Master Data Analysis with Python - Intro to Pandas

person icon Teddy Petrou

Master Data Analysis with Python - Intro to Pandas

Begin your data analysis journey with Python by mastering the fundamentals of the pandas library

updated on icon Updated on Nov, 2023

language icon Language - English

person icon Teddy Petrou

architecture icon Data Analysis,Python,Development,Pandas

Lectures -29

Resources -3

Duration -1 hours


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Course Description

Master Data Analysis with Python - Intro to Pandas  targets those who want to completely master doing data analysis with pandas. This course provides an introduction to the two primary pandas objects, the Data Frame and Series. This is a brand new free course updated for the latest version of pandas.

This course is taught by expert instructor Ted Petrou, author of the highly-rated text books Pandas Cookbook and Master Data Analysis with Python. Ted has taught over 1,000 hours of live in-person data science courses that use the pandas library. Pandas is a difficult library to use effectively and is often taught incorrectly with poor practices. Ted is extremely adept at using pandas and is known for developing best practices on how to use the library.

All of the material and exercises are written in Jupyter Notebooks available for you to download. This allows you to read the notes, run the code, and write solutions to the exercises all in a single place.

This course targets those who have an interest in becoming experts and completely mastering the pandas library for data analysis in a professional environment. This course does not cover all of the pandas library, just a small and fundamental portion of it. If you are looking for a brief introduction of the entire pandas library, this course is not it. It takes many dozens of hours, lots of practice, and rigorous understanding to be successful using pandas for data analysis in a professional environment.

Intro to Pandas is first in the Master Data Analysis with Python series which includes the following sequence of courses:

  • Intro to Pandas

  • Selecting Subsets of Data with Pandas

  • Essential Pandas Commands

  • Grouping Data with Pandas

  • Time Series with Pandas

  • Cleaning Data with Pandas

  • Joining Data with Pandas

  • Data Visualization

  • Advanced Pandas

  • Exploratory Data Analysis

This course assumes no previous pandas experience. The only prerequisite knowledge is to understand the fundamentals of Python.

Who this course is for:

  • Those who want to begin a comprehensive path for mastering the pandas library with best practices to analyze data


What will you learn in this course:

  • Best practices from pandas expert Ted Petrou author of Master Data Analysis with Python
  • Introduction to the pandas Data Frame and Series
  • Understanding the different data types available within a Data Frame
  • Accessing the Data Frame components - the index, columns, and values
  • Setting a meaningful index in a Data Frame
  • Completing a five-step process for data exploration


What are the prerequisites for this course?

  • It is necessary to understand the fundamentals of the Python programming language. No prior experience with pandas needed.

Master Data Analysis with Python - Intro to Pandas


Check out the detailed breakdown of what’s inside the course

2 Lectures
  • play icon Course Overview 02:19 02:19
  • play icon Resources
Course Contents
6 Lectures
What is pandas?
3 Lectures
The DataFrame and Series
6 Lectures
Data Types and Missing Values
4 Lectures
Setting a Meaningful Index
7 Lectures
Five-Step Process for Data Exploration
1 Lectures

Instructor Details

Teddy Petrou

Teddy Petrou

I am the author of Pandas Cookbook and Master Data Analysis with Python, highly rated texts on performing real-world data analysis with Pandas.

I am the founder of Dunder Data, a company that teaches the fundamentals of data science and machine learning. I enjoy discovering best practices on how to use and teach data analysis with Python.

I also enjoy creating open-source Python libraries and am author of Dexplot, Bar Chart Race, DataFrame Image, and Jupyter to Medium.

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