Data Manipulation with pandas
Use the world’s most popular Python data science package to manipulate data and calculate summary statistics.
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Course Description
pandas is the world's most popular Python library, used for everything from data manipulation to data analysis. In this course, you'll learn how to manipulate DataFrames, as you extract, filter, and transform real-world datasets for analysis. Using pandas you’ll explore all the core data science concepts. Using real-world data, including Walmart sales figures and global temperature time series, you’ll learn how to import, clean, calculate statistics, and create visualizations—using pandas to add to the power of Python!
- 1
Transforming DataFrames
FreeLet’s master the pandas basics. Learn how to inspect DataFrames and perform fundamental manipulations, including sorting rows, subsetting, and adding new columns. - 2
Aggregating DataFrames
In this chapter, you’ll calculate summary statistics on DataFrame columns, and master grouped summary statistics and pivot tables. - 3
Slicing and Indexing DataFrames
Indexes are supercharged row and column names. Learn how they can be combined with slicing for powerful DataFrame subsetting. - 4
Creating and Visualizing DataFrames
Learn to visualize the contents of your DataFrames, handle missing data values, and import data from and export data to CSV files.
Alex Yarosh
Adel Nehme
Amy Peterson
Justin SaddlemyerPrerequisites
Intermediate Python
Richie Cotton
Curriculum Architect at DataCamp
Richie is a Learning Solutions Architect at DataCamp. He has been using R since 2004, in the fields of proteomics, debt collection, and chemical health and safety. He has released almost 30 R packages on CRAN and Bioconductor – most famously the assertive suite of packages – as well as creating and contributing to many others. He also has written two books on R programming, Learning R and Testing R Code.
Maggie Matsui
Curriculum Manager at DataCamp
Maggie is a Curriculum Manager at DataCamp. She holds a Bachelor's degree in Statistics and Computer Science from Brown University, where she spent lots of time teaching math, programming, and statistics as a tutor and teaching assistant. She's passionate about teaching all things data-related and making programming accessible to everyone.
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Harvard Business School
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Decision Science Analytics, USAA
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