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# Create custom aggregate functions (UDAs)
This documentation page is also available as an interactive notebook. You can launch the notebook in
Kaggle or Colab, or download it for use with an IDE or local Jupyter installation, by clicking one of the
above links.
export const quartoRawHtml = [`
| region |
product |
amount |
quantity |
| North |
Widget |
100. |
5 |
| North |
Gadget |
250. |
2 |
| North |
Widget |
150. |
8 |
| South |
Widget |
200. |
10 |
| South |
Gadget |
175. |
3 |
| East |
Widget |
125. |
6 |
`, `
`, `
| region |
amount_variance |
| East |
0. |
| North |
3888.889 |
| South |
156.25 |
`, `
| region |
products |
| East |
Widget |
| North |
Widget, Gadget, Widget |
| South |
Widget, Gadget |
`, `
| region |
amounts |
| East |
[125.] |
| North |
[100., 250., 150.] |
| South |
[200., 175.] |
`, `
| region |
avg_price |
| East |
125. |
| North |
146.667 |
| South |
194.231 |
`, `
| region |
top_product |
| East |
Widget |
| North |
Widget |
| South |
Widget |
`];
Build reusable aggregation logic for group-by queries and analytics.
## Problem
You need aggregations beyond the built-in `sum`, `count`, `mean`, `min`,
`max` — such as collecting values into a list, concatenating strings, or
computing custom statistics.
## Solution
**What’s in this recipe:**
* Define a UDA (User-Defined Aggregate) with the `@pxt.uda` decorator
* Use UDAs in `group_by` queries
* Create UDAs with multiple inputs
### Setup
```python theme={null}
%pip install -qU pixeltable
```
```python theme={null}
import pixeltable as pxt
pxt.drop_dir('uda_demo', force=True)
pxt.create_dir('uda_demo')
```
Connected to Pixeltable database at: postgresql+psycopg://postgres:@/pixeltable?host=/Users/pjlb/.pixeltable/pgdata
Created directory 'uda\_demo'.
\
### Create sample data
```python theme={null}
sales = pxt.create_table(
'uda_demo/sales',
{
'region': pxt.String,
'product': pxt.String,
'amount': pxt.Float,
'quantity': pxt.Int,
},
)
sales.insert(
[
{
'region': 'North',
'product': 'Widget',
'amount': 100.0,
'quantity': 5,
},
{
'region': 'North',
'product': 'Gadget',
'amount': 250.0,
'quantity': 2,
},
{
'region': 'North',
'product': 'Widget',
'amount': 150.0,
'quantity': 8,
},
{
'region': 'South',
'product': 'Widget',
'amount': 200.0,
'quantity': 10,
},
{
'region': 'South',
'product': 'Gadget',
'amount': 175.0,
'quantity': 3,
},
{
'region': 'East',
'product': 'Widget',
'amount': 125.0,
'quantity': 6,
},
]
)
sales.collect()
```
Created table 'sales'.
Inserting rows into \`sales\`: 0 rows \[00:00, ? rows/s]
Inserting rows into \`sales\`: 6 rows \[00:00, 609.56 rows/s]
Inserted 6 rows with 0 errors.
### Variance UDA (not built-in)
```python theme={null}
# A UDA is a class that inherits from pxt.Aggregator
# It must implement: __init__, update, and value
@pxt.uda
class variance(pxt.Aggregator):
"""Compute population variance using Welford's online algorithm."""
def __init__(self):
self.count = 0
self.mean = 0.0
self.m2 = 0.0 # Sum of squared differences from mean
def update(self, val: float) -> None:
if val is not None:
self.count += 1
delta = val - self.mean
self.mean += delta / self.count
delta2 = val - self.mean
self.m2 += delta * delta2
def value(self) -> float:
if self.count < 1:
return 0.0
return self.m2 / self.count # Population variance
```
```python theme={null}
# Use like any built-in aggregate
sales.select(variance(sales.amount)).collect()
```
```python theme={null}
# Use in group_by queries
sales.group_by(sales.region).select(
sales.region, amount_variance=variance(sales.amount)
).collect()
```
### String concatenation UDA
```python theme={null}
@pxt.uda
class string_agg(pxt.Aggregator):
"""Concatenate strings with a comma separator."""
def __init__(self):
self.values = []
def update(self, val: str) -> None:
if val is not None:
self.values.append(val)
def value(self) -> str:
return ', '.join(self.values)
```
```python theme={null}
# List all products sold in each region
sales.group_by(sales.region).select(
sales.region, products=string_agg(sales.product)
).collect()
```
### Collect values into a list
```python theme={null}
@pxt.uda
class collect_list(pxt.Aggregator):
"""Collect all values into a list."""
def __init__(self):
self.items = []
def update(self, val: float) -> None:
if val is not None:
self.items.append(val)
def value(self) -> list[float]:
return self.items
```
```python theme={null}
# Get all amounts per region as a list
sales.group_by(sales.region).select(
sales.region, amounts=collect_list(sales.amount)
).collect()
```
### Weighted average UDA
```python theme={null}
@pxt.uda
class weighted_avg(pxt.Aggregator):
"""Compute weighted average: sum(value * weight) / sum(weight)."""
def __init__(self):
self.weighted_sum = 0.0
self.weight_sum = 0.0
def update(self, value: float, weight: float) -> None:
if value is not None and weight is not None:
self.weighted_sum += value * weight
self.weight_sum += weight
def value(self) -> float:
if self.weight_sum == 0:
return 0.0
return self.weighted_sum / self.weight_sum
```
```python theme={null}
# Compute quantity-weighted average price per region
sales.group_by(sales.region).select(
sales.region, avg_price=weighted_avg(sales.amount, sales.quantity)
).collect()
```
### Mode UDA (most frequent value)
```python theme={null}
from collections import Counter
@pxt.uda
class mode(pxt.Aggregator):
"""Find the most frequent value in a group."""
def __init__(self):
self.counts = Counter()
def update(self, val: str) -> None:
if val is not None:
self.counts[val] += 1
def value(self) -> str:
if not self.counts:
return None
return self.counts.most_common(1)[0][0]
```
```python theme={null}
# Find most common product per region
sales.group_by(sales.region).select(
sales.region, top_product=mode(sales.product)
).collect()
```
## Explanation
**UDA structure:**
```python theme={null}
@pxt.uda
class my_aggregate(pxt.Aggregator):
def __init__(self): # Initialize state
self.state = initial_value
def update(self, val: InputType) -> None: # Called for each row
# Update internal state with val
def value(self) -> OutputType: # Called at the end
return self.state
```
**Key points:**
* Always handle `None` values in `update()`
* Multiple parameters in `update()` enable multi-column aggregations
(like `weighted_avg`)
* Return type annotation on `value()` determines output column type
## See also
* [UDFs in Pixeltable](../../../platform/udfs-in-pixeltable) -
Complete guide to custom functions
* [Join
tables](/howto/cookbooks/core/query-join-tables) -
Combine data before aggregating
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