@@ -18,23 +18,40 @@ tools in the PyData space.
18
18
We'd like to make it easier for users to find these project, if you know of other
19
19
substantial projects that you feel should be on this list, please let us know.
20
20
21
+ .. _ecosystem.stats :
22
+
23
+ Statistics and Machine Learning
24
+ -------------------------------
25
+
21
26
`Statsmodels <http://statsmodels.sourceforge.net >`__
22
- ----------------------------------------------------
27
+ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
23
28
24
29
Statsmodels is the prominent python "statistics and econometrics library" and it has
25
30
a long-standing special relationship with pandas. Statsmodels provides powerful statistics,
26
31
econometrics, analysis and modeling functionality that is out of pandas' scope.
27
32
Statsmodels leverages pandas objects as the underlying data container for computation.
28
33
34
+ `sklearn-pandas <https://github.com/paulgb/sklearn-pandas >`__
35
+ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
36
+
37
+ Use pandas DataFrames in your scikit-learn ML pipeline.
38
+
39
+
40
+
41
+ .. _ecosystem.visualization :
42
+
43
+ Visualization
44
+ -------------
45
+
29
46
`Vincent <https://github.com/wrobstory/vincent >`__
30
- --------------------------------------------------
47
+ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
31
48
32
49
The `Vincent <https://github.com/wrobstory/vincent >`__ project leverages `Vega <https://github.com/trifacta/vega >`__
33
50
(that in turn, leverages `d3 <http://d3js.org/ >`__) to create plots . It has great support
34
51
for pandas data objects.
35
52
36
53
`yhat/ggplot <https://github.com/yhat/ggplot >`__
37
- ------------------------------------------------
54
+ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
38
55
39
56
Hadley Wickham's `ggplot2 <http://ggplot2.org/ >`__ is a foundational exploratory visualization package for the R language.
40
57
Based on `"The Grammer of Graphics" <http://www.cs.uic.edu/~wilkinson/TheGrammarOfGraphics/GOG.html >`__ it
@@ -44,27 +61,32 @@ but a faithful implementation for python users has long been missing. Although s
44
61
(as of Jan-2014), the `yhat/ggplot <https://github.com/yhat/ggplot >`__ project has been
45
62
progressing quickly in that direction.
46
63
47
-
48
64
`Seaborn <https://github.com/mwaskom/seaborn >`__
49
- ------------------------------------------------
65
+ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
50
66
51
67
Although pandas has quite a bit of "just plot it" functionality built-in, visualization and
52
68
in particular statistical graphics is a vast field with a long tradition and lots of ground
53
69
to cover. The `Seaborn <https://github.com/mwaskom/seaborn >`__ project builds on top of pandas
54
70
and `matplotlib <http://matplotlib.org >`__ to provide easy plotting of data which extends to
55
71
more advanced types of plots then those offered by pandas.
56
72
73
+ `Bokeh <http://bokeh.pydata.org >`__
74
+ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
75
+
76
+ Bokeh is a Python interactive visualization library for large datasets that natively uses
77
+ the latest web technologies. Its goal is to provide elegant, concise construction of novel
78
+ graphics in the style of Protovis/D3, while delivering high-performance interactivity over
79
+ large data to thin clients.
80
+
81
+ .. _ecosystem.domain :
82
+
83
+ Domain Specific
84
+ ---------------
57
85
58
86
`Geopandas <https://github.com/kjordahl/geopandas >`__
59
- -----------------------------------------------------
87
+ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
60
88
61
89
Geopandas extends pandas data objects to include geographic information which support
62
90
geometric operations. If your work entails maps and geographical coordinates, and
63
91
you love pandas, you should take a close look at Geopandas.
64
92
65
- `sklearn-pandas <https://github.com/paulgb/sklearn-pandas >`__
66
- -------------------------------------------------------------
67
-
68
- Use pandas DataFrames in your scikit-learn ML pipeline.
69
-
70
-
0 commit comments