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  • 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
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  • Section 1 : Introduction 1 Lectures 00:02:19

    • Lecture 1 :
  • 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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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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