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

Reading data from an Excel file

To read data from an Excel file, you will need to use a different reader function from pandas. Generally, working with Excel files can be challenging since the file can contain formatted multi-line headers, merged header cells, and images. They may also contain multiple worksheets with custom names (labels). Therefore, it is vital that you always inspect the Excel file first. The most common scenario is reading from an Excel file that contains data partitioned into multiple sheets, which is the focus of this recipe.

In this recipe, you will be using the pandas.read_excel() function and will examine the various parameters available to ensure the data is read properly as a DataFrame with a DatetimeIndex for time series analysis. In addition, you will explore different options to read Excel files with multiple sheets.