'])
data = users.inner_join(message_tuples)
# [('sarah',
#    (
#      {'date_created':'08/08','news_email':True,'email':'sarah@gmail.com'},
#      [{'date':'10/10','message':'what is a...','user':'sarah'}...]
#    )
#  ),...]

# From here you can imagine doing more complex analysis

CSV, Aggregate Functions, and Set functions

In examples/camping_purchases.csv there is a list of camping purchases. Let's do some cost analysis and compare it to the required camping gear list stored in examples/gear_list.txt.

purchases = seq.csv('examples/camping_purchases.csv')
total_cost = purchases.select(lambda row: int(row[2])).sum()
# 1275

most_expensive_item = purchases.max_by(lambda row: int(row[2]))
# ['4', 'sleeping bag', ' 350']

purchased_list = purchases.select(lambda row: row[1])
gear_list = seq.open('examples/gear_list.txt').map(lambda row: row.strip())
missing_gear = gear_list.difference(purchased_list)
# ['water bottle','gas','toilet paper','lighter','spoons','sleeping pad',...]

In addition to the aggregate functions shown above (sum and max_by) there are many more. Similarly, there are several more set like functions in addition to difference.

PyFunctional can read and write to SQLite3 database files. In the example below, users are read from examples/users.db which stores them as rows with columns id:Int and name:String.

db_path = 'examples/users.db'
users = seq.sqlite3(db_path, 'select * from user').to_list()
# [(1, 'Tom'), (2, 'Jack'), (3, 'Jane'), (4, 'Stephan')]]

sorted_users = seq.sqlite3(db_path, 'select * from user order by name').to_list()
# [(2, 'Jack'), (3, 'Jane'), (4, 'Stephan'), (1, 'Tom')]

Writing to a SQLite3 database is similarly easy

import sqlite3
from collections import namedtuple

with sqlite3.connect(':memory:') as conn:
    conn.execute('CREATE TABLE user (id INT, name TEXT)')
    conn.commit()
    User = namedtuple('User', 'id name')

    # Write using a specific query
    seq([(1, 'pedro'), (2, 'fritz')]).to_sqlite3(conn, 'INSERT INTO user (id, name) VALUES (?, ?)')

    # Write by inserting values positionally from a tuple/list into named table
    seq([(3, 'sam'), (4, 'stan')]).to_sqlite3(conn, 'user')

    # Write by inferring schema from namedtuple
    seq([User(name='tom', id=5), User(name='keiga', id=6)]).to_sqlite3(conn, 'user')

    # Write by inferring schema from dict
    seq([dict(name='david', id=7), dict(name='jordan', id=8)]).to_sqlite3(conn, 'user')

    # Read everything back to make sure it wrote correctly
    print(list(conn.execute('SELECT * FROM user')))

    # [(1, 'pedro'), (2, 'fritz'), (3, 'sam'), (4, 'stan'), (5, 'tom'), (6, 'keiga'), (7, 'david'), (8, 'jordan')]

Just as PyFunctional can read from csv, json, jsonl, sqlite3, and text files, it can also write them. For complete API documentation see the collections API table or the official docs.

PyFunctional will auto-detect files compressed with gzip, lzma/xz, and bz2. This is done by examining the first several bytes of the file to determine if it is compressed so therefore requires no code changes to work.

To write compressed files, every to_ function has a parameter compression which can be set to the default None for no compression, gzip or gz for gzip compression, lzma or xz for lzma compression, and bz2 for bz2 compression.

The only change required to enable parallelism is to import from functional import pseq instead of from functional import seq and use pseq where you would use seq. The following operations are run in parallel with more to be implemented in a future release:

map/select
filter/filter_not/where
flat_map

Parallelization uses python multiprocessing and squashes chains of embarrassingly parallel operations to reduce overhead costs. For example, a sequence of maps and filters would be executed all at once rather than in multiple loops using multiprocessing.

Shortform documentation is below and full documentation is at docs.pyfunctional.pedro.ai.

All of PyFunctional streams can be accessed through the seq object. The primary way to create a stream is by calling seq with an iterable. The seq callable is smart and is able to accept multiple types of parameters as shown in the examples below.

# Passing a list
seq([1, 1, 2, 3]).to_set()
# [1, 2, 3]

# Passing direct arguments
seq(1, 1, 2, 3).map(lambda x: x).to_list()
# [1, 1, 2, 3]

# Passing a single value
seq(1).map(lambda x: -x).to_list()
# [-1]

seq also provides entry to other streams as attribute functions as shown below.

# number range
seq.range(10)

# text file
seq.open('filepath')

# json file
seq.json('filepath')

# jsonl file
seq.jsonl('filepath')

# csv file
seq.csv('filepath')
seq.csv_dict_reader('filepath')

# sqlite3 db and sql query
seq.sqlite3('filepath', 'select * from data')

For more information on the parameters that these functions can take, reference the streams documentation

Transformations and Actions APIs

Below is the complete list of functions which can be called on a stream object from seq. For complete documentation reference transformation and actions API.

Function Description Type map(func)/select(func) Maps func onto elements of sequence transformation starmap(func)/smap(func) Applies func to sequence with itertools.starmap transformation filter(func)/where(func) Filters elements of sequence to only those where func(element) is True transformation filter_not(func) Filters elements of sequence to only those where func(element) is False transformation flatten() Flattens sequence of lists to a single sequence transformation flat_map(func) Maps func to each element, then merges the result to one flat sequence. func must return an iterable transformation group_by(func) Groups sequence into (key, value) pairs where key=func(element) and value is from the original sequence transformation group_by_key() Groups sequence of (key, value) pairs by key transformation reduce_by_key(func) Reduces list of (key, value) pairs using func transformation count_by_key() Counts occurrence of each key in sequence of (key, value) pairs transformation count_by_value() Counts occurrence of each value in the sequence transformation union(other) Union of unique elements in sequence and other transformation intersection(other) Intersection of unique elements in sequence and other transformation difference(other) New sequence with unique elements present in sequence but not in other transformation symmetric_difference(other) New sequence with unique elements present in sequence or other, but not both transformation distinct() Returns distinct elements of sequence. Elements must be hashable transformation distinct_by(func) Returns distinct elements of sequence using func as a key transformation drop(n) Drops the first n elements of the sequence transformation drop_right(n) Drops the last n elements of the sequence transformation drop_while(func) Drops elements while func evaluates to True, returning the rest transformation take(n) Returns sequence of first n elements transformation take_while(func) Takes elements while func evaluates to True, dropping the rest transformation init() Returns sequence without the last element transformation tail() Returns sequence without the first element transformation inits() Returns consecutive inits of sequence transformation tails() Returns consecutive tails of sequence transformation zip(other) Zips the sequence with other transformation zip_with_index(start=0) Zips the sequence with the index starting at start on the right side transformation zip_with_next() Zips the sequence minus last element with itself minus first element, resulting in adjacent elements paired with each other transformation enumerate(start=0) Zips the sequence with the index starting at start on the left side transformation cartesian(*iterables, repeat=1) Returns cartesian product from itertools.product transformation inner_join(other) Returns inner join of sequence with other. Must be a sequence of (key, value) pairs transformation outer_join(other) Returns outer join of sequence with other. Must be a sequence of (key, value) pairs transformation left_join(other) Returns left join of sequence with other. Must be a sequence of (key, value) pairs transformation right_join(other) Returns right join of sequence with other. Must be a sequence of (key, value) pairs transformation join(other, join_type='inner') Returns join of sequence with other as specified by join_type. Must be a sequence of (key, value) pairs transformation partition(func) Partitions the sequence into elements that satisfy func(element) and those that don't transformation grouped(size) Partitions the elements into groups of size size transformation sorted(key=None, reverse=False)/order_by(func) Returns elements sorted according to python sorted transformation reverse() Returns the reversed sequence transformation slice(start, until) Sequence starting at start and including elements up to until transformation head(no_wrap=None) / first(no_wrap=None) Returns first element in sequence (if no_wrap=True, the result will never be wrapped with Sequence) action head_option(no_wrap=None) Returns first element in sequence or None if its empty (if no_wrap=True, the result will never be wrapped with Sequence) action last(no_wrap=None) Returns last element in sequence (if no_wrap=True, the result will never be wrapped with Sequence) action last_option(no_wrap=None) Returns last element in sequence or None if its empty (if no_wrap=True, the result will never be wrapped with Sequence) action len() / size() Returns length of sequence action count(func) Returns count of elements in sequence where func(element) is True action empty() Returns True if the sequence has zero length action non_empty() Returns True if sequence has non-zero length action all() Returns True if all elements in sequence are truthy action exists(func) Returns True if func(element) for any element in the sequence is True action for_all(func) Returns True if func(element) is True for all elements in the sequence action find(func) Returns the first element for which func(element) evaluates to True action any() Returns True if any element in sequence is truthy action max() Returns maximal element in sequence action min() Returns minimal element in sequence action max_by(func) Returns element with maximal value func(element) action min_by(func) Returns element with minimal value func(element) action sum()/sum(projection) Returns the sum of elements possibly using a projection action product()/product(projection) Returns the product of elements possibly using a projection action average()/average(projection) Returns the average of elements possibly using a projection action aggregate(func)/aggregate(seed, func)/aggregate(seed, func, result_map) Aggregates using func starting with seed or first element of list then applies result_map to the result action fold_left(zero_value, func) Reduces element from left to right using func and initial value zero_value action fold_right(zero_value, func) Reduces element from right to left using func and initial value zero_value action make_string(separator) Returns string with separator between each str(element) action dict(default=None) / to_dict(default=None) Converts a sequence of (Key, Value) pairs to a dictionary. If default is not None, it must be a value or zero argument callable which will be used to create a collections.defaultdict action list() / to_list() Converts sequence to a list action set() / to_set() Converts sequence to a set action to_file(path) Saves the sequence to a file at path with each element on a newline action to_csv(path) Saves the sequence to a csv file at path with each element representing a row action to_jsonl(path) Saves the sequence to a jsonl file with each element being transformed to json and printed to a new line action to_json(path) Saves the sequence to a json file. The contents depend on if the json root is an array or dictionary action to_sqlite3(conn, tablename_or_query, *args, **kwargs) Saves the sequence to a SQLite3 db. The target table must be created in advance action to_pandas(columns=None) Converts the sequence to a pandas DataFrame action cache() Forces evaluation of sequence immediately and caches the result action for_each(func) Executes func on each element of the sequence action peek(func) Executes func on each element of the sequence and returns it transformation

Whenever possible, PyFunctional will compute lazily. This is accomplished by tracking the list of transformations that have been applied to the sequence and only evaluating them when an action is called. In PyFunctional this is called tracking lineage. This is also responsible for PyFunctional's ability to cache the results of computations to prevent expensive re-computation. This is predominantly done to preserve sensible behavior and used sparingly. For example, calling size() will cache the underlying sequence. If this was not done and the input was an iterator, then further calls would operate on an expired iterator since it was used to compute the length. Similarly, repr also caches since it is most often used during interactive sessions where it's undesirable to keep recomputing the same value. Below are some examples of inspecting lineage.

def times_2(x):
    return 2 * x

elements = (
   seq(1, 1, 2, 3, 4)
      .map(times_2)
      .peek(print)
      .distinct()
)

elements._lineage
# Lineage: sequence -> map(times_2) -> peek(print) -> distinct

l_elements = elements.to_list()
# Prints: 1
# Prints: 1
# Prints: 2
# Prints: 3
# Prints: 4

elements._lineage
# Lineage: sequence -> map(times_2) -> peek(print) -> distinct -> cache

l_elements = elements.to_list()
# The cached result is returned so times_2 is not called and nothing is printed

Files are given special treatment if opened through the seq.open and related APIs. functional.util.ReusableFile implements a wrapper around the standard python file to support multiple iterations over a single file object while correctly handling iteration termination and file closing.

Even though functions like first() are supposed to return a single element, if the element is an iterable, then it is wrapped into a Sequence. For instance:

>>> s = seq(list(), list())
>>> type(s.first())
<class 'functional.pipeline.Sequence'>

That behaviour can be changed with no_wrap option:

>>> type(s.first(no_wrap=True))
<class 'list'>

The option is also accepted by seq()/pseq() as well as Sequence() constructor, for example:

>>> type(seq([list(), list()], no_wrap=True).last())
<class 'list'>

SQL based query planner and interpreter
_ lambda operator

Contributing and Bug Fixes

Any contributions or bug reports are welcome. Thus far, there is a 100% acceptance rate for pull requests and contributors have offered valuable feedback and critique on code. It is great to hear from users of the package, especially what it is used for, what works well, and what could be improved.

To contribute, create a fork of PyFunctional, make your changes, then make sure that they pass. In order to be merged, all pull requests must:

Pass all the unit tests
Pass all the pylint tests, or ignore warnings with explanation of why it's correct to do so
Not significantly reduce coverage without a good reason (coveralls.io)
Edit the CHANGELOG.md file in the Next Release heading with changes

Gitter for chat

Supported Python Versions

PyFunctional 1.6 is tested against Python 3.12 and Python 3.13.
PyFunctional 1.5 is tested against Python 3.8 to 3.11. PyPy3 is not tested, but bug fixed on best effort basis.
PyFunctional 1.4 supports and is tested against Python 3.6, Python 3.7, and PyPy3
PyFunctional 1.4 and above do not support python 2.7
PyFunctional 1.4 works in Python 3.5, but is not tested against it
PyFunctional 1.4 and above partially works in 3.8, parallel processing currently has issues, but other feature work fine
PyFunctional 1.3 and below supports and was tested against Python 2.7, Python 3.5, Python 3.6, PyPy2, and PyPy3

Changelog

To learn more about me (the author) visit my webpage at pedro.ai.

I created PyFunctional while using Python extensively, and finding that I missed the ease of use for manipulating data that Spark RDDs and Scala collections have. The project takes the best ideas from these APIs as well as LINQ to provide an easy way to manipulate data when using Scala is not an option or PySpark is overkill.

These people have generously contributed their time to improving PyFunctional

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