March 8, 2019

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EntilZha/PyFunctional

Python library for creating data pipelines with chain functional programming


repo name	EntilZha/PyFunctional
repo link	https://github.com/EntilZha/PyFunctional
homepage	http://www.pyfunctional.org
language	Python
size (curr.)	660 kB
stars (curr.)	1631
created	2015-02-05
license	MIT License

PyFunctional

Features

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.

Installation
Examples
Writing to Files
Parallel Execution
Github Shortform Documentation
Contributing and Bug Fixes
Changelog

Installation

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

Examples

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

Simple Example

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()

Aggregates and Joins

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, lets 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 lets 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 are a list of camping purchases. Lets do some cost analysis and compare it 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.

Reading/Writing SQLite3

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')]

Writing to files

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.

Compressed Files

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.

Parallel Execution

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

Documentation

Shortform documentation is below and full documentation is at docs.pyfunctional.org.

Streams API

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)`	Apply `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)`	`func` must return an iterable. Maps `func` to each element, then merges the result to one flat sequence	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 occurrences of each `key` in list of `(key, value)` pairs	transformation
`count_by_value()`	Counts occurrence of each value in a list	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)`	Drop the first `n` elements of the sequence	transformation
`drop_right(n)`	Drop the last `n` elements of the sequence	transformation
`drop_while(func)`	Drop elements while `func` evaluates to `True`, then returns the rest	transformation
`take(n)`	Returns sequence of first `n` elements	transformation
`take_while(func)`	Take elements while `func` evaluates to `True`, then drops 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
`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 which 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()` / `first()`	Returns first element in sequence	action
`head_option()`	Returns first element in sequence or `None` if its empty	action
`last()`	Returns last element in sequence	action
`last_option()`	Returns last element in sequence or `None` if its empty	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 element that first evaluates `func(element)` 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)`	Aggregate using `func` starting with `seed` or first element of list then apply `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)`	Save 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

Lazy Execution

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 the ability for PyFunctional to cache results of computation 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 its undesirable to keep recomputing the same value. Below are some examples of inspecting lineage.

def times_2(x):
    print(x)
    return 2 * x
elements = seq(1, 1, 2, 3, 4).map(times_2).distinct()
elements._lineage
# Lineage: sequence -> map(times_2) -> distinct

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

elements._lineage
# Lineage: sequence -> map(times_2) -> 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 iteration over a single file object while correctly handling iteration termination and file closing.

Road Map Idea

SQL based query planner and interpreter
_ lambda operator
Prepare for 1.0 next release

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 when running on TravisCI (you may need to sign up for an account and link Github). 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 its correct to do so
Achieve 100% test coverage on coveralls.io)
Edit the CHANGELOG.md file in the Next Release heading with changes

Contact

Gitter for chat

Supported Python Versions

PyFunctional supports and is tested against Python 2.7, 3.5, 3.6, and PyPy

Changelog

About me

To learn more about me (the author) visit my webpage at pedrorodriguez.io.

I am a PhD student in Computer Science at the University of Colorado at Boulder. My research interests include large-scale machine learning, distributed computing, and adjacent fields. I completed my undergraduate degree in Computer Science at UC Berkeley in 2015. I have previously done research in the UC Berkeley AMPLab with Apache Spark, worked at Trulia as a data scientist, and worked as a data scientist at Oracle Data Cloud this past summer.

I created PyFunctional while using Python extensively at Trulia, 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.

Contributors

These people have generously contributed their time to improving PyFunctional