Example Data Details

This notebook presents some ways to use the package to give insights of the data

First, import the package

import dvb.datascience as ds

C:\ProgramData\Anaconda3\lib\site-packages\deap\tools\_hypervolume\pyhv.py:33: ImportWarning: Falling back to the python version of hypervolume module. Expect this to be very slow.
  "module. Expect this to be very slow.", ImportWarning)
C:\ProgramData\Anaconda3\lib\importlib\_bootstrap_external.py:426: ImportWarning: Not importing directory C:\ProgramData\Anaconda3\lib\site-packages\mpl_toolkits: missing __init__
  _warnings.warn(msg.format(portions[0]), ImportWarning)
C:\ProgramData\Anaconda3\lib\importlib\_bootstrap_external.py:426: ImportWarning: Not importing directory c:\programdata\anaconda3\lib\site-packages\mpl_toolkits: missing __init__
  _warnings.warn(msg.format(portions[0]), ImportWarning)

Describe the data

p = ds.Pipeline()
p.addPipe('read', ds.data.SampleData('iris'))
p.addPipe('describe', ds.eda.Describe(), [('read', 'df', 'df')])
p.transform(name="describe", close_plt=True)

'Drawing diagram using blockdiag'

Transform describe

	sepal length (cm)	sepal width (cm)	petal length (cm)	petal width (cm)	target
count	150.000000	150.000000	150.000000	150.000000	150.000000
mean	5.843333	3.054000	3.758667	1.198667	1.000000
std	0.828066	0.433594	1.764420	0.763161	0.819232
min	4.300000	2.000000	1.000000	0.100000	0.000000
25%	5.100000	2.800000	1.600000	0.300000	0.000000
50%	5.800000	3.000000	4.350000	1.300000	1.000000
75%	6.400000	3.300000	5.100000	1.800000	2.000000
max	7.900000	4.400000	6.900000	2.500000	2.000000

Dump the data

p = ds.Pipeline()
p.addPipe('read', ds.data.SampleData('iris'))
p.addPipe('dump', ds.eda.Dump(), [('read', 'df', 'df')])
p.transform(name="dump", close_plt=True)

'Drawing diagram using blockdiag'

Transform dump

	sepal length (cm)	sepal width (cm)	petal length (cm)	petal width (cm)	target
0	5.1	3.5	1.4	0.2	0
1	4.9	3.0	1.4	0.2	0
2	4.7	3.2	1.3	0.2	0
3	4.6	3.1	1.5	0.2	0
4	5.0	3.6	1.4	0.2	0
5	5.4	3.9	1.7	0.4	0
6	4.6	3.4	1.4	0.3	0
7	5.0	3.4	1.5	0.2	0
8	4.4	2.9	1.4	0.2	0
9	4.9	3.1	1.5	0.1	0
10	5.4	3.7	1.5	0.2	0
11	4.8	3.4	1.6	0.2	0
12	4.8	3.0	1.4	0.1	0
13	4.3	3.0	1.1	0.1	0
14	5.8	4.0	1.2	0.2	0
15	5.7	4.4	1.5	0.4	0
16	5.4	3.9	1.3	0.4	0
17	5.1	3.5	1.4	0.3	0
18	5.7	3.8	1.7	0.3	0
19	5.1	3.8	1.5	0.3	0
20	5.4	3.4	1.7	0.2	0
21	5.1	3.7	1.5	0.4	0
22	4.6	3.6	1.0	0.2	0
23	5.1	3.3	1.7	0.5	0
24	4.8	3.4	1.9	0.2	0
25	5.0	3.0	1.6	0.2	0
26	5.0	3.4	1.6	0.4	0
27	5.2	3.5	1.5	0.2	0
28	5.2	3.4	1.4	0.2	0
29	4.7	3.2	1.6	0.2	0
30	4.8	3.1	1.6	0.2	0
31	5.4	3.4	1.5	0.4	0
32	5.2	4.1	1.5	0.1	0
33	5.5	4.2	1.4	0.2	0
34	4.9	3.1	1.5	0.1	0
35	5.0	3.2	1.2	0.2	0
36	5.5	3.5	1.3	0.2	0
37	4.9	3.1	1.5	0.1	0
38	4.4	3.0	1.3	0.2	0
39	5.1	3.4	1.5	0.2	0
40	5.0	3.5	1.3	0.3	0
41	4.5	2.3	1.3	0.3	0
42	4.4	3.2	1.3	0.2	0
43	5.0	3.5	1.6	0.6	0
44	5.1	3.8	1.9	0.4	0
45	4.8	3.0	1.4	0.3	0
46	5.1	3.8	1.6	0.2	0
47	4.6	3.2	1.4	0.2	0
48	5.3	3.7	1.5	0.2	0
49	5.0	3.3	1.4	0.2	0
50	7.0	3.2	4.7	1.4	1
51	6.4	3.2	4.5	1.5	1
52	6.9	3.1	4.9	1.5	1
53	5.5	2.3	4.0	1.3	1
54	6.5	2.8	4.6	1.5	1
55	5.7	2.8	4.5	1.3	1
56	6.3	3.3	4.7	1.6	1
57	4.9	2.4	3.3	1.0	1
58	6.6	2.9	4.6	1.3	1
59	5.2	2.7	3.9	1.4	1
60	5.0	2.0	3.5	1.0	1
61	5.9	3.0	4.2	1.5	1
62	6.0	2.2	4.0	1.0	1
63	6.1	2.9	4.7	1.4	1
64	5.6	2.9	3.6	1.3	1
65	6.7	3.1	4.4	1.4	1
66	5.6	3.0	4.5	1.5	1
67	5.8	2.7	4.1	1.0	1
68	6.2	2.2	4.5	1.5	1
69	5.6	2.5	3.9	1.1	1
70	5.9	3.2	4.8	1.8	1
71	6.1	2.8	4.0	1.3	1
72	6.3	2.5	4.9	1.5	1
73	6.1	2.8	4.7	1.2	1
74	6.4	2.9	4.3	1.3	1
75	6.6	3.0	4.4	1.4	1
76	6.8	2.8	4.8	1.4	1
77	6.7	3.0	5.0	1.7	1
78	6.0	2.9	4.5	1.5	1
79	5.7	2.6	3.5	1.0	1
80	5.5	2.4	3.8	1.1	1
81	5.5	2.4	3.7	1.0	1
82	5.8	2.7	3.9	1.2	1
83	6.0	2.7	5.1	1.6	1
84	5.4	3.0	4.5	1.5	1
85	6.0	3.4	4.5	1.6	1
86	6.7	3.1	4.7	1.5	1
87	6.3	2.3	4.4	1.3	1
88	5.6	3.0	4.1	1.3	1
89	5.5	2.5	4.0	1.3	1
90	5.5	2.6	4.4	1.2	1
91	6.1	3.0	4.6	1.4	1
92	5.8	2.6	4.0	1.2	1
93	5.0	2.3	3.3	1.0	1
94	5.6	2.7	4.2	1.3	1
95	5.7	3.0	4.2	1.2	1
96	5.7	2.9	4.2	1.3	1
97	6.2	2.9	4.3	1.3	1
98	5.1	2.5	3.0	1.1	1
99	5.7	2.8	4.1	1.3	1
100	6.3	3.3	6.0	2.5	2
101	5.8	2.7	5.1	1.9	2
102	7.1	3.0	5.9	2.1	2
103	6.3	2.9	5.6	1.8	2
104	6.5	3.0	5.8	2.2	2
105	7.6	3.0	6.6	2.1	2
106	4.9	2.5	4.5	1.7	2
107	7.3	2.9	6.3	1.8	2
108	6.7	2.5	5.8	1.8	2
109	7.2	3.6	6.1	2.5	2
110	6.5	3.2	5.1	2.0	2
111	6.4	2.7	5.3	1.9	2
112	6.8	3.0	5.5	2.1	2
113	5.7	2.5	5.0	2.0	2
114	5.8	2.8	5.1	2.4	2
115	6.4	3.2	5.3	2.3	2
116	6.5	3.0	5.5	1.8	2
117	7.7	3.8	6.7	2.2	2
118	7.7	2.6	6.9	2.3	2
119	6.0	2.2	5.0	1.5	2
120	6.9	3.2	5.7	2.3	2
121	5.6	2.8	4.9	2.0	2
122	7.7	2.8	6.7	2.0	2
123	6.3	2.7	4.9	1.8	2
124	6.7	3.3	5.7	2.1	2
125	7.2	3.2	6.0	1.8	2
126	6.2	2.8	4.8	1.8	2
127	6.1	3.0	4.9	1.8	2
128	6.4	2.8	5.6	2.1	2
129	7.2	3.0	5.8	1.6	2
130	7.4	2.8	6.1	1.9	2
131	7.9	3.8	6.4	2.0	2
132	6.4	2.8	5.6	2.2	2
133	6.3	2.8	5.1	1.5	2
134	6.1	2.6	5.6	1.4	2
135	7.7	3.0	6.1	2.3	2
136	6.3	3.4	5.6	2.4	2
137	6.4	3.1	5.5	1.8	2
138	6.0	3.0	4.8	1.8	2
139	6.9	3.1	5.4	2.1	2
140	6.7	3.1	5.6	2.4	2
141	6.9	3.1	5.1	2.3	2
142	5.8	2.7	5.1	1.9	2
143	6.8	3.2	5.9	2.3	2
144	6.7	3.3	5.7	2.5	2
145	6.7	3.0	5.2	2.3	2
146	6.3	2.5	5.0	1.9	2
147	6.5	3.0	5.2	2.0	2
148	6.2	3.4	5.4	2.3	2
149	5.9	3.0	5.1	1.8	2