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

Transcript

1. Using Functional Programming for efficient Data Processing and Analysis PyCon — Portland, Oregon — May 17, 2017 by Reuben Cummings @reubano
2. Part I
3. • Managing Director, Nerevu Development • Founder of Arusha Coders • Author of several popular Python packages Who am I?
4. Hands-on workshop (don't be a spectator) Image Credit www.flickr.com/photos/16210667@N02
5. what are you looking to get out of this workshop?
6. What is data? Image Credit www.flickr.com/photos/147437926@N08
7. 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
8. 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...
9. Organization vs Storage flat binary structured unstructured
10. How do you process data? Image Credit www.flickr.com/photos/sugagaga
11. E — extract T — transform L — load
12. ETL: Extract sources Python
13. ETL: Extract sources Python
14. ETL: Transform Python Python
15. ETL: Transform Python Python
16. ETL: Load Python destination
17. ETL: Load Python destination
18. What is functional programming? Image Credit www.flickr.com/photos/shonk
19. Let's make a rectangle!
20. 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
21. Rectangle (imperative) >>> r = Rectangle(2, 3) >>> r.length 2 >>> r.area 6 >>> r.grow(2) >>> r.length 4 >>> r.area 12
22. Expensive Rectangle (imperative) from time import sleep class ExpensiveRectangle(Rectangle): @property def area(self): sleep(5) return self.length * self.width
23. Expensive Rectangle (imperative) >>> r = ExpensiveRectangle(2, 3) >>> r.area 6 >>> r.area 6
24. Infinite Squares (imperative) def sum_area(rects): area = 0 for r in rects: area += r.area return area
25. 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>
26. 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
27. Now let's get functional!
28. 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]
29. >>> 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
30. Rectangle (functional) >>> big_r = grow_rect(r, 2) >>> get_length(big_r) 4 >>> get_area(big_r) 12
31. Expensive Rectangle (functional) from functools import lru_cache @lru_cache() def exp_get_area (rect): sleep(5) return rect[0] * rect[1]
32. Expensive Rectangle (functional) >>> r = make_rect(2, 3) >>> exp_get_area(r) 6 >>> exp_get_area(r) 6
33. Infinite Squares (functional) def accumulate_area(rects): accum = 0 for r in rects: accum += get_area(r) yield accum
34. 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
35. 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
36. Exercise #1 Image Credit: Me
37. Exercise #1 (Problem) x y z
38. Exercise #1 (Problem) x ₒ factor y h
39. Exercise #1 (Problem) x y z x ₒ factor y h
40. Exercise #1 (Problem) (z ÷ h) z h
41. Exercise #1 (Problem) x y z x ₒ factor y h ratio =
42. Exercise #1 (Problem) z = √(x2 + y2 ) ratio = function1(x, y, factor) hyp = function2(rectangle)
43. 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
44. 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)
45. 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
46. Part II
47. You might not need pandas Image Credit www.flickr.com/photos/harlequeen
48. Obtaining data
49. 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'}
50. 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…',
51. pip install xlrd
52. 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)
53. 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'}
54. Transforming data
55. 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)
56. grouping data >>> data = ((key, list(g)) for key, g in groups) >>> next(data) (200, [{'amount': 200, 'item': 'a'}, {'amount': 200, 'item': 'b'}])
57. aggregating data >>> key = 'amount' >>> value = sum(r.get(key, 0) for r in records) >>> {**records[0], key: value} {'a': 'item', 'amount': 800}
58. Storing data
59. 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)
60. Introducing meza
61. pip install meza
62. Obtaining data
63. csv data >>> from meza.io import read >>> >>> records = read('output.csv') >>> next(records) {'amount': '200', 'item': 'a'}
64. 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…', … }
65. 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'}
66. Transforming data
67. 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}
68. merging >>> from meza.process import merge >>> >>> records = [ ... {'a': 200}, {'b': 300}, {'c': 400}] >>> >>> merge(records) {'a': 200, 'b': 300, 'c': 400}
69. 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)
70. normalization >>> from meza.process import normalize >>> >>> records = [ ... { ... 'color': 'blue', 'setosa': 5, ... 'versi': 6 ... }, { ... 'color': 'red', 'setosa': 3, ... 'versi': 5 ... }]
71. normalization >>> kwargs = { ... 'data': 'length', 'column':'species', ... 'rows': ['setosa', 'versi']} >>> >>> data = normalize(records, **kwargs) >>> next(data) {'color': 'blue', 'length': 5, 'species': 'setosa'}
72. 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
73. Storing data
74. 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)
75. JSON files >>> json = cv.records2json(records) >>> write('output.json', json)
76. Exercise #2 Image Credit: Me
77. Exercise #2 (Problem) • create a list of dicts with keys "factor", "length", "width", and "ratio" (for factors 1 - 20)
78. 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…} ]
79. 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"
80. Exercise #2 (Problem) from statistics import median from meza.process import group records[0]['ratio'] // .25
81. 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)
82. Exercise #2 (Problem) from meza.convert import records2csv from meza.io import write key median 0 0.108… 1 0.343…
83. 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)]
84. 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)
85. 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)
86. Exercise #2 (Solution) $ csvlook results.csv | key | median | | --- | ------ | | 0 | 0.108… | | 1 | 0.343… | | 2 | 0.632… | | 4 | 1.000… |
87. Part III
88. Introducing riko
89. pip install riko
90. Obtaining data
91. Python Events Calendar https://www.python.org/events/python- events/
92. Python Events Calendar https://www.python.org/events/python- events/
93. 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'}]
94. 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}))
95. Python Events Calendar >>> stream = flow.output >>> next(stream) {'event': 'PyDataBCN 2017', 'location': 'Barcelona, Spain'} >>> next(stream) {'event': 'PyConWEB 2017', 'location': 'Munich, Germany'}
96. Transforming data
97. 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)
98. 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}))
99. 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'}
100. Parallel processing
101. 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}])]
102. 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'}
103. Python Events Calendar >>> stream[-1] {'event': 'PyDays Vienna 2017'}
104. Exercise #3 Image Credit: Me
105. 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.
106. Exercise #3 (Problem) from riko.collections import SyncPipe url = 'https://www.python.org/jobs/feed/rss' # use the 'fetch', 'tokenizer', 'subelement', # and 'filter' pipes
107. Exercise #3 (Problem) • write the 'link', 'location', and 'title' fields of each record to a json file
108. Exercise #3 (Problem) from meza.fntools import dfilter from meza.convert import records2json from meza.io import write
109. 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]
110. 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'}]
111. 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}))
112. 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'}
113. 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]
114. 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" }, {
115. Thank you! Reuben Cummings @reubano
116. Extra Slides Image Credit www.flickr.com/photos/jeremybrooks
117. 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)