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

View More author details

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

Writing time series data to a relational database (PostgreSQL and MySQL)

In this recipe, you will write your DataFrame to a relational database such as PostgreSQL. The approach is the same for any relational database system supported by the SQLAlchemy Python library. You will experience how SQLAlchemy makes switching the backend database (called dialect) simple without altering the code. The abstraction layer provided by the SQLAlchemy library makes it feasible to switch to any supported database, such as from PostgreSQL to Amazon Redshift, using the same code.

The sample list of supported relational databases (dialects) in SQLAlchemy includes the following:

Microsoft SQL Server
MySQL/MariaDB
PostgreSQL
Oracle
SQLite

Additionally, external dialects are available to install and use with SQLAlchemy to support other databases (dialects), such as Snowflake, Microsoft SQL Server, and Google BigQuery. Please visit the official page of SQLAlchemy for a list of available dialects: https://docs...