NumPy is a Python package which stands for ‘Numerical Python’. It is a library consisting of multidimensional array objects and a collection of routines for processing of array.
Operations using NumPy
Using NumPy, a developer can perform the following operations −
- Mathematical and logical operations on arrays.
- Fourier transforms and routines for shape manipulation.
- Operations related to linear algebra. It has in-built functions for linear algebra and random number generation.
NumPy – A Replacement for MatLab
It is often used along with packages like SciPy (Scientific Python) and Mat−plotlib (plotting library). This combination is widely used as a replacement for MatLab, a popular platform for technical computing. However, Python alternative to MatLab is now seen as a more modern and complete programming language.
It is open source, which is an added advantage of NumPy.
ndarray Object
The most important object defined in NumPy is an N-dimensional array type called ndarray. It describes the collection of items of the same type. Items in the collection can be accessed using a zero-based index. Every item in an ndarray takes the same size of block in the memory. Each element in ndarray is an object of data-type object (called dtype). Any item extracted from ndarray object (by slicing) is represented by a Python object of one of array scalar types.
We will see lots of examples on using NumPy library of python in Data science work in the next chapters.
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