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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
  • ▶ 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

Ex 3.9 Dealing with Missing Data:

Objective: Explore tools and techniques for identifying and dealing with missing data.

  1. Download the Ex3.9_MissingData.ipynb file.
  2. Using the IthacaDailyClimateJan2018.csv file, work through each of the steps in the Jupyter Notebook which should include both your code and the corresponding output that match the screengrabs of the completed steps 2, 4, and 8 provided below.

Expected output: Ithaca January 2018 climate DataFrame loaded with missing values visible using isnull()

 

Expected output: Ithaca climate DataFrame after filling missing values using fillna() or forward fill

 

Expected output: Ithaca climate DataFrame after dropping rows with missing values using dropna()

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  • Ex 3.9 Dealing with Missing Data:
  • Objective: Explore tools and techniques for identifying and dealing with missing data.