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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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4 Hours15 Videos56 Exercises194,550 Learners4850 XPData Analyst TrackData Manipulation TrackData Scientist TrackPython Programmer Track

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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 DataFrames

    Free

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

    Play Chapter Now
    Introducing DataFrames
    50 xp
    Inspecting a DataFrame
    100 xp
    Parts of a DataFrame
    100 xp
    Sorting and subsetting
    50 xp
    Sorting rows
    100 xp
    Subsetting columns
    100 xp
    Subsetting rows
    100 xp
    Subsetting rows by categorical variables
    100 xp
    New columns
    50 xp
    Adding new columns
    100 xp
    Combo-attack!
    100 xp
  2. 2

    Aggregating DataFrames

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

    Play Chapter Now
    Summary statistics
    50 xp
    Mean and median
    100 xp
    Summarizing dates
    100 xp
    Efficient summaries
    100 xp
    Cumulative statistics
    100 xp
    Counting
    50 xp
    Dropping duplicates
    100 xp
    Counting categorical variables
    100 xp
    Grouped summary statistics
    50 xp
    What percent of sales occurred at each store type?
    100 xp
    Calculations with .groupby()
    100 xp
    Multiple grouped summaries
    100 xp
    Pivot tables
    50 xp
    Pivoting on one variable
    100 xp
    Fill in missing values and sum values with pivot tables
    100 xp
  3. 3

    Slicing and Indexing DataFrames

    Indexes are supercharged row and column names. Learn how they can be combined with slicing for powerful DataFrame subsetting.

    Play Chapter Now
    Explicit indexes
    50 xp
    Setting and removing indexes
    100 xp
    Subsetting with .loc[]
    100 xp
    Setting multi-level indexes
    100 xp
    Sorting by index values
    100 xp
    Slicing and subsetting with .loc and .iloc
    50 xp
    Slicing index values
    100 xp
    Slicing in both directions
    100 xp
    Slicing time series
    100 xp
    Subsetting by row/column number
    100 xp
    Working with pivot tables
    50 xp
    Pivot temperature by city and year
    100 xp
    Subsetting pivot tables
    100 xp
    Calculating on a pivot table
    100 xp
  4. 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.

    Play Chapter Now
    Visualizing your data
    50 xp
    Which avocado size is most popular?
    100 xp
    Changes in sales over time
    100 xp
    Avocado supply and demand
    100 xp
    Price of conventional vs. organic avocados
    100 xp
    Missing values
    50 xp
    Finding missing values
    100 xp
    Removing missing values
    100 xp
    Replacing missing values
    100 xp
    Creating DataFrames
    50 xp
    List of dictionaries
    100 xp
    Dictionary of lists
    100 xp
    Reading and writing CSVs
    50 xp
    CSV to DataFrame
    100 xp
    DataFrame to CSV
    100 xp
    Wrap-up
    50 xp

In the following tracks

Data Analyst Data Manipulation Data Scientist Python Programmer

Datasets

Avocado pricesWalmart salesHomelessness dataTemperatures

Collaborators

alexandrayaroshAlex YaroshAAN94Adel Nehmeamy-4121b590-cc52-442a-9779-03eb58089e08Amy Petersonjustin-saddlemyerJustin Saddlemyer

Prerequisites

Intermediate Python
Richie Cotton Headshot

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.
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Maggie Matsui Headshot

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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Lloyds Banking Group

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Harvard Business School

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