4.1. Data Structure

4.1.1. TableData

class tabledata.TableData(table_name: Optional[str], headers: Sequence[str], rows: Sequence[T_co], dp_extractor: Optional[dataproperty._extractor.DataPropertyExtractor] = None, type_hints: Optional[Sequence[Union[str, Type[typepy.type._base.AbstractType], None]]] = None, max_workers: Optional[int] = None, max_precision: Optional[int] = None)

Class to represent a table data structure.

Parameters:

• table_name – Name of the table.
• headers – Table header names.
• rows – Data of the table.

as_dataframe() → pandas.DataFrame

Returns:

Table data as a pandas.DataFrame instance.

Return type:

pandas.DataFrame

Sample Code:

from tabledata import TableData

TableData(
    "sample",
    ["a", "b"],
    [[1, 2], [3.3, 4.4]]
).as_dataframe()

Output:

     a    b
0    1    2
1  3.3  4.4

Dependency Packages:  

• pandas

as_dict(default_key: str = 'table') → Dict[str, List[OrderedDict[str, Any]]]

Parameters:default_key – Key of a returning dictionary when the table_name is empty. Returns:Table data as a dict instance. Return type:dict

Sample Code:

from tabledata import TableData

TableData(
    "sample",
    ["a", "b"],
    [[1, 2], [3.3, 4.4]]
).as_dict()

Output:

{'sample': [OrderedDict([('a', 1), ('b', 2)]), OrderedDict([('a', 3.3), ('b', 4.4)])]}

as_tuple() → Iterator[Tuple]

Returns:

Rows of the tuple.

Return type:

list of namedtuple

Sample Code:

from tabledata import TableData

records = TableData(
    "sample",
    ["a", "b"],
    [[1, 2], [3.3, 4.4]]
).as_tuple()
for record in records:
    print(record)

Output:

Row(a=1, b=2)
Row(a=Decimal('3.3'), b=Decimal('4.4'))

column_dp_list

dp_extractor

equals(other: tabledata._core.TableData, cmp_by_dp: bool = True) → bool

filter_column(patterns: Optional[str] = None, is_invert_match: bool = False, is_re_match: bool = False, pattern_match: tabledata._constant.PatternMatch = <PatternMatch.OR: 0>) → tabledata._core.TableData

static from_dataframe(dataframe: pandas.DataFrame, table_name: str = '', type_hints: Optional[Sequence[Optional[Type[typepy.type._base.AbstractType]]]] = None, max_workers: Optional[int] = None) → TableData

Initialize TableData instance from a pandas.DataFrame instance.

Parameters:

• dataframe (pandas.DataFrame) –
• table_name (str) – Table name to create.

has_value_dp_matrix

header_dp_list

headers

Table header names.

Type:Sequence[str]

in_tabledata_list(other: Sequence[TableData], cmp_by_dp: bool = True) → bool

is_empty() → bool

Returns:True if the data headers or value_matrix is empty. Return type:bool

is_empty_header() → bool

bool: True if the data headers is empty.

is_empty_rows() → bool

Returns:True if the tabular data has no rows. Return type:bool

max_workers

num_columns

num_rows

Number of rows in the tabular data. None if the rows is neither list nor tuple.

Type:Optional[int]

rows

Original rows of tabular data.

Type:Sequence

table_name

Name of the table.

Type:str

transpose() → tabledata._core.TableData

validate_rows() → None

Raises:ValueError –

value_dp_matrix

DataProperty for table data.

Type:DataPropertyMatrix

value_matrix

Converted rows of tabular data.

Type:DataPropertyMatrix