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Using Functional Programming for efficient Data...

Using Functional Programming for efficient Data Processing and Analysis

A PyCon workshop on Functional Programming
Video: https://www.youtube.com/watch?v=9kDUTJahXBM
Code: https://github.com/reubano/pycon17-tute

Reuben Cummings

May 17, 2017
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  1. Using Functional Programming for efficient Data Processing and Analysis PyCon

    — Portland, Oregon — May 17, 2017 by Reuben Cummings @reubano
  2. • Managing Director, Nerevu Development • Founder of Arusha Coders

    • Author of several popular Python packages Who am I?
  3. Organization room presenter 1 matt 3 james 6 reuben You

    can't afford to have security be an optional or "nice-to- have"... structured unstructured
  4. Storage flat binary type,day tutorial,wed talk,fri poster,sun keynote,fri 00103e0 b0e6

    04... 00105f0 e4e7 03... 0010600 0be8 04... 00105b0 c4e4 02... 00106e0 b0e9 04...
  5. Rectangle (imperative) class Rectangle(object): def __init__(self, length, width): self.length =

    length self.width = width @property def area(self): return self.length * self.width def grow(self, amount): self.length *= amount
  6. Rectangle (imperative) >>> r = Rectangle(2, 3) >>> r.length 2

    >>> r.area 6 >>> r.grow(2) >>> r.length 4 >>> r.area 12
  7. Infinite Squares (imperative) >>> from itertools import count >>> >>>

    squares = ( ... Rectangle(x, x) for x in count(1)) >>> squares <generator object <genexpr> at 0x11233ca40> >>> next(squares) <__main__.Rectangle at 0x1123a8400>
  8. Infinite Squares (imperative) >>> sum_area(squares) KeyboardInterrupt Traceback (most recent call

    last) <ipython-input-196-6a83df34d1b4> in <module>() ----> 1 sum_area(squares) <ipython-input-193-3d117e0b93c3> in sum_area(rects) 3 4 for r in rects: ----> 5 area += r.area
  9. Rectangle (functional) def make_rect(length, width): return (length, width) def grow_rect(rect,

    amount): return (rect[0] * amount, rect[1]) def get_length (rect): return rect[0] def get_area (rect): return rect[0] * rect[1]
  10. >>> grow_rect(r, 2) (4, 3) >>> get_length(r) 2 >>> get_area(r)

    6 Rectangle (functional) >>> r = make_rect(2, 3) >>> get_length(r) 2 >>> get_area(r) 6
  11. Infinite Squares (functional) >>> from itertools import islice >>> >>>

    squares = ( ... make_rect(x, x) for x in count(1)) >>> >>> area = accumulate_area(squares) >>> next(islice(area, 6, 7)) 140 >>> next(area) 204
  12. Infinite Squares (functional) >>> from itertools import accumulate >>> >>>

    squares = ( ... make_rect(x, x) for x in count(1)) >>> >>> area = accumulate(map(get_area, squares)) >>> next(islice(area, 6, 7)) 140 >>> next(area) 204
  13. Exercise #1 (Problem) z = √(x2 + y2 ) ratio

    = function1(x, y, factor) hyp = function2(rectangle)
  14. Exercise #1 (Problem) z = √(x2 + y2 ) x

    y z x ₒ factor y h ratio = function1(x, y, factor) hyp = function2(rectangle) >>> get_ratio(1, 2, 2) 0.7905694150420948
  15. Exercise #1 (Solution) from math import sqrt, pow def get_hyp(rect):

    sum_s = sum(pow(r, 2) for r in rect) return sqrt(sum_s) def get_ratio(length, width, factor=1): rect = make_rect(length, width) big_rect = grow_rect(rect, factor) return get_hyp(rect) / get_hyp(big_rect)
  16. Exercise #1 (Solution) >>> get_ratio(1, 2, 2) 0.7905694150420948 >>> get_ratio(1,

    2, 3) 0.6201736729460423 >>> get_ratio(3, 4, 2) 0.6933752452815365 >>> get_ratio(3, 4, 3) 0.5076730825668095
  17. csv data >>> from csv import DictReader >>> from io

    import StringIO >>> >>> csv_str = 'Type,Day\ntutorial,wed\ntalk,fri' >>> csv_str += '\nposter,sun' >>> f = StringIO(csv_str) >>> data = DictReader(f) >>> dict(next(data)) {'Day': 'wed', 'Type': 'tutorial'}
  18. JSON data >>> from urllib.request import urlopen >>> from ijson

    import items >>> >>> json_url = 'https://api.github.com/users' >>> f = urlopen(json_url) >>> data = items(f, 'item') >>> next(data) {'avatar_url': 'https://avatars3.githubuserco…', 'events_url': 'https://api.github.com/users/…', 'followers_url': 'https://api.github.com/use…', 'following_url': 'https://api.github.com/use…',
  19. xls(x) data >>> from urllib.request import urlretrieve >>> from xlrd

    import open_workbook >>> >>> xl_url = 'https://github.com/reubano/meza' >>> xl_url += '/blob/master/data/test/test.xlsx' >>> xl_url += '?raw=true' >>> xl_path = urlretrieve(xl_url)[0] >>> book = open_workbook(xl_path) >>> sheet = book.sheet_by_index(0) >>> header = sheet.row_values(0)
  20. xls(x) data >>> nrows = range(1, sheet.nrows) >>> rows =

    (sheet.row_values(x) for x in nrows) >>> data = ( ... dict(zip(header, row)) for row in rows) >>> >>> next(data) {' ': ' ', 'Some Date': 30075.0, 'Some Value': 234.0, 'Sparse Data': 'Iñtërnâtiônàližætiøn', 'Unicode Test': 'Ādam'}
  21. grouping data >>> import itertools as it >>> from operator

    import itemgetter >>> >>> records = [ ... {'item': 'a', 'amount': 200}, ... {'item': 'b', 'amount': 200}, ... {'item': 'c', 'amount': 400}] >>> >>> keyfunc = itemgetter('amount') >>> _sorted = sorted(records, key=keyfunc) >>> groups = it.groupby(_sorted, keyfunc)
  22. grouping data >>> data = ((key, list(g)) for key, g

    in groups) >>> next(data) (200, [{'amount': 200, 'item': 'a'}, {'amount': 200, 'item': 'b'}])
  23. aggregating data >>> key = 'amount' >>> value = sum(r.get(key,

    0) for r in records) >>> {**records[0], key: value} {'a': 'item', 'amount': 800}
  24. csv files >>> from csv import DictWriter >>> >>> records

    = [ ... {'item': 'a', 'amount': 200}, ... {'item': 'b', 'amount': 400}] >>> >>> header = list(records[0].keys()) >>> with open('output.csv', 'w') as f: ... w = DictWriter(f, header) ... w.writeheader() ... w.writerows(records)
  25. csv data >>> from meza.io import read >>> >>> records

    = read('output.csv') >>> next(records) {'amount': '200', 'item': 'a'}
  26. JSON data >>> from meza.io import read_json >>> >>> f

    = urlopen(json_url) >>> records = read_json(f, path='item') >>> next(records) {'avatar_url': 'https://avatars3.githubuserco…', 'events_url': 'https://api.github.com/users/…', 'followers_url': 'https://api.github.com/use…', 'following_url': 'https://api.github.com/use…', … }
  27. xlsx data >>> from meza.io import read_xls >>> >>> records

    = read_xls(xl_path) >>> next(records) {'Some Date': '1982-05-04', 'Some Value': '234.0', 'Sparse Data': 'Iñtërnâtiônàližætiøn', 'Unicode Test': 'Ādam'}
  28. aggregation >>> from meza.process import aggregate >>> >>> records =

    [ ... {'a': 'item', 'amount': 200}, ... {'a': 'item', 'amount': 300}, ... {'a': 'item', 'amount': 400}] ... >>> aggregate(records, 'amount', sum) {'a': 'item', 'amount': 900}
  29. merging >>> from meza.process import merge >>> >>> records =

    [ ... {'a': 200}, {'b': 300}, {'c': 400}] >>> >>> merge(records) {'a': 200, 'b': 300, 'c': 400}
  30. grouping >>> from meza.process import group >>> >>> records =

    [ ... {'item': 'a', 'amount': 200}, ... {'item': 'a', 'amount': 200}, ... {'item': 'b', 'amount': 400}] >>> >>> groups = group(records, 'item') >>> next(groups)
  31. normalization >>> from meza.process import normalize >>> >>> records =

    [ ... { ... 'color': 'blue', 'setosa': 5, ... 'versi': 6 ... }, { ... 'color': 'red', 'setosa': 3, ... 'versi': 5 ... }]
  32. normalization >>> kwargs = { ... 'data': 'length', 'column':'species', ...

    'rows': ['setosa', 'versi']} >>> >>> data = normalize(records, **kwargs) >>> next(data) {'color': 'blue', 'length': 5, 'species': 'setosa'}
  33. normalization before after color setosa versi blue 5 6 red

    3 5 color length species blue 5 setosa blue 6 versi red 3 setosa red 5 versi
  34. csv files >>> from meza import convert as cv >>>

    from meza.io import write >>> >>> records = [ ... {'item': 'a', 'amount': 200}, ... {'item': 'b', 'amount': 400}] >>> >>> csv = cv.records2csv(records) >>> write('output.csv', csv)
  35. Exercise #2 (Problem) • create a list of dicts with

    keys "factor", "length", "width", and "ratio" (for factors 1 - 20)
  36. Exercise #2 (Problem) records = [ { 'factor': 1, 'length':

    2, 'width': 2, 'ratio': 1.0 }, { 'factor': 2, 'length': 2, 'width': 2, 'ratio': 0.6324… }, { 'factor': 3, 'length': 2, 'width': 2, 'ratio': 0.4472…} ]
  37. Exercise #2 (Problem) • create a list of dicts with

    keys "factor", "length", "width", and "ratio" (for factors 1 - 20) • group the records by quartiles of the "ratio" value, and aggregate each group by the median "ratio"
  38. Exercise #2 (Problem) • create a list of dicts with

    keys "factor", "length", "width", and "ratio" (for factors 1 - 20) • group the records by quartiles of the "ratio" value, and aggregate each group by the median "ratio" • write the records out to a csv file (1 row per group)
  39. Exercise #2 (Solution) >>> length = width = 2 >>>

    records = [ ... { ... 'length': length, ... 'width': width, ... 'factor': f, ... 'ratio': get_ratio(length, width, f) ... } ... ... for f in range(1, 21)]
  40. Exercise #2 (Solution) >>> from statistics import median >>> from

    meza import process as pr >>> >>> def aggregator(group): ... ratios = (g['ratio'] for g in group) ... return median(ratios) >>> >>> kwargs = {'aggregator': aggregator} >>> gkeyfunc = lambda r: r['ratio'] // .25 >>> groups = pr.group( ... records, gkeyfunc, **kwargs)
  41. Exercise #2 (Solution) >>> from meza import convert as cv

    >>> from meza.io import write >>> >>> results = [ ... {'key': k, 'median': g} ... for k, g in groups] >>> >>> csv = cv.records2csv(results) >>> write('results.csv', csv)
  42. Exercise #2 (Solution) $ csvlook results.csv | key | median

    | | --- | ------ | | 0 | 0.108… | | 1 | 0.343… | | 2 | 0.632… | | 4 | 1.000… |
  43. Python Events Calendar >>> from riko.collections import SyncPipe >>> >>>

    url = 'www.python.org/events/python-events/' >>> _xpath = '/html/body/div/div[3]/div/section' >>> xpath = '{}/div/div/ul/li'.format(_xpath) >>> xconf = {'url': url, 'xpath': xpath} >>> kwargs = {'emit': False, 'token_key': None} >>> epath = 'h3.a.content' >>> lpath = 'p.span.content' >>> rrule = [{'field': 'h3'}, {'field': 'p'}]
  44. Python Events Calendar >>> flow = ( ... SyncPipe('xpathfetchpage', conf=xconf)

    ... .subelement( ... conf={'path': epath}, ... assign='event', **kwargs) ... .subelement( ... conf={'path': lpath}, ... assign='location', **kwargs) ... .rename(conf={'rule': rrule}))
  45. Python Events Calendar >>> stream = flow.output >>> next(stream) {'event':

    'PyDataBCN 2017', 'location': 'Barcelona, Spain'} >>> next(stream) {'event': 'PyConWEB 2017', 'location': 'Munich, Germany'}
  46. Python Events Calendar >>> dpath = 'p.time.datetime' >>> frule =

    { ... 'field': 'date', 'op': 'after', ... 'value':'2017-06-01'} >>> >>> flow = ( ... SyncPipe('xpathfetchpage', conf=xconf) ... .subelement( ... conf={'path': epath}, ... assign='event', **kwargs)
  47. Python Events Calendar ... .subelement( ... conf={'path': lpath}, ... assign='location',

    **kwargs) ... .subelement( ... conf={'path': dpath}, ... assign='date', **kwargs) ... .rename(conf={'rule': rrule}) ... .filter(conf={'rule': frule}))
  48. Python Events Calendar >>> stream = flow.output >>> next(stream) {'date':

    '2017-06-06T00:00:00+00:00', 'event': 'PyCon Taiwan 2017', 'location': 'Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei 11529, Taiwan'}
  49. Python Events Calendar >>> from meza.process import merge >>> from

    riko.collections import SyncCollection >>> >>> _type = 'xpathfetchpage' >>> source = {'url': url, 'type': _type} >>> xpath2 = '{}/div/ul/li'.format(_xpath) >>> sources = [ ... merge([source, {'xpath': xpath}]), ... merge([source, {'xpath': xpath2}])]
  50. Python Events Calendar >>> sc = SyncCollection(sources, parallel=True) >>> flow

    = (sc.pipe() ... .subelement( ... conf={'path': epath}, ... assign='event', **kwargs) ... .rename(conf={'rule': rrule})) >>> >>> stream = flow.list >>> stream[0] {'event': 'PyDataBCN 2017'}
  51. Exercise #3 (Problem) • fetch the Python jobs rss feed

    • tokenize the "summary" field by newlines ("\n") • use "subelement" to extract the location (the first "token") • filter for jobs located in the U.S.
  52. Exercise #3 (Solution) >>> from riko.collections import SyncPipe >>> >>>

    url = 'https://www.python.org/jobs/feed/rss' >>> fetch_conf = {'url': url} >>> tconf = {'delimiter': '\n'} >>> rule = { ... 'field': 'location', 'op': 'contains'} >>> vals = ['usa', 'united states'] >>> frule = [ ... merge([rule, {'value': v}]) ... for v in vals]
  53. Exercise #3 (Solution) >>> fconf = {'rule': frule, 'combine': 'or'}

    >>> kwargs = {'emit': False, 'token_key': None} >>> path = 'location.content.0' >>> rrule = [ ... {'field': 'summary'}, ... {'field': 'summary_detail'}, ... {'field': 'author'}, ... {'field': 'links'}]
  54. Exercise #3 (Solution) >>> flow = (SyncPipe('fetch', conf=fetch_conf) ... .tokenizer(

    ... conf=tconf, field='summary', ... assign='location') ... .subelement( ... conf={'path': path}, ... assign='location', **kwargs) ... .filter(conf=fconf) ... .rename(conf={'rule': rrule}))
  55. Exercise #3 (Solution) >>> stream = flow.list >>> stream[0] {'dc:creator':

    None, 'id': 'https://python.org/jobs/2570/', 'link': 'https://python.org/jobs/2570/', 'location': 'College Park,MD,USA', 'title': 'Python Developer - MarketSmart', 'title_detail': 'Python Developer - MarketSmart', 'y:published': None, 'y:title': 'Python Developer - MarketSmart'}
  56. Exercise #3 (Solution) >>> from meza import convert as cv

    >>> from meza.fntools import dfilter >>> from meza.io import write >>> >>> fields = ['link', 'location', 'title'] >>> records = [ ... dfilter( ... item, blacklist=fields, ... inverse=True) ... for item in stream]
  57. Exercise #3 (Solution) >>> json = cv.records2json(records) >>> write('pyjobs.json', json)

    $ head -n7 pyjobs.json [ { "link": "https://python.org/jobs/2570/", "location": "College Park,MD,USA", "title": "Python Developer - MarketSmart" }, {
  58. Infinite Squares (functional) def accumulate_area2(rects, accum=0): it = iter(rects) try:

    area = get_area(next(it)) except StopIteration: return accum += area yield accum yield from accumulate_area2(it, accum)