Data Cleaning and Preprocessing with pandas

with Martin Ganchev

Introducing you to the fundamentals of the quintessential Python data analysis library, pandas, and its core data structures – the Series and DataFrame objects.

3 hours 27 lessons
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27 High Quality Lessons
3 Practical Tasks
3 Hours of Content
Certificate of Achievement

Course Overview

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.

Topics covered

data analysisData processingProgrammingPython

What You'll Learn

This course introduces you to one of the most widely used data analysis libraries – pandas. With pandas you’ll be able to solve your analytic tasks in an easy and professional way!

Develop a basic understanding of the pandas library 
Navigate through the pandas documentation 
Practice with fundamental programming tools 
Study collecting, cleaning, and preprocessing data 
Work with pandas Series and DataFrames 
Practice data selection with pandas 


Student feedback


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Nice course. It makes a lot of sense after having completed the Data preprocessing with Numpy course, as there the full stages of data preprocessing are specified. One can then see this pandas course as building on top of that other course, usefully highlighting the differences and advantages of using pandas for mixed data sets and more efficient visualisations. In this perspective, this can be considered a course on data preprocessing, while on its own probably not. Overall, the concepts are clearly explained and well showcase the strengths of pandas for data manipulation and visualisation.
I have mixed feelings on this course. The name suggests there will be some data cleaning and preprocessing done or discussed but none of that really happens. At its current state I believe 'Introduction to pandas' would be a more suitable name for the course as it discusses some of the basics of the library. I enjoyed the explanation of loc and iloc as it gave me a better understanding of them. The final rating reflects only the fact that as it stands the course seems unfinished, lacking sections on actual data cleaning and preprocessing.
very informative and useful, but packed too closely without deep explanations(unlike python programmer BootCamp) into a 2-hour module. It felt like someone reading out keyword-definition phrases(had to rewind a lot). the course is oriented on the basic usage of pandas for a career but the questions in practice exams are tricky and high-order-thinking like nowhere was it mentioned explicitly in the course that dfs can be made only by a list of lists
It's just a pity that there's no pratical project tutorial in this course, I wonder how much knowledge I can apply to a real world? The course is mainly about some basic concept of pandas, but non of them are related to data cleaning even preprocessing. Hope we have more lessons about those topic and some real world projects like we did in "Data Preprocessing with NumPy". Thank you.
I really appreciate the new information and knowledge I gained from this course; however, I wish if the quality of this course is the same as the NumPy course. for example, I wish if the instructor discussed the company load data, and data cleaning in this course. Anyway, good course and good content as usual. Thx
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Martin Ganchev

“In this course, I will help you streamline the data cleaning and data preprocessing stages of analysis with pandas – the go-to library for data science and analytics professionals. By the end of this brief practical training, you will be able to handle the pandas Series object and DataFrames with confidence and ease.”

Martin Ganchev

Worked at the European Commission

Data Cleaning and Preprocessing with pandas

with Martin Ganchev

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