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

Writing Large Datasets

In this recipe, you will explore how the choice of the 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 an in-memory columnar format for optimized data analysis performance. To persist this in-memory columnar and store it, you can use pandas to_orc, to_feather, and to_parquet writer functions to persist your data to disk.

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

Getting Ready

In this recipe, you will be working with the New...

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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 (13) Chapters

Writing Large Datasets

Getting Ready

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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 (13) Chapters

Writing Large Datasets

Getting Ready

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