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Interactive Python REPL automation with common helpers and best practices
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Basé sur la classification professionnelle SOC
| name | python-repl |
| description | Interactive Python REPL automation with common helpers and best practices |
Enhances Python REPL workflows with bundled utility functions for data analysis, debugging, and performance profiling.
This skill bundles Python REPL helpers, common imports, and execution patterns for efficient Python development in gptme.
This skill includes bundled utility functions for common Python tasks:
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)
Use bundled helpers for debugging:
from python_helpers import inspect_df, describe_object
inspect_df(df) # Quick dataframe overview
describe_object(obj) # Object introspection
Required packages are listed in requirements.txt:
# Helpers auto-import pandas, numpy
df = pd.read_csv('data.csv')
inspect_df(df) # Show overview
from python_helpers import time_function
@time_function
def slow_operation():
# Your code here
pass
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