improved output semantic for python objects

when we provide the right semantics to the outputs we increase the tactile representation on assistive techonology. these improvements make it easier for screen reader users to surface lists, tables, and links created from python.

we use the itemscope and itemtype properties as the proper semantic attributes to provide type information. these types extend to css selectors that can tailor specific type representations.

itemscope indicates that representation was generated for known type

    import nbconvert_a11y.tables
    from nbconvert_a11y.outputs import repr_html, repr_semantic
    import pandas
    %reload_ext nbconvert_a11y.tables
    %reload_ext nbconvert_a11y.outputs

comparing the standard dict repr with the semantic repr

    data = {'a': 1, 'b': 'abc', 'd': True, 'e': False}
    data["f"] = data
    print(repr(data))
    data

{'a': 1, 'b': 'abc', 'd': True, 'e': False, 'f': {...}}

a

1

b

abc

d

True

e

False

f

<dict object at 0x7fdcc9302a40>

comparing the standard None repr with the semantic repr

    print(repr(None))
    display(None)

None

None

comparing the standard false repr with the semantic repr

    print(repr(False))
    False

False

False

comparing the standard true repr with the semantic repr

    print(repr(True))
    True

True

True

comparing the standard int repr with the semantic repr

    print(repr(1000))
    1000

1000

1000

comparing the standard float repr with the semantic repr

    print(repr(x := 3.14159))
    x

3.14159

3.14159

comparing the standard list repr with the semantic repr

    print(repr(x:= [1,2,3,]))
    x

[1, 2, 3]

1. 1
2. 2
3. 3

comparing the standard tuple repr with the semantic repr

    print(repr(x:= (1,2,3,)))
    x

(1, 2, 3)

1. 1
2. 2
3. 3

    print(repr(x:= {1,2,3,}))
    x

{1, 2, 3}

• 1
• 2
• 3

comparing the standard dict repr with the semantic repr with recursion

    x = dict(a=1, b="abc", c=True)
    x.update(z=x)
    print(repr(x))
    x

{'a': 1, 'b': 'abc', 'c': True, 'z': {...}}

a

1

b

abc

c

True

z

<dict object at 0x7fdcc9355900>

    import pandas

comparing the standard dataframe repr with the semantic repr

    df = pandas.DataFrame(range(x, x+10) for x in range(10))
    display({"text/html": df._repr_html_()}, raw=True)
    df

	0	1	2	3	4	5	6	7	8	9
0	0	1	2	3	4	5	6	7	8	9
1	1	2	3	4	5	6	7	8	9	10
2	2	3	4	5	6	7	8	9	10	11
3	3	4	5	6	7	8	9	10	11	12
4	4	5	6	7	8	9	10	11	12	13
5	5	6	7	8	9	10	11	12	13	14
6	6	7	8	9	10	11	12	13	14	15
7	7	8	9	10	11	12	13	14	15	16
8	8	9	10	11	12	13	14	15	16	17
9	9	10	11	12	13	14	15	16	17	18

rows

10

columns

10

indexes

rows

1

columns

1

index	0	1	2	3	4	5	6	7	8	9
0	0	1	2	3	4	5	6	7	8	9
1	1	2	3	4	5	6	7	8	9	10
2	2	3	4	5	6	7	8	9	10	11
3	3	4	5	6	7	8	9	10	11	12
4	4	5	6	7	8	9	10	11	12	13
5	5	6	7	8	9	10	11	12	13	14
6	6	7	8	9	10	11	12	13	14	15
7	7	8	9	10	11	12	13	14	15	16
8	8	9	10	11	12	13	14	15	16	17
9	9	10	11	12	13	14	15	16	17	18

comparing the standard ndarray repr with the semantic repr

    print(repr(df.values))
    df.values

array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9],
       [ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10],
       [ 2,  3,  4,  5,  6,  7,  8,  9, 10, 11],
       [ 3,  4,  5,  6,  7,  8,  9, 10, 11, 12],
       [ 4,  5,  6,  7,  8,  9, 10, 11, 12, 13],
       [ 5,  6,  7,  8,  9, 10, 11, 12, 13, 14],
       [ 6,  7,  8,  9, 10, 11, 12, 13, 14, 15],
       [ 7,  8,  9, 10, 11, 12, 13, 14, 15, 16],
       [ 8,  9, 10, 11, 12, 13, 14, 15, 16, 17],
       [ 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]])

rows

10

columns

10

indexes

rows

0

columns

0

0	1	2	3	4	5	6	7	8	9
1	2	3	4	5	6	7	8	9	10
2	3	4	5	6	7	8	9	10	11
3	4	5	6	7	8	9	10	11	12
4	5	6	7	8	9	10	11	12	13
5	6	7	8	9	10	11	12	13	14
6	7	8	9	10	11	12	13	14	15
7	8	9	10	11	12	13	14	15	16
8	9	10	11	12	13	14	15	16	17
9	10	11	12	13	14	15	16	17	18

customize table size through pandas options

    df = pandas.DataFrame(range(x, x+100) for x in range(200))
    with pandas.option_context("display.max_columns", 4, "display.max_rows", 4):
        display(df)

rows

200

columns

100

indexes

rows

1

columns

1

index	0	1	hidden	98	99
0	0	1	hidden	98	99
1	1	2	hidden	99	100
hidden	hidden	hidden	hidden	hidden	hidden
198	198	199	hidden	296	297
199	199	200	hidden	297	298

custom ndarray size with pandas options

    with pandas.option_context("display.max_columns", 4, "display.max_rows", 4):
        display(df.values)

rows

200

columns

100

indexes

rows

0

columns

0

0	1	hidden	98	99
1	2	hidden	99	100
hidden	hidden	hidden	hidden	hidden
198	199	hidden	296	297
199	200	hidden	297	298