Cleaning and Working with Dataframes in Python from Pluralsight

Learn to rename columns, tidy up messy data, and convert data types for efficient analysis. Say goodbye to data headaches and hello to streamlined insights.

In today's data-driven world, cleaning and organizing data has become an essential task for businesses and organizations. Messy data can lead to incorrect insights, which can lead to poor decision-making.

In this course, Cleaning and Working with Dataframes in Python, you’ll gain the ability to clean and organize messy data using the powerful pandas library in Python.

First, you’ll explore how to rename columns in a dataframe for more intuitive data access. You'll learn how to assign column names manually using the .columns dataframe attribute and how to rename an existing column in a dataframe using the rename() function.

Next, you’ll discover how to alter columns in a dataframe for a tidy data set. You'll learn how to drop a list of columns with a single call to drop(), and you'll define the purpose of the in place and axis parameters.

Finally, you’ll learn how to apply these skills to solve real-world problems. When you’re finished with this course, you’ll have the skills and knowledge of cleaning and working with dataframes - using pandas in Python - needed to clean and organize messy data and obtain accurate insights. You'll be ready to take on data cleaning challenges and become a more efficient data professional.

What's inside

Syllabus

Course Overview

Dataframes and Exploratory Data Analysis

Modifying Dataframe Columns and Composition

Cleaning and Manipulating Dataframes

Summary

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches pandas in Python, a must-learn for modern data science and machine learning

Taught by Jake Lyman, a well-established instructor in the data science and programming communities

Focuses on hands-on learning through lab exercises, a preferred method for most students

Designed for beginners, so anyone can jump right in regardless of their current data handling skills

Covers renaming columns, data tidying, and data type conversions, which are in-demand skills in business and data analysis

Requires Python programming experience, which may be a barrier for total beginners

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 Cleaning and Working with Dataframes in Python with these activities:

Review course materials

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Reviewing the course materials will help you familiarize yourself with the key concepts and terminology used throughout the course.

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Read the course syllabus and description
Go through the first few sections of the course materials

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Reach out to potential mentors in your field
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Joining a study group can provide you with a support network and help you learn from others.

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Find a study group that fits your schedule and learning style
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Follow tutorials on data cleaning

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Following tutorials on data cleaning can help you learn the techniques and tools used in the industry.

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Find tutorials on data cleaning from reputable sources
Follow the tutorials and complete the exercises

Practice manipulating dataframes

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Practicing manipulating dataframes will help you develop the skills necessary to clean and organize data.

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Complete the practice exercises in the course materials
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Choose a topic for your blog post
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Create a data visualization

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Creating a data visualization will help you learn how to present data in a clear and concise way.

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Choose a dataset to work with
Clean and prepare the data
Create a data visualization using a tool like Tableau or Power BI

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Form a team or work on the competition individually
Submit your solution to the competition

Career center

Learners who complete Cleaning and Working with Dataframes in Python will develop knowledge and skills that may be useful to these careers:

Business Analyst

As a Business Analyst, one of your key roles is to help decision-makers understand and analyze data. Cleaning and Working with Dataframes in Python gives you the skills needed to prepare data for robust analysis and accurate insights, an integral skillset for the role.

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Data Management Specialist

A key responsibility for the Data Management Specialist is to ensure the security, quality, and organization of data. The Cleaning and Working with Dataframes in Python provides you with the skills to handle the data organization aspect of this process.

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Data Scientist

In a multifaceted role such as Data Scientist, knowing how to manipulate and clean data is a key component. The course Cleaning and Working with Dataframes in Python can help you become more efficient with the preparation side of data analysis, saving time and producing better results.

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Statistician

Statisticians collect and analyze data to help organizations make informed decisions. This often involves acquiring data in a raw format that must be cleaned and organized before it can be used. The Cleaning and Working with Dataframes in Python course is a great place to learn the basics of organizing and cleaning data.

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Data Engineer

Data Engineers design, build, and maintain data systems. This includes organizing and cleaning large amounts of data, tasks that can be made more efficient with the skills learned in Cleaning and Working with Dataframes in Python. Because this course uses the popular Pandas library, you will be able to apply your knowledge immediately.

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Database Administrator

Database Administrators set up, manage, and maintain databases. This involves organizing and cleaning data to ensure optimal performance. Cleaning and Working with Dataframes in Python can teach you the basics of data cleaning and organization as it relates to working with databases.

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Data Analyst

As a Data Analyst, you will spend much of your time extracting insights from data and presenting them to stakeholders. The course Cleaning and Working with Dataframes in Python teaches you how to organize, clean, and prepare data for more accurate analysis and efficient visualizations. While a Bachelor’s degree is common, this course can help you gain the hands-on experience that many employers seek.

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Data Architect

Data Architects should have a solid understanding of data organization and cleaning. Cleaning and Working with Dataframes in Python will help you to build a foundation in these skills, which are crucial for a successful career as a Data Architect.

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Market Research Analyst

Market Research Analysts need to be able to find and organize data to create reports that can help their clients make important decisions and influence successful marketing campaigns. Cleaning and Working with Dataframes in Python will teach you to organize and clean data efficiently, giving you a head start in this career.

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Operations Research Analyst

Operations Research Analysts collect, analyze, and interpret data to make recommendations on how to improve an organization's efficiency. Cleaning and Working with Dataframes in Python can help you acquire some of the skills needed to work with raw data, a skillset often used by Operations Research Analysts.

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Financial Analyst

Cleaning and Working with Dataframes in Python may be a useful course for Financial Analysts, especially those who work with large amounts of raw data. This course will help you to organize and clean data in a way that makes it easier to analyze and understand.

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Quantitative Analyst

The Cleaning and Working with Dataframes in Python course can be helpful for Quantitative Analysts, as the skills taught in this course can be used to prepare data for analysis, model building, and forecasting.

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Actuary

Cleaning and Working with Dataframes in Python may be a helpful course for Actuaries, as the skills taught in this course can be used to prepare data for analysis and modeling.

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Computer Programmer

Similar to a Software Engineer, Computer Programmers may find it helpful to learn the basics of data cleaning and manipulation. Cleaning and Working with Dataframes in Python gives you a great foundation for working with data, a skill that is becoming more important for Computer Programmers as software becomes more data-driven.

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Software Engineer

While not a traditional career path from this course, Software Engineers may find the lessons in cleaning and manipulation useful when working with data-driven features or building data analysis software. This course uses Python's Pandas library, a popular library used by Software Engineers for data-related tasks.

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Cleaning and Working with Dataframes in Python

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