modin.pandas.qcut¶

snowflake.snowpark.modin.pandas.general.qcut(x: np.ndarray | Series, q: int | ListLikeOfFloats, labels: ListLike | bool | None = None, retbins: bool = False, precision: int = 3, duplicates: Literal['raise'] | Literal['drop'] = 'raise') → Series[source]¶

Quantile-based discretization function.

Discretize variable into equal-sized buckets based on rank or based on sample quantiles.

Parameters:

• x (1-D ndarray or Series) – The data across which to compute buckets. If a Snowpark pandas Series is passed, the computation is distributed. Otherwise, if a numpy array or list is provided, the computation is performed client-side instead.

• 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 bin. Must be of the same length as the resulting bins. If False, return only integer indicators of the bins. If True, raise an error.

  labels=False will run binning computation in Snowflake; other values are not yet supported in Snowpark pandas.

• retbins (bool, default False) – Whether to return the (bins, labels) or not. Can be useful if bins is given as a scalar. retbins=True is not yet supported in Snowpark pandas.

• 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:

Since Snowpark pandas does not yet support the pd.Categorical type, unlike native pandas, the return value is always a Series.

Return type:

Series