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#!/usr/bin/env python3
# © 2018; -*- encoding: utf-8 -*-
# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at
import datatable as dt
from datatable.lib import core
def aggregate(dt_in, min_rows=500, n_bins=500, nx_bins=50, ny_bins=50,
nd_max_bins=500, max_dimensions=50, seed=0, progress_fn=None,
Aggregate datatable in-place.
dt_in: datatable
Frame to be aggregated in-place.
min_rows: int
Minimum number of rows in a dataset to perform an aggregation on.
n_bins: int
Number of bins for 1D aggregation.
nx_bins: int
Number of x bins for 2D aggregation.
ny_bins: int
Number of y bins for 2D aggregation.
nd_max_bins: int
Maximum number of exemplars for ND aggregation, not a hard limit.
max_dimensions: int
Number of columns at which start using the projection method.
seed: int
Seed to be used for the projection method.
progress_fn: object
Python function for progress reporting accepting two arguments:
- `progress`, that is a value from 0 to 1;
- `status_code`, 0 – in progress, 1 – completed.
nthreads: int
Number of OpenMP threads ND aggregator will use. Default is 0,
i.e. automatically figure out the optimal number.
The target datatable is aggregated in-place and gets an additional
column `count` with the number of members for a particular exemplar.
The function returns a new one-column datatable that contains exemplar_ids
for each of the original rows.
if progress_fn is not None and not callable(progress_fn):
raise dt.TypeError("`progress_fn` argument should be a function")
dt_members = core.aggregate(dt_in, min_rows, n_bins, nx_bins, ny_bins,
nd_max_bins, max_dimensions, seed, progress_fn,
return dt_members