VertexFrame categorical_summary

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categorical_summary(self, *column_inputs)

[ALPHA] Compute a summary of the data in a column(s) for categorical or numerical data types.

Parameters:

*column_inputs : str | tuple(str, dict)

Comma-separated column names to summarize or tuple containing column name and dictionary of optional parameters. Optional parameters (see below for details): top_k (default = 10), threshold (default = 0.0)

Returns:

: dict

Summary for specified column(s) consisting of levels with their frequency and percentage

The returned value is a Map containing categorical summary for each specified column.

For each column, levels which satisfy the top k and/or threshold cutoffs are displayed along with their frequency and percentage occurrence with respect to the total rows in the dataset.

Missing data is reported when a column value is empty (“”) or null.

All remaining data is grouped together in the Other category and its frequency and percentage are reported as well.

User must specify the column name and can optionally specify top_k and/or threshold.

Optional parameters:

top_k

Displays levels which are in the top k most frequently occurring values for that column.

threshold

Displays levels which are above the threshold percentage with respect to the total row count.

top_k and threshold

Performs level pruning first based on top k and then filters out levels which satisfy the threshold criterion.

defaults

Displays all levels which are in Top 10.

Examples

>>> frame.categorical_summary('source','target')
>>> frame.categorical_summary(('source', {'top_k' : 2}))
>>> frame.categorical_summary(('source', {'threshold' : 0.5}))
>>> frame.categorical_summary(('source', {'top_k' : 2}), ('target',
... {'threshold' : 0.5}))

Sample output (for last example above):

>>> {u'categorical_summary': [{u'column': u'source', u'levels': [
... {u'percentage': 0.32142857142857145, u'frequency': 9, u'level': u'thing'},
... {u'percentage': 0.32142857142857145, u'frequency': 9, u'level': u'abstraction'},
... {u'percentage': 0.25, u'frequency': 7, u'level': u'physical_entity'},
... {u'percentage': 0.10714285714285714, u'frequency': 3, u'level': u'entity'},
... {u'percentage': 0.0, u'frequency': 0, u'level': u'Missing'},
... {u'percentage': 0.0, u'frequency': 0, u'level': u'Other'}]},
... {u'column': u'target', u'levels': [
... {u'percentage': 0.07142857142857142, u'frequency': 2, u'level': u'thing'},
... {u'percentage': 0.07142857142857142, u'frequency': 2,
...  u'level': u'physical_entity'},
... {u'percentage': 0.07142857142857142, u'frequency': 2, u'level': u'entity'},
... {u'percentage': 0.03571428571428571, u'frequency': 1, u'level': u'variable'},
... {u'percentage': 0.03571428571428571, u'frequency': 1, u'level': u'unit'},
... {u'percentage': 0.03571428571428571, u'frequency': 1, u'level': u'substance'},
... {u'percentage': 0.03571428571428571, u'frequency': 1, u'level': u'subject'},
... {u'percentage': 0.03571428571428571, u'frequency': 1, u'level': u'set'},
... {u'percentage': 0.03571428571428571, u'frequency': 1, u'level': u'reservoir'},
... {u'percentage': 0.03571428571428571, u'frequency': 1, u'level': u'relation'},
... {u'percentage': 0.0, u'frequency': 0, u'level': u'Missing'},
... {u'percentage': 0.5357142857142857, u'frequency': 15, u'level': u'Other'}]}]}