AbstractNumeric

class bach.SeriesAbstractNumeric

(engine, base_node, index, name, expression, group_by, instance_dtype, order_by=None, **kwargs)

​[source]

Abstract Series class that represents the base numeric types and its specific operations.

Operations

All common arithmetic operations are supported, as well as common aggregation functions, and statistical functions.

Supported arithmetic operations:

• add (+), subtract (-)
• multiply (*), divide (/), floordiv (//), mod (%)
• lshift (<<) and rshift(>>) for Integer types

Methods

SeriesAbstractNumeric.cut(bins[, right])	Segments values into bins.
SeriesAbstractNumeric.mean([partition, skipna])	Get the mean/average of the input values.
SeriesAbstractNumeric.minmax_scale([...])	Scales series based on a given range.
SeriesAbstractNumeric.qcut(q)	Segments values into equal-sized buckets based on rank or sample quantiles.
SeriesAbstractNumeric.quantile([partition, q])	When q is a float or len(q) == 1, the resultant series index will remain In case multiple quantiles are calculated, the resultant series index will have all calculated quantiles as index values.
SeriesAbstractNumeric.round([decimals])	Round the value of this series to the given amount of decimals.
SeriesAbstractNumeric.scale([with_mean, ...])	Standardizes series based on mean and population standard deviation.
SeriesAbstractNumeric.sem([partition, ...])	Get the unbiased standard error of the mean.
SeriesAbstractNumeric.std([partition, ...])	Get the standard deviation of the input values Normalized by N-1 by default.
SeriesAbstractNumeric.sum([partition, ...])	Get the sum of the input values.
SeriesAbstractNumeric.var([partition, ...])	Get the sample variance of the input values (square of the sample standard deviation) Normalized by N-1 by default.