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The core functionality of NumPy is its "ndarray", for n-dimensional array, data structure. These arrays are strided views on memory. [9] In contrast to Python's built-in list data structure, these arrays are homogeneously typed: all elements of a single array must be of the same type.
tidyr – help transform data specifically into tidy data, where each variable is a column, each observation is a row; each row is an observation, and each value is a cell. readr – help read in common delimited, text files with data; purrr – a functional programming toolkit; tibble – a modern implementation of the built-in data frame data ...
Data for these collections can be imported from various file formats such as comma-separated values, JSON, Parquet, SQL database tables or queries, and Microsoft Excel. [8] A Series is a 1-dimensional data structure built on top of NumPy's array. [9]: 97 Unlike in NumPy, each data point has an associated label. The collection of these labels is ...
Dask Bag [15] is an unordered collection of repeated objects, a hybrid between a set and a list. Dask Bag is used to parallelize computation of semi-structured or unstructured data, such as JSON records, text data, log files or user-defined Python objects using operations such as filter, fold, map and groupby.
Function rank is an important concept to array programming languages in general, by analogy to tensor rank in mathematics: functions that operate on data may be classified by the number of dimensions they act on. Ordinary multiplication, for example, is a scalar ranked function because it operates on zero-dimensional data (individual numbers).
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NumPy, a BSD-licensed library that adds support for the manipulation of large, multi-dimensional arrays and matrices; it also includes a large collection of high-level mathematical functions. NumPy serves as the backbone for a number of other numerical libraries, notably SciPy. De facto standard for matrix/tensor operations in Python.
Pairwise summation is the default summation algorithm in NumPy [9] and the Julia technical-computing language, [10] where in both cases it was found to have comparable speed to naive summation (thanks to the use of a large base case).