Please download the reading material on NumPy from the resource section.
The core functionality of NumPy is its "ndarray", for n-dimensional array, and data structure. These arrays are stridden views on memory. 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.
Such arrays can also be viewed into memory buffers allocated by C/C++, Python, and Fortran extensions to the C Python interpreter without the need to copy data around, giving a degree of compatibility with existing numerical libraries.
File :
NumPy Reading Material.ipynb
3.2 MB
Reading Material - Numpy Broadcasting
Please download the reading material from the resource section.
The following concepts will be discussed in this notebook
- Broadcasting in NumPy
File :
Broadcasting.ipynb
9 kB
Reading Material - Vectorization
Please download the reading material from the resource section.
The following concept will be discussed in the notebook
- Vectorization
File :
Vectorization.ipynb
5.3 kB
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