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 Jun 2022

Publisher Packt

ISBN-13 9781801075541

Length 630 pages

Edition 1st Edition

Languages

Python

Tools

Keras

Concepts

Data Analysis

Author (1):

Tarek A. Atwan

View More author details

Table of Contents (18) Chapters

Preface

1. Chapter 1: Getting Started with Time Series Analysis

2. Chapter 2: Reading Time Series Data from Files FREE CHAPTER

3. Chapter 3: Reading Time Series Data from Databases

4. Chapter 4: Persisting Time Series Data to Files

5. Chapter 5: Persisting Time Series Data to Databases

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

7. Chapter 7: Handling Missing Data

8. Chapter 8: Outlier Detection Using Statistical Methods

9. Chapter 9: Exploratory Data Analysis and Diagnosis

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

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

12. Chapter 12: Forecasting Using Supervised Machine Learning

13. Chapter 13: Deep Learning for Time Series Forecasting

14. Chapter 14: Outlier Detection Using Unsupervised Machine Learning

15. Chapter 15: Advanced Techniques for Complex Time Series

16. Index

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Plotting time series data with interactive visualizations using hvPlot

In this recipe, you will explore the hvPlot library to create interactive visualizations. hvPlot works well with pandas DataFrames to render interactive visualizations with minimal effort. You will be using the same closing_price.csv dataset to explore the library.

Getting ready

You can download the Jupyter notebooks and datasets needed from the GitHub repository. Please refer to the Technical requirements section of this chapter.

How to do it…

Start by importing the libraries needed. Notice that hvPlot has a pandas extension, which makes it more convenient. This will allow you to use the same syntax as in the previous recipe:
```
import pandas as pd
import hvplot.pandas 
import hvplot as hv
closing_price_n = closing_price.div(closing_price.iloc[0])
```

When plotting using pandas, you use the .plot() method, for example, closing_price_n.plot(). Similarly, hvPlot allows you to render an interactive...