PowerQuery Guide to Pandas
PowerQuery Guide to Pandas
A Comparative Approach to Learn Pandas
About the Book
PowerQuery Guide to Pandas is designed to make readers familiar with the syntax and core concepts of Python language and the Pandas library by relating readers to familiar concepts in PowerQuery for Excel and PowerBI. With this book, I'll provide a comparative approach to learning Pandas and hopefully serve as good introduction to the Python language.
Learn the following concepts and topics in this book
* Data Types
* Input and Output
* Control Structures
* Structured Data Records
* Data Wrangling
* Dealing with Missing Data
* Grouping Data
* Combining Data
* ... and much more!
PowerQuery is great tool and should be part of every accountant's toolbox. However, Python is more mature and has wider reach in data analytics, processing, and automation. Level up your skills by learning Python and Pandas with the help of this book!
Today's business environment is becoming more and more data-driven. Stay relevant.
Buy this book and jump-start your Python journey!
NOTE! This book is written the Agile Way. That means the book is currently unfinished and in-progress. As I continue to complete the chapters, we will re-publish the book with the new and updated content. Readers will receive an email once a new version is published!
Send any questions, comments, or feedback about this book to my Linkedin or Twitter accounts. Thank you!
About the Author
Table of Contents
- 1. Introduction
-
2. About the Book
- 2.1 Why did I write this book
- 2.2 Who is the Target Audience
- 2.3 Prerequisites
- 2.4 Conventions in the Book
- 2.5 Feedback
-
3. The Basics
- 3.1 Version
- 3.2 Case Sensitivity
- 3.3 Indentation
- 3.4 Comments
- 3.5 Variables
- 3.6 The Help System
- 3.7 Coding Tools
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4. Input and Output
- 4.1 Input
- 4.2 Output
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5. Data Types
- 5.1 Built-in Data Types
- 5.2 Field Data Types
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6. Control Structures
- 6.1 Conditional Statements
- 6.2 Loops
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7. Error Handling
- 7.1 If Statement
- 7.2 Try Statement
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8. Functions
- 8.1 Function Basics
- 8.2 Optional Parameters & Default Values
- 8.3 Return Multiple Values
- 8.4 Type Hinting
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9. Classes
- 9.1 Basics
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10. Strings
- 10.1 Basics
- 10.2 Transforming a String Field
- 10.3 String Slicing
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11. Date and Time
- 11.1 Basics
- 11.2 Transforming a Datetime Field
- 11.3 Date Formatting
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12. Structured Data Records
- 12.2 List
- 12.3 Records and Dictionaries
- 12.4 Creating a Table/Dataframe from List, Records or Dictionaries
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13. Introduction to Data Wrangling
- 13.2 Rename Columns
- 13.3 Reorder Columns
- 13.4 Sorting
- 13.5 Unique Values
- 13.6 Dealing with Duplicates
- 13.7 Getting Info
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14. Adding Columns
- 14.1 Duplicating a Column
- 14.2 Extracting Values from a Column
- 14.3 Conditional Column
- 14.4 Index Column
- 14.5 Thru a Formula
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15. Subsetting Data
- 15.1 Selecting Columns and Rows
- 15.2 loc and iloc (Pandas only)
- 15.3 Removing Columns and Rows
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16. Dealing with Missing Data
- 16.1 Fill Forward and Fill Backward
- 16.2 Recode/Replace Values
- 16.3 Dropping Missing Data
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17. Grouping Data
- 17.1 Aggregate a Single Column
- 17.2 Aggregate Multiple Columns
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18. Pivoting and Unpivoting Data
- 18.1 Pivot
- 18.2 Unpivot/Melt
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19. Combining Data
- 19.1 Merge
- 19.2 Append
- 20. Conclusion
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