User:Niraj/Teaching-18

Teaching lesson plan 18 Subject: Python programming

Date: 4 Feb 2024

Time: 60 minutes

Period: 3rd

Teaching Item: Array Input and Output in NumPy

Class: Bachelor

Objective:

Students will learn how to input and output array data using NumPy, understand various file formats supported for array storage, and apply file input/output operations to efficiently work with array data.

Materials Needed:

• Python interpreter with NumPy installed or IDE
• Projector

1. Introduction to Array Input and Output (10 mins)

• Define array input and output operations:
  • Array input/output refers to the process of reading from and writing to external files or streams.
  • It allows for storing and retrieving array data from various formats.
• Discuss the importance of array input/output in data analysis, machine learning, and scientific computing.

2. NumPy File Formats (10 mins)

• Introduce different file formats supported by NumPy for array storage:
  • Text files: CSV (comma-separated values) and plain text files.
  • Binary files: NumPy's native .npy format for efficient binary storage.
  • Compressed files: NumPy's .npz format for storing multiple arrays in a single compressed file.
• Discuss the advantages and use cases of each file format.

3. Reading and Writing Text Files (15 mins)

• Demonstrate how to read and write array data from/to text files using NumPy functions:
  • numpy.loadtxt(): Read data from a text file.
  • numpy.savetxt(): Write data to a text file.
• Show examples of loading and saving arrays in CSV and plain text formats.

4. Reading and Writing Binary Files (15 mins)

• Explain how to read and write array data from/to binary files using NumPy functions:
  • numpy.load(): Load data from a .npy binary file.
  • numpy.save(): Save data to a .npy binary file.
• Demonstrate the usage of these functions for efficient binary storage and retrieval.

5. Working with Compressed Files (10 mins)

• Introduce NumPy's compressed file format .npz for storing multiple arrays:
  • numpy.load() can also load data from a .npz file containing multiple arrays.
  • Show how to save and load multiple arrays into/from a single compressed file.
• Discuss the benefits of using compressed files for efficient storage and transmission of array data.

6. Error Handling and Exception Handling (5 mins)

• Discuss error handling strategies when working with file input/output operations:
  • Handle potential errors such as file not found, permission denied, or file format mismatches.
  • Show how to use try and except blocks to handle exceptions gracefully.

7. Exercise (15 mins)

• Provide a programming exercise where students:
  • Write code to load array data from a text file, perform some operations, and save the modified array to a binary file.
  • Experiment with different file formats and compression techniques to understand their impact on file size and performance.

8. Conclusion (5 mins)

• Recap the key points covered in the lesson:
  • NumPy supports various file formats for array input and output, including text files, binary files, and compressed files.
  • Text files are suitable for human-readable data exchange but may be slower for large datasets.
  • Binary files offer faster storage and retrieval but are not human-readable.
  • Compressed files provide a space-efficient way to store multiple arrays in a single file.
• Encourage students to practice using array input/output operations with different file formats and to explore additional file handling techniques.