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

Performing data quality checks

Missing data are values not captured or not observed in the dataset. Values can be missing for a particular feature (column), or an entire observation (row). When ingesting the data using pandas, missing values will show up as either NaN, NaT, or NA.

Sometimes, in a given data set, missing observations are replaced with other values from the source system; for example, this can be a numeric filler such as 99999 or 0, or a string such as missing or N/A. When missing values are represented by 0, you need to be cautious and investigate further to determine whether those zero values are legitimate or if they are indicative of missing data.

In this recipe, you will explore how to identify the presence of missing data.

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.

You will be using two datasets from the Ch7 folder: clicks_missing_multiple...