

In [19]:

    
from IPython.display import HTML, Markdown
import dx
import pandas as pd
import numpy as np

display(Markdown("""
# Pragmatic Yet Unwieldy Notebook

_Unnamed Notebook Author_

2018
"""))

print("Mixed media; HTML, Markdown, Tabular data, maybe Vega\n")

display(HTML("""
A daring interpretative piece, <i>Pragmatic Yet Unwieldy Notebook</i> was part of a series of strung together
outputs made in the fall of 2018. Critics suggested it is a commentary on modern life's obsession with the incessant
buzzing of the machines around us and our deep attempt to connect with other beings, be they human or machine. The partly
strung together notebook unashamedly displays the author's inability to break apart cells into discrete chunks, daring the
nteract contributors to criticism.
"""))

df = pd.DataFrame(np.random.randint(0,100,size=(100, 4)), columns=list('ABCD'))

dx.enable()
display(df)
dx.disable()

print("tensorflow likes to mix diagnostic noise amidst your useful outputs "*100)









    




Pragmatic Yet Unwieldy Notebook
Unnamed Notebook Author
2018







    



Mixed media; HTML, Markdown, Tabular data, maybe Vega







    





A daring interpretative piece, Pragmatic Yet Unwieldy Notebook was part of a series of strung together
outputs made in the fall of 2018. Critics suggested it is a commentary on modern life's obsession with the incessant
buzzing of the machines around us and our deep attempt to connect with other beings, be they human or machine. The partly
strung together notebook unashamedly displays the author's inability to break apart cells into discrete chunks, daring the
nteract contributors to criticism.







    







  
    
      
      A
      B
      C
      D
    
  
  
    
      0
      16
      28
      11
      62
    
    
      1
      69
      23
      80
      34
    
    
      2
      38
      69
      0
      88
    
    
      3
      56
      67
      40
      78
    
    
      4
      19
      28
      89
      5
    
    
      5
      32
      44
      36
      13
    
    
      6
      79
      34
      8
      26
    
    
      7
      41
      25
      48
      58
    
    
      8
      41
      2
      26
      94
    
    
      9
      42
      12
      23
      93
    
    
      10
      36
      22
      89
      14
    
    
      11
      81
      5
      36
      72
    
    
      12
      46
      1
      21
      39
    
    
      13
      44
      79
      78
      53
    
    
      14
      12
      38
      78
      42
    
    
      15
      84
      69
      81
      76
    
    
      16
      2
      15
      45
      80
    
    
      17
      18
      42
      83
      24
    
    
      18
      93
      71
      8
      33
    
    
      19
      99
      75
      60
      69
    
    
      20
      90
      40
      31
      95
    
    
      21
      79
      58
      97
      65
    
    
      22
      48
      20
      75
      97
    
    
      23
      43
      4
      24
      41
    
    
      24
      57
      13
      41
      54
    
    
      25
      41
      86
      18
      57
    
    
      26
      0
      11
      50
      56
    
    
      27
      76
      13
      54
      46
    
    
      28
      40
      21
      68
      76
    
    
      29
      70
      36
      88
      91
    
    
      ...
      ...
      ...
      ...
      ...
    
    
      70
      46
      64
      19
      72
    
    
      71
      88
      54
      29
      38
    
    
      72
      28
      34
      18
      90
    
    
      73
      68
      14
      38
      59
    
    
      74
      4
      16
      16
      60
    
    
      75
      25
      70
      9
      33
    
    
      76
      82
      2
      22
      80
    
    
      77
      71
      79
      4
      40
    
    
      78
      19
      86
      3
      78
    
    
      79
      37
      71
      62
      97
    
    
      80
      41
      69
      11
      98
    
    
      81
      10
      12
      98
      20
    
    
      82
      65
      36
      99
      15
    
    
      83
      98
      6
      1
      77
    
    
      84
      89
      72
      83
      30
    
    
      85
      52
      84
      30
      25
    
    
      86
      51
      82
      56
      75
    
    
      87
      48
      13
      52
      22
    
    
      88
      53
      74
      80
      12
    
    
      89
      80
      14
      23
      11
    
    
      90
      32
      41
      84
      11
    
    
      91
      53
      55
      74
      42
    
    
      92
      17
      40
      40
      91
    
    
      93
      44
      56
      79
      69
    
    
      94
      39
      40
      61
      90
    
    
      95
      16
      84
      43
      27
    
    
      96
      12
      49
      84
      44
    
    
      97
      3
      26
      32
      39
    
    
      98
      77
      76
      34
      70
    
    
      99
      75
      89
      98
      2
    
  

100 rows × 4 columns







    



tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs tensorflow likes to mix diagnostic noise amidst your useful outputs

	A	B	C	D
0	16	28	11	62
1	69	23	80	34
2	38	69	0	88
3	56	67	40	78
4	19	28	89	5
5	32	44	36	13
6	79	34	8	26
7	41	25	48	58
8	41	2	26	94
9	42	12	23	93
10	36	22	89	14
11	81	5	36	72
12	46	1	21	39
13	44	79	78	53
14	12	38	78	42
15	84	69	81	76
16	2	15	45	80
17	18	42	83	24
18	93	71	8	33
19	99	75	60	69
20	90	40	31	95
21	79	58	97	65
22	48	20	75	97
23	43	4	24	41
24	57	13	41	54
25	41	86	18	57
26	0	11	50	56
27	76	13	54	46
28	40	21	68	76
29	70	36	88	91
...	...	...	...	...
70	46	64	19	72
71	88	54	29	38
72	28	34	18	90
73	68	14	38	59
74	4	16	16	60
75	25	70	9	33
76	82	2	22	80
77	71	79	4	40
78	19	86	3	78
79	37	71	62	97
80	41	69	11	98
81	10	12	98	20
82	65	36	99	15
83	98	6	1	77
84	89	72	83	30
85	52	84	30	25
86	51	82	56	75
87	48	13	52	22
88	53	74	80	12
89	80	14	23	11
90	32	41	84	11
91	53	55	74	42
92	17	40	40	91
93	44	56	79	69
94	39	40	61	90
95	16	84	43	27
96	12	49	84	44
97	3	26	32	39
98	77	76	34	70
99	75	89	98	2

	A	B	C	D
0	16	28	11	62
1	69	23	80	34
2	38	69	0	88
3	56	67	40	78
4	19	28	89	5
5	32	44	36	13
6	79	34	8	26
7	41	25	48	58
8	41	2	26	94
9	42	12	23	93
10	36	22	89	14
11	81	5	36	72
12	46	1	21	39
13	44	79	78	53
14	12	38	78	42
15	84	69	81	76
16	2	15	45	80
17	18	42	83	24
18	93	71	8	33
19	99	75	60	69
20	90	40	31	95
21	79	58	97	65
22	48	20	75	97
23	43	4	24	41
24	57	13	41	54
25	41	86	18	57
26	0	11	50	56
27	76	13	54	46
28	40	21	68	76
29	70	36	88	91
...	...	...	...	...
70	46	64	19	72
71	88	54	29	38
72	28	34	18	90
73	68	14	38	59
74	4	16	16	60
75	25	70	9	33
76	82	2	22	80
77	71	79	4	40
78	19	86	3	78
79	37	71	62	97
80	41	69	11	98
81	10	12	98	20
82	65	36	99	15
83	98	6	1	77
84	89	72	83	30
85	52	84	30	25
86	51	82	56	75
87	48	13	52	22
88	53	74	80	12
89	80	14	23	11
90	32	41	84	11
91	53	55	74	42
92	17	40	40	91
93	44	56	79	69
94	39	40	61	90
95	16	84	43	27
96	12	49	84	44
97	3	26	32	39
98	77	76	34	70
99	75	89	98	2

	A	B	C	D
0	16	28	11	62
1	69	23	80	34
2	38	69	0	88
3	56	67	40	78
4	19	28	89	5
5	32	44	36	13
6	79	34	8	26
7	41	25	48	58
8	41	2	26	94
9	42	12	23	93
10	36	22	89	14
11	81	5	36	72
12	46	1	21	39
13	44	79	78	53
14	12	38	78	42
15	84	69	81	76
16	2	15	45	80
17	18	42	83	24
18	93	71	8	33
19	99	75	60	69
20	90	40	31	95
21	79	58	97	65
22	48	20	75	97
23	43	4	24	41
24	57	13	41	54
25	41	86	18	57
26	0	11	50	56
27	76	13	54	46
28	40	21	68	76
29	70	36	88	91
...	...	...	...	...
70	46	64	19	72
71	88	54	29	38
72	28	34	18	90
73	68	14	38	59
74	4	16	16	60
75	25	70	9	33
76	82	2	22	80
77	71	79	4	40
78	19	86	3	78
79	37	71	62	97
80	41	69	11	98
81	10	12	98	20
82	65	36	99	15
83	98	6	1	77
84	89	72	83	30
85	52	84	30	25
86	51	82	56	75
87	48	13	52	22
88	53	74	80	12
89	80	14	23	11
90	32	41	84	11
91	53	55	74	42
92	17	40	40	91
93	44	56	79	69
94	39	40	61	90
95	16	84	43	27
96	12	49	84	44
97	3	26	32	39
98	77	76	34	70
99	75	89	98	2