What you’ll learn You will understand that NumPy – Numerical Python is used for scientific computing and data analysis You will get clarity that NumPy uses n-dimensional, homogenous object (ndarray) NumPy are fast, use less memory, are convenient and use vectorized code (Code does not contain explicit looping and indexing etc) You will learn how to create array’s in NumPy You will clearly understand the comparison between NumPy and standard python You will learn the structure of Arrays Indexing, Subsetting, Slicing and Iterating through Arrays Execution speed in NumPy and Standard Python Lists NumPy Arrays – Few Operations Basic mathematical operations/linear algebra operations/functions Playing with arrays using resize, reshape & stack creation Requirements Basic experience with the Python programming language Strong knowledge of data types (strings, integers, floating points, booleans) etc Description The Ultimate NumPy Tutorial for Data Science Beginners: What is the NumPy library in Python? NumPy stands for Numerical Python and is one of the most useful scientific libraries in Python programming. It provides support for large multidimensional array objects and various tools to work with them. Various other libraries like Pandas, Matplotlib, and Scikit-learn are built on top of this amazing library. Python Lists vs NumPy Arrays – What’s the Difference? If you’re familiar with Python, you might be wondering why use NumPy arrays when we already have Python lists? After all, these Python lists act as an array that can store elements of various types. This is a perfectly valid question and the answer to this is hidden in the way Python stores an object in memory. A Python object is actually a pointer to a memory location that stores all the details about the object, like bytes and the value. Although this extra information is what makes Python a dynamically typed language, it also comes at a cost which becomes apparent when storing a large collection of objects, like in an array. Python lists are essentially an array of pointers, each pointing to a location that contains the information related to the element. This adds a lot of overhead in terms of memory and computation. And most of this information is rendered redundant when all the objects stored in the list are of the same type! To overcome this problem, we use NumPy arrays that contain only homogeneous elements, i.e. elements having the same data type. This makes it more efficient at storing and manipulating the array. This difference becomes apparent when the array has a large number of elements, say thousands or millions. Also, with NumPy arrays, you can perform element-wise operations, something which is not possible using Python lists! This is the reason why NumPy arrays are preferred over Python lists when performing mathematical operations on a large amount of data. Continue reading...
