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  • Best practices from pandas expert Ted Petrou author of Master Data Analysis with Python
  • Select subsets of data from DataFrames with just the brackets, loc, and iloc
  • Learn how to simultaneously select rows and columns
  • Learn how to filter for specific criteria using boolean selection
  • Learn a more intuitive procedure for filtering data with the query method
  • Take a challenging Certification Exam to validate your knowledge gained

Master Data Analysis with Python - Selecting Subsets of Data targets those who want to completely master doing data analysis with pandas. This is the second course in the Master Data Analysis with Python series and provides extremely detailed coverage of how to do any type of subset selection within a pandas DataFrame or Series.

Exercises and a Free Certification Exam

Over 100 exercises with detailed solutions are available for you to practice. Additionally, there is a free challenging certification exam offered for those who want to validate their knowledge gained.

Expert Instructor

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.

Become an Expert

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.

The Master Data Analysis with Python Series

Selecting Subsets of Data is second 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.

  • This is the second course of the Master Data Analysis with Python series. You will need to take Master Data Analysis with Python - Intro to Pandas 2022 first.
  • Those who want to begin a comprehensive path for mastering the pandas library with best practices to analyze data
  • Those who want to test their knowledge gained by taking a challenging certification exam
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  • Section 1 : Selecting Subsets of Data from DataFrames with just the brackets 3 Lectures 00:10:52

    • Lecture 1 :
    • Intro to Subset Selection Preview
    • Lecture 2 :
    • Selecting with Just the Brackets.mp4
    • Lecture 3 :
    • Exercises
  • Section 2 : Selecting Subsets of Data from DataFrames with loc 4 Lectures 00:21:55

    • Lecture 1 :
    • Simultaneous row and column subset selection
    • Lecture 2 :
    • Slice Notation with loc
    • Lecture 3 :
    • Other Subset Selections with loc
    • Lecture 4 :
    • Exercises
  • Section 3 : Selecting Subsets of Data with iloc 2 Lectures 00:09:13

    • Lecture 1 :
    • Simultaneous row and column subset selection
    • Lecture 2 :
    • Exercises
  • Section 4 : Selecting Subsets of Data from a Series 2 Lectures 00:09:11

    • Lecture 1 :
    • Selecting Subsets of Data from a Series
    • Lecture 2 :
    • Exercises
  • Section 5 : Boolean Selection Single Conditions 3 Lectures 00:16:51

    • Lecture 1 :
    • Boolean Selection Single Conditions
    • Lecture 2 :
    • Practical Boolean Selection
    • Lecture 3 :
    • Exercises
  • Section 6 : Boolean Selection Multiple Conditions_ 4 Lectures 00:25:09

    • Lecture 1 :
    • Different logical operators for boolean Series
    • Lecture 2 :
    • Inverting a condition with the not operator
    • Lecture 3 :
    • Many equality conditions in a single column
    • Lecture 4 :
    • Exercises - Boolean Selection Multiple Conditions
  • Section 7 : Boolean Selection More 5 Lectures 00:19:04

    • Lecture 1 :
    • Boolean Selection on a Series
    • Lecture 2 :
    • Simultaneous boolean selection of rows and column labels with loc
    • Lecture 3 :
    • Column to Column Comparison
    • Lecture 4 :
    • Filter for Missing Values
    • Lecture 5 :
    • Exercises - Boolean Selection More
  • Section 8 : Filtering with the query Method 7 Lectures 00:19:39

    • Lecture 1 :
    • Intro to the query Method
    • Lecture 2 :
    • Column to Column Comparison within the query method
    • Lecture 3 :
    • Arithmetic Operations within query
    • Lecture 4 :
    • Reference Variable Names
    • Lecture 5 :
    • Selecting Columns with query
    • Lecture 6 :
    • Summary of the query Method
    • Lecture 7 :
    • Exercises
  • Section 9 : Miscellaneous Subset Selection 3 Lectures 00:09:32

    • Lecture 1 :
    • Selecting a Column with Dot Notation
    • Lecture 2 :
    • Selecting rows with just the brackets using slice notation
    • Lecture 3 :
    • Selecting a single cell with at and iat
  • Section 10 : Taking the Certification Exam 3 Lectures 00:10:09

    • Lecture 1 :
    • Going to the Exam Website
    • Lecture 2 :
    • Completing the Exam
    • Lecture 3 :
    • Submitting the Exam
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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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