data-formats

name	data-formats
description	Reading, writing, and converting common data formats (CSV, Excel, JSON, YAML) with correct handling of encoding, types, and edge cases.

Data Formats Skill

Excel (.xlsx) Files

Reading Excel

import pandas as pd
df = pd.read_excel('input.xlsx', engine='openpyxl')
# Check what you got
print(df.columns.tolist())
print(df.dtypes)
print(df.head())

Writing Excel

df.to_excel('output.xlsx', index=False, engine='openpyxl')

CRITICAL: Excel Formula Evaluation

openpyxl writes formula strings but does NOT compute them. Verifiers read VALUES, not formulas.

Problem: Cell with =SUM(A1:A3) shows as 0 or #N/A when read back.

Solutions (in order of preference):

1. Compute values in Python and write computed values directly
2. Use gnumeric to recalculate: ssconvert --recalc file.xlsx file.xlsx
3. Use LibreOffice: libreoffice --headless --calc --convert-to xlsx file.xlsx

Common Excel Pitfalls

• Type mismatch: Number 2025 vs string "2025" breaks MATCH/VLOOKUP
• Missing packages: pip3 install --break-system-packages openpyxl xlsxwriter
• Sheet names: Check with pd.ExcelFile('input.xlsx').sheet_names
• Multiple sheets: pd.read_excel('input.xlsx', sheet_name='Sheet2')

CSV Files

import pandas as pd
# Always specify encoding
df = pd.read_csv('input.csv', encoding='utf-8')
# Check for issues
print(f"Shape: {df.shape}")
print(f"Columns: {df.columns.tolist()}")
print(df.head())

# Write
df.to_csv('output.csv', index=False, encoding='utf-8')

CSV Pitfalls

• Delimiter: Some files use ; or \t — check with head -2 file.csv
• Encoding: Try encoding='latin-1' if utf-8 fails
• Header: Some files have no header — use header=None
• Mixed types: Use dtype=str to read everything as strings first

JSON Files

import json
with open('input.json', encoding='utf-8') as f:
    data = json.load(f)

# Write with proper formatting
with open('output.json', 'w', encoding='utf-8') as f:
    json.dump(data, f, indent=2, ensure_ascii=False)

YAML Files

# Install: pip3 install --break-system-packages pyyaml
import yaml
with open('input.yaml') as f:
    data = yaml.safe_load(f)

General Tips

• Always check output file exists and has content before finishing
• Verify column names match exactly what the task expects
• Watch for NaN values: df.fillna(0) or df.dropna()
• Numeric precision: use round(value, N) for expected decimal places