Pandas Intermediatetype
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Apr 20, 2020
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Advanced Pandas Techniques (with full output)
Using your sample data:
1️⃣ groupby() + agg() — Multiple Aggregations
Output:
Product | Quantity Ordered | Sales | discount_percentage |
AA Batteries (4-pack) | 3 | 11.52 | 5.0 |
Lightning Cable | 3 | 44.85 | 10.0 |
USB-C Cable | 3 | 35.85 | 17.5 |
2️⃣ pivot_table() — Like Excel PivotTable
Output:
Product | Los Angeles | New York City | San Francisco |
AA Batteries (4-pack) | 0.00 | 11.52 | 0.00 |
Lightning Cable | 44.85 | 0.00 | 0.00 |
USB-C Cable | 0.00 | 0.00 | 35.85 |
3️⃣ apply() — Custom Row-wise Logic
Output:
Product | Sales | discount_percentage | Discounted Sales |
USB-C Cable | 23.90 | 20 | 19.12 |
Lightning Cable | 14.95 | 10 | 13.46 |
AA Batteries (4-pack) | 11.52 | 5 | 10.94 |
USB-C Cable | 11.95 | 15 | 10.16 |
Lightning Cable | 29.90 | 10 | 26.91 |
4️⃣ query() — Readable Filtering
Output:
Product | Sales |
USB-C Cable | 23.90 |
Lightning Cable | 29.90 |
5️⃣ rolling() — Moving Averages (3-row window)
Output:
Sales | Sales Rolling Mean |
23.90 | NaN |
14.95 | NaN |
11.52 | 16.79 |
11.95 | 12.81 |
29.90 | 17.79 |
6️⃣ eval() — Fast Column Math
Same result as apply, but faster
Sales | discount_percentage | FinalPrice |
23.90 | 20 | 19.12 |
14.95 | 10 | 13.46 |
11.52 | 5 | 10.94 |
11.95 | 15 | 10.16 |
29.90 | 10 | 26.91 |
7️⃣ nlargest() — Top-N Rows
Output:
Product | Sales |
Lightning Cable | 29.90 |
USB-C Cable | 23.90 |
Lightning Cable | 14.95 |
8️⃣ Multiple Conditions (masking)
Output:
City | Sales |
Los Angeles | 29.90 |
9️⃣ groupby() by Day of Week
Output:
Weekday | Sales |
Saturday | 38.85 |
Sunday | 23.47 |
Monday | 29.90 |
🔟 nunique() — Count Unique Users
Output:
✅ Summary Table
Technique | Description |
.groupby().agg() | Multi-metric aggregation by group |
pivot_table() | Like Excel PivotTables |
apply() | Custom row/column logic |
query() | SQL-style filtering |
eval() | Efficient column calculations |
rolling() | Moving windows (e.g. averages) |
nlargest() | Top-N filtering |
nunique() | Count of unique values |
- Author:NotionNext
- URL:https://tangly1024.com/article/1dbd698f-3512-80dc-ad0e-c3f9e51a7e3b
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