profiler-viz 0.1.0

A beautiful Python profiler with interactive HTML visualization

Profiler Viz

A beautiful Python profiler with interactive HTML visualization. Profile your Python functions and get stunning visual reports with charts, sortable tables, and call tree analysis.

Features

✨ Beautiful HTML Reports - Interactive visualizations with charts and graphs

📊 Performance Charts - See top functions by time and call count at a glance

🔍 Sortable Statistics - Click column headers to sort by any metric

🌳 Call Tree Analysis - Click any function to see its callers and callees

🎯 Easy to Use - Just add a decorator to your function

Installation

pip install profiler-viz

Or install from source:

git clone https://github.com/nndat/profiler_viz.git
cd profiler-viz
pip install -e .

Quick Start

Basic Usage

from profiler_viz import func_profile

@func_profile()
def my_function():
    # Your code here
    result = sum(range(1000000))
    return result

# Run your function
my_function()

This will automatically:

• Profile the function execution
• Generate a .prof file
• Create a beautiful HTML report
• Both files named: my_function_YYYYMMDDHHmmss.{prof,html}

Custom Output Directory

from profiler_viz import func_profile

@func_profile(dest="./profiling_results")
def process_data():
    # Your code here
    pass

process_data()

Convert Existing .prof Files

from profiler_viz import view_profile_html

# Convert a .prof file to HTML
view_profile_html("my_profile.prof")

# Or specify output path
view_profile_html("my_profile.prof", "report.html")

Or use the command line:

profiler-viz my_profile.prof

HTML Report Features

The generated HTML report includes:

📈 Overview Section

• Total functions profiled
• Total function calls
• Primitive calls
• Total execution time

📊 Performance Charts

1. Top 10 Functions by Cumulative Time - Functions that take the most time including their callees
2. Top 10 Functions by Total Time - Functions that take the most time excluding callees
3. Top 10 Most Called Functions - Functions called most frequently
4. Time Distribution Pie Chart - Visual breakdown of where time is spent

📋 Sortable Statistics Table

• Click any column header to sort
• Filter functions by name or file path
• View detailed metrics:
  • ncalls - Number of calls
  • tottime - Total time in function (excluding sub-functions)
  • percall - Time per call
  • cumtime - Cumulative time (including sub-functions)
  • filename:lineno(function) - Location and name

🌳 Call Tree Modal

• Click any row to see:
  • Who calls this function (callers)
  • What this function calls (callees)
  • Call counts and timing for each relationship

Examples

Example 1: Profile a Recursive Function

from profiler_viz import func_profile

@func_profile()
def fibonacci(n):
    if n <= 1:
        return n
    return fibonacci(n-1) + fibonacci(n-2)

result = fibonacci(20)

Example 2: Profile Data Processing

from profiler_viz import func_profile
import pandas as pd

@func_profile(dest="./analysis_reports")
def analyze_data(filename):
    df = pd.read_csv(filename)
    # Complex data processing
    result = df.groupby('category').agg({
        'value': ['sum', 'mean', 'std']
    })
    return result

analyze_data("large_dataset.csv")

Example 3: Profile with Custom Logic

from profiler_viz import func_profile

@func_profile(dest="./benchmarks")
def benchmark_algorithm(data_size):
    data = list(range(data_size))
    # Test different sorting approaches
    sorted_data = sorted(data, reverse=True)
    return sorted_data

# Run benchmark
benchmark_algorithm(1000000)

Understanding the Output

File Naming

Files are automatically named using the pattern:

<function_name>_YYYYMMDDHHmmss.prof
<function_name>_YYYYMMDDHHmmss.html

For example:

my_function_20231128143022.prof
my_function_20231128143022.html

Metrics Explained

• ncalls: Number of calls. If shown as X/Y, Y is primitive calls, X is total calls
• tottime: Time spent in this function alone (excluding calls to sub-functions)
• percall: Average time per call (tottime / ncalls)
• cumtime: Total time spent in this function including all sub-functions
• percall: Average cumulative time (cumtime / primitive calls)

What to Look For

1. High cumtime - Functions that take the most overall time
2. High tottime - Functions doing expensive operations themselves
3. High ncalls - Functions called many times (candidates for caching/optimization)
4. High percall - Individual calls that are slow

Tips and Best Practices

1. Start with cumtime - Sort by cumulative time to find the biggest bottlenecks
2. Check the call tree - Click on slow functions to understand the call chain
3. Look for patterns - Use the filter box to find related functions
4. Compare runs - Profile before and after optimizations to measure impact
5. Use custom directories - Organize profiles by feature or test case

Requirements

• Python 3.7+
• No external dependencies (uses only Python standard library)

License

MIT License - see LICENSE file for details

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Support

For issues, questions, or contributions, please visit: https://github.com/nndat/profiler_viz/issues