User:Niraj/Teaching-19

Teaching lesson plan 19 Subject: Python programming

Date: 5 Feb 2024

Time: 60 minutes

Period: 3rd

Teaching Item: Introduction to Series in Pandas

Class: Bachelor

Objective:

Students will learn about Series in pandas, understand their structure and capabilities, and apply them to manipulate and analyze one-dimensional labeled data effectively.

Materials Needed:

• Python interpreter with pandas installed
• Jupyter Notebook or any Python IDE
• Sample dataset (e.g., CSV file)
• Projector

1. Introduction to Pandas and Series (10 mins)

• Brief overview of pandas library:
  • Pandas is a powerful data manipulation and analysis library in Python.
  • It provides data structures and functions for working with structured data, similar to R's data frames.
• Introduce the concept of Series in pandas:
  • Series is a one-dimensional labeled array capable of holding any data type (e.g., integers, strings, floats, etc.).
  • Each element in a Series has a corresponding label, called an index.

2. Creating Series (15 mins)

• Discuss different ways to create Series objects in pandas:
  • From a Python list or NumPy array.
  • Using a dictionary where keys become the index labels.
  • Specifying data and index separately.
  • Using built-in functions like pd.Series().
• Demonstrate each method with examples and discuss their advantages.

3. Accessing Elements and Indexing (15 mins)

• Explain how to access elements and perform indexing on Series:
  • Using integer-based indexing.
  • Using label-based indexing.
  • Slicing for selecting subsets of data.
• Show examples of accessing elements and performing indexing operations on Series.

4. Operations on Series (15 mins)

• Discuss various operations that can be performed on Series:
  • Arithmetic operations (e.g., addition, subtraction, multiplication, division).
  • Element-wise mathematical functions (e.g., np.sqrt(), np.log()).
  • Comparison operations (e.g., greater than, less than).
  • Boolean indexing for filtering data.
• Demonstrate each operation with examples and discuss their applications.

5. Handling Missing Data (10 mins)

• Introduce techniques for handling missing data (NaN) in Series:
  • pd.isnull() and pd.notnull() functions for detecting missing values.
  • fillna() method for filling missing values with specified values.
  • dropna() method for removing rows with missing values.
• Show examples of handling missing data in Series.

6. Exercise (15 mins)

• Provide a programming exercise where students:
  • Load a sample dataset into a pandas Series.
  • Perform indexing, slicing, and filtering operations on the Series.
  • Apply arithmetic operations and mathematical functions to manipulate the data.
  • Handle missing data using appropriate techniques.

7. Conclusion (5 mins)

• Recap the key points covered in the lesson:
  • Series in pandas are one-dimensional labeled arrays capable of holding any data type.
  • Series can be created from Python lists, dictionaries, NumPy arrays, or by specifying data and index separately.
  • Operations such as indexing, slicing, arithmetic operations, and handling missing data can be performed on Series.
• Encourage students to practice using Series in pandas for data manipulation tasks and to explore additional functionalities offered by the library.