Python Libraries Bundle
Dive into learning all about Python libraries with this 5-course value bundle! Follow along with expert instructor Brett Romero as he guides you through working with Scrapy, SciPy, NumPy, iPython (Jupyter), and BeautifulSoup. Project files included! Total Length: 5 hrs
All the courses you buy, when you need them, Please inform and I will always resend the link to you. However, You should download early and conveniently study anytime, anywhere.
This Python Libraries Bundle includes these courses:
- Python Scrapy: Scrape Web Data Using Python
- Python SciPy: The Open Source Python Library
- Python NumPy: Scientific Computing with Python
- Learn iPython: The Full Python IDE
- Python BeautifulSoup: Extract Web Data Beautifully
Python Scrapy: Scrape Web Data Using Python
With this Python Scrapy: Scrape Web Data Using Python course, you’ll learn how to scrape using XPath or CSS. With the large number of examples from both techniques, you’re sure to find a solution that fits for you.
Whether you’re targeting data on a single page or multiple, Scrapy can handle the job. No matter if the data is within a list, you can scrape specific patterns right out of the list. Building up your specific Scrapy job isn’t a difficult task.
Scrapy is a Python library. If you’re familiar with Python, XPath or CSS, you’ll feel right at home using Scrapy.
Python SciPy: The Open Source Python Library
Computational computing can be a complex topic. How to perform various mathematical functions in code isn’t straight forward.
With this Python SciPy: The Open Source Python Library course, you’ll walk through a number of examples showing exactly how to create and execute complex computational computing functions.
The course starts with an explanation of what SciPy is. Then we see how to install it. From there, we get into simple mathematical computations, and and move into more advanced computations. The last few lessons demonstrate the full capabilities of SciPy.
SciPy is for those that need to perform rigorous, complex computations and not have the program bog down computing them. If you’re ready to see how to create even the most complex mathematical functions in code, this course is for you.
Python NumPy: Scientific Computing with Python
This Python NumPy: Scientific Computing with Python course provides a thorough understanding of NumPy’s features and when to use them.
Begin with an introduction to NumPy and take a tour of NumPy’s features. Then you’ll move on to topics such as matrices, deviations, Eigen values, and covariance. You’ll finish with a real-world project utilizing the included resource files.
NumPy is mainly used in matrix computing. We’ll do a number of examples specific to matrix computing, which will allow you to see the various scenarios in which NumPy is helpful. There are a few computational computing libraries available for Python. It’s important to know when to choose one over the other. Through rigorous exercises, you’ll experience where NumPy is powerful and develop an understanding of the scenarios in which NumPy is most useful.
Learn iPython: The Full Python IDE
Coding Python from the command line isn’t a fun experience when you begin getting into longer form code. The command line simply isn’t designed for that. That’s where iPython comes in. At the end of this Learn iPython – the Full Python IDE course, you’ll have a thorough understanding of iPython. It may even become your go-to Python editor. You’ll also know the differences between iPython and Jupyter.
Through a number of examples with various scenarios, you’ll develop an understanding of how iPython is an extremely efficient Python editor for long form code compared to the command line. You’ll know how to enter in code, markdown for comments, and rearrange/edit code as needed.
Python BeautifulSoup: Extract Web Data Beautifully
Learn to parse and extract from HTML streams with this Python BeautifulSoup: Extract Web Data Beautifully training course.
BeautifulSoup is a popular Python library for extracting data from HTML or live pages. It isn’t limited to a single webpage. You can extract data from multiple webpages. In fact, one of the examples we use does just that. Knowing how to find data within the HTML tree is key to getting targeted data. This course will show you how to identify that data within the HTML tree. Then you’ll build a parsing rule to extract it using BeautifulSoup.
With a number of examples to ensure you know exactly how to find data, build parsing rules and the needed code to execute the extract, you’ll walk away from this course feeling confident in your abilities to retrieve data from webpages.
Jupyter is an open-source web application Notebook that allows you to create and share documents. These documents can contain live code, equations, visualizations, and even narrative text. Jupyter Notebook is commonly used for data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more. Jupyter Notebooks are revolutionizing the way data scientists and engineers work together!
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