Categorical Column Profile

class dataprofiler.profilers.categorical_column_profile.CategoricalColumn(name, options=None)

Bases: dataprofiler.profilers.base_column_profilers.BaseColumnProfiler

Categorical column profile subclass of BaseColumnProfiler. Represents a column int the dataset which is a categorical column.

Initialization of column base properties and itself.

Parameters

name (String) – Name of data

type = 'category'

diff(other_profile, options=None)

Finds the differences for CategoricalColumns.

Parameters

other_profile (CategoricalColumn) – profile to find the difference with

Returns

the CategoricalColumn differences

Return type

dict

property profile

Property for profile. Returns the profile of the column. For categorical_count, it will display the top k categories most frequently occurred in descending order.

property categories

Property for categories.

property categorical_counts

Property for the counts of each category.

property unique_ratio

Property for unique_ratio. Returns ratio of unique categories to sample_size

property is_match

Property for is_match. Returns true if column is categorical.

update(df_series)

Updates the column profile.

Parameters

df_series (pandas.core.series.Series) – Data to profile.

Returns

None

property gini_impurity

Property for Gini Impurity. Gini Impurity is a way to calculate likelihood of an incorrect classification of a new instance of a random variable.

G = Σ(i=1; J): P(i) * (1 - P(i)), where i is the category classes. We are traversing through categories and calculating with the column

Returns

None or Gini Impurity probability

col_type = None

property unalikeability

Property for Unlikeability. Unikeability checks for “how often observations differ from one another” Reference: Perry, M. and Kader, G. Variation as Unalikeability. Teaching Statistics, Vol. 27, No. 2 (2005), pp. 58-60.

U = Σ(i=1,n)Σ(j=1,n): (Cij)/(n**2-n) Cij = 1 if i!=j, 0 if i=j

Returns

None or unlikeability probability