Python REPL Skill

Enhances Python REPL workflows with bundled utility functions for data analysis, debugging, and performance profiling.

Overview

This skill bundles Python REPL helpers, common imports, and execution patterns for efficient Python development in gptme.

Bundled Scripts

Helper Functions (python_helpers.py)

This skill includes bundled utility functions for common Python tasks:

• Data inspection (inspect_df, describe_object)
• Quick plotting (quick_plot)
• Performance profiling (time_function)
• Common imports setup (setup_common_imports)

Usage Patterns

Data Analysis

When working with data, automatically import common libraries and set up display options:

import numpy as np
import pandas as pd

pd.set_option("display.max_rows", 100)

Debugging

Use bundled helpers for debugging:

from python_helpers import inspect_df, describe_object

inspect_df(df)  # Quick dataframe overview
describe_object(obj)  # Object introspection

Dependencies

Required packages are listed in requirements.txt:

• ipython: Interactive Python shell
• numpy: Numerical computing
• pandas: Data manipulation

Best Practices

1. Use helpers: Leverage bundled helper functions instead of reimplementing
2. Import once: Common imports are handled by pre-execute hook
3. Profile performance: Use time_function for performance-sensitive code

Examples

Quick Data Analysis

# Helpers auto-import pandas, numpy
df = pd.read_csv("data.csv")
inspect_df(df)  # Show overview

Performance Profiling

from python_helpers import time_function


@time_function
def slow_operation():
    # Your code here
    pass

Related

• Tool: ipython