Master Data Analysis with Python - Intro to Pandas

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

Created by : Rated :4.3 632 views

Last Updated: 2023-03-20 11:03:11 English Course Type: Self Learning

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

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 DataFrame 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 textbooks 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.

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

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

Section 1 : Introduction 1 Lectures 00:02:19
Lecture 1 :
Course Overview Preview
Section 2 : Course Contents 6 Lectures 00:14:54
Lecture 1 :
Downloading the Course Material
Lecture 2 :
Exploring the Course Contents
Lecture 3 :
Opening the Material with Jupyter Notebooks
Lecture 4 :
Working through a Course Material
Lecture 5 :
Differences with Video Notebooks
Lecture 6 :
When to Open a New Notebook
Section 3 : What is pandas? 3 Lectures 00:13:16
Lecture 1 :
What is pandas?
Lecture 2 :
Which version of pandas to use?
Lecture 3 :
Pandas examples
Section 4 : The DataFrame and Series 6 Lectures 00:16:39
Lecture 1 :
Intro to the DataFrame and Series
Lecture 2 :
DataFrame Components
Lecture 3 :
Selecting a Series
Lecture 4 :
Components of a Series
Lecture 5 :
Getting Help in a Jupyter Notebook
Lecture 6 :
Exercises
Section 5 : Data Types and Missing Values 4 Lectures 00:14:34
Lecture 1 :
Intro to Data Types and Missing Values
Lecture 2 :
Finding the Data Type of Each Column
Lecture 3 :
Getting More Metadata
Lecture 4 :
Exercises
Section 6 : Setting an Index of a DataFrame 7 Lectures 00:19:53
Lecture 1 :
Setting an Index of a DataFrame
Lecture 2 :
Accessing the Index, Columns, and Data
Lecture 3 :
Accessing the Components of a Series
Lecture 4 :
The Default Index
Lecture 5 :
Setting an Index on Read
Lecture 6 :
Choosing a Good Index
Lecture 7 :
Exercises
Section 7 : Five-Step Process for Data Exploration 1 Lectures 00:05:21
Lecture 1 :
Five-Step Process for Data Exploration

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Theodore Petrou,

3120 Course Views

4 Courses

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.