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

Introducing DataFrames

Inspecting a DataFrame

Parts of a DataFrame

Sorting and subsetting

Sorting rows

Subsetting columns

Subsetting rows

Subsetting rows by categorical variables

New columns

Adding new columns

Combo-attack!

Transforming DataFrames

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

Summary statistics

Mean and median

Summarizing dates

Efficient summaries

Cumulative statistics

Counting

Dropping duplicates

Counting categorical variables

Grouped summary statistics

What percent of sales occurred at each store type?

Calculations with .groupby()

Multiple grouped summaries

Pivot tables

Pivoting on one variable

Fill in missing values and sum values with pivot tables

Aggregating DataFrames

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

Explicit indexes

Setting and removing indexes

Subsetting with .loc[]

Setting multi-level indexes

Sorting by index values

Slicing and subsetting with .loc and .iloc

Slicing index values

Slicing in both directions

Slicing time series

Subsetting by row/column number

Working with pivot tables

Pivot temperature by city and year

Subsetting pivot tables

Calculating on a pivot table

Slicing and Indexing DataFrames

Learn to visualize the contents of your DataFrames, handle missing data values, and import data from and export data to CSV files.

Visualizing your data

Which avocado size is most popular?

Changes in sales over time

Avocado supply and demand

Price of conventional vs. organic avocados

Missing values

Finding missing values

Removing missing values

Replacing missing values

Creating DataFrames

List of dictionaries

Dictionary of lists

Reading and writing CSVs

CSV to DataFrame

DataFrame to CSV

Wrap-up

Creating and Visualizing DataFrames

Avocado prices

Walmart sales

Homelessness data

Temperatures

Course Glossary: Data Manipulation with pandas

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!

The videos contain live transcripts, which can be accessed 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.

<h2>Discover Data Manipulation with pandas</h2>
With this course, you’ll learn why pandas is the world's most popular Python library, used for everything from data manipulation to data analysis. You’ll explore how to manipulate DataFrames, as you extract, filter, and transform real-world datasets for analysis. 
<br><br>
With 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.
<br><br>
<h2>Work with pandas Data to Explore Core Data Science Concepts</h2>
You’ll start by mastering the pandas basics, including how to inspect DataFrames and perform some fundamental manipulations. You’ll also learn about aggregating DataFrames, before moving on to slicing and indexing. 
<br><br>
You’ll wrap up the course by learning how to visualize the contents of your DataFrames, working with a dataset that contains weekly US avocado sales. 
<br><br>
<h2>Learn to Manipulate DataFrames</h2>
By completing this pandas course, you’ll understand how to use this Python library for data manipulation. You’ll have an understanding of DataFrames and how to use them, as well as be able to visualize your data in Python.

Intermediate Python

This data manipulation with pandas course will show you how to manipulate DataFrames as you extract, filter, and transform real-world datasets for analysis. 

Data Manipulation with pandas

Learn how to import and clean data, calculate statistics, and create visualizations with pandas. 

Associate Data Scientist  in Python

Data Analyst  in Python

Data Manipulation  in Python

Python Data Fundamentals

Avocado supply and demand

Data Manipulation with pandas

Exercise instructions

Hands-on interactive exercise