AIO Connect

List of Functions

one_function(df, primary_dimension, …[, …])

Multi-Dimensional ABC Analysis provides ABC classification for a multi-dimensional, granular input.

Definition of Functions

aioconnect.one_function(df, primary_dimension, numeric_dimension, secondary_dimensions=None, A=0.8, B=0.95, classified_only=False)

Multi-Dimensional ABC Analysis provides ABC classification for a multi-dimensional, granular input.

Parameters

dfPandas.DataFrame

DataFrame holding the object to be classified, if applicable additional secondary_dimensions, and numeric values used for classification, e.g.

df.columns = [“product”, “country”, “quantity”].

primary_dimensionstring

Column name in input DataFrame holding object to be classified, e.g. product.

secondary_dimensionlist of strings = None

List of columns names in input DataFrame holding additional attributes of primary_dimension to structure classification on a more granular level, e.g. country, region, city

numeric_dimensionstring

Column name in input DataFrame holding numeric values to be used for classification.

A, Bfloat = 0.8, 0.95

Threshold for classification.

classified_onlybool = False

Provides DataFrame with columns primary_dimension, secondary_dimension, numeric_dimension and class in originally provided naming.

Returns

df_groupedPandas.DataFrame

input DataFrame grouped by provided primary- & secondary dimensions with respective classification and cumulative values.

Examples

>>> import aio
>>> # create sample data
>>> products, quantities = {}, {}
>>> np.random.seed(seed=0)
>>> for i in range(1000):
>>>     products[i] = "{:04d}".format(np.random.randint(15))
>>>     quantities[i] = np.random.randint(1000)
>>> # prepare sample data DataFrame
>>> df = pd.DataFrame()
>>> df["Product"] = products.values()
>>> df["Quantity"] = quantities.values()
>>>
>>> results = aio.abc_analysis(
>>>     df, primary_dimension="Product", numeric_dimension="Quantity"
>>> )

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