The document discusses using code and programming to automate repetitive tasks. It provides examples of using JavaScript regular expressions and Python to analyze log file data and add new columns and tags. Programming allows tasks to be repeated accurately and efficiently. The document encourages planning an approach, choosing the right tools, and structuring work for repeatability. Coding skills are useful for handling large and complex data in a focused, transparent way.

1. Coding for
Marketers
@robinlord8

2. If we handle what
robots can't do

3. We can take advantage
of what they do well

4. One idea to make your life easier

5. First steps to put that into practice

6. BIG
Repeated Tasks
Analytics
Hell

7. BIG
Repeated Tasks
Analytics
Hell

12. Policy Breach Notice
Dear Customer,
Some of your webpages include users' personally identifiable information
(PII) in the URL. As a result, users' PII is accessible to any third-party with a
tracking tag on these pages.
We have further identified that these pages contain Google tagging products
(such as AdWords conversion tracking, remarketing, DoubleClick for
Publishers, Floodlight and Google Analytics).

13. https://www.distilled.net/resources/how-to-keep-personally-identifiable-information-out-of-google-analytics/

14. Tools
Regular expressions - find me this thing
Regex - find me this thing

15. Tools
Regular expressions - find me this thing
Regex
Find me this thing

16. some words and some numbers: 1234

17. some words and some numbers: 1234
some

18. some words and some numbers: 1234
[a-zA-Z]+

19. some words and some numbers: 1234
d+

20. Tools
Coding language, runs in browser - do this thing
JavaScript
Do this thing

21. https://websitesetup.org/javascript-cheat-sheet/
https://htmlcheatsheet.com/js/

22. var newTitle = document.title.replace(emailRegex, 'PII');
document.title = newTitle

23. var newTitle = document.title.replace(emailRegex, 'PII');

24. var newTitle = document.title.replace(emailRegex, 'PII');
A name - whatever I want

25. var newTitle = document.title.replace(emailRegex, 'PII');
The titleThe web page

26. var newTitle = document.title.replace(emailRegex, 'PII');
with thisReplace this

27. document.title = Amazon.co.uk: archie.lord@test.com
newTitle = Amazon.co.uk: PII

28. document.title = newTitle

29. document.title = newTitle
What we made

30. document.title = newTitle
The titleThe web page

31. document.title = Amazon.co.uk: PII
newTitle = Amazon.co.uk: PII

32. BIG
Repeated Tasks
Analytics
Hell

33. BIG
Repeated Tasks
JavaScript and
RegEx

34. BIG
Repeated Tasks
JavaScript and
RegEx

35. 1 month later - new data
Scheduled work

36. 1 month later - new data
Shifting sands

37. Us using Excel
Our problems

39. Tools
Jupyter turns thisJupyter turns this

40. Tools Into thisInto this

41. Tools
Regular expressions - find me this thing
Regex - find me this thing

42. Code
Coding language - versatile and relatively friendly, good for data processing
Python - friendly, good with data

43. URL User Agent Status Hits
https://surgery.biz/ Mozilla 200 1,000,003
Logfiles – an example of big repeated analysis

44. Import
https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html

45. Resource
https://realpython.com/python-first-steps/

46. log_data = pandas.read_csv('robin/logs.csv')
https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html

47. log_data = pandas.read_csv('robin/logs.csv')
https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html
Whatever name I want

48. log_data = pandas.read_csv('robin/logs.csv')
https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html
Open csv file

49. log_data = pandas.read_csv('robin/logs.csv')
https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html
csv file location

50. log_data = pandas.read_csv('logsFinal05.csv')
https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html
This changes

51. log_data = pandas.read_csv('logsFinal10.csv')
https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html
This changes

52. log_data = pandas.read_csv('logsFinal10.csv')
https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html
This stays the same

53. Repeatability

54. Testing for numbers

55. URL Status Hits
https://surgery.biz/ 200 1,000,003
https://surgery.biz/ 200 500,005
http://surgery.biz/ 301 300,000

56. URL Status Hits Active
https://surgery.biz/ 200 1,000,003 True
https://surgery.biz/ 200 500,005 True
http://surgery.biz/ 301 300,000 False

57. URL Status Hits Active
https://surgery.biz/ 200 1,000,003 True
https://surgery.biz/ 200 500,005 True
http://surgery.biz/ 301 300,000 False
log_data['Active'] = log_data['Status']==200

58. URL Status Hits Active
https://surgery.biz/ 200 1,000,003 True
https://surgery.biz/ 200 500,005 True
http://surgery.biz/ 301 300,000 False
log_data['Active'] = log_data['Status']==200
Our data we named

59. URL Status Hits Active
https://surgery.biz/ 200 1,000,003 True
https://surgery.biz/ 200 500,005 True
http://surgery.biz/ 301 300,000 False
log_data['Active'] = log_data['Status']==200
Column we're filling

60. URL Status Hits Active
https://surgery.biz/ 200 1,000,003 True
https://surgery.biz/ 200 500,005 True
http://surgery.biz/ 301 300,000 False
log_data['Active'] = log_data['Status']==200
Our data

61. URL Status Hits Active
https://surgery.biz/ 200 1,000,003 True
https://surgery.biz/ 200 500,005 True
http://surgery.biz/ 301 300,000 False
log_data['Active'] = log_data['Status']==200
Column we're checking

62. URL Status Hits Active
https://surgery.biz/ 200 1,000,003 True
https://surgery.biz/ 200 500,005 True
http://surgery.biz/ 301 300,000 False
log_data['Active'] = log_data['Status']==200
Our check

63. URL Status Hits High Hits
https://surgery.biz/ 200 1,000,003 True
https://surgery.biz/ 200 500,005 True
http://surgery.biz/ 301 300,000 False
log_data['High Hits'] = log_data['Hits']>500000

64. URL Status Hits High Hits
https://surgery.biz/ 200 1,000,003 True
https://surgery.biz/ 200 500,005 True
http://surgery.biz/ 301 300,000 False
log_data['High Hits'] = log_data['Hits']>500000
Column we're filling

65. URL Status Hits High Hits
https://surgery.biz/ 200 1,000,003 True
https://surgery.biz/ 200 500,005 True
http://surgery.biz/ 301 300,000 False
log_data['High Hits'] = log_data['Hits']>500000
Column we're checking

66. URL Status Hits High Hits
https://surgery.biz/ 200 1,000,003 True
https://surgery.biz/ 200 500,005 True
http://surgery.biz/ 301 300,000 False
log_data['High Hits'] = log_data['Hits']>500000
Our check

67. Testing for words

68. URL Status Hits Http
https://surgery.biz/ 200 1,000,003 False
https://surgery.biz/ 200 500,005 False
http://surgery.biz/ 301 300,000 True

69. URL Status Hits Http
https://surgery.biz/ 200 1,000,003 False
https://surgery.biz/ 200 500,005 False
http://surgery.biz/ 301 300,000 True

70. URL Status Hits Http
https://surgery.biz/ 200 1,000,003 False
https://surgery.biz/ 200 500,005 False
http://surgery.biz/ 301 300,000 True
log_data['Http'] = log_data['URL'].str.contains('http:')

71. URL Status Hits Http
https://surgery.biz/ 200 1,000,003 False
https://surgery.biz/ 200 500,005 False
http://surgery.biz/ 301 300,000 True
log_data['Http'] = log_data['URL'].str.contains('http:')
Column we're filling

72. URL Status Hits Http
https://surgery.biz/ 200 1,000,003 False
https://surgery.biz/ 200 500,005 False
http://surgery.biz/ 301 300,000 True
log_data['Http'] = log_data['URL'].str.contains('http:')
Column we're checking

73. URL Status Hits Http
https://surgery.biz/ 200 1,000,003 False
https://surgery.biz/ 200 500,005 False
http://surgery.biz/ 301 300,000 True
log_data['Http'] = log_data['URL'].str.contains('http:')
Our check

74. URL Active 301 High Hits Http
https://surgery.biz/ True False True False
https://surgery.biz/ True False True False
http://surgery.biz/ False True False True

75. Eeeeeeeverything

76. Eeeeeeeverything

77. Eeeeeeeverything

78. Eeeeeeeverything
Our output

79. Eeeeeeeverything
Our output

80. Eeeeeeeverything
Our output

81. Eeeeeeeverything
Our output

83. Repeatability

84. For loop

85. checklist=['plastic', 'legal', 'dress', 'sue']
for item in checklist:

86. checklist=['plastic', 'legal', 'dress', 'sue']
for item in checklist:
log_data[item] = log_data['URLs'].str.contains(item)

87. checklist=['plastic', 'legal', 'dress', 'sue']
for item in checklist:
log_data[item] = log_data['URLs'].str.contains(item)

88. checklist=['plastic', 'legal', 'dress', 'sue']
for item in checklist:
log_data['plastic']=log_data['URLs'].str.contains('plastic')
URL plastic
http://…sue-us False

89. checklist=['plastic', 'legal', 'dress', 'sue']
for item in checklist:
log_data['legal'] = log_data['URLs'].str.contains('legal')
URL plastic legal
http://…sue-us False False

90. checklist=['plastic', 'legal', 'dress', 'sue']
for item in checklist:
log_data['dress'] = log_data['URLs'].str.contains('dress')
URL plastic legal dress
http://…sue-us False False False

91. checklist=['plastic', 'legal', 'dress', 'sue']
for item in checklist:
log_data[ ] = log_data['URLs'].str.contains( )
URL plastic legal dress sue
http://…sue-us False False False True

92. checklist=['plastic', 'legal', 'dress', 'sue']

93. checklist=['plastic', 'legal', 'dress', 'sue', 'dresses', 'sale',
'product', 'free-trial', 'best-medical-decision-of-my-life',
'dr-nick']

94. checklist=['plastic', 'legal', 'dress', 'sue', 'dresses', 'sale',
'product', 'free-trial', 'best-medical-decision-of-my-life',
'dr-nick', 'many', 'more', 'checks', 'its', 'so', 'easy']

95. checklist=['plastic', 'legal', 'dress', 'sue', 'dresses', 'sale',
'product', 'free-trial', 'best-medical-decision-of-my-life',
'dr-nick', 'many', 'more', 'checks', 'its', 'so', 'easy', 'to', 'do',
'and', 'you', 'just', 'update', 'and', 'run', 'your', 'list',
'whenever', 'you', 'want', 'no', 'kidding', 'there', 'is', 'time',
'to', 'mess', 'around', 'with', 'stuff', 'like', 'this', 'because', 'we',
'aren't', 'repeating', 'work', 'in', 'excel']

96. for item in checklist:
log_data[item] = log_data['URLs'].str.contains(item)
H('contact',A2)),'contact',IF(ISNUMBER(SEARCH('dresses',A2)),'dresses',IF(ISNU
MBER(SEARCH('sale',A2)),'sale',IF(ISNUMBER(SEARCH('product',A2)),'product',
IF(ISNUMBER(SEARCH('free-trial',A2)),'free-trial',IF(ISNUMBER(SEARCH('best-
medical-decision-of-my-life',A2)),'best-medical-decision-of-my-
life',IF(ISNUMBER(SEARCH('dr-nick',A2)),'dr-
nick',IF(ISNUMBER(SEARCH('many',A2)),'many',IF(ISNUMBER(SEARCH('more',
A2)),'more',IF(ISNUMBER(SEARCH('checks',A2)),'checks',IF(ISNUMBER(SEARC
H('its',A2)),'its',IF(ISNUMBER(SEARCH('so',A2)),'so',IF(ISNUMBER(SEARCH('eas
y',A2)),'easy',IF(ISNUMBER(SEARCH('to',A2)),'to',IF(ISNUMBER(SEARCH('do',A
2)),'do',IF(ISNUMBER(SEARCH('and',A2)),'and',IF(ISNUMBER(SEARCH('you',A2)
),'you',IF(ISNUMBER(SEARCH('just',A2)),'just',IF(ISNUMBER(SEARCH('update',A
2)),'update',IF(ISNUMBER(SEARCH('and',A2)),'and',IF(ISNUMBER(SEARCH('run'
,A2)),'run',IF(ISNUMBER(SEARCH('your',A2)),'your',IF(ISNUMBER(SEARCH('list',
A2)),'list',IF(ISNUMBER(SEARCH('whenever',A2)),'whenever',IF(ISNUMBER(SEA
RCH('you',A2)),'you',IF(ISNUMBER(SEARCH('want',A2)),'want',IF(ISNUMBER(SE
ARCH('no',A2)),'no',IF(ISNUMBER(SEARCH('kidding',A2)),'kidding',IF(ISNUMBE
R(SEARCH('there',A2)),'there',IF(ISNUMBER(SEARCH('is',A2)),'is',IF(ISNUMBER(
SEARCH('time',A2)),'time',IF(ISNUMBER(SEARCH('to',A2)),'to',IF(ISNUMBER(SE
ARCH('mess',A2)),'mess',IF(ISNUMBER(SEARCH('around',A2)),'around',IF(ISNU
MBER(SEARCH('with',A2)),'with',IF(ISNUMBER(SEARCH('stuff',A2)),'stuff',IF(ISN
UMBER(SEARCH('like',A2)),'like',IF(ISNUMBER(SEARCH('this',A2)),'this',IF(ISNU
MBER(SEARCH('because',A2)),'because',IF(ISNUMBER(SEARCH('we',A2)),'we',IF
(ISNUMBER(SEARCH('aren't',A2)),'aren't',IF(ISNUMBER(SEARCH('repeating',A2
)),'repeating',IF(ISNUMBER(SEARCH('work',A2)),'work',IF(ISNUMBER(SEARCH(
'in',A2)),'in',IF(ISNUMBER(SEARCH('excel',A2)),'excel','other')))))))))))))))))))))))))
)))))))))))))))))))))))

97. Eeeeeeeverything
Our output

98. Eeeeeeeverything
Our output

99. Repeatability

100. BIG
Repeated Tasks
JavaScript and
RegEx

101. Python
JavaScript and
RegEx

102. Some first steps

103. Specific

105. 1 month later - new data

107. General
General

108. It's boring

109. It's boring
Mindless
Repetitive
Fiddly
Time consuming

110. Code is good at
Accuracy
Speed
Comfort with data
Focus
Transparency

111. Code is good at
Accuracy
Speed
Comfort with data
Focus
Transparency
We need to handle:
Conclusions
Communication
Direction

112. Plan your approach:

113. Choose your tools

114. Thinking time and sense checks

115. Structure

116. @robinlord8

117. Credits

118. Credits by slide number
26 – Iron Giant, 1992, Warner Bros, Pictures
45 – Sherlock, 2010-2018, Hartswood Films, BBC Wales, WGBH
75 – Final Space, 2018, Conaco

119. Resources

120. Resources
1. Regex101 - https://regex101.com/
2. Google Tag Manager (a way to run JavaScript, will need to be added to your site, like Google Analytics) - https://www.google.com/analytics/tag-manager/
3. Simo Ahava blog - https://www.simoahava.com/
4. Sam's GTM PII fix blog post - https://www.distilled.net/resources/how-to-keep-personally-identifiable-information-out-of-google-analytics/
5. JavaScript cheat sheets - https://websitesetup.org/javascript-cheat-sheet/, https://htmlcheatsheet.com/js/
6. Anaconda - download everything you need to start with Python - including Jupyter - https://www.anaconda.com/download/
7. Pandas documentation - https://pandas.pydata.org/pandas-docs/stable/
8. Python beginner resource - https://realpython.com/python-first-steps/
9. Stack Overflow (if you Google your problem, this will probably come up anyway) - https://stackoverflow.com/