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Merging DataFrames with pandas

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  • 14 Videos
  • 56 Exercises
  • 4 hours 
  • 2,128 Participants
  • 4650 XP

Instructor(s):

Dhavide Aruliah
Dhavide Aruliah

Dhavide Aruliah is Director of Training at Continuum Analytics, the creator and driving force behind Anaconda—the leading Open Data Science platform powered by Python. Dhavide was previously an Associate Professor at the University of Ontario Institute of Technology (UOIT). He served as Program Director for various undergraduate & postgraduate programs at UOIT. His research interests include computational inverse problems, numerical linear algebra, & high-performance computing. The materials for this course were produced by the Continuum training team.

Collaborator(s):

Hugo Bowne-Anderson Hugo Bowne-Anderson

Yashas Roy Yashas Roy

Course Description

As a Data Scientist, you'll often find that the data you need is not in a single file. It may be spread across a number of text files, spreadsheets, or databases. You want to be able to import the data of interest as a collection of DataFrames and figure out how to combine them to answer your central questions. This course is all about the act of combining, or merging, DataFrames, an essential part of any working Data Scientist's toolbox. You'll hone your pandas skills by learning how to organize, reshape, and aggregate multiple data sets to answer your specific questions.

Prerequisites:

Merging data 

Here, you'll learn all about merging pandas DataFrames. You'll explore different techniques for merging, and learn about left joins, right joins, inner joins, and outer joins, as well as when to use which. You'll also learn about ordered merging, which is useful when you want to merge DataFrames whose columns have natural orderings, like date-time columns.