pyradigms package

Module contents

This is the main pyradigms module

class pyradigms.Pyradigm(entries: DataFrame | None = None, x: List[str] = _Nothing.NOTHING, y: List[str] = _Nothing.NOTHING, z: List[str] = _Nothing.NOTHING, sort_orders: Dict = _Nothing.NOTHING, filters: Dict = _Nothing.NOTHING, ignore: List[str] = _Nothing.NOTHING, print_columns: List[str] = ['Form'], print_sep: str = ' ')

Bases: object

Pyradigm instances hold the data from which paradigms are created, as well as parameters and methods used to generate them.

category_joiner = ' / '

The string(s) used to concatenate labels of categories combined on an axis.

compose_paradigm(csv_output=None, **kwargs)

The central function of pyradigms, creating paradigms. In addition to the list below, you can pass any argument described for the Pyradigm class.

Parameters:

csv_output (str) – CSV file to save paradigm to.

Returns:

If one paradigm is generated, a pandas DataFrame. If multiple paradigms are generated, a dict of DataFrames.

decompose_paradigm(paradigm, z_value=None, **kwargs)

Decompose a paradigm by specifying the parameters shown on the x and y axes.

Parameters:

• paradigm (DataFrame) – The paradigm as a pandas DataFrame. Note: the y axis labels of the must be the index, not the first column. To achieve that, use pd.read_csv("file.csv", dtype=str, index_col=0) or df.set_index()

• x (list) – The parameters shown on the x axis (columns)

• y (list) – The parameters shown on the y axis (index)

• separators (list) – Strings by which x and y labels (combined categories) will be split.

• print_column (str) – Name of the column where paradigm cells will be stored.

• z_value (str) – if a z parameter is specified for the paradigm to be decomposed, you can assign a value manually.

Returns: a pandas DataFrame object

entries: DataFrame

The data in a wide format pandas DataFrame

filters: Dict

Pass parameter names as keys, value lists to be filtered as values. Example: {"Number": ["SG", "DU"]}

classmethod from_csv(path, data_format='wide', **kwargs)

Create a new Pyradigm object from a CSV file.

Parameters:

• path (str) – path to the CSV file to be read

• data_format (str) –

  • "wide" (default): Parameters in columns, entries in rows.

  • "long": Columns: ID, Parameter, Value

  • "paradigm": Decompose a paradigm by specifying at least x and y (kwargs are passed to Pyradigm.decompose_paradigm())

Returns:

a Pyradigm object

classmethod from_dataframe(df: DataFrame, data_format='wide', **kwargs)

Create a new Pyradigm from a pandas dataframe.

Parameters:

df (DataFrame) – a pandas dataframe containing the data in wide format

Returns:

a Pyradigm object

classmethod from_dict(records)

Create a new Pyradigm object from a list of dicts (records)

Parameters:

records (list) – a list containing dicts representing the data in wide format

Returns:

a Pyradigm object

classmethod from_text(text, x_sep=',', y_sep='\n')

Create a new Pyradigm object from a string.

Parameters:

• text (str) – a string containing the data in wide format

• x_sep (str) – how columns are separated

• y_sep (str) – how rows are separated

Returns:

a Pyradigm object

ignore: List[str]

Parameters which will be ignored completely.

output_folder = None

The folder into which generated paradigms will be written.

print_columns: List[str]

The column in wide format which holds the values in the cells (usually forms, IDs, values…)

print_sep: str

The string used to combine columns to be printed in the cells

separators = ['.']

The first item is used to concatenate labels of values combined in a column or row label. Subsequent items are only used when decomposing paradigms.

sort_orders: Dict

Pass parameter names as keys, ordered value lists as values. Example: {"Number": ["SG", "DU", "PL"]}

to_long()

Arrange the entries in long format. If there is no column ID, one will be created.

Returns: a pandas DataFrame object

to_markdown(data_format='paradigm', pyd_kwargs=None, **kwargs)

Print a markdown representation of the data. **kwargs are passed to pandas’ to_markdown and to tabulate, so you can use tablefmt.

Parameters:

• data_format (str) –

  • "paradigm" (default): The data rendered as a paradigm.

  • "wide": Parameters in columns, entries in rows.

  • "long": Columns: ID, Parameter, Value

• pyd_kwargs (dict) – Any parameters to be passed to Pyradigm.compose_paradigm() if data_format=="paradigm"

Returns:

A markdown string.

update(**kwargs)

with_multi_index = False

If False, the value labels for categories sharing an axis will be joined in single cells. If True, a pandas MultiIndex will be used.

x: List[str]

The parameters to be represented on the x axis

y: List[str]

The parameters to be represented on the y axis

z: List[str]

The parameters to be represented on the z axis