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Pandas for Data Science

Genevieve M. Lipp, Nick Eubank, Kyle Bradbury, and Andrew D. Hilton

How can you effectively use Python to clean, sort, and store data? What are the benefits of using the Pandas library for data science? What best practices can data scientists leverage to better work with multiple types of datasets? In the third course of Data Science Python Foundations Specialization from Duke University, Python users will learn about how Pandas — a common library in Python used for data science — can ease their workflow.

We recommend you should take this course after the first two courses of the specialization. However, if you hold a prerequisite knowledge of basic algebra, Python programming, and NumPy, you should be able to complete the material in this course.

In the first week, we’ll discuss Python file concepts, including the programming syntax that allows you to read and write to a file. Then in the following weeks, we’ll transition into discussing Pandas more specifically and the pros and cons of using this library for specific data projects. By the end of this course, you should be able to know when to use Pandas, how to load and clean data in Pandas, and how to use Pandas for data manipulation. This will prepare you to take the next step in your data scientist journey using Python; creating larger software programs.

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What's inside

Syllabus

Week 1: Pandas for Data Science

This week, you will learn how to read data from files into your python program, and write that corresponding data to a file. We’ll be working primarily with string-type data in this unit and will give special attention to the way that python handles strings. Additionally we’ll go over some basic debugging in python using exception traces, and you’ll leverage these to create your own python program that is capable of reading and writing to a file.

Week 2: Tabular Data with Pandas

This Week, you’ll learn how to begin to utilize Pandas, one of the most commonly used libraries in Data Science with python. Pandas is predominantly used for working with tabular data. By the end of this week you’ll be able to identify the hallmarks and quirks of working with tabular data, describe the benefits and limitations of using Pandas, and be able to perform some basic data manipulation techniques in Pandas.

Week 3: Loading and Cleaning Data

This week, you will learn how to perform basic file operations in Pandas, as well as how to clean up large datasets. You’ll learn to read and write from common tabular file formats, and Pandas-specific intricacies for working with that data. Additionally, you’ll learn best practices for cleaning your data.

Week 4: Data Manipulation

This week you will learn how to combine datasets from different sources. Pandas has different methods of combining data depending on your preferred outcome, and you’ll be able to differentiate between when to use each kind. Additionally, we’ll go over computationally efficient ways of querying your data, which, while similar to selecting data via subsetting in its outcomes, has a distinct set of advantages.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Suitable for Python users looking to expand their data analysis skills with Pandas library

Assumes basic knowledge of algebra, Python programming, and NumPy, making it accessible to those with prior programming experience

Provides hands-on practice with Pandas through file operations, data cleaning, and manipulation techniques

Part of the Data Science Python Foundations Specialization from Duke University, offering a structured learning path

Focuses specifically on using Pandas library, which is widely used in data science

Requires students to complete the first two courses of the specialization, potentially limiting accessibility for those new to the topic

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Pandas for Data Science with these activities:

Review basic Python programming concepts

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Before starting this course, strengthen your foundation in Python programming to facilitate your learning.

Browse courses on Python

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Review the basics of Python syntax, data types, and control flow
Practice writing simple Python programs to reinforce your understanding

Read 'Python for Data Analysis' by Wes McKinney

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Supplement your learning by reading a comprehensive book on Python for data analysis.

View Python for Data Analysis: Data Wrangling with... on Amazon

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Obtain a copy of the book
Read through the chapters relevant to the Pandas library
Apply the concepts and techniques presented in the book to your own Pandas projects

Identify a mentor or experienced user of Pandas

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Seek guidance and support from someone with expertise in Pandas to enhance your learning.

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Reach out to potential mentors in your network or online communities
Explain your learning goals and ask for their guidance
Schedule regular meetings or discussions

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Practice writing Python code

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Practice writing Python code to reinforce your understanding of the Pandas library.

Browse courses on Python

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Find a coding challenge or exercise online
Implement the solution in Python using the Pandas library
Check your solution against the provided answer or test cases

Follow tutorials on Pandas data manipulation

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Seek out and complete tutorials to enhance your skills in data manipulation using Pandas.

Browse courses on Pandas

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Search for tutorials on data manipulation with Pandas
Select a tutorial that aligns with your learning objectives
Follow the tutorial step-by-step and implement the techniques in your own Python code

Create a cheat sheet of Pandas functions

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Create a quick reference guide to summarize the most important Pandas functions and their usage.

Browse courses on Pandas

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Identify the most commonly used Pandas functions
Write down the syntax and a brief description of each function
Organize the functions into logical categories or groups

Join a study group or online forum for Pandas

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Engage with other learners to discuss Pandas concepts, share tips, and support each other's learning.

Browse courses on Pandas

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Find or create a study group or online forum focused on Pandas
Actively participate in discussions and ask questions
Collaborate with others on projects or exercises

Build a data analysis project using Pandas

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Apply your Pandas skills to a real-world data analysis project to solidify your understanding.

Browse courses on Data Analysis

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Identify a dataset that aligns with your interests
Load the data into a Pandas DataFrame
Clean and explore the data
Perform data analysis using Pandas functions
Visualize and interpret your results

Career center

Learners who complete Pandas for Data Science will develop knowledge and skills that may be useful to these careers:

Data Scientist

A Data Scientist uses code to gather data, analyze it, and translate it into actionable solutions. A good command of Python code and computational thinking is required to perform these tasks, and a course in Pandas could make all the difference by building a base of knowledge that is suitable for use in a Data Science career. What's more, this course, Pandas for Data Science, was created by Duke University's Data Science Python Foundations Specialization, a highly regarded course that may help you make yourself a standout candidate for jobs in a competitive job market.

See salaries and explore the career path for Data Scientist

Data Analyst

A Data Analyst is a professional who takes raw data and turns it into actionable insights by cleaning, analyzing, and presenting it. This course, Pandas for Data Science, may help you succeed in this role as it introduces you to the Pandas library in Python, which is very useful for working with tabular data. That said, in this course you will also learn about the pros and cons of using this library for specific data projects, and thus you'll gain some valuable perspective that may serve you well in this career.

See salaries and explore the career path for Data Analyst

Data Engineer

A Data Engineer builds, maintains, and manages the data pipelines that fuel a company's information systems. Because you'll be working with tabular data, it will be essential to have a solid understanding of Python, as well as a go-to library for this type of work. This course will help on both counts, as it will not only introduce you to Python but also teach you about the Pandas library, which is frequently used by Data Engineers.

See salaries and explore the career path for Data Engineer

Statistician

A Statistician collects, analyzes, interprets, and presents data. Given that you may often need to use Python to perform your duties as a Statistician, this course may help build a strong foundation for you in Python by introducing you to the Pandas library and allowing you to gain experience using it. Indeed, knowledge and practical experience with Pandas may give you an edge over other job candidates.

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Software Engineer

A Software Engineer designs, develops, tests, and maintains software. While you may not need to use Python or a library like Pandas on a daily basis in this role, being well versed in them is certainly not a bad idea. What's more, this course may be helpful if you wish to become a Full-Stack Software Engineer, who designs and develops both the front-end and back-end of software applications.

See salaries and explore the career path for Software Engineer

Market Researcher

A Market Researcher conducts research on target markets and develops plans to reach them. One of the core tools used by Market Researchers is data analysis. If you choose to use Python, this course may help you develop job-ready skills because it will introduce you to the Pandas library, which is frequently used by data professionals.

See salaries and explore the career path for Market Researcher

Financial Analyst

A Financial Analyst conducts research and performs data analysis to make investment recommendations. This may involve using Python and Pandas to clean, analyze, and present data. This course will introduce you to both, which may improve your marketability as a job candidate.

See salaries and explore the career path for Financial Analyst

Quantitative Analyst

A Quantitative Analyst uses mathematical and statistical models to analyze financial data. This course may be useful for building a foundation of knowledge in Python, as it will teach you about the Pandas library, which can be used for data analysis.

See salaries and explore the career path for Quantitative Analyst

Risk Analyst

A Risk Analyst identifies, assesses, and manages risks within an organization. Because you may occasionally need to use Python for data analysis, this course may be useful if you wish to enter this field. This is especially true in an era where vast amounts of data are collected and analyzed by businesses.

See salaries and explore the career path for Risk Analyst

Auditor

An Auditor examines financial records to ensure accuracy and compliance. Because you may occasionally need to use Python for data analysis, this course may be of some use to you, particularly in instances where you are examining very large amounts of data.

See salaries and explore the career path for Auditor

Operations Research Analyst

An Operations Research Analyst uses mathematical and analytical techniques to improve efficiency and productivity. In some cases, this may involve using Python. If so, this course may be of some use to you, especially if you learn to use Pandas, a library that is frequently used for data manipulation.

See salaries and explore the career path for Operations Research Analyst

Business Analyst

A Business Analyst analyzes and solves business problems. You may occasionally need to use Python for tasks that involve data analysis, in which case this course may be useful. Especially if you learn the Pandas library, which is commonly used for this purpose.

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Management Consultant

A Management Consultant provides advice to businesses on how to improve their performance. You may occasionally need to use Python for tasks that involve data analysis, in which case this course may be useful. Especially if you learn the Pandas library, which is commonly used for this purpose.

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Product Manager

A Product Manager is responsible for the development and marketing of a product. You may occasionally need to use Python for tasks that involve data analysis, in which case this course may be useful. Especially if you learn the Pandas library, which is commonly used for this purpose.

See salaries and explore the career path for Product Manager

Project Manager

A Project Manager plans, executes, and closes projects. You may occasionally need to use Python for tasks that involve data analysis, in which case this course may be useful. Especially if you learn the Pandas library, which is commonly used for this purpose.

See salaries and explore the career path for Project Manager

Reading list

We've selected 19 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Pandas for Data Science.

The Elements of Statistical Learning

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Classic textbook on statistical learning. It useful reference if you want to learn more about the theoretical foundations of data science.

The Elements of Statistical Learning: Data Mining,...

Hardcover

$$$

The Elements of Statistical Learning: Data Mining,...

Hardcover

$$$

The Elements of Statistical Learning: Data Mining,...

Kindle Edition

$$$

Python for Data Analysis

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Provides a comprehensive overview of Python for data analysis, including coverage of Pandas, NumPy, and other essential libraries. It good choice for anyone who wants to learn how to use Python for data science projects.

Pandas for Data Science

What's inside

Syllabus

Good to know

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Activities

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