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Data Processing with Optimus

You're reading from   Data Processing with Optimus Supercharge big data preparation tasks for analytics and machine learning with Optimus using Dask and PySpark

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Product type Paperback
Published in Sep 2021
Publisher Packt
ISBN-13 9781801079563
Length 300 pages
Edition 1st Edition
Languages
Concepts
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Authors (2):
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Dr. Argenis Leon Dr. Argenis Leon
Author Profile Icon Dr. Argenis Leon
Dr. Argenis Leon
Luis Aguirre Contreras Luis Aguirre Contreras
Author Profile Icon Luis Aguirre Contreras
Luis Aguirre Contreras
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Toc

Table of Contents (16) Chapters Close

Preface 1. Section 1: Getting Started with Optimus
2. Chapter 1: Hi Optimus! FREE CHAPTER 3. Chapter 2: Data Loading, Saving, and File Formats 4. Section 2: Optimus – Transform and Rollout
5. Chapter 3: Data Wrangling 6. Chapter 4: Combining, Reshaping, and Aggregating Data 7. Chapter 5: Data Visualization and Profiling 8. Chapter 6: String Clustering 9. Chapter 7: Feature Engineering 10. Section 3: Advanced Features of Optimus
11. Chapter 8: Machine Learning 12. Chapter 9: Natural Language Processing 13. Chapter 10: Hacking Optimus 14. Chapter 11: Optimus as a Web Service 15. Other Books You May Enjoy

Joining data

The join operation is used to merge entries from a data source to another using a common column as a key to pair the data correctly. The concept of joining is commonly seen in database technologies, in which we also see the different types of joins, such as inner join, outer join, left join, and right join. These joins are better represented in the following diagram:

Figure 4.1 – Inner, outer, left, and right joins

When joining data, we must identify the key column for both dataframes. Let's look at an example:

df_a = op.create.dataframe({
    "id": [143, 225, 545, 765, 152],
    "name": ["Alice", "Bob", "Charlie", "Dan", "Frank"] 
}) 
df_b = op.create.dataframe({
    "id": [225, 545, 765, 152, 329],
    "city": ["Bradford", "Norwich", "Bath...
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