Numpy Concepts

Internal

• Numpy

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

a = np.array(...)

Do not from numpy import *. The numpy namespace is large and contains a number of functions that conflict with built-in Python functions, like min() and max().

ndarray

ndarray

Pseudorandom Number Generation

Pseudorandom Number Generation

Universal Functions

Universal Functions