User:Niraj/Teaching-22

Teaching lesson plan 22 Subject: Python programming

Date: 8 Feb 2024

Time: 60 minutes

Period: 3rd

Teaching Item: Handling Missing Data and Index Hierarchy in Pandas

Class: Bachelor

Objective:

Students will learn how to handle missing data effectively and understand the concept of index hierarchy in pandas, enabling them to clean and structure datasets for analysis.

Materials Needed:

• Python interpreter with pandas installed
• Jupyter Notebook or IDE
• Sample dataset with missing data
• Projector

1. Introduction to Missing Data (10 mins)

• Define missing data:
  • Missing data refers to the absence of values in a dataset, which can occur due to various reasons such as data entry errors or incomplete records.
  • Handling missing data is crucial in data analysis to ensure accurate results.
• Discuss the impact of missing data on analysis and decision-making.

2. Identifying Missing Data (10 mins)

• Introduce techniques for identifying missing data in pandas:
  • Using the isnull() method to detect missing values.
  • Using the notnull() method to identify non-missing values.
  • Summing the boolean results to count missing values.
• Demonstrate each technique with examples and discuss their applications.

3. Handling Missing Data (15 mins)

• Discuss strategies for handling missing data in pandas:
  • Removing missing values using dropna() method.
  • Imputing missing values with mean, median, or mode using fillna() method.
  • Forward filling or backward filling missing values using ffill() or bfill() methods.
• Show examples of each strategy and discuss their pros and cons.

4. Introduction to Index Hierarchy (10 mins)

• Introduce the concept of index hierarchy in pandas:
  • Index hierarchy, also known as MultiIndex, allows for creating multiple levels of index labels in pandas.
  • It enables representing and working with higher-dimensional data in a structured manner.
• Discuss the advantages of index hierarchy in organizing and analyzing complex datasets.

5. Creating Index Hierarchy (10 mins)

• Explain how to create index hierarchy in pandas DataFrame:
  • Using the set_index() method to specify multiple columns as index levels.
  • Creating MultiIndex objects directly using tuples or arrays.
• Demonstrate the creation of index hierarchy with examples and discuss different scenarios.

6. Indexing and Slicing with Index Hierarchy (10 mins)

• Discuss how to index and slice data with index hierarchy in pandas:
  • Using hierarchical indexing to select subsets of data based on index levels.
  • Accessing and manipulating data at different levels of index hierarchy.
• Show examples of indexing and slicing operations with index hierarchy.

7. Exercise (15 mins)

• Provide a programming exercise where students:
  • Load a sample dataset with missing data into a pandas DataFrame.
  • Handle missing data using appropriate techniques discussed in the lesson.
  • Create index hierarchy for the DataFrame based on multiple columns.
  • Perform indexing and slicing operations on the DataFrame with index hierarchy.

8. Conclusion (5 mins)

• Recap the key points covered in the lesson:
  • Handling missing data is essential in data analysis to ensure accurate results.
  • Pandas provides various methods for identifying and handling missing data, including removal, imputation, and filling techniques.
  • Index hierarchy in pandas allows for organizing and analyzing higher-dimensional datasets efficiently.
  • Hierarchical indexing enables indexing and slicing operations at different levels of index hierarchy.
• Encourage students to practice handling missing data and working with index hierarchy in their own projects and to explore additional functionalities offered by pandas.