Pandas: Data Manipulation - qcut() function

Last update on August 19 2022 21:50:52 (UTC/GMT +8 hours)

qcut() function

Bin values into discrete intervals.

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.

Syntax:

pandas.qcut(x, q, labels=None, retbins=False, precision=3, duplicates='raise')

Parameters:

Name	Description	Type	Required / Optional
x		1d ndarray or Series	Required
q	Number of quantiles. 10 for deciles, 4 for quartiles, etc. Alternately array of quantiles, e.g. [0, .25, .5, .75, 1.] for quartiles	integer or array of quantiles	Required
labels	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.	array or boolean ,default None	Required
retbins	Whether to return the bins or not. Can be useful if bins is given as a scalar.	bool	Optional
precision	The precision at which to store and display the bins labels.	int	Optional
duplicates	If bin edges are not unique, raise ValueError or drop non-uniques.	{default ‘raise’, ‘drop’},	optional

Returns: out : Categorical, Series, or array 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 : ndarray of floats
Returned only if retbins is True.

Notes: Out of bounds values will be NA in the resulting Categorical object.

Example:

Download the Pandas DataFrame Notebooks from here.

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