Book Image

Artificial Intelligence with Python - Second Edition

By : Prateek Joshi
Book Image

Artificial Intelligence with Python - Second Edition

By: Prateek Joshi

Overview of this book

Artificial Intelligence with Python, Second Edition is an updated and expanded version of the bestselling guide to artificial intelligence using the latest version of Python 3.x. Not only does it provide you an introduction to artificial intelligence, this new edition goes further by giving you the tools you need to explore the amazing world of intelligent apps and create your own applications. This edition also includes seven new chapters on more advanced concepts of Artificial Intelligence, including fundamental use cases of AI; machine learning data pipelines; feature selection and feature engineering; AI on the cloud; the basics of chatbots; RNNs and DL models; and AI and Big Data. Finally, this new edition explores various real-world scenarios and teaches you how to apply relevant AI algorithms to a wide swath of problems, starting with the most basic AI concepts and progressively building from there to solve more difficult challenges so that by the end, you will have gained a solid understanding of, and when best to use, these many artificial intelligence techniques.
Table of Contents (26 chapters)
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Handling time series data with Pandas

Pandas is arguably the most important library in Python. Learning to use its methods well is paramount, and it will serve you well as you use Python for any of your other projects. In addition to time series analysis, many more functions can be performed with Pandas including:

  • DataFrame manipulation with integrated indexing
  • Methods to read data from a variety of different file formats and write data into in-memory data structures
  • Data sorting
  • Data filtering
  • Missing value imputation
  • Reshaping and pivoting datasets
  • Label-based slicing, indexing, and creation of subsets
  • Efficient column insertion and deletion
  • Group by operations on datasets
  • Merging and joining of datasets

In this section, we will use it to convert a sequence of numbers into time series data and visualize it. Pandas provides options to add timestamps, organize data, and then efficiently operate on it.

Create a new Python file and...