[flatten list]铺平 list of lists #array

100%

[flatten list]铺平 list of lists #array

✓ Published0🌍 Public

Last edited Feb 18, 2019

Created on Oct 22, 2018

This example demonstrates two ways to flatten nested lists of arrays in Python. The first method uses a lambda function with a list comprehension to produce a new flattened list. The second method defines a generator function, `flatten`, which recursively yields items from nested iterables while ignoring strings and bytes, using the `Iterable` class from the `collections` module. The example data `[1, 2, [3, 4, [5, 6], 7], 8]` is processed by the generator, printing each number in order.

AI-generated description

Similar vizzes

Loading thumbnail…

Flatten

This example demonstrates a JavaScript function that flattens a nested array structure into a single-level array. The code defines a `flat` function using `Array.reduce()`, which iterates through each element and checks if it is an array using `Array.isArray()`. If an element is an array, the function recursively calls itself on that sub-array and concatenates the result; otherwise, it directly concatenates the element. The visualization highlights the recursive reduction process, showing how nested data gets progressively consolidated until a completely flat result emerges.

RRonolibert

62% match

Loading thumbnail…

flatten Arrays

This example visualizes the recursive flattening of nested arrays into a single linear sequence. It shows a branching tree of values collapsing inward as the algorithm traverses each level, with individual elements animating into a final horizontal row. The code uses a custom `flatten` function that recursively iterates through nested arrays and pushes non-array values into a result array. Rendering relies on D3’s force simulation to position the collapsing nodes and transition paths, while the data itself is generated from arbitrary nested array structures defined in the source gist.

MMAKIO135

62% match

Loading thumbnail…

[多个lists 元素各种组合] #array

This example demonstrates how to generate all possible combinations of elements from multiple lists, such as job attributes like `c=[1,2]`, `edu=['高中','专科']`, and `exp=['不限','1-3年']`. It uses Python’s `itertools.combinations` and `itertools.product` to produce every combination of one or more items, optionally constrained to full-length tuples when `full_length=True`. The output is a list of tuples, as shown in the commented result, which can be used for scenario enumeration or filtering in data processing pipelines.

SShawn91

57% match

Loading thumbnail…

[合并多个已经排序的 lists] 多个已经排序过的 lists，将这些 lists 合并并排序 #array

This example demonstrates how to merge multiple pre-sorted lists into a single sorted sequence. It uses Python's `heapq.merge` function, which accepts two sorted lists—here, `a = [1, 4, 7, 10]` and `b = [2, 5, 6, 11]`—and returns a generator yielding items in ascending order. The code then iterates through the generator with a `for` loop, printing each merged value. The key constraint is that `heapq.merge` only works correctly when all input lists are already sorted, as shown in this minimal, console-based illustration.

SShawn91

53% match

Loading thumbnail…

Flatten an array in Javascript without recursion

This example compares two JavaScript functions for flattening nested arrays, showing that the iterative approach is faster than the recursive one. The code uses the Benchmark.js library to time both methods on a nested array of integers, with the suite reporting each function’s operations per second. The recursive version, `flattenRecursive`, relies on `concat` and `forEach`, while the iterative `flatten` uses a `while` loop with `shift` to mutate the list in place. The output, logged via `console.log`, highlights the fastest method, demonstrating the performance trade-off between recursion and iteration.

HHypercubed

52% match

Loading thumbnail…

See https://twitter.com/ftrain/status/267503513477713920

A Python function transforms a flat list into a nested structure, visually mapping upward trends as branching sublists. The example shows how `ftrain.py` recursively groups consecutive larger values, producing output like `[1, [2, [3, 3, [4, 4]], 2], 1]` from input `[1, 2, 3, 3, 4, 4, 2, 1]`. It uses a stack-based algorithm to detect increases and pop back on decreases, demonstrating a compact data reshaping technique. The source is a gist by RandomEtc, likely intended as a conceptual illustration rather than a formal visualization.

RRandomEtc

52% match

Loading thumbnail…

Gist e8e67bab8a73e27d741e5a1d793bb5bd

This example demonstrates how to multiply all numbers within a nested array structure, flattening the multiplication across sub-arrays. The code uses nested `for` loops to iterate through each inner array, updating a running `product` variable. A test case with `[[1,2],[3,4],[5,6,7]]` is provided at the end, showing the expected result of 5040. The visualization highlights the logic of nested iteration and cumulative multiplication, with the data source being the hardcoded test array. No external libraries or rendering APIs are used, as the focus is on the algorithmic pattern in plain JavaScript.

DDocwali777

48% match

Loading thumbnail…

[split pandas cell into rows]将一个包含字符串或 list 的 cell，使用特定字符，将 cell 分成多行，同行其他 cells 复制值 #pandas

This example shows a pandas data-wrangling technique that transforms a DataFrame by splitting a single cell containing a delimited string or list into multiple rows while duplicating the other column values across the new rows. The function `splitDataFrameList` accepts a DataFrame, the target column name, and a separator string, then uses `df.apply` along the rows and a helper function to parse each cell. It relies on the `collections.Iterable` check and the `str.split` method to separate values, ultimately constructing a new DataFrame from the accumulated row dictionaries via `pd.DataFrame`.

SShawn91

46% match