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Lazy loaded imagePandas Intermediate
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Apr 20, 2020
Apr 20, 2025
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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
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