> ## Documentation Index
> Fetch the complete documentation index at: https://docs.pixeltable.com/llms.txt
> Use this file to discover all available pages before exploring further.
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# Sample data for training and testing
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 = [`
| Need |
Method |
| Quick experiment |
Random sample of N rows |
| Balanced classes |
Stratified by label |
| Reproducible |
Fixed seed |
`, `
| text |
label |
score |
| Fantastic! |
positive |
0.91 |
| It is okay |
neutral |
0.5 |
| Average product |
neutral |
0.55 |
| Highly recommend |
positive |
0.92 |
| Great product! |
positive |
0.9 |
`, `
| text |
label |
score |
| Terrible |
negative |
0.1 |
| It is okay |
neutral |
0.5 |
| Fantastic! |
positive |
0.91 |
| Highly recommend |
positive |
0.92 |
| Great product! |
positive |
0.9 |
`, `
| text |
label |
score |
| Terrible |
negative |
0.1 |
| It is okay |
neutral |
0.5 |
| Fantastic! |
positive |
0.91 |
`, `
| text |
label |
score |
| Fantastic! |
positive |
0.91 |
| Highly recommend |
positive |
0.92 |
| Great product! |
positive |
0.9 |
`, `
| Method |
Parameter |
Behavior |
| Fixed count |
n=100 |
Exactly 100 rows |
| Percentage |
fraction=0.1 |
10% of rows |
| Per-class |
n_per_stratum=10 |
10 from each class |
`, `
| Use case |
Parameters |
| Proportional |
fraction=0.1, stratify_by=col |
| Equal allocation |
n_per_stratum=10, stratify_by=col |
| Reproducible |
Add seed=42 |
`];
Create training, validation, and test splits with random or stratified
sampling.
## Problem
You have a large dataset and need to create subsets for ML
training—random samples for quick experiments, stratified samples for
balanced classes, or reproducible splits for benchmarking.
## Solution
**What’s in this recipe:**
* Random sampling with `sample(n=...)`
* Percentage-based sampling with `sample(fraction=...)`
* Stratified sampling with `stratify_by=`
You use `query.sample()` to create random subsets, with optional
stratification for balanced class distribution.
### Setup
```python theme={null}
%pip install -qU pixeltable
```
```python theme={null}
import pixeltable as pxt
```
```python theme={null}
# Create a fresh directory
pxt.drop_dir('sampling_demo', force=True)
pxt.create_dir('sampling_demo')
```
Connected to Pixeltable database at: postgresql+psycopg://postgres:@/pixeltable?host=/Users/pjlb/.pixeltable/pgdata
Created directory 'sampling\_demo'.
\
### Create sample dataset
```python theme={null}
# Create a dataset with labels
data = pxt.create_table(
'sampling_demo/data',
{'text': pxt.String, 'label': pxt.String, 'score': pxt.Float},
)
# Insert sample data with imbalanced classes
samples = [
{'text': 'Great product!', 'label': 'positive', 'score': 0.9},
{'text': 'Love it', 'label': 'positive', 'score': 0.85},
{'text': 'Amazing quality', 'label': 'positive', 'score': 0.95},
{'text': 'Best purchase ever', 'label': 'positive', 'score': 0.88},
{'text': 'Highly recommend', 'label': 'positive', 'score': 0.92},
{'text': 'Fantastic!', 'label': 'positive', 'score': 0.91},
{'text': 'Terrible', 'label': 'negative', 'score': 0.1},
{'text': 'Waste of money', 'label': 'negative', 'score': 0.15},
{'text': 'It is okay', 'label': 'neutral', 'score': 0.5},
{'text': 'Average product', 'label': 'neutral', 'score': 0.55},
]
data.insert(samples)
```
Created table 'data'.
Inserting rows into \`data\`: 0 rows \[00:00, ? rows/s]
Inserting rows into \`data\`: 10 rows \[00:00, 857.13 rows/s]
Inserted 10 rows with 0 errors.
10 rows inserted, 20 values computed.
### Random sampling
```python theme={null}
# Sample exactly N rows
data.sample(n=5, seed=42).collect()
```
```python theme={null}
# Sample a percentage of rows
sample_50pct = data.sample(fraction=0.5, seed=42).collect()
```
### Stratified sampling
```python theme={null}
# Stratified sampling: 50% from each class
data.sample(fraction=0.5, stratify_by=data.label, seed=42).collect()
```
```python theme={null}
# Equal allocation: N rows from each class
data.sample(n_per_stratum=1, stratify_by=data.label, seed=42).collect()
```
### Sampling from filtered data
```python theme={null}
# Sample from filtered query (high-confidence predictions only)
data.where(data.score > 0.8).sample(n=3, seed=42).collect()
```
### Persist samples as tables
```python theme={null}
# Create a persistent table from a sample for dev/test
train_sample = data.sample(fraction=0.8, seed=42)
test_sample = data.sample(fraction=0.2, seed=43)
# Persist as new tables
train_table = pxt.create_table('sampling_demo/train', source=train_sample)
test_table = pxt.create_table('sampling_demo/test', source=test_sample)
```
Created table 'train'.
Inserting rows into \`train\`: 0 rows \[00:00, ? rows/s]
Inserting rows into \`train\`: 9 rows \[00:00, 3080.27 rows/s]
Created table 'test'.
Inserting rows into \`test\`: 0 rows \[00:00, ? rows/s]
Inserting rows into \`test\`: 3 rows \[00:00, 1333.92 rows/s]
## Explanation
**Sampling methods:**
**Stratification options:**
**Tips:**
* Always set `seed` for reproducible experiments
* Use stratified sampling for imbalanced datasets
* Combine with `.where()` to sample from subsets
## See also
* [Export for ML
training](/howto/cookbooks/data/data-export-pytorch) -
PyTorch DataLoader export
* [Import Hugging Face
datasets](/howto/cookbooks/data/data-import-huggingface) -
Load pre-split datasets
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