Debugging Tips

Quickly Locating and Solving Problems

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Debugging Levels

1. Print Debugging (Simplest)
2. Assertions (Assert)
3. Logging
4. Debugger

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Print Debugging

Basic Usage

def calculate_tax(income, rate):
    print(f"Debug: income={income}, rate={rate}")  # Debug output
    tax = income * rate
    print(f"Debug: tax={tax}")
    return tax

result = calculate_tax(100000, 0.25)

Variable Inspection

data = [1, 2, 3, 4, 5]
print(f"Type: {type(data)}")
print(f"Length: {len(data)}")
print(f"Content: {data}")

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Assertions (Assert)

def calculate_mean(data):
    assert len(data) > 0, "Data cannot be empty"
    assert all(isinstance(x, (int, float)) for x in data), "Must be numbers"

    return sum(data) / len(data)

#  Normal
result = calculate_mean([1, 2, 3])

#  Triggers assertion
result = calculate_mean([])  # AssertionError: Data cannot be empty

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Logging

import logging

# Configure logging
logging.basicConfig(
    level=logging.DEBUG,
    format='%(asctime)s - %(levelname)s - %(message)s',
    filename='analysis.log'
)

# Use logging
logging.debug("Starting data processing")
logging.info("Loaded 1000 rows of data")
logging.warning("Found 15 missing values")
logging.error("File read failed")
logging.critical("System crash")

Logging Levels:

• DEBUG: Detailed information
• INFO: General information
• WARNING: Warnings
• ERROR: Errors
• CRITICAL: Critical errors

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Practical Techniques

Technique 1: Conditional Breakpoints

for i, resp in enumerate(respondents):
    if resp['id'] == 1001:  # Only debug specific data
        print(f"Debugging ID 1001: {resp}")

Technique 2: Check Data Types

def process_income(income):
    print(f"income type: {type(income)}")
    print(f"income value: {repr(income)}")

    if not isinstance(income, (int, float)):
        raise TypeError(f"Expected number, got {type(income)}")

Technique 3: Using pdb (Python Debugger)

import pdb

def analyze_data(df):
    print("Starting analysis")
    pdb.set_trace()  # Program pauses here
    result = df.mean()
    return result

# Debugging commands:
# n (next): Next line
# s (step): Step into function
# c (continue): Continue execution
# p variable: Print variable
# q (quit): Quit

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Common Problem Debugging

Problem 1: Data Type Errors

#  Hidden type issue
ages = ['25', '30', '35']  # String list
mean = sum(ages) / len(ages)  # TypeError

#  Debug
print(f"ages type: {type(ages)}")
print(f"First element type: {type(ages[0])}")

# Fix
ages = [int(x) for x in ages]

Problem 2: DataFrame Column Name Errors

import pandas as pd

df = pd.DataFrame({'age': [25, 30], 'income': [50000, 75000]})

#  KeyError
mean_incom = df['incom'].mean()  # Spelling error

#  Debug
print("Available columns:", df.columns.tolist())
print("Column types:", [type(col) for col in df.columns])

Problem 3: Loop Index Issues

data = [10, 20, 30]

#  IndexError
for i in range(len(data) + 1):  # Range error
    print(data[i])

#  Debug
print(f"Data length: {len(data)}")
print(f"Loop range: {list(range(len(data) + 1))}")

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Debugging Checklist

Data Issues

• [ ] Check data types
• [ ] Check data length/shape
• [ ] Check missing values
• [ ] Check anomalous values

Logic Issues

• [ ] Print intermediate results
• [ ] Check conditional statements
• [ ] Verify loop ranges
• [ ] Confirm function parameters

Performance Issues

• [ ] Use time measurement
• [ ] Check algorithm complexity
• [ ] Look for repeated calculations

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Practical Debugging Template

import logging
import traceback

# Configuration
logging.basicConfig(
    level=logging.DEBUG,
    format='%(asctime)s - %(levelname)s - %(message)s'
)

def safe_process(data):
    """Processing function with complete debugging"""
    try:
        # Input validation
        logging.debug(f"Input data type: {type(data)}")
        logging.debug(f"Input data length: {len(data)}")

        # Main processing
        result = process_logic(data)

        # Output validation
        logging.debug(f"Output result: {result}")
        return result

    except Exception as e:
        logging.error(f"Processing failed: {e}")
        logging.error(traceback.format_exc())
        return None

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Summary

Debugging Order:

1. Read error message (Most important!)
2. Add print statements
3. Check data types and values
4. Use logging
5. Use debugger (pdb)

Module 8 Complete!

Next module is a Core Focus: Module 9 Libraries (NumPy/Pandas/Visualization, etc.)

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