This slide is part of Python Data Visualization series event held by AIC x PyLadies TW. Part 2: Python plot packages, e.g., matplotlib, seaborn, plotly, bokeh

1. Data Vis
資料視覺化
# python-packages
2018-05-18

2. Topics
● Data Science Process
● Data Visualization in Python
○ Matplotlib
○ Seaborn
○ Plotly
○ Bokeh
● Summary

3. 起點
● 有資料，缺乏想法／故事
○ 要從資料中發現以前沒發現的事情
● 有想法/故事，缺乏資料
○ 先從怎麼收資料／收什麼資料開始做起

4. data vis steps
By Farcaster at English Wikipedia, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=40129394

5. data vis steps
By Farcaster at English Wikipedia, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=40129394

6. data vis steps
By Farcaster at English Wikipedia, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=40129394

7. data vis steps
By Farcaster at English Wikipedia, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=40129394

8. data vis steps
By Farcaster at English Wikipedia, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=40129394
探索型
視覺化

9. data vis steps
By Farcaster at English Wikipedia, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=40129394

10. data vis steps
By Farcaster at English Wikipedia, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=40129394
解釋型
視覺化

11. data vis steps
By Farcaster at English Wikipedia, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=40129394

12. ● 探索型：
○ Exploratory Data Analysis 階段
○ e.g., Dashboard, Jupyter Notebook
● 解釋型：
○ 已經有一個故事／發現
○ 想要使聽眾作出決定／行動
○ 通常使用的資料是靜態的
2 Types of Visualization Goals

13. Data Visualization in Python
● Useful packages: Matplotlib, Seaborn, Bokeh
● Data: Nasa dataset on Kaggle

14. Python Packages
● Matplotlib:
大家長
● Seaborn:
Kaggle裡的分析圖趨勢
● Bokeh:
產生網頁版的互動圖表
● Plotly:
Based on D3.js, 有各種語言的介
面, e.g., Python, JS

15. 觀察別人做了什麼分析
Kaggle Kernel

17. 觀察別人用了什麼工具
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
Learn from Kaggle Kernels

18. import matplotlib.pyplot as plt
● Background: from matlab to matplotlib
● Structure layers
○ backend layer
○ artist layer
○ scripting layer
■ pyplot, pylab
Matplotlib

19. Anaconda Python Environment:
● Good for data analysis tasks
● Virtual environment by
Anaconda
○ conda create -n <yourenvname>
python=3.4 anaconda
Environment
Jupyter Notebook:
● Good for demo, presentation,
trial and error...
● Magic line in jupyter notebook:
○ %matplotlib inline

20. Preferable Functions
● Goals:
○ Generate charts EASILY
○ Make some adjustments
● Aspects:
○ Chart types
○ Customizable elements
○ Grids / subplots

21. 1
Our Goal
2 43

22. 1. Gallery
https://matplotlib.org/gallery.html
a. Chart types
histogram, scatter
b. Customizable elements
colorbar, colormap
c. Grids/ subplots
subplots, subplot2grid
2. Implementation
3. Pros and cons
Matplotlib

23. Demo goal:
(meteorite-landings.csv)
Implementation

24. From gallery: scatter
Scatter Plot

25. figure
axes
Fig, axes

26. Subplots (random data):
Matplotlib: Subplots

27. axes
(0,0)
axes
(1,0)
axes
(1,1)
Figsize, sharex, sharey

28. Subplot2grid (random data):
Matplotlib: Subplot2grid

29. (0,0)
(1,0) (1,2)
subplot2grid

30. Axis visibility:
Axis

31. set_visible

32. Nasa meteorite-landings dataset
Dataset

33. ‘lat’, ‘long’:
Real Data

35. ‘lat’, ‘long’ and ‘mass’:
Goal!

36. Add:
Revise:

37. Pros and Cons
● 優點：
○ 可用的元件很多，可塑性高
● 缺點：
○ 世界觀很大，有很多種教學寫法達到同一件事，對初學不易理解
○ 預設的樣式大部分需要很多微調

38. 1. Gallery
https://seaborn.pydata.org/examples/index.html
a. Chart types
heatmap, structured heatmap
b. Customizable elements
color palettes, style
c. Matrix /grid/ jointplot
scatterplot matrix, facetgrid, jointplot
2. Implementation
3. Pros and cons
Seaborn

39. Demo goal: (meteorite-landings)
Implementation

40. Jointplot (random data):
Start

42. Jointplot: (meteorite-landings)
Jointplot

44. Combine with Matplotlib
Goal!

45. Add:
Cla, sca

46. Pros and Cons
● 優點：
○ 比較美觀的預設樣式
○ 用一兩行就可以達成多維度同時比較的圖表
○ 可以和 matplotlib 做組合
● 缺點
○ 要做比較細節的客製化的時候，學習曲線可能突然變高

47. 1. Gallery
a. Chart types
https://plot.ly/python/
b. Customizable elements
https://images.plot.ly/plotly-documentation/images/python_cheat_sheet
.pdf
c. Matrix /grid
https://plot.ly/python/#multiple-axes-subplots-and-insets
2. Implementation
a. Offline mode: https://plot.ly/python/offline/
b. 3D scatter:
https://medium.com/@ichitsai/vis-plot-ly-offline-python-%E8%B3%87%E
6%96%99%E8%A6%96%E8%A6%BA%E5%8C%96-f4b540c130f8
3. Pros and cons
Plotly

48. Goal

49. import

50. Scatter plot

51. Trace, go (graph objects), iplot

52. Subplots

53. tools.make_subplots
code:
result:

54. tools.make_subplots

55. Subplots with Real Data

56. Trace, Histogram, Scatter, marker

57. Goal!

59. Pros and Cons
● 優點：
○ 有互動的框架，不用自己煩惱 zoom, pan 等等功能
○ 背後是一個致力開發的公司
○ 有很多語言的 API
○ 除了寫 code 也有好用的 UI
○ 最近還有 dashboard 系列
● 缺點：
○ 有些資料會自動判斷是什麼類型，可能會錯誤處理
■ 例如 id: 1234 他會以為是數值型資料，要額外再加 prefix 給他

60. Bokeh
1. Gallery
http://bokeh.pydata.org/en/latest/docs/gallery.html
selection_histogram, code
2. Jupyter notebook environment:
output_notebook()
show([obj])
3. Special functions
4. Pros and cons

61. Bokeh Default Demo

62. Nasa Dataset Screenshot

63. Bokeh Special Functions
● Toolbar:
TOOLS = "pan,wheel_zoom,box_select,lasso_select,reset"
● Figure:
p = figure(
tools=TOOLS, toolbar_location="above",
plot_width=300, plot_height=100,
y_axis_location="right", title="Nasa Meteorite-Landings")
● Plot:
p.scatter(x, y, size=3, color=’red’, alpha=0.6)

64. Special Functions
● Layout:
layout = column(
row(horizontal_hist, Spacer(width=100, height=100)),
row(scatter, vertical_hist))
show(layout)
● Ticker:
p.yaxis.ticker = FixedTicker(ticks=[0,50,60])

65. Pros and Cons
● 優點：
○ 用 python 直接寫出網頁版互動圖表
○ 可以結合資料分析流程直接 產生結果
● 缺點：
○ 有些資料會自動判斷是什麼類型，可能會錯誤處理
○ 可能會有些不合法字符沒有寫在文件上，造成很難 debug
■ E.g., “:”

66. ● PlotDB (d3.js)
Useful Tools Recommended

67. from Data, Tools to Storytelling
Story-
telling
Data Tools

68. References
● Matplotlib 簡介與使用
○ by Shan-Ho Yang
○ https://www.slideshare.net/vrootic/matplotlib-64469117
● Matplotlib库的常用知识
○ http://www.cnblogs.com/yinheyi/p/6056314.html
● 淺談資料視覺化以及 ggplot2 實踐
○ https://leemengtaiwan.github.io/data-visualization-from-matplotlib-to-ggplot2.html
● Matplotlib Hierarchy
○ http://www.aosabook.org/en/matplotlib.html

69. Q & A