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

Plotting time series data with interactive visualizations using hvPlot

Interactive visualizations allow us to analyze data more efficiently compared to static visuals. Simple interactions, such as zooming in and out or slicing through the visual, can unearth additional insights for further investigation.

In this recipe, we will explore the hvPlot library to create interactive visualizations. HvPlot offers a high-level API for data visualization and integrates seamlessly with various data sources, including pandas, Xarray, Dask, Polars, NetworkX, Streamlit, and GeoPandas. Utilizing hvPlot with pandas for rendering interactive visualizations requires minimal effort, allowing you to create dynamic visualizations with few modifications to the original code. We will use the 'closing_price.csv' dataset to explore the capabilities of the library in this recipe.