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

Handling missing data with univariate imputation using scikit-learn

Scikit-Learn is a very popular machine learning library in Python. The scikit-learn library offers a plethora of options for everyday machine learning tasks and algorithms such as classification, regression, clustering, dimensionality reduction, model selection, and preprocessing.

Additionally, the library offers multiple options for univariate and multivariate data imputation.

Getting ready

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

This recipe will utilize the three functions prepared earlier (read_dataset, rmse_score, and plot_dfs).

You will be using four datasets from the Ch7 folder: clicks_original.csv, clicks_missing.csv, co2_original.csv, and co2_missing_only.csv. The datasets are available from the GitHub repository.