Data Cleaning and Preprocessing with pandas

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

With Martin Ganchev

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Course Overview

pandas is one of today’s most successful data analysis libraries out there. A favorite to 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. Technically, pandas has been built on NumPy because the former needs the computational power and abilities of the latter. But what makes pandas truly great is its ability to operate with the data in an easy-to-use way, allowing you to focus almost entirely on your analytic task. And in this course, you will learn how to work with this powerful Python library and its core data structures – the pandas Series and DataFrames.

27 High Quality Lessons
3 Practical Tasks
2 Hours of Video
Certificate of Achievement

Skills you will gain

data analysisdata processingprogrammingpython

What You'll Learn

This course delivers information about one of the most widely used data analysis libraries – pandas. Learn pandas and 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 

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

Martin Ganchev
Worked at European Commission
Data Cleaning and Preprocessing with pandas

With Martin Ganchev

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