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

Working with large data files

One of the advantages of using pandas is that it provides data structures for in-memory analysis, which results in a performance advantage when working with data. However, this advantage can also become a constraint when working with large datasets, as the amount of data you can load is limited by the available memory. When datasets exceed the available memory, it can lead to performance degradation, especially when pandas creates intermediate copies of the data for certain operations.

In real-world scenarios, there are general best practices to mitigate these limitations, including:

Sampling or loading a small number of rows for your Exploratory Data Analysis (EDA): Before applying your data analysis strategy to the entire dataset, it is a good practice to sample or load a small number of rows. This allows you to get a better understanding of your data, gain some intuition, and identify unnecessary columns that can be eliminated, thus reducing the overall...