NumPy.pptx Bachelor of Computer Application
- 1. What is NumPy? NumPy stands for Numerical Python. NumPy is a Python library used for working with arrays. It also has functions for working in domain of linear algebra, fourier transform, and matrices. NumPy was created in 2005 by Travis Oliphant. It is an open source project and you can use it freely. NumPy is a general-purpose array-processing package. It provides a high- performance multidimensional array object and tools for working with these arrays. It is the fundamental package for scientific computing with Python. Why Use NumPy? •In Python we have lists that serve the purpose of arrays, but they are slow to process. •NumPy aims to provide an array object that is up to 50x faster than traditional Python lists. •The array object in NumPy is called ndarray, it provides a lot of supporting functions that make working with ndarray very easy. •Arrays are very frequently used in data science, where speed and resources are very important.
- 2. •It is capable of performing Fourier Transform and reshaping the data stored in multidimensional arrays. •NumPy provides the in-built functions for linear algebra and random number generation. Why is NumPy Faster Than Lists? NumPy arrays are stored at one continuous place in memory unlike lists, so processes can access and manipulate them very efficiently. This behavior is called locality of reference in computer science.This is the main reason why NumPy is faster than lists. Also it is optimized to work with latest CPU architectures. NumPy – Environment setup Standard Python distribution doesn't come bundled with NumPy module. A lightweight alternative is to install NumPy using popular Python package installer, pip. pip install numpy.
- 3. NumPy as np NumPy is usually imported under the np alias. alias: In Python alias are an alternate name for referring to the same thing. Create an alias with the as keyword while importing: Checking NumPy Version The version string is stored under __version__ attribute.
- 4. NumPy Array Creation Creation of One-dimensional arrays Creation of n-dimensional arrays
- 5. Example: In this example, we are creating a two- dimensional array that has the rank of 2 as it has 2 axes. The first axis(dimension) is of length 2, i.e., the number of rows, and the second axis(dimension) is of length 3, i.e., the number of columns. The overall shape of the array can be represented as (2, 3)
- 6. Check Number of Dimensions? NumPy Arrays provides the ndim attribute that returns an integer that tells us how many dimensions the array have. NumPy Array Reshaping Reshaping means changing the shape of an array. The shape of an array is the number of elements in each dimension. By reshaping we can add or remove dimensions or change number of elements in each dimension.
- 7. Reshape From 1-D to 2-D Reshape From 1-D to 3-D Can We Reshape Into any Shape? Yes, as long as the elements required for reshaping are equal in both shapes. We can reshape an 8 elements 1D array into 4 elements in 2 rows 2D array but we cannot reshape it into a 3 elements 3 rows 2D array as that would require 3x3 = 9 elements.
- 8. Returns Copy or View?