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Pandas

  • Course Code: Data Analysis / BI - Pandas
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 2 Days Audience: This course is geared for Python experienced developers, analysts or others who wants to use pandas to automate repetitive spreadsheet functionality and derive insight from data by sorting columns, filtering data subsets, and creating multi-leveled indices.

Course Snapshot 

  • Duration: 2 days 
  • Skill-level: Foundation-level Pandas skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for Python experienced developers, analysts or others who wants to use pandas to automate repetitive spreadsheet functionality and derive insight from data by sorting columns, filtering data subsets, and creating multi-leveled indices.  
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation, or remote instructor led delivery, or CBT/WBT (by request). 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

Pandas makes it easy to dive into Python-based data analysis. You’ll learn to use pandas to automate repetitive spreadsheet functionality and derive insight from data by sorting columns, filtering data subsets, and creating multi-leveled indices. Each lesson is a self-contained tutorial, letting you dip in when you need to troubleshoot tricky problems. Best of all, you won’t be learning from sterile or randomly created data. You’ll start with a variety of datasets that are big, small, incomplete, broken, and messy and learn how to clean and format them for proper analysis 

Working in a hands-on learning environment, led by our Pandas expert instructor, students will learn about and explore: 

  • Import a CSV, identify issues with its data structures, and convert it to the proper format 
  • Sort, filter, pivot, and draw conclusions from a dataset and its subsets. 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Identify trends from text-based and time-based data 
  • Organize, group, merge, and join separate datasets 
  • Real-world datasets that are easy to download and explore 

Audience & Pre-Requisites 

This course is geared for attendees with Python skills who wish to use pandas to automate repetitive spreadsheet functionality and derive insight from data by sorting columns, filtering data subsets, and creating multi-leveled indices 

Pre-Requisites:  Students should have  

  • developers with some knowledge of Python.  
  • experienced with spreadsheet software who know the basics of Python. 

Course Agenda / Topics 

  1. Introducing Pandas 
  • Data in the 21st Century 
  • Introducing pandas 
  • Importing a Dataset 
  • Manipulating a DataFrame 
  • Counting Values in a Series 
  • Filtering a Column by One or More Criteria 
  • Grouping Data 
  1. Python Crash Course 
  • Simple Data Types 
  • Operators 
  • Variables 
  • Functions 
  • Objects and Methods 
  • Lists 
  • Tuples 
  • Dictionaries 
  • Sets 
  • Modules, Classes, and Datetimes 
  1. NumPy Crash Course 
  • Dimensions 
  • The ndarray Object 
  • The nan Object 
  1. The Series Object 
  • Overview of a Series 
  • Create a Series from Python Objects 
  • Retrieving the First and Last Rows 
  • Mathematical Operations 
  • Passing the Series to Python’s Built-In Functions 
  • Coding Challenges / Exercises 
  1. Series Methods 
  • Importing a Dataset with the read_csv Method 
  • Sorting a Series 
  • Overwriting a Series with the inplace Parameter 
  • Counting Values with the value_counts Method 
  • Invoking a Function on Every Series Value with the apply Method 
  • Coding Challenge: Deriving Insights from a Series 
  1. The DataFrame Object 
  • Overview of a DataFrame 
  • Similarities between Series and DataFrames 
  • Sorting a DataFrame 
  • Sort by Index 
  • Setting a New Index 
  • Selecting Columns or Rows from a DataFrame 
  • Select Rows from a DataFrame 
  • Extract Value from Series 
  • Rename Column or Row 
  • Resetting an Index 
  • Coding Challenge 
  1. Filtering a DataFrame 
  • Optimizing A Dataset for Memory Usage 
  • Filtering by a Single Condition 
  • Filtering by Multiple Conditions 
  • Filtering by Condition 
  • Dealing with Duplicates 
  • Coding Challenge 
  1. Working with Text Data 
  • String Casing 
  • String Slicing 
  • Boolean Methods 
  • Splitting Strings 
  • Coding Challenge 
  • A Note on Regular Expressions 
  1. MultiIndex DataFrames 
  • The MultiIndex Object 
  • MultiIndex DataFrames 
  • Sorting A MultiIndex 
  • Indexing with a MultiIndex 
  • Cross Sections 
  • Manipulating the Index 
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