Interactive Course

Data Manipulation with pandas

Use the world’s most popular Python data science package to manipulate data and calculate summary statistics.

  • 4 hours
  • 15 Videos
  • 56 Exercises
  • 42,682 Participants
  • 4,850 XP

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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. 1

    Transforming Data


    Let’s master the pandas basics. Learn how to inspect DataFrames and perform fundamental manipulations, including sorting rows, subsetting, and adding new columns.

  2. Aggregating Data

    In this chapter, you’ll calculate summary statistics on DataFrame columns, and master grouped summary statistics and pivot tables.

  3. Slicing and indexing

    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.

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Lloyd's Banking Group


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Harvard Business School


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Decision Science Analytics @ USAA

Maggie Matsui
Maggie Matsui

Senior Content Developer at DataCamp

Maggie is a Senior Content Developer 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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Richie Cotton
Richie Cotton

Curriculum Architect at DataCamp

Richie runs the Content Quality team 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.

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