# Debugging and Profiling

These libraries help you with Python development: the debugger enables you to step through code, analyze stack frames and set breakpoints etc., and the profilers run code and give you a detailed breakdown of execution times, allowing you to identify bottlenecks in your programs. Auditing events provide visibility into runtime behaviors that would otherwise require intrusive debugging or patching.

- Audit events table
- `bdb` — Debugger framework
- `faulthandler` — Dump the Python traceback
  - Dumping the traceback
  - Fault handler state
  - Dumping the tracebacks after a timeout
  - Dumping the traceback on a user signal
  - Issue with file descriptors
  - Example
- `pdb` — The Python Debugger
  - Command-line interface
  - Debugger commands
- The Python Profilers
  - Introduction to the profilers
  - Instant User’s Manual
  - `profile` and `cProfile` Module Reference
  - The `Stats` Class
  - What Is Deterministic Profiling?
  - Limitations
  - Calibration
  - Using a custom timer
- `timeit` — Measure execution time of small code snippets
  - Basic Examples
  - Python Interface
  - Command-Line Interface
  - Examples
- `trace` — Trace or track Python statement execution
  - Command-Line Usage
    - Main options
    - Modifiers
    - Filters
  - Programmatic Interface
- `tracemalloc` — Trace memory allocations
  - Examples
    - Display the top 10
    - Compute differences
    - Get the traceback of a memory block
    - Pretty top
      - Record the current and peak size of all traced memory blocks
  - API
    - Functions
    - DomainFilter
    - Filter
    - Frame
    - Snapshot
    - Statistic
    - StatisticDiff
    - Trace
    - Traceback