toucan_data_sdk/fakir/fake_data_generator.py
def fake_data_generator(conf: List[dict]) -> pd.DataFrame:
    """
    `conf` is a list of dictionaries like
      {'type': 'label', 'values': ['Paris', 'Marseille', 'Lyons'], 'name': 'Cities'}
    and each dictionary will add a column.

    There are two different behaviours:
    - type: 'label' -> the new column will be taken into account for a cartesian product
                       with all other labels
    - type: 'number' -> the new column will contain simple numbers
    """

    # First create all the lines with the cartesian product of all the
    # possible values of 'label' columns
    label_confs = [x for x in conf if x['type'] == 'label']
    label_names = [x['name'] for x in label_confs]
    label_values = [x['values'] for x in label_confs]
    df = pd.DataFrame(list(product(*label_values)), columns=label_names)

    # Then add all the 'number' columns
    number_confs = [x for x in conf if x['type'] == 'number']
    for num_conf in number_confs:
        num_column = np.random.uniform(low=num_conf['min'], high=num_conf['max'], size=df.shape[0])
        df[num_conf['name']] = num_column.round(num_conf.get('digits', 4))

    return df
def fake_data_generator(conf: List[dict]) -> pd.DataFrame:
    """
    `conf` is a list of dictionaries like
      {'type': 'label', 'values': ['Paris', 'Marseille', 'Lyons'], 'name': 'Cities'}
    and each dictionary will add a column.

    There are two different behaviours:
    - type: 'label' -> the new column will be taken into account for a cartesian product
                       with all other labels
    - type: 'number' -> the new column will contain simple numbers
    """

    # First create all the lines with the cartesian product of all the
    # possible values of 'label' columns
    label_confs = [x for x in conf if x['type'] == 'label']
    label_names = [x['name'] for x in label_confs]
    label_values = [x['values'] for x in label_confs]
    df = pd.DataFrame(list(product(*label_values)), columns=label_names)

    # Then add all the 'number' columns
    number_confs = [x for x in conf if x['type'] == 'number']
    for num_conf in number_confs:
        num_column = np.random.uniform(low=num_conf['min'], high=num_conf['max'], size=df.shape[0])
        df[num_conf['name']] = num_column.round(num_conf.get('digits', 4))

    return df
def fake_data_generator ( conf : List [ dict ] ) -> pd . DataFrame : # First create all the lines with the cartesian product of all the # possible values of 'label' columns label_confs = [ x for x in conf if x [ 'type' ] == 'label' ] label_names = [ x [ 'name' ] for x in label_confs ] label_values = [ x [ 'values' ] for x in label_confs ] df = pd . DataFrame ( list ( product ( * label_values ) ) , columns = label_names ) # Then add all the 'number' columns number_confs = [ x for x in conf if x [ 'type' ] == 'number' ] for num_conf in number_confs : num_column = np . random . uniform ( low = num_conf [ 'min' ] , high = num_conf [ 'max' ] , size = df . shape [ 0 ] ) df [ num_conf [ 'name' ] ] = num_column . round ( num_conf . get ( 'digits' , 4 ) ) return df
`conf` is a list of dictionaries like
      {'type': 'label', 'values': ['Paris', 'Marseille', 'Lyons'], 'name': 'Cities'}
    and each dictionary will add a column.

    There are two different behaviours:
    - type: 'label' -> the new column will be taken into account for a cartesian product
                       with all other labels
    - type: 'number' -> the new column will contain simple numbers
conf is a list of dictionaries like { type : label values : [ Paris Marseille Lyons ] name : Cities } and each dictionary will add a column .