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# 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.
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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.
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itemscope indicates that representation was generated for known type
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itemscope indicates that representation was generated for known type
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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
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comparing the standard dict repr with the semantic repr
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comparing the standard dict repr with the semantic repr
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data = { 'a' : 1 , 'b' : 'abc' , 'd' : True , 'e' : False }
data [ "f" ] = data
print ( repr ( data ))
data
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comparing the standard None repr with the semantic repr
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comparing the standard None repr with the semantic repr
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print ( repr ( None ))
display ( None )
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comparing the standard false repr with the semantic repr
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comparing the standard false repr with the semantic repr
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comparing the standard true repr with the semantic repr
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comparing the standard true repr with the semantic repr
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comparing the standard int repr with the semantic repr
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comparing the standard int repr with the semantic repr
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comparing the standard float repr with the semantic repr
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comparing the standard float repr with the semantic repr
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print ( repr ( x := 3.14159 ))
x
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comparing the standard list repr with the semantic repr
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comparing the standard list repr with the semantic repr
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print ( repr ( x := [ 1 , 2 , 3 ,]))
x
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comparing the standard tuple repr with the semantic repr
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comparing the standard tuple repr with the semantic repr
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print ( repr ( x := ( 1 , 2 , 3 ,)))
x
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print ( repr ( x := { 1 , 2 , 3 ,}))
x
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comparing the standard dict repr with the semantic repr with recursion
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comparing the standard dict repr with the semantic repr with recursion
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x = dict ( a = 1 , b = "abc" , c = True )
x . update ( z = x )
print ( repr ( x ))
x
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comparing the standard dataframe repr with the semantic repr
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comparing the standard dataframe repr with the semantic repr
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df = pandas . DataFrame ( range ( x , x + 10 ) for x in range ( 10 ))
display ({ "text/html" : df . _repr_html_ ()}, raw = True )
df
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comparing the standard ndarray repr with the semantic repr
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comparing the standard ndarray repr with the semantic repr
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print ( repr ( df . values ))
df . values
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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]])
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customize table size through pandas options
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customize table size through pandas options
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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 )
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hidden
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custom ndarray size with pandas options
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custom ndarray size with pandas options
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with pandas . option_context ( "display.max_columns" , 4 , "display.max_rows" , 4 ):
display ( df . values )
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