9. Files and Exceptions

Objectives

• Understand the notions of files and persistent data.
• Read, write and update files.
• Read and write CSV files, a common format for machine-learning datasets.
• Serialize objects into the JSON data-interchange format—commonly used to transmit over the Internet—and deserialize JSON into objects.
• Use the with statement to ensure that resources are properly released, avoiding “resource leaks.”
• Use the try statement to delimit code in which exceptions may occur and handle those exceptions with associated except clauses.

Objectives (cont.)

• Use the try statement’s else clause to execute code when no exceptions occur in the try suite.
• Use the try statement’s finally clause to execute code regardless of whether an exception occurs in the try.
• raise exceptions to indicate runtime problems.
• Understand the traceback of functions and methods that led to an exception.
• Use pandas to load into a DataFrame and process the Titanic Disaster CSV dataset.

Outline

• 9.1 Introduction
• 9.2 Files
• 9.3 Text-File Processing

  • 9.3.1 Writing to a Text File: Introducing the with Statement
  • 9.3.2 Reading Data from a Text File

• 9.4 Updating Text Files
• 9.5 Serialization with JSON
• 9.6 Focus on Security: pickle Serialization and Deserialization
• 9.7 Additional Notes Regarding Files
• 9.8 Handling Exceptions

  • 9.8.1 Division by Zero and Invalid Input
  • 9.8.2 try Statements
  • 9.8.3 Catching Multiple Exceptions in One except Clause
  • 9.8.4 What Exceptions Does a Function or Method Raise?
  • 9.8.5 What Code Should Be Placed in a try Suite?

• 9.9 finally Clause
• 9.10 Explicitly Raising an Exception
• 9.11 (Optional) Stack Unwinding and Tracebacks
• 9.12 Intro to Data Science: Working with CSV Files

  • 9.12.1 Python Standard Library Module csv
  • 9.12.2 Reading CSV Files into Pandas DataFrames
  • 9.12.3 Reading the Titanic Disaster Dataset
  • 9.12.4 Simple Data Analysis with the Titanic Disaster Dataset
  • 9.12.5 Passenger Age Histogram

• 9.13 Wrap-Up

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