actableai.data_imputation.data package

Submodules

actableai.data_imputation.data.data_frame module

class actableai.data_imputation.data.data_frame.DataFrame(d: Union[str, pandas.core.frame.DataFrame])

Bases: pandas.core.frame.DataFrame

auto_fix(errors: Optional[actableai.data_imputation.error_detector.cell_erros.CellErrors] = None, *detectors: actableai.data_imputation.error_detector.base_error_detector.BaseErrorDetector) → actableai.data_imputation.data.data_frame.DataFrame

property column_types: actableai.data_imputation.type_recon.type_detector.DfTypes

detect_error(*detectors: actableai.data_imputation.error_detector.base_error_detector.BaseErrorDetector) → actableai.data_imputation.error_detector.cell_erros.CellErrors

enable_debug(enable: bool = True)

property fix_info

property fix_strategy

classmethod from_dict(data, orient='columns', dtype=None, columns=None) → actableai.data_imputation.data.data_frame.DataFrame

Construct DataFrame from dict of array-like or dicts.

Creates DataFrame object from dictionary by columns or by index allowing dtype specification.

Parameters

• data (dict) – Of the form {field : array-like} or {field : dict}.
• orient ({'columns', 'index'}, default 'columns') – The “orientation” of the data. If the keys of the passed dict should be the columns of the resulting DataFrame, pass ‘columns’ (default). Otherwise if the keys should be rows, pass ‘index’.
• dtype (dtype, default None) – Data type to force, otherwise infer.
• columns (list, default None) – Column labels to use when orient='index'. Raises a ValueError if used with orient='columns'.

Return type

DataFrame

See also

DataFrame.from_records

DataFrame from structured ndarray, sequence of tuples or dicts, or DataFrame.

DataFrame

DataFrame object creation using constructor.

Examples

By default the keys of the dict become the DataFrame columns:

>>> data = {'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']}
>>> pd.DataFrame.from_dict(data)
   col_1 col_2
0      3     a
1      2     b
2      1     c
3      0     d

Specify orient='index' to create the DataFrame using dictionary keys as rows:

>>> data = {'row_1': [3, 2, 1, 0], 'row_2': ['a', 'b', 'c', 'd']}
>>> pd.DataFrame.from_dict(data, orient='index')
       0  1  2  3
row_1  3  2  1  0
row_2  a  b  c  d

When using the ‘index’ orientation, the column names can be specified manually:

>>> pd.DataFrame.from_dict(data, orient='index',
...                        columns=['A', 'B', 'C', 'D'])
       A  B  C  D
row_1  3  2  1  0
row_2  a  b  c  d

override_column_type(column: str, column_type: actableai.data_imputation.meta.types.ColumnType)

property possible_column_types: Dict[str, Set[actableai.data_imputation.meta.types.ColumnType]]

actableai.data_imputation.data.loader module

actableai.data_imputation.data.loader.load_data(d: Union[str, pandas.core.frame.DataFrame]) → pandas.core.frame.DataFrame

Module contents

class actableai.data_imputation.data.DataFrame(d: Union[str, pandas.core.frame.DataFrame])

Bases: pandas.core.frame.DataFrame

auto_fix(errors: Optional[actableai.data_imputation.error_detector.cell_erros.CellErrors] = None, *detectors: actableai.data_imputation.error_detector.base_error_detector.BaseErrorDetector) → actableai.data_imputation.data.data_frame.DataFrame

property column_types: actableai.data_imputation.type_recon.type_detector.DfTypes

detect_error(*detectors: actableai.data_imputation.error_detector.base_error_detector.BaseErrorDetector) → actableai.data_imputation.error_detector.cell_erros.CellErrors

enable_debug(enable: bool = True)

property fix_info

property fix_strategy

classmethod from_dict(data, orient='columns', dtype=None, columns=None) → actableai.data_imputation.data.data_frame.DataFrame

Construct DataFrame from dict of array-like or dicts.

Creates DataFrame object from dictionary by columns or by index allowing dtype specification.

Parameters

• data (dict) – Of the form {field : array-like} or {field : dict}.
• orient ({'columns', 'index'}, default 'columns') – The “orientation” of the data. If the keys of the passed dict should be the columns of the resulting DataFrame, pass ‘columns’ (default). Otherwise if the keys should be rows, pass ‘index’.
• dtype (dtype, default None) – Data type to force, otherwise infer.
• columns (list, default None) – Column labels to use when orient='index'. Raises a ValueError if used with orient='columns'.

Return type

DataFrame

See also

DataFrame.from_records

DataFrame from structured ndarray, sequence of tuples or dicts, or DataFrame.

DataFrame

DataFrame object creation using constructor.

Examples

By default the keys of the dict become the DataFrame columns:

>>> data = {'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']}
>>> pd.DataFrame.from_dict(data)
   col_1 col_2
0      3     a
1      2     b
2      1     c
3      0     d

Specify orient='index' to create the DataFrame using dictionary keys as rows:

>>> data = {'row_1': [3, 2, 1, 0], 'row_2': ['a', 'b', 'c', 'd']}
>>> pd.DataFrame.from_dict(data, orient='index')
       0  1  2  3
row_1  3  2  1  0
row_2  a  b  c  d

When using the ‘index’ orientation, the column names can be specified manually:

>>> pd.DataFrame.from_dict(data, orient='index',
...                        columns=['A', 'B', 'C', 'D'])
       A  B  C  D
row_1  3  2  1  0
row_2  a  b  c  d

override_column_type(column: str, column_type: actableai.data_imputation.meta.types.ColumnType)

property possible_column_types: Dict[str, Set[actableai.data_imputation.meta.types.ColumnType]]