Python Yappi is a performance profiling tool for Python applications. It allows you to measure the execution time of different parts of your Python code so that you can identify bottlenecks and areas of improvement in your code. Yappi stands for “Yet Another Python Profiler.”
Here are some key features and capabilities of Python Yappi:
- Function Profiling: Yappi can profile individual functions, providing information on how much time is spent in each function.
- Thread Profiling: It supports profiling of multi-threaded Python applications, allowing you to analyze the performance of different threads.
- CPU and Wall Time Profiling: Yappi can profile both CPU time and wall time, which includes time spent waiting for external resources such as I/O operations.
- Statistics: It provides statistics about function execution, including the number of calls, total time spent, and time per call.
- Context Switches: Yappi can also report the number of context switches that occur during profiling, which can be useful for identifying threading-related issues.
- Integration: Yappi can be easily integrated into your Python code by importing the module and adding a few lines of code to start and stop profiling.
Here’s a basic example of how to use Yappi for profiling a Python script:
import yappi # Start profiling yappi.set_clock_type("cpu") # Use CPU time for profiling yappi.start() # Your code to profile goes here # Stop profiling yappi.stop() # Print profiling statistics yappi.get_func_stats().print_all()Code language: Python (python)
Keep in mind that Yappi is just one of several profiling tools available for Python.
Depending on your specific needs and preferences, you may also want to explore other profiling tools like cProfile, Pyflame, or line_profiler. Each tool has its own strengths and weaknesses, and the choice of profiler depends on your profiling goals and the nature of your Python application.
What Format Does Yappi Output?
Yappi can output profiling data in different formats to make it easier to analyze and visualize the results. The primary formats supported by Yappi are:
- Text: Yappi can output profiling data in a human-readable text format, which is suitable for viewing in the console or saving to a text file. This format provides a summary of function statistics, including the number of calls, total time, time per call, and more.
- JSON: Yappi can also output profiling data in JSON format. This format is useful when you want to process the profiling data programmatically or integrate it with other tools and systems.
- PStat: Yappi can save profiling data in the “pstat” format, which is a binary format used by Python’s built-in
cProfilemodule. This format allows you to visualize profiling data using tools like
Here’s an example of how to save profiling data in these formats:
import yappi # Start profiling yappi.set_clock_type("cpu") # Use CPU time for profiling yappi.start() # Your code to profile goes here # Stop profiling yappi.stop() # Save profiling data in different formats yappi.get_func_stats().save("profile.txt", type="text") yappi.get_func_stats().save("profile.json", type="json") yappi.get_func_stats().save("profile.pstat", type="pstat")Code language: Python (python)
You can then use various tools to analyze and visualize the saved profiling data in the respective formats. For example, to visualize “pstat” data, you can use the built-in
pstats module in Python, and for JSON data, you can use libraries like
matplotlib or custom scripts to generate custom reports.
The choice of format depends on your specific needs for analyzing and sharing the profiling data.
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