Making basic, good-looking plots in Python is tough. Matplotlib gives you great control, but at the expense of being very detailed. The rise of pandas has made Python the go-to language for data wrangling and munging but many people are still reluctant to leave R because of its outstanding data viz packages. ggplot is a port of the popular R package ggplot2 into Python. It provides a high level grammar that allow users to quickly and easily make good looking plots. An example may be found here: http://blog.yhathq.com/posts/ggplot-for-python.html Greg will show you how to use ggplot to analyze data from the MLB's open data source, pitchf/x. He will take you through the basics of ggplot and show how easy it is to create histograms, plot smoothed curves, customize colors & shapes. http://www.meetup.com/PyData-Boston/events/184382092/

1. analyzing MLB data with
ggplot
Greg Lamp

2. ggplot
● What is it?
● Alternatives
● How it works
● Why should I use it?
● Brief case study
● Questions

3. Here I am on
the Internet.
Founder/CTO @ Yhat
Hi, I’m Greg!

4. What is
ggplot?

6. DSL for graphics

7. DSL for graphics
scatterplot
histogram
labels
color
shape

8. What about
matplotlib?

10. a quick example

12. matplotlib ggplot

13. it’s not all bad!

14. matplotlib
syntax, api,
default themes,
learning curve

15. matplotlib
maturity, ipython,
customization, community
syntax, api,
default themes,
learning curve

16. What about
d3.js?

17. d3.js

18. ggplot

19. ggplot d3.js

20. How it works

21. Format

22. ggplot

24. data frame

25. “aesthetics”

26. Aesthetics

30. color

31. shape

32. size

33. ...fill, alpha, slope,
intercept, ymin,
ymax, ...

34. Geoms,
Stats, &
Scales

35. geom_point

36. geom_area

37. ...there are many

38. stat_smooth

39. ...there are a few

40. scale_color_brewer

41. scale_color_gradient

42. ...there are many

43. Layers

44. ggplot()

45. +
ggplot() geom_point()

46. + +
ggplot() geom_point() stat_smooth()

47. + +
ggplot() geom_point() stat_smooth()+ +

48. ggplot() +
geom_point() +
stat_smooth()

49. Why is this
good?

50. Makes “reasonable
assumptions”

51. not real colors

52. matplotlib freaks

53. still not real colors
...but i can guess
what you mean

55. Concise yet
expressive

59. Looks pretty good
(and is easy to customize)

61. Seaborngithub.com/mwaskom/seaborn

62. Case Study

66. pitch speed

68. 103.4 mph

70. Load ggplot and pandas

71. Read in our pitch f/x data

72. define the x-
axis
pass in your data frame

73. add a histogram

74. How does fatigue
impact velocity?

75. ...not helpful

78. What about at the
individual level?

81. Justin
Verlander

85. ggplot let’s you
fail quicker

86. Finding Help

87. /tagged/python-ggplot

88. http://ggplot.yhathq.com

89. What’s next?

91. Thanks!
@theglamp
greg@yhathq.com