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

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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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Performing data quality checks

Missing data are values not captured or 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, missing observations are replaced with other values in 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 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.csv and...

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You're reading from Time Series Analysis with Python Cookbook Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation

Table of Contents (18) Chapters

Performing data quality checks

Getting ready

Authors (1)

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You're reading from Time Series Analysis with Python Cookbook Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation

Table of Contents (18) Chapters

Performing data quality checks

Getting ready

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