Data Allocation in Numpy Array

In NumPy, data is allocated contiguously in memory, following a well-defined layout consisting of the data buffer, shape, and strides. This is essential for efficient data access, vectorized operations, and compatibility with low-level libraries like BLAS and LAPACK.

1. Data Buffer: The data buffer in NumPy is a single, flat block of memory that stores the actual elements of the array, regardless of its dimensionality. This enables efficient element-wise operations and data access.
2. Shape: The shape of an array is a tuple of integers that represents the dimensions along each axis. Each integer corresponds to the size of the array along a specific dimension, which defines the number of elements along each axis and is essential for correctly indexing and reshaping the array.
3. Strides: Strides are tuples of integers that define the number of bytes to step in each dimension when moving from one element to the next. They determine the spacing between elements in memory and measure how many bytes are required to move from one element to another in each dimension.

Python lists are a substitute for arrays, but they fail to deliver the performance required while computing large sets of numerical data.

To address this issue we use the NumPy library of Python. NumPy offers an array object called ndarray. They are similar to standard Python sequences but differ in certain key factors.