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  2. NumPy - Wikipedia

    en.wikipedia.org/wiki/NumPy

    NumPy (pronounced / ˈ n ʌ m p aɪ / NUM-py) is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. [3]

  3. CuPy - Wikipedia

    en.wikipedia.org/wiki/CuPy

    CuPy is an open source library for GPU-accelerated computing with Python programming language, providing support for multi-dimensional arrays, sparse matrices, and a variety of numerical algorithms implemented on top of them. [3] CuPy shares the same API set as NumPy and SciPy, allowing it to be a drop-in replacement to run NumPy/SciPy code on GPU.

  4. Row- and column-major order - Wikipedia

    en.wikipedia.org/wiki/Row-_and_column-major_order

    Support for multi-dimensional arrays may also be provided by external libraries, which may even support arbitrary orderings, where each dimension has a stride value, and row-major or column-major are just two possible resulting interpretations. Row-major order is the default in NumPy [19] (for Python).

  5. Array slicing - Wikipedia

    en.wikipedia.org/wiki/Array_slicing

    If the array abstraction does not support true negative indices (as for example the arrays of Ada and Pascal do), then negative indices for the bounds of the slice for a given dimension are sometimes used to specify an offset from the end of the array in that dimension. In 1-based schemes, -1 generally would indicate the second-to-last item ...

  6. Array (data type) - Wikipedia

    en.wikipedia.org/wiki/Array_(data_type)

    An array data structure can be mathematically modeled as an abstract data structure (an abstract array) with two operations get(A, I): the data stored in the element of the array A whose indices are the integer tuple I. set(A, I, V): the array that results by setting the value of that element to V. These operations are required to satisfy the ...

  7. pandas (software) - Wikipedia

    en.wikipedia.org/wiki/Pandas_(software)

    By default, a Pandas index is a series of integers ascending from 0, similar to the indices of Python arrays. However, indices can use any NumPy data type, including floating point, timestamps, or strings. [4]: 112 Pandas' syntax for mapping index values to relevant data is the same syntax Python uses to map dictionary keys to values.

  8. Google JAX - Wikipedia

    en.wikipedia.org/wiki/Google_JAX

    # imports from jax import grad import jax.numpy as jnp # define the logistic function def logistic (x): return jnp. exp (x) / (jnp. exp (x) + 1) # obtain the gradient function of the logistic function grad_logistic = grad (logistic) # evaluate the gradient of the logistic function at x = 1 grad_log_out = grad_logistic (1.0) print (grad_log_out)

  9. Dask (software) - Wikipedia

    en.wikipedia.org/wiki/Dask_(software)

    A Dask array comprises many smaller n-dimensional Numpy arrays and uses a blocked algorithm to enable computation on larger-than-memory arrays. During an operation, Dask translates the array operation into a task graph, breaks up large Numpy arrays into multiple smaller chunks, and executes the work on each chunk in parallel.