non-numerical dataframes

dataframes will be challenging for folks with dyscalculia. the wai-adapt has started writing guidance for concepts like this, but dataframes are likely out of scope.

this notebook sketches an approach towards subtle visual identificaition of a table value in a distribution. before making this i realized that there is no way to explore a table of numbers with dyscalculia. i imagine i'll be thinking more about the visual, audible, and tactile aspects of numeric information.

we'll want a systematic approach where we explore how the dimensions of line, tone/weight, and, lastly, color can present numerical concepts using visual gestalts.

an interesting outcome of this approach is that scrolling activates an animation of the values. it reverses back into albers assertion that notice color in motion and when it is changing. simple concepts like this have significant potential for activiting ones participation in exploring data.

%%
        df = pipe(numpy.linspace(0, 360, 101), pandas.Index).rename("index").to_series().mul(numpy.pi/180).apply(lambda x: pandas.Series(dict(x=numpy.cos(x), y=numpy.sin(x))))
        style_attributes = lambda extra="": compose_left(operator.methodcaller("items"), map(F"--%s{extra}: %s".__mod__), ";".join, "{};".format)

{% set df = df.reset_index() %}
{% set summary = df.describe() %}
{% set groups = [df, df.sample(20)] %}
<style>
.dataframe {
    --height: 600px;
    display: block;
    position: relative;
    width: 100%;
    border: 5px red solid;
    {%- for col in df.columns -%}
    --d{{col}}: calc(var(--{{col}}-max) - var(--{{col}}-min));
    {%- endfor -%}
    --dx: calc(var(--x-max) - var(--x-min));
    --dy: calc(var(--y-max) - var(--y-min));
    tbody, thead {
        tr {
            border: 4px white solid;
            position: relative;
            td {
                {%- for col in df.columns -%}
                &:nth-of-type({{loop.index}}) {
                    --rel: calc((var(--{{col}}) - var(--{{col}}-min)) / var(--d{{col}}));
                }
                {%- endfor -%}
                background: linear-gradient(90deg, 
                        rgba(0,0,0,0) calc(100% * var(--rel) - .25rem), 
                        rgba(128,128,128,.5) calc(100% * var(--rel)), 
                        rgba(0,0,0,0) calc(100% * var(--rel) + .25rem)
                    );
            }

        }
    }
}

</style>

%%
<table class="dataframe" min"].pipe(style_attributes("-min"))}}{{summary.loc["max"].pipe(style_attributes("-max"))}}"="" style="{{summary.loc[">
<caption>a table of sin and cosine data with visual representations in each cell.</caption>
<thead>
<tr>
<th>index</th>
         {%- for k in df.columns -%}
             <th>{{k}}</th>
         {%- endfor -%}
    </tr>
    {%-for i, row in summary.iterrows() -%}
    <tr style="{{style_attributes()(row)}}">
<th scope="row">{{row.name}}</th>
        {%- for k in df.columns -%}
        <td>{{row.loc[k]}}</td>
        {%- endfor -%}
    </tr>
    {%- endfor -%}
    </thead>
     {%- for g in groups -%}
     <tbody>
         {%- for i, row in g.iterrows() -%}
            <tr style="{{style_attributes()(row)}}">
<th>{{loop.index}}</th>
             {%- for k in df.columns -%}
                 <td>{{row.loc[k]}}</td>
             {%- endfor -%}
            </tr>
         {%- endfor -%}
     </tbody>
     {%- endfor -%}
</table>

    df = pipe(numpy.linspace(0, 360, 101), pandas.Index).rename("index").to_series().mul(numpy.pi/180).apply(lambda x: pandas.Series(dict(x=numpy.cos(x), y=numpy.sin(x))))
    style_attributes = lambda extra="": compose_left(operator.methodcaller("items"), map(F"--%s{extra}: %s".__mod__), ";".join, "{};".format)

%%

a table of sin and cosine data with visual representations in each cell.

index	index	x	y
count	101.0	101.0	101.0
mean	180.0	0.009900990099009927	5.681783374570121e-18
std	105.48061433268201	0.7140735186932855	0.7071067811865475
min	0.0	-1.0	-1.0
25%	90.0	-0.6845471059286887	-0.6845471059286887
50%	180.0	6.123233995736766e-17	0.0
75%	270.0	0.7289686274214116	0.6845471059286887
max	360.0	1.0	1.0
1	0.0	1.0	0.0
2	3.6	0.9980267284282716	0.06279051952931337
3	7.2	0.9921147013144779	0.12533323356430426
4	10.8	0.9822872507286887	0.18738131458572463
5	14.4	0.9685831611286311	0.2486898871648548
6	18.0	0.9510565162951535	0.3090169943749474
7	21.6	0.9297764858882513	0.368124552684678
8	25.2	0.9048270524660196	0.42577929156507266
9	28.8	0.8763066800438636	0.4817536741017153
10	32.4	0.8443279255020151	0.5358267949789967
11	36.0	0.8090169943749475	0.5877852522924731
12	39.6	0.7705132427757891	0.6374239897486897
13	43.2	0.7289686274214116	0.6845471059286887
14	46.800000000000004	0.6845471059286886	0.7289686274214116
15	50.4	0.6374239897486897	0.7705132427757893
16	54.0	0.5877852522924731	0.8090169943749475
17	57.6	0.5358267949789965	0.8443279255020151
18	61.2	0.48175367410171516	0.8763066800438637
19	64.8	0.42577929156507266	0.9048270524660196
20	68.4	0.36812455268467786	0.9297764858882515
21	72.0	0.30901699437494745	0.9510565162951535
22	75.60000000000001	0.24868988716485474	0.9685831611286311
23	79.2	0.18738131458572452	0.9822872507286887
24	82.8	0.12533323356430426	0.9921147013144779
25	86.4	0.0627905195293133	0.9980267284282716
26	90.0	6.123233995736766e-17	1.0
27	93.60000000000001	-0.0627905195293134	0.9980267284282716
28	97.2	-0.12533323356430437	0.9921147013144778
29	100.8	-0.1873813145857246	0.9822872507286887
30	104.4	-0.24868988716485485	0.9685831611286311
31	108.0	-0.30901699437494734	0.9510565162951536
32	111.60000000000001	-0.368124552684678	0.9297764858882513
33	115.2	-0.4257792915650727	0.9048270524660195
34	118.8	-0.48175367410171505	0.8763066800438637
35	122.4	-0.5358267949789969	0.844327925502015
36	126.0	-0.587785252292473	0.8090169943749475
37	129.6	-0.6374239897486897	0.7705132427757893
38	133.20000000000002	-0.6845471059286887	0.7289686274214114
39	136.8	-0.7289686274214117	0.6845471059286885
40	140.4	-0.7705132427757894	0.6374239897486895
41	144.0	-0.8090169943749473	0.5877852522924732
42	147.6	-0.8443279255020151	0.5358267949789967
43	151.20000000000002	-0.8763066800438636	0.4817536741017152
44	154.8	-0.9048270524660196	0.4257792915650725
45	158.4	-0.9297764858882515	0.36812455268467775
46	162.0	-0.9510565162951535	0.3090169943749475
47	165.6	-0.9685831611286311	0.24868988716485482
48	169.20000000000002	-0.9822872507286887	0.18738131458572457
49	172.8	-0.9921147013144779	0.1253332335643041
50	176.4	-0.9980267284282716	0.06279051952931314
51	180.0	-1.0	1.2246467991473532e-16
52	183.6	-0.9980267284282716	-0.06279051952931335
53	187.20000000000002	-0.9921147013144779	-0.12533323356430429
54	190.8	-0.9822872507286886	-0.18738131458572477
55	194.4	-0.9685831611286311	-0.24868988716485502
56	198.0	-0.9510565162951536	-0.3090169943749473
57	201.6	-0.9297764858882515	-0.3681245526846779
58	205.20000000000002	-0.9048270524660195	-0.42577929156507266
59	208.8	-0.8763066800438635	-0.4817536741017154
60	212.4	-0.844327925502015	-0.5358267949789968
61	216.0	-0.8090169943749476	-0.587785252292473
62	219.6	-0.7705132427757893	-0.6374239897486896
63	223.20000000000002	-0.7289686274214116	-0.6845471059286887
64	226.8	-0.6845471059286886	-0.7289686274214116
65	230.4	-0.6374239897486895	-0.7705132427757894
66	234.0	-0.5877852522924732	-0.8090169943749473
67	237.6	-0.5358267949789971	-0.8443279255020149
68	241.20000000000002	-0.48175367410171527	-0.8763066800438636
69	244.8	-0.42577929156507216	-0.9048270524660198
70	248.4	-0.3681245526846778	-0.9297764858882515
71	252.0	-0.30901699437494756	-0.9510565162951535
72	255.6	-0.2486898871648553	-0.968583161128631
73	259.2	-0.18738131458572463	-0.9822872507286887
74	262.8	-0.12533323356430373	-0.9921147013144779
75	266.40000000000003	-0.06279051952931321	-0.9980267284282716
76	270.0	-1.8369701987210297e-16	-1.0
77	273.6	0.06279051952931372	-0.9980267284282716
78	277.2	0.12533323356430423	-0.9921147013144779
79	280.8	0.18738131458572513	-0.9822872507286886
80	284.40000000000003	0.24868988716485493	-0.9685831611286311
81	288.0	0.30901699437494723	-0.9510565162951536
82	291.6	0.36812455268467825	-0.9297764858882512
83	295.2	0.4257792915650726	-0.9048270524660196
84	298.8	0.4817536741017157	-0.8763066800438634
85	302.40000000000003	0.5358267949789968	-0.844327925502015
86	306.0	0.5877852522924729	-0.8090169943749476
87	309.6	0.63742398974869	-0.770513242775789
88	313.2	0.6845471059286886	-0.7289686274214116
89	316.8	0.7289686274214119	-0.6845471059286883
90	320.40000000000003	0.7705132427757894	-0.6374239897486896
91	324.0	0.8090169943749473	-0.5877852522924734
92	327.6	0.8443279255020153	-0.5358267949789963
93	331.2	0.8763066800438636	-0.4817536741017153
94	334.8	0.9048270524660197	-0.4257792915650722
95	338.40000000000003	0.9297764858882515	-0.36812455268467786
96	342.0	0.9510565162951535	-0.3090169943749476
97	345.6	0.9685831611286312	-0.2486898871648545
98	349.2	0.9822872507286887	-0.18738131458572468
99	352.8	0.9921147013144779	-0.1253332335643038
100	356.40000000000003	0.9980267284282716	-0.06279051952931326
101	360.0	1.0	-2.4492935982947064e-16
1	172.8	-0.9921147013144779	0.1253332335643041
2	356.40000000000003	0.9980267284282716	-0.06279051952931326
3	248.4	-0.3681245526846778	-0.9297764858882515
4	140.4	-0.7705132427757894	0.6374239897486895
5	187.20000000000002	-0.9921147013144779	-0.12533323356430429
6	36.0	0.8090169943749475	0.5877852522924731
7	320.40000000000003	0.7705132427757894	-0.6374239897486896
8	72.0	0.30901699437494745	0.9510565162951535
9	64.8	0.42577929156507266	0.9048270524660196
10	162.0	-0.9510565162951535	0.3090169943749475
11	219.6	-0.7705132427757893	-0.6374239897486896
12	111.60000000000001	-0.368124552684678	0.9297764858882513
13	133.20000000000002	-0.6845471059286887	0.7289686274214114
14	50.4	0.6374239897486897	0.7705132427757893
15	226.8	-0.6845471059286886	-0.7289686274214116
16	338.40000000000003	0.9297764858882515	-0.36812455268467786
17	115.2	-0.4257792915650727	0.9048270524660195
18	295.2	0.4257792915650726	-0.9048270524660196
19	201.6	-0.9297764858882515	-0.3681245526846779
20	284.40000000000003	0.24868988716485493	-0.9685831611286311