This slide is part of Python Data Visualization series event held by AIC x PyLadies TW. Part 3: Dashboard introduction, taking Redash for example

1. Data Vis
資料視覺化
#kaggle-hands-on #dashboard
2018-05-25

2. Topics
● Redash Introduction
○ Build local version by docker and docker-compose
○ Query json file
○ Join data from different sources
● Kaggle Usage: find EDA examples

4. Redash Features
● Sharable queries
● Scheduling queries
● Various data source interface
● Open source with updated docker images

5. Redash Dependency
● Python
● Nginx
○ Web server
● Celery
○ Distributed Message
Queue
● Redis
● PostgreSQL
○ DB

6. Query Json File

10. Redash

11. Join Data from
Different
Sources

12. Steps
1. Create query from Google Spreadsheets
a. Google service credential setting
b. Dataset: world population
http://www.worldometers.info/world-population/world-population-by-year/
2. Query json for ‘year’ column
a. Data preprocessing
3. Join 2 views

13. Create Query from Google Spreadsheet
https://redash.io/help/data-sources/querying-a-google-spreadsheet

14. Google Service Account Setting

15. Spreadsheet Sharing Settings

16. Fill Doc ID in Redash Query

17. Steps
1. Create query from Google Spreadsheets
a. Google service credential setting
b. Dataset: world population
http://www.worldometers.info/world-population/world-population-by-year/
2. Query json for ‘year’ column
a. Data preprocessing
3. Join 2 views

18. Create json query for ‘year’

19. https://gist.github.com/tsaiichi/15c8ab58bef71bad05135381ad4de63c

20. Steps
1. Create query from Google Spreadsheets
a. Google service credential setting
b. Dataset: world population
http://www.worldometers.info/world-population/world-population-by-year/
2. Query json for ‘year’ column
a. Data preprocessing
3. Join 2 views

22. https://gist.github.com/tsaiichi/71b496e2c3ad58ec036f3a6fe1730812

23. res_dict = {
rows: {
year: ‘2018’, reclat: 23,
reclong: 130, ...
population: 7632819325
}
}
JSON {}
year_dict = {
2018: 7632819325,
2017: 7550262101,
...
}
q
3
q
4

25. Build Local
Redash
https://redash.io/help/data-sources/supported-
data-sources
By docker-compose,
docker images
https://github.com/getredash/redash

26. Part of Docker-compose

27. DB Files Created by Docker

28. Docker-compose Up

29. Docker ps -a

31. Differences with Superset
Data and Visualization
● Redash has more data source interface
● Redash visualization is based on plotly,
while Superset is based on D3.js
● Local Redash can do query in Python
Account
● Superset can integrated with LDAP
● Redash access control are group-based,
while Superset has different role levels
● Redash user can reset password by Email

32. Activity:
Vis Hands-on and Sharing

33. 遊戲規則：
1. 請大家在下面列表中挑一個 Kaggle 資料集（鐵達尼號、
神奇寶貝、Hacker Rank），請大家分組討論 2~4 個小故
事／發現
2. 用 Python 或是各種你可以做圖的方式做圖，例如
PlotDB、Excel、Google Spreadsheets、手繪都可以 ，來
表達你們覺得這個資料可以看到的故事
3. 每組最後 Demo 2 張以上的圖 給大家看你們的成果
（為了大家能在時間內做出來，可以多多利用 Kaggle 裡
面的 kernel 觀摩別人的作品）

34. 南極為什麼有那麼多隕石：倖存者偏差
其實，在世界各地，隕石出現的可能性是
大致相等的，只不過降落在南極的隕石更
加容易保存下來，並且非常容易被冰蓋考
察的科學家發現罷了。
原文網址
：https://kknews.cc/zh-mo/news/mqkeb6
g.html

36. Titanic
Data
● https://www.kaggle.com/c/titanic/data
Kernels
● https://www.kaggle.com/kabure/titanic-eda-keras-neural-networks-guide
● https://www.kaggle.com/arthurtok/introduction-to-ensembling-stacking-in
-python

37. Hacker Rank
Data
https://www.kaggle.com/hackerrank/developer-survey-2018/data
● HackerRank-Developer-Survey-2018-Values.csv
Kernel
https://www.kaggle.com/ginterhauser/pretty-and-easy-data-visualisation-in-pyt
hon

38. Pokemon
Data
https://www.kaggle.com/abcsds/pokemon/data
Kernel
https://www.kaggle.com/ndrewgele/visualizing-pok-mon-stats-with-seaborn

39. References
● https://www.slideshare.net/toyama0919/redash-is-awesome

40. Thanks!
Contact us:
PyLadies TW
● https://www.facebook.com
/groups/pyladies.taiwan/
● pyladies.tw@gmail.com