Data Cleaning and Preprocessing with pandas

Name: Data Cleaning and Preprocessing with pandas Course
Price: 36 USD

with Martin Ganchev

4.7/5

(2,048)

Master Python’s quintessential pandas library and its core data structures – Series and DataFrame objects. Elevate your data analysis skills for real-world challenges

3 hours of content 21766 students

Start for free

What you get:

3 hours of content
23 Interactive exercises
9 Coding exercises
18 Downloadable resources
World-class instructor
Closed captions
Q&A support
Future course updates
Course exam
Certificate of achievement

Data Cleaning and Preprocessing with pandas

A course by Martin Ganchev

Start for free

What you get:

3 hours of content
23 Interactive exercises
9 Coding exercises
18 Downloadable resources
World-class instructor
Closed captions
Q&A support
Future course updates
Course exam
Certificate of achievement

Start for free

What you get:

3 hours of content
23 Interactive exercises
9 Coding exercises
18 Downloadable resources
World-class instructor
Closed captions
Q&A support
Future course updates
Course exam
Certificate of achievement

What you learn

Add the pandas library to your data analysis toolkit.
Learn how to install and import Python packages.
Gain proficiency with pandas Series and DataFrame objects.
Explore methods to clean and preprocess data using pandas.
Solve real-world data preprocessing problems using pandas.

Top Choice of Leading Companies Worldwide

Industry leaders and professionals globally rely on this top-rated course to enhance their skills.

Course Description

pandas is one of today’s most celebrated data analysis libraries. A favorite of many, its versatile functionalities can be leveraged for manipulation of many types of data - numeric, text, Boolean, and more. That’s one of the features that make pandas the go-to choice for analysts, especially during the data cleaning and preprocessing stages. pandas is built on NumPy and takes advantage of its computational power and abilities. But what sets pandas apart is its ability to operate with data in an easy-to-use way, allowing you to focus almost entirely on your analytic task. In this course, you will learn how to work with this powerful Python library and its core data structures – the pandas Series and DataFrames.

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1.1 Introduction to the pandas Library

6 min

1.3 Installing and Running pandas

6 min

1.4 Introduction to pandas Series

9 min

1.7 Working with Attributes in Python

5 min

1.10 Using an Index in pandas

4 min

1.13 Label-based vs Position-based Indexing

5 min

Curriculum

1. pandas - Basics

15 Lessons 82 Min

In this section, you will develop a basic understanding of the pandas library and practice with fundamental programming tools such as methods, parameters, arguments, attributes, and index values. You will also learn how to work with the pandas Series and DataFrame objects. In the end, we will present the pandas documentation and will show how you can navigate through it.

Introduction to the pandas Library
6 min
Installing and Running pandas
6 min
Introduction to pandas Series
9 min
Working with Attributes in Python
5 min
Using an Index in pandas
4 min
Label-based vs Position-based Indexing
5 min
More on Working with Indices in Python
6 min
Using Methods in Python - Part I
5 min
Using Methods in Python - Part II
3 min
Parameters vs Arguments
5 min
The pandas Documentation
10 min
Introduction to pandas DataFrames
5 min
Creating DataFrames from Scratch - Part I
6 min
Creating DataFrames from Scratch - Part II
5 min
Additional Notes on Using DataFrames
2 min
2. Data Cleaning and Data Preprocessing

1 Lesson 5 Min

Only about 20% of the work of a data analytics or science team goes to statistical analysis, making visualization or predictive models. The bulk of the time is consumed by collecting, cleaning, and preprocessing data. That is why in this section, we’ve provided a single lecture that aims at clarifying the meaning of and difference between the data cleaning and data preprocessing stages.

Data Cleaning and Data Preprocessing
5 min
3. pandas Series

5 Lessons 21 Min

Here, we will introduce you to working with one of the two core data structures of pandas – the pandas Series object. You will also discover several common methods and learn how to apply them to a pandas Series.

.unique(), .nunique()
4 min
Converting Series into Arrays
5 min
.sort_values()
4 min
Attribute and Method Chaining
4 min
.sort_index()
4 min
4. pandas DataFrames

8 Lessons 44 Min

This section focuses on the other fundamental object in pandas - the DataFrame. The DataFrame is universally known as the most important structure in this library. Here, we will revise its characteristics as well as comment on several popular related methods. In addition, we will show you how to deal with various techniques for data selection in a DataFrame.

A Revision to pandas DataFrames
5 min
A Note on Working with the Anaconda Assistant Read now
1 min
Using the Anaconda Assistant: Importing Data with pandas
5 min
Common Attributes for Working with DataFrames
4 min
Data Selection in pandas DataFrames
7 min
Data Selection - Indexing Data with .iloc[]
6 min
Data Selection - Indexing Data with .loc[]
4 min
A Few Comments on Using .loc[] and .iloc[]
12 min

Topics

PythonData AnalysisProgrammingData PreprocessingPandas

Tools & Technologies

Course Requirements

Python (version 3.8 or later), pandas library, and a code editor or IDE (e.g., Jupyter Notebook, Spyder, or VS Code)
Completion of an introductory Python course is required.
Familiarity with NumPy is helpful but not mandatory.

Who Should Take This Course?

Level of difficulty: Intermediate

Aspiring data analysts, data scientists, data engineers, AI engineers
Graduate students who need Python and pandas for their studies

Exams and Certification

A 365 Financial Analyst Course Certificate is an excellent addition to your LinkedIn profile—demonstrating your expertise and willingness to go the extra mile to accomplish your goals.

Meet Your Instructor

Martin Ganchev

Worked at

15 Courses

41531 Reviews

612707 Students

Martin Ganchev has been creating high-quality online education content since 2016, helping over one million students worldwide gain valuable business, finance, and data skills. As the second employee at 365 Careers, he played a key role in building some of the company's most successful courses, known for their clarity, practical value, and engaging delivery. With a Master of Science in Economic and Social Sciences from Bocconi University, Martin specializes in statistics, econometrics, Python, SQL, and business intelligence—equipping students to analyze data rigorously, automate workflows, and turn complex results into clear business insights.