perbandingan Excel vs SQL vs Python dalam analisis berbagai tugas

tabel perbandingan Excel vs SQL vs Python dalam menyelesaikan berbagai tugas analisis data umum:

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`

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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