Numpy Concepts
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Overview
NumPy is the short for Numerical Python. It provides data structures, algorithms and library glue needed for most scientific applications involving numerical data in Python. NumPy contains:
- A fast and efficient multidimensional array object
ndarray. For numerical data, NumPy arrays are more efficient for storing and manipulating data than other built-in Python data structures. The reason is that NumPy stores data in continuous block of memory, independent of other built-in Python objects. Also, NumPy library or algorithms is written in C and can operate on this memory without any type checking or other overhead. - Functions for performing element-wise computations with arrays, and mathematical operations between arrays.
- Tools for reading and writing array-based datasets to disk.
- Linear algebra operations, Fourier transform, random number generation.
- A mature C API to enable Python extensions and native C or C++ code to access NumPy's data structures.
One of NumPy's primary uses is as a container for data to be passed between algorithms and libraries. NumPy is one of the most important foundational packages for numerical computing in Python and a dependency for Pandas.
NumPy does not provide time series manipulation, which is present in Pandas.
Import Convention
import numpy as np