Features

class nimble.core.data.Features

Methods that apply to the features axis of a Base object.

This object can be used to iterate over the features and contains methods that operate over the data in the associated Base object feature-by-feature.

A feature is an abstract slice of all data elements of the same kind across different contexts. In a concrete sense, features can be thought of as the data columns but a column can be organized in many ways. To optimize for machine learning, each column should be modified to meet the definition of a feature.

Methods

append(toAppend, *[, useLog])

Append features to this object.

calculate(function[, features, useLog])

Apply a calculation to each feature.

copy([toCopy, start, end, number, ...])

Copy certain features of this object.

count(condition[, points])

The number of features which satisfy the condition.

delete([toDelete, start, end, number, ...])

Remove certain features from this object.

extract([toExtract, start, end, number, ...])

Move certain features of this object into their own object.

fillMatching(fillWith, matchingElements[, ...])

Replace given values in each feature with other values.

getIndex(identifier)

The index of a feature.

getIndices(names)

The indices of a list of feature names.

getName(index)

The name of the feature at the provided index.

getNames()

The feature names ordered by index.

hasName(name)

Determine if feature name exists.

insert(insertBefore, toInsert, *[, useLog])

Insert more features into this object.

matching(function, *[, useLog])

Identifying features matching the given criteria.

max([groupByFeature])

Returns a nimble object representing the maximum value along the features axis.

mean([groupByFeature])

Returns a nimble object representing the mean value along the features axis.

median([groupByFeature])

Returns a nimble object representing the median value along the features axis.

medianAbsoluteDeviation([groupByFeature])

Returns a nimble object representing the median absolute deviation along the features axis.

min([groupByFeature])

Returns a nimble object representing the minimum value along the features axis.

mode([grouByFeature])

Returns a nimble object representing the mode along the features axis.

normalize(function[, applyResultTo, ...])

Modify all features in this object using the given function.

permute([order, useLog])

Permute the indexing of the features.

plot([features, horizontal, outPath, show, ...])

Bar chart comparing features.

plotMeans([features, horizontal, outPath, ...])

Plot feature means with 95% confidence interval bars.

plotStatistics(statistic[, features, ...])

Bar chart comparing an aggregate statistic between features.

populationStandardDeviation([groupByFeature])

Returns a nimble object representing the population standard deviation along the features axis.

proportionMissing([groupByFeature])

Returns a nimble object representing the proportion of values that are None or NaN along the features axis.

proportionZero([groupByFeature])

Returns a nimble object representing the proportion of values that are equal to zero along the features axis.

quartiles([groupByFeature])

Returns a nimble object representing the quartiles along the features axis.

repeat(totalCopies, copyFeatureByFeature, *)

Create an object using copies of this object's features.

replace(data[, features, useLog])

Replace the data in one or more of the features in this object.

report([basicStatistics, ...])

Report containing a summary and statistics for each feature.

retain([toRetain, start, end, number, ...])

Keep only certain features of this object.

setNames(assignments[, oldIdentifiers, useLog])

Set or rename one or more feature names of this object.

similarities(similarityFunction)

Calculate similarities between features.

sort([by, reverse, useLog])

Arrange the features in this object.

splitByParsing(feature, rule, resultingNames, *)

Split a feature into multiple features.

standardDeviation([groupByFeature])

Returns a nimble object representing the standard deviation along the features axis.

statistics(statisticsFunction[, groupByFeature])

Calculate feature statistics.

sum([groupByFeature])

Returns a nimble object representing the sum along the features axis.

transform(function[, features, useLog])

Modify this object by applying a function to each feature.

unique()

Only the unique features from this object.

uniqueCount([groupByFeature])

Returns a nimble object representing the number of unique values along the features axis.

variance([groupByFeature])

Returns a nimble object representing the variance along the features axis.