You're reading from Time Series Analysis with Python Cookbook Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation

Product type Paperback

Published in Apr 2025

Publisher

ISBN-13 9781805124283

Length 98 pages

Edition 2nd Edition

Languages

Python

Concepts

Data Analysis

Author (1):

Tarek A. Atwan

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Table of Contents (13) Chapters

1. Time Series Analysis with Python Cookbook, Second Edition: Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation FREE CHAPTER

2. Getting Started with Time Series Analysis

3. Reading Time Series Data from Files

4. Reading Time Series Data from Databases

5. Persisting Time Series Data to Files

6. Persisting Time Series Data to Databases

7. Working with Date and Time in Python

8. Handling Missing Data

9. Outlier Detection Using Statistical Methods

10. Exploratory Data Analysis and Diagnosis

11. Building Univariate Time Series Models Using Statistical Methods

12. Additional Statistical Modeling Techniques for Time Series

13. Outlier Detection Using Unsupervised Machine Learning

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Databases extend what you can store to include text, images, and media files and are designed for efficient read-and-write operations at a massive scale. Databases can store terabytes and petabytes of data with efficient and optimized data retrieval capabilities, such as when performing analytical operations on data warehouses and data lakes. A data warehouse is a database designed to store large amounts of structured data, mostly integrated from multiple source systems, built specifically to support business intelligence reporting, dashboards, and advanced analytics. A data lake, on the other hand, stores a large amount of structured, semi-structured, or unstructured data in its raw format. In this chapter, we will continue to use the pandas library to read data from databases. We will create time series DataFrames by reading data from relational (SQL) databases and non-relational (NoSQL) databases.

Additionally, you will...