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Python for Geeks

Python for Geeks

By : Asif
4.5 (20)
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Python for Geeks

Python for Geeks

4.5 (20)
By: Asif

Overview of this book

Python is a multipurpose language that can be used for multiple use cases. Python for Geeks will teach you how to advance in your career with the help of expert tips and tricks. You'll start by exploring the different ways of using Python optimally, both from the design and implementation point of view. Next, you'll understand the life cycle of a large-scale Python project. As you advance, you'll focus on different ways of creating an elegant design by modularizing a Python project and learn best practices and design patterns for using Python. You'll also discover how to scale out Python beyond a single thread and how to implement multiprocessing and multithreading in Python. In addition to this, you'll understand how you can not only use Python to deploy on a single machine but also use clusters in private as well as in public cloud computing environments. You'll then explore data processing techniques, focus on reusable, scalable data pipelines, and learn how to use these advanced techniques for network automation, serverless functions, and machine learning. Finally, you'll focus on strategizing web development design using the techniques and best practices covered in the book. By the end of this Python book, you'll be able to do some serious Python programming for large-scale complex projects.
Table of Contents (20 chapters)
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1
Section 1: Python, beyond the Basics
5
Section 2: Advanced Programming Concepts
9
Section 3: Scaling beyond a Single Thread
13
Section 4: Using Python for Web, Cloud, and Network Use Cases

Introducing advanced tricks with pandas DataFrame

pandas is an open source Python library that provides tools for high-performance data manipulation to make data analysis quick and easy. The typical uses of the pandas library are to reshape, sort, slice, aggregate, and merge data.

The pandas library is built on top of the NumPy library, which is another Python library that is used for working with arrays. The NumPy library is significantly faster than traditional Python lists because data is stored at one continuous location in memory, which is not the case with traditional lists.

The pandas library deals with three key data structures, as follows:

  • Series: This is a single-dimensional array-like object that contains an array of data and an array of data labels. The array of data labels is called an index. The index can be specified automatically using integers from 0 to n-1 if not explicitly specified by a user.
  • DataFrame: This is a representation of tabular data...

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