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Course Description
Being able to combine and work with multiple datasets is an essential skill for any aspiring Data Scientist. pandas is a crucial cornerstone of the Python data science ecosystem, with Stack Overflow recording 5 million views for pandas questions. Learn to handle multiple DataFrames by combining, organizing, joining, and reshaping them using pandas. You'll work with datasets from the World Bank and the City Of Chicago. You will finish the course with a solid skillset for data-joining in pandas.The videos contain live transcripts you can reveal by clicking "Show transcript" at the bottom left of the videos.The course glossary can be found on the right in the resources section.To obtain CPE credits you need to complete the course and reach a score of 70% on the qualified assessment. You can navigate to the assessment by clicking on the CPE credits callout on the right.
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Start Course for FreeWhat you'll learn
- Concatenate and Manage DataFrame Integrity
- Filter, Query, and Reshape Data for Analysis
- Merge Using Index, Hierarchical, and Time-Series Structures
- Perform and Customize DataFrame Joins
Prerequisites
Curriculum
Course outline
1
Data Merging Basics
Learn how you can merge disparate data using inner joins. By combining information from multiple sources you’ll uncover compelling insights that may have previously been hidden. You’ll also learn how the relationship between those sources, such as one-to-one or one-to-many, can affect your result.
- Inner join50 XP
- What column to merge on?50 XP
- Your first inner join100 XP
- Inner joins and number of rows returned100 XP
- One-to-many relationships50 XP
- One-to-many classification100 XP
- One-to-many merge100 XP
- Merging multiple DataFrames50 XP
- Total riders in a month100 XP
- Three table merge100 XP
- One-to-many merge with multiple tables100 XP
2
Merging Tables With Different Join Types
Take your knowledge of joins to the next level. In this chapter, you’ll work with TMDb movie data as you learn about left, right, and outer joins. You’ll also discover how to merge a table to itself and merge on a DataFrame index.
3
Advanced Merging and Concatenating
In this chapter, you’ll leverage powerful filtering techniques, including semi-joins and anti-joins. You’ll also learn how to glue DataFrames by vertically combining and using the pandas.concat function to create new datasets. Finally, because data is rarely clean, you’ll also learn how to validate your newly combined data structures.
4
Merging Ordered and Time-Series Data
In this final chapter, you’ll step up a gear and learn to apply pandas' specialized methods for merging time-series and ordered data together with real-world financial and economic data from the city of Chicago. You’ll also learn how to query resulting tables using a SQL-style format, and unpivot data using the melt method.
Joining Data with pandas
Course
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