Source code for mars.dataframe.base.qcut

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# Licensed under the Apache License, Version 2.0 (the "License");
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import numpy as np
import pandas as pd
from pandas.api.types import is_integer

from ...core import ENTITY_TYPE
from ...tensor import tensor as astensor
from ...tensor.statistics.percentile import percentile
from ..core import DATAFRAME_TYPE, SERIES_TYPE
from ..initializer import DataFrame, Series
from .cut import cut

[docs]def qcut(x, q, labels=None, retbins=False, precision=3, duplicate='raise'): """ Quantile-based discretization function. Discretize variable into equal-sized buckets based on rank or based on sample quantiles. For example 1000 values for 10 quantiles would produce a Categorical object indicating quantile membership for each data point. Parameters ---------- x : 1d tensor or Series q : int or list-like of float Number of quantiles. 10 for deciles, 4 for quartiles, etc. Alternately array of quantiles, e.g. [0, .25, .5, .75, 1.] for quartiles. labels : array or False, default None Used as labels for the resulting bins. Must be of the same length as the resulting bins. If False, return only integer indicators of the bins. If True, raises an error. retbins : bool, optional Whether to return the (bins, labels) or not. Can be useful if bins is given as a scalar. precision : int, optional The precision at which to store and display the bins labels. duplicates : {default 'raise', 'drop'}, optional If bin edges are not unique, raise ValueError or drop non-uniques. Returns ------- out : Categorical or Series or tensor of integers if labels is False The return type (Categorical or Series) depends on the input: a Series of type category if input is a Series else Categorical. Bins are represented as categories when categorical data is returned. bins : tensor of floats Returned only if `retbins` is True. Notes ----- Out of bounds values will be NA in the resulting Categorical object Examples -------- >>> import mars.dataframe as md >>> md.qcut(range(5), 4).execute() ... # doctest: +ELLIPSIS [(-0.001, 1.0], (-0.001, 1.0], (1.0, 2.0], (2.0, 3.0], (3.0, 4.0]] Categories (4, interval[float64]): [(-0.001, 1.0] < (1.0, 2.0] ... >>> md.qcut(range(5), 3, labels=["good", "medium", "bad"]).execute() ... # doctest: +SKIP [good, good, medium, bad, bad] Categories (3, object): [good < medium < bad] >>> md.qcut(range(5), 4, labels=False).execute() array([0, 0, 1, 2, 3]) """ if is_integer(q): q = np.linspace(0, 1, q + 1) if isinstance(x, (DATAFRAME_TYPE, SERIES_TYPE, pd.DataFrame, pd.Series)): x = DataFrame(x) if x.ndim == 2 else Series(x) bins = x.quantile(q) else: x = astensor(x) if isinstance(q, ENTITY_TYPE): q = q * 100 else: q = [iq * 100 for iq in q] bins = percentile(x, q) return cut(x, bins, labels=labels, retbins=retbins, precision=precision, include_lowest=True, duplicates=duplicate)