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 Jun 2022

Publisher Packt

ISBN-13 9781801075541

Length 630 pages

Edition 1st Edition

Languages

Python

Tools

Keras

Concepts

Data Analysis

Author (1):

Tarek A. Atwan

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Table of Contents (18) Chapters

Preface

1. Chapter 1: Getting Started with Time Series Analysis

2. Chapter 2: Reading Time Series Data from Files FREE CHAPTER

3. Chapter 3: Reading Time Series Data from Databases

4. Chapter 4: Persisting Time Series Data to Files

5. Chapter 5: Persisting Time Series Data to Databases

6. Chapter 6: Working with Date and Time in Python

7. Chapter 7: Handling Missing Data

8. Chapter 8: Outlier Detection Using Statistical Methods

9. Chapter 9: Exploratory Data Analysis and Diagnosis

10. Chapter 10: Building Univariate Time Series Models Using Statistical Methods

11. Chapter 11: Additional Statistical Modeling Techniques for Time Series

12. Chapter 12: Forecasting Using Supervised Machine Learning

13. Chapter 13: Deep Learning for Time Series Forecasting

14. Chapter 14: Outlier Detection Using Unsupervised Machine Learning

15. Chapter 15: Advanced Techniques for Complex Time Series

16. Index

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Reading data from a relational database

In this recipe, you will read data from PostgreSQL, a popular open source relational database.

You will explore two methods for connecting to and interacting with PostgreSQL. First, you will start by using psycopg2, a PostgreSQL Python connector, to connect and query the database, then parse the results into a pandas DataFrame. In the second approach, you will query the same database again but this time using SQLAlchemy, an object-relational mapper (ORM) that is well integrated with pandas.

Getting ready

In this recipe, it is assumed that you have the latest PostgreSQL installed. At the time of writing, version 14 is the latest stable version (version 15 is still in beta).

To connect to and query the database in Python, you will need to install psycopg2, a popular PostgreSQL database adapter for Python. You will also need to install SQLAlchemy, which provides flexibility regarding how you want to manage the database, whether it is...

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

Table of Contents (18) Chapters

Reading data from a relational database

Getting ready

Authors (1)

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

Table of Contents (18) Chapters

Reading data from a relational database

Getting ready

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Authors (1)

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