VertexFrame top_k¶
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top_k
(self, column_name, k, weights_column=None)¶ Most or least frequent column values.
Parameters: column_name : unicode
The column whose top (or bottom) K distinct values are to be calculated.
k : int32
Number of entries to return (If k is negative, return bottom k).
weights_column : unicode (default=None)
The column that provides weights (frequencies) for the topK calculation. Must contain numerical data. Default is 1 for all items.
Returns: : Frame
An object with access to the frame of data.
Calculate the top (or bottom) K distinct values by count of a column. The column can be weighted. All data elements of weight <= 0 are excluded from the calculation, as are all data elements whose weight is NaN or infinite. If there are no data elements of finite weight > 0, then topK is empty.
Examples
For this example, we calculate the top 5 movie genres in a data frame: Consider the following frame containing four columns.
>>> frame.inspect() [#] rank city population_2013 population_2010 change county ============================================================================ [0] 1 Portland 609456 583776 4.40% Multnomah [1] 2 Salem 160614 154637 3.87% Marion [2] 3 Eugene 159190 156185 1.92% Lane [3] 4 Gresham 109397 105594 3.60% Multnomah [4] 5 Hillsboro 97368 91611 6.28% Washington [5] 6 Beaverton 93542 89803 4.16% Washington [6] 15 Grants Pass 35076 34533 1.57% Josephine [7] 16 Oregon City 34622 31859 8.67% Clackamas [8] 17 McMinnville 33131 32187 2.93% Yamhill [9] 18 Redmond 27427 26215 4.62% Deschutes >>> top_frame = frame.top_k("county", 2) [===Job Progress===] >>> top_frame.inspect() [#] county count ====================== [0] Washington 4.0 [1] Clackamas 3.0