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 Jan 2026

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

2. Chapter 1: Getting Started with Time Series Analysis FREE CHAPTER

3. Chapter 2: Reading Time Series Data from Files

4. Chapter 3: Reading Time Series Data from Databases

5. Chapter 4: Persisting Time Series Data to Files

6. Chapter 5: Persisting Time Series Data to Databases

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

8. Chapter 7: Handling Missing Data

9. Chapter 8: Outlier Detection Using Statistical Methods

10. Chapter 9: Exploratory Data Analysis and Diagnosis

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

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

13. Chapter 12: Forecasting Using Supervised Machine Learning

Writing large datasets

In this recipe, you will explore how the choice of different file formats can impact the overall write and read performance. You will explore Parquet, Optimized Row Columnar (ORC), and Feather, and compare their performance to other popular file formats such as JSON and CSV.The three file formats, ORC, Feather, and Parquet, are columnar file formats, making them efficient for analytical needs and showing improved querying performance overall. The three file formats are also supported in Apache Arrow (PyArrow), which offers a standardized in-memory columnar data format for optimized data analysis performance. To persist this in-memory columnar and store it, you can use the pandas to_orc, to_feather, and to_parquet methods to write your data to disk.

Arrow provides the in-memory representation of the data as a columnar format, while Feather, ORC, and Parquet allow us to store this representation on disk.