tabel perbandingan Excel vs SQL vs Python dalam menyelesaikan berbagai tugas analisis data umum:
📊 Perbandingan Excel vs SQL vs Python
| Task |
Excel |
SQL |
Python (pandas) |
| 1. Filter rows |
Filter/Slicer, IF, FILTER() |
WHERE clause |
df[df['col'] == value] |
| 2. Select columns |
Klik kolom / pilih secara manual |
SELECT column1, column2 |
df[['col1', 'col2']] |
| 3. Sort data |
Sort tool (ascending/descending) |
ORDER BY column ASC/DESC |
df.sort_values('col', ascending=True) |
| 4. Grouping |
Pivot Table / SUBTOTAL() |
GROUP BY column |
df.groupby('col') |
| 5. Counting |
COUNT(), Pivot Table |
COUNT(column) |
df['col'].count() or df.groupby(...).count() |
| 6. Average |
AVERAGE(), Pivot Table |
AVG(column) |
df['col'].mean() |
| 7. Sum |
SUM() |
SUM(column) |
df['col'].sum() |
| 8. Remove duplicates |
"Remove Duplicates" tool |
SELECT DISTINCT |
df.drop_duplicates() |
| 9. Join tables |
VLOOKUP / XLOOKUP / Power Query |
JOIN, LEFT JOIN, INNER JOIN |
pd.merge(df1, df2, on='key', how='left') |
| 10. Create new columns |
Formula (e.g., =A1+B1) |
Use expressions in SELECT |
df['new'] = df['A'] + df['B'] |
| 11. Handle missing data |
IFERROR(), filter blanks |
Depends on DB engine (IS NULL, COALESCE) |
df.fillna(), df.dropna() |
| 12. Exporting data |
Save As (CSV, XLSX) |
INTO OUTFILE (MySQL) |
df.to_csv(), df.to_excel() |
| 13. Visualization |
Charts, PivotChart |
Tidak native, butuh tools tambahan |
matplotlib, seaborn, plotly |
🔍 Kapan pakai yang mana?
| Kriteria |
Cocok Pakai Excel |
Cocok Pakai SQL |
Cocok Pakai Python (pandas) |
| User non-teknis / cepat |
✅ Ya |
🚫 Kurang cocok |
🚫 Butuh coding |
| Data besar (>100K rows) |
🚫 Tidak ideal |
✅ Sangat cocok |
✅ Cocok (dengan RAM cukup) |
| Automasi analisis |
🚫 Terbatas |
⚠️ Terbatas |
✅ Sangat cocok |
| Advanced analisis/statistik |
🚫 Minim |
🚫 Tidak fokus |
✅ Ya (via SciPy, statsmodels, dll.) |
| Machine Learning |
❌ Tidak bisa |
❌ Tidak bisa |
✅ Sangat cocok (scikit-learn, dsb) |
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