list(itertools.accumulate(my_list, operator.add))

Built-in Data Structures

Meaning

It computes the running total (or other binary accumulation) of an iterable by applying a binary function cumulatively. This eliminates the need for explicit loops to maintain intermediate sums, reducing boilerplate and potential errors. Use it when you need a sequence of prefix results from a series of values.

Primary Function

Cumulative reduction

Communicative Purpose

Enables computation of running totals or other cumulative results without manual loops

Pattern

list(itertools.accumulate(iterable, function))

Core Structure

itertools.accumulate(..., ...)

Função primária

Cumulative reduction

Propósito comunicativo

Enables computation of running totals or other cumulative results without manual loops

Situações de gatilho

Financial analysis: calculating cumulative revenue over months; Signal processing: generating cumulative sum of sensor readings; Game development: tracking cumulative score updates

Contextos

Data analysis scripts, scientific computing notebooks, backend services processing transaction streams

Padrão

list(itertools.accumulate(iterable, function))

Estrutura central

itertools.accumulate(..., ...)

Slots de substituição

iterable: a sequence of numeric values; function: a binary callable such as operator.add or a lambda

Colocados típicos

import itertools
import operator
often used with list() conversion
appears in data processing loops

Substituições comuns

Explicit for-loop accumulation
numpy.cumsum for arrays
list comprehension with sum(my_list[:i+1])

Erros comuns

Forgetting to import itertools or operator, expecting a list directly from accumulate, using accumulate on non-numeric data without a suitable function

Similar / contraste

itertools.accumulate with default addition (omitted function) vs. functools.reduce for a single total; map for element-wise transformation

Interferências

Coming from MATLAB or R: expecting a built-in cumsum function that returns a list directly; in Python accumulate returns an iterator

Família do chunk

itertools.accumulate patterns
cumulative reductions
prefix sum patterns

Nuance

The result is an iterator; converting to list materializes all intermediate sums, which may be memory-intensive for large sequences. Works with any type supporting the binary function (e.g., strings concatenated with operator.add).

Efeito pragmático

Makes the intent of computing a running total explicit and reduces boilerplate code

Dica de memória

Imagine walking through the list and keeping a running total as you go

Nota

Remember that accumulate returns an iterator; converting to list materializes all intermediate sums which may be memory-intensive for large sequences.

Upgrade path

Using itertools.accumulate with custom functions for more complex aggregations, or switching to numpy.cumsum for numeric arrays

Frequência: MediumFormulaicidade: Semi-fixedTipo de construção: iterator transformation via higher-order functionPrioridade de aquisição: Recognition firstPrioridade de output: BothTag de espaçamento: Medium-term

Examples

import itertools, operator sales = [100, 200, 150, 300] cumulative_sales = list(itertools.accumulate(sales, operator.add)) print(cumulative_sales) # [100, 300, 450, 750]

import itertools, operator values = [2, 3, 4, 5] cumulative_product = list(itertools.accumulate(values, operator.mul)) print(cumulative_product) # [2, 6, 24, 120]

import itertools words = ['a', 'b', 'c'] cumulative_concat = list(itertools.accumulate(words, lambda x, y: x + y)) print(cumulative_concat) # ['a', 'ab', 'abc']