Best Resources to Learn NumPy and Pandas
Introduction to NumPy and Pandas
One of the core Python libraries for scientific computing is called NumPy. The library is renowned for its dynamic programming features, including advanced syntax, compatibility for various hardware and computational devices, numerical computing utilities, the versatility of Python, the efficiency of compiled code, and more. An essential Python package for data scientists is NumPy.
A Python library providing capabilities for data analysis is called Pandas. You may alter data to get an understanding by using this library. Pandas were designed by Wes McKinney and were first designed to do statistical analysis of financial records; since Pandas' 2009 release has become increasingly well-known as a data analysis tool.
You may import data from sources like Excel using Pandas. Pandas enable you to prepare your data for analysis. Before processing and extracting any insight and understanding from your information, "cleaning" your data, sometimes referred to as "data wrangling" or "data munging," is the process of deleting inaccurate data. Clean and reliable data are essential. If not, the outcomes of your analytical method will be, at best, distorted and, at worst, meaningless.
You'll probably utilize Jupyter Notebook software when working on data science assignments. Using Jupyter Notebook, you may clean and modify data. You may use it to conduct activities like statistical modelling and machine learning. You can enter the program in it and execute it, just like a code editor.
Resources to Learn NumPy
Python NumPy Tutorial - Learn NumPy Arrays with Examples
You will learn about every feature of NumPy in this beautifully complied NumPy course. This course covers the basic data structure of NumPy, i.e., multidimensional NumPy arrays, wherein they have taught how NumPy is used in place of lists for multidimensional array operations, NumPy Arrays vs. Lists, the reason behind why NumPy is so important in Python, NumPy unique methods, etc. The sample codes in this tutorial demonstrate the benefits of using a NumPy array over a list after every topic.
Linear Regression with NumPy and Python [A Project on Coursera]
This course is ideal for you if you're searching for an online course that gives a hands-on experience where they provide exercise so you can use NumPy alongside the theoretical understanding. This guided project of Coursera is smartly designed to teach you the machine learning algorithm linear regression while giving you the experience of NumPy and Python.
It is strange to state, but you won't use any of the well-known pre-defined machine learning libraries with all the ready-to-use algorithms, including the most frequently used scikit-learn and stats models, in this 1.5-hour online project.
To gain a deeper grasp of the foundations, the purpose of this project is for you to develop all the machinery for the machine learning models by yourself, such as gradient descent and relatively more straightforward one linear regression, to teach you how machine learning libraries work at the backend.
Rhyme, a hands-on computer programming and project platform from Coursera, are used as the foundation for this course. On Rhyme, you will get a chance to work on projects directly in your browser. You will get immediate access to cloud workstations that have already been set up and have all of the data and equipment you require for completing the project.
The Complete NumPy Course for Data Science
The three hours of video content in this course are sufficient to teach the fundamentals to intermediate users of the NumPy library. This straightforward course is ideal for you if you want to become a data scientist or learn to process N-dimensional data. The only prerequisite is that you are familiar with the fundamentals of the Python programming language.
You begin by building straightforward NumPy arrays, comprehending array characteristics, and studying NumPy indexing and slicing. Later, you will use array operations, such as array broadcasting, to address the challenges of doing math on various arrays of various forms.
You will use the arithmetic function on arrays. Learn about adding an image filter and one-hot encoding, which converts the image's colour.
Doing More with Python NumPy [Udemy Course]
This tutorial offers more than 4 hrs of video content; this course is a fantastic way to study the NumPy library in-depth. After completing the course, you will learn more about arrays in Python, including why we should use NumPy instead of the standard Python script for array operations. It includes various knowledge-testing quizzes.
You will begin by learning about the fundamentals of arrays, including what they are as a type of data container, ways to view your arrays, and ways to do array indexing and slicing on both 2D and 3D arrays.
You can also evaluate the performance of doing similar functioning on a Python list against NumPy arrays. You will study array broadcasting and explore how to execute specific arrays using various NumPy techniques.
Working with Multidimensional Data Using NumPy
To work with complex data, you must develop the ability to work with multidimensional data. Before putting the data into a machine learning algorithm or visualizing your data, they need to be in the proper numeric form.
This course will first cover the fundamentals of producing, printing, and applying math operations on the multidimensional array. You'll discover how to use NumPy to edit images, index your arrays, and retrieve specific things inside the array.
You will later do certain functions of arrays of various shapes.
Resources to Learn Pandas
Pandas Python Library for Beginners in Data Science
A one-hour video tutorial called "Pandas Python Library for Beginners in Data Science" is designed for university students who have never encountered Pandas and are eager to understand their syntax. The project offers a variety of problems and their answers to motivate students to practice using Pandas.
Learners will have mastered the fundamentals of pandas after this assignment. To maximize the value of your data, they will likewise be ready to acquire insight into it, clean it up, and perform some simple preparation.
Analyzing Data with Python [Course on edX]
You learn how to use various data analysis tools in this course. Among these are SciPy, an environment of tools for maths, science, and technology; Pandas; NumPy, an acronym for "Numerical Python" and a Python module employed for mathematical calculations; and scikit-learn, an acronym for "Scientific Python" and a Python package employed for machine learning.
Introduction to Data Science in Python [Free Coursera Course]
Another excellent course from the University of Michigan is available via the Coursera platform to teach users how to utilize the NumPy and pandas libraries for mathematical computation.
The course's over 30 hours of lectures and information are intended for beginning programmers who have some familiarity with the Python coding language.
The course will begin with a few Python techniques and operations that data analysts and pandas will employ, lambda functions, such as date & time, and regular expression.
After that, switch to pandas and understand how to abstract Series & DataFrames, use indexing, and handle missing values. You will also study how to create summary reports and combine data frames.
Intermediate Pandas Python Library for Data Science
Understanding sophisticated feature engineering techniques to handle missing values in the dataset, how to filter, pick, and split data for more straightforward processing, as well as the many types of joining, filtering, and binning data function, will be aided by taking an introductory Pandas Python Library for Data Science course.
This project is intended for users who are already comfortable with pandas for data analysis but wish to fully capitalize on its potential by mastering more complicated procedures. Students will be eligible to use Pandas to manage structured data at the end of the project.
Pandas with Python [Free Course on Udemy]
This course, which teaches you how to group and connect data as well as process highly complex data and more, is recommended for those who have at least a basic grasp of the python programming language and wish to delve further into the panda's library and learn a few of its advanced capabilities.
Data frame and series are the two most fundamental operations in Pandas. Along with knowing them, it is essential to understand when to apply each of these in your work. Then discover date ranges, data inspection, and data frame slicing.
Additionally, you will learn about importing data from various file types, dealing with text data, merging data frames, and data visualization.