Categorical Column Profile¶

Contains class for categorical column profiler.

class dataprofiler.profilers.categorical_column_profile.CategoricalColumn(name: str | None, options: CategoricalOptions = None)¶

Bases: dataprofiler.profilers.base_column_profilers.BaseColumnProfiler[CategoricalColumn]

Categorical column profile subclass of BaseColumnProfiler.

Represents a column int the dataset which is a categorical column.

Initialize column base properties and itself.

Parameters: name (String) – Name of data

type = 'category'¶

diff(other_profile: dataprofiler.profilers.categorical_column_profile.CategoricalColumn, options: Optional[dict] = None) → dict¶

Find the differences for CategoricalColumns.

Parameters: other_profile (CategoricalColumn) – profile to find the difference with
Returns: the CategoricalColumn differences
Return type: dict

report(remove_disabled_flag: bool = False) → dict¶

Return report.

This is a private abstract method.

Parameters: remove_disabled_flag (boolean) – flag to determine if disabled options should be excluded in the report.

property profile: dict¶

Return the profile of the column.

For categorical_count, it will display the top k categories most frequently occurred in descending order.

property categories: list[str]¶: Return categories.

property categorical_counts: dict[str, int]¶: Return counts of each category.

property unique_ratio: float¶: Return ratio of unique categories to sample_size.

property unique_count: int¶: Return ratio of unique categories to sample_size.

property is_match: bool¶: Return true if column is categorical.

col_type = None¶

update(df_series: pandas.core.series.Series) → dataprofiler.profilers.categorical_column_profile.CategoricalColumn¶

Update the column profile.

Parameters: df_series (pandas.core.series.Series) – Data to profile.
Returns: updated CategoricalColumn
Return type: CategoricalColumn

name: str | None¶

sample_size: int¶

metadata: dict¶

times: dict¶

thread_safe: bool¶

property gini_impurity: float | None¶

Return 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

property unalikeability: float | None¶

Return 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