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  • 📓 CIS 340 Notebook
  • 📋 CIS 340 Schedule
  • ▶ Group Assignments & Presentation Info
    • Group Assignments
    • Data Analysis Group Presentation
  • ▶ Labs
    • ▶ Lab 1: NumPy Arrays
      • Objective
    • ▶ Lab 2: Exploring Summary Statistics with Pandas
      • Objective
    • ▶ Lab 3: Filtering and Sorting
      • Objective
    • ▶ Lab 4: groupby()
      • Objective
    • ▶ Lab 5: Reading Tables from an Azure SQL Database
      • Objective
    • ▶ Lab 6: Matplotlib and Seaborn plots
      • Objective
  • ▶ 1 Getting Started
    • ▶ Ex 1.1: Intro to Jupyter Notebooks
      • Objective
      • Class Workflow
      • CIS 340 Software and Installs
      • Jupyter Notebooks
      • Markdown
  • ▶ 2 NumPy
    • ▶ Ex 2.1: Intro to NumPy
      • Objective
      • The Data Analytics Ecosystem
      • NumPy ndarrays
    • ▶ 2.2: Indexing and Slicing
      • Objective
      • Basic Indexing and Slicing
      • Importing Data Into NumPy Arrays
  • ▶ 3 Pandas
    • ▶ Ex 3.1: Reading CSV Files
      • Objective
      • Pandas DataFrames
    • ▶ Ex 3.2: Reading Excel Files
      • Objective
      • Bringing Code into Notebooks
      • uv Package Manager
      • More on DataFrame Attributes
      • Understanding Data Types
      • Changing Data Types
    • ▶ Ex 3.3: Custom Datasets & Intro to Git
      • Objective
      • Examining Summary Statistics
      • Creating Custom Datasets
      • Git Version Control
    • ▶ Ex 3.4: Filtering
      • Objective
      • Filtering DataFrames
    • ▶ Ex 3.5: Sorting and Top N
      • Objective
      • Converting Types and Sorting
      • Top and Bottom N
    • ▶ Ex 3.6: Groupby Operations - One Column
      • Objective
      • groupby() One Column
      • Adding New Columns to a DataFrame
    • ▶ Ex 3.7: Groupby Operations - Two Columns
      • Objective
      • groupby() Two Columns
    • ▶ Ex 3.8: Screenscraping and Data Cleansing
      • Objective
      • Screenscraping
      • Using First Row as Column Names
      • Data Cleansing
    • ▶ Ex 3.9: Dealing with Missing Data
      • Objective
      • Missing Data
    • ▶ Ex 3.10: Data Cleansing II and MCP Development
      • Objective
      • Inconsistent Data
      • Introduction to MCP (Model Context Protocol)
      • Building an MCP Server for Claude Desktop
    • ▶ Ex 3.11: Reading Tables from a Database
      • Objective
      • Reading Tables from a Database
  • ▶ 4 Visualizing Data with Python
    • ▶ Ex 4.1: Matplotlib and Seaborn
      • Objective
      • Matplotlib
      • Seaborn
    • ▶ Ex 4.2: Working with Different Datasets
      • Objective
      • Visualizing Joined Datasets
    • ▶ Ex 4.3: Bokeh-LineChart
      • Objective
      • Intro to Bokeh
    • ▶ Ex 4.4: Time Series and Bokeh Layouts
      • Objective
      • Layouts
      • Hover Tooltip

Lab 6: Matplotlib and Seaborn plots

Description: Download the Lab 6 - Matplotlib and Seaborn Plots.ipynb Jupyter Notebook file from Canvas. Use the skills that were introduced in the demonstration and exercise files from 4.1 and 4.2 to create Matplotlib and Seaborn plots similar to the below in parts 1 through 5. Submit screengrabs of your code and plots, similar to the screenshots below.

Part 1:

Expected output: Matplotlib plot created in Lab 6 part 1 using skills from exercises 4.1 and 4.2

 

Part 2:

Expected output: Matplotlib plot created in Lab 6 part 2 using skills from exercises 4.1 and 4.2

 

Part 3:

Expected output: Seaborn plot created in Lab 6 part 3 using skills from exercises 4.1 and 4.2

 

Part 4:

Expected output: Seaborn plot created in Lab 6 part 4 using skills from exercises 4.1 and 4.2

 

Part 5:

Expected output: Matplotlib or Seaborn plot created in Lab 6 part 5 using skills from exercises 4.1 and 4.2

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  • Lab 6: Matplotlib and Seaborn plots
  • Description: Download the Lab 6 - Matplotlib and Seaborn Plots.ipynb Jupyter Notebook file from Canvas. Use the skills that were introduced in the demonstration and exercise files from 4.1 and 4.2 to create Matplotlib and Seaborn plots similar to the below in parts 1 through 5. Submit screengrabs of your code and plots, similar to the screenshots below.