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Data Manipulation with pandas

Beginner

4.5+

Updated 12/2024

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

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PythonData Manipulation4 hours15 videos56 exercises4,850 XP422,293Statement of Accomplishment

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Course Description

Discover Data Manipulation with pandas

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.

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.

Work with pandas Data to Explore Core Data Science Concepts

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.

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.

Learn to Manipulate DataFrames

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.

Prerequisites

Intermediate Python

1

Transforming DataFrames

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!

2

Aggregating DataFrames

Summary statistics

Mean and median

Summarizing dates

Efficient summaries

Cumulative statistics

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

3

Slicing and Indexing DataFrames

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

4

Creating and Visualizing DataFrames

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

Data Manipulation with pandas

Course
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*4.5

from 283 reviews

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Top reviews

CELISMAR O.

4 days

That is a great course! Rich in content, well explained and comprehensive. I just believe that an exercises review would improve significantly the student understanding. I really recommend it!

chaker s.

6 days

Good course

phil m.

9 days

Excellent module. It would be great to have some context on uses of a pandas series , I can understand why you'd need pandas subsets of a dataframe.

Hanseeka D.

13 days

This course is enhance my understanding of pandas.

John C.

17 days

Good content, good pace, excellent exercises!

"That is a great course! Rich in content, well explained and comprehensive. I just believe that an exercises review would improve significantly the student understanding. I really recommend it!"

CELISMAR O.

"Good course"

chaker s.

"Excellent module. It would be great to have some context on uses of a pandas series , I can understand why you'd need pandas subsets of a dataframe."

phil m.

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