#175 gdtest_sec_index_hero

---
title: Starter Demo
description: A minimal example showing the basics of data processing.
image: "data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='600' height='300'%3E%3Crect fill='%234A90D9' width='600' height='300'/%3E%3Ctext x='300' y='160' text-anchor='middle' fill='white' font-size='28' font-family='sans-serif'%3EStarter Demo%3C/text%3E%3C/svg%3E"
---

# Starter Demo

This demo walks through the most basic usage of the package.

```python
from gdtest_sec_index_hero import process

result = process([1, 2, None, 3], strict=True)
print(result)  # [1, 2, 3]
```

The `strict` parameter filters out `None` values, giving you a clean list.

---
title: Advanced Demo
description: A comprehensive example with chained operations and validation.
image: "data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='600' height='300'%3E%3Crect fill='%2342B883' width='600' height='300'/%3E%3Ctext x='300' y='160' text-anchor='middle' fill='white' font-size='28' font-family='sans-serif'%3EAdvanced Demo%3C/text%3E%3C/svg%3E"
---

# Advanced Demo

This demo chains multiple operations together.

```python
from gdtest_sec_index_hero import process, summarize, validate

data = process([10, 20, None, 30, 40], strict=True)
stats = summarize(data)
print(stats)  # {'count': 4, 'sum': 100, 'mean': 25.0}

schema = {'count': int, 'sum': int, 'mean': float}
assert validate(schema, stats)
```

---
title: Data Pipeline
description: Build a complete ETL pipeline from raw data to validated output.
image: "data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='600' height='300'%3E%3Crect fill='%23E8853D' width='600' height='300'/%3E%3Ctext x='300' y='160' text-anchor='middle' fill='white' font-size='28' font-family='sans-serif'%3EData Pipeline%3C/text%3E%3C/svg%3E"
---

# Data Pipeline

A step-by-step guide to building a data pipeline.

## Step 1: Extract

```python
raw = [100, None, 200, None, 300]
```

## Step 2: Transform

```python
from gdtest_sec_index_hero import process
clean = process(raw, strict=True)  # [100, 200, 300]
```

## Step 3: Load

```python
from gdtest_sec_index_hero import summarize
output = summarize(clean)
print(output)  # {'count': 3, 'sum': 600, 'mean': 200.0}
```

---
title: Visualization
description: Create summary visualizations from processed data.
image: "data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='600' height='300'%3E%3Crect fill='%237C4DFF' width='600' height='300'/%3E%3Ctext x='300' y='160' text-anchor='middle' fill='white' font-size='28' font-family='sans-serif'%3EVisualization%3C/text%3E%3C/svg%3E"
---

# Visualization

After processing and summarizing data, you can visualize the results.

```python
from gdtest_sec_index_hero import process, summarize

data = process([5, 15, 25, 35, 45])
stats = summarize(data)
print(f"Mean: {stats['mean']}, Total: {stats['sum']}")
```

Use these summary statistics as input to your favorite plotting library.

---
title: Error Handling
description: Learn how to handle edge cases and invalid inputs gracefully.
---

# Error Handling

When working with real-world data you need to handle edge cases.

```python
from gdtest_sec_index_hero import process, validate

# Empty lists are handled cleanly
result = process([], strict=True)
print(result)  # []

# Validation catches missing fields
assert not validate({'name': str}, {'age': 30})
```

---
title: Batch Processing
description: Process multiple datasets in batch mode.
---

# Batch Processing

Process several datasets at once.

```python
from gdtest_sec_index_hero import process, summarize

datasets = [
    [10, 20, 30],
    [5, None, 15],
    [100, 200, 300],
]

for ds in datasets:
    clean = process(ds, strict=True)
    print(summarize(clean))
```

---
title: Common Schema Patterns
description: Reusable validation schemas for typical data structures.
---

# Common Schema Patterns

Define reusable schemas for common record types.

```python
from gdtest_sec_index_hero import validate

user_schema = {'name': str, 'age': int}
event_schema = {'type': str, 'timestamp': float}

assert validate(user_schema, {'name': 'Alice', 'age': 30})
assert validate(event_schema, {'type': 'click', 'timestamp': 1.0})
```

---
title: Performance Tips
description: Optimize processing speed for large datasets.
---

# Performance Tips

Tips for working efficiently with large volumes of data.

- Use `strict=False` (the default) when you know data is clean
- Pre-validate schemas once, then batch-process records
- Use `summarize()` to get aggregate stats without storing intermediates

---
title: Integration Guide
description: Integrate the package with pandas, Polars, and other frameworks.
---

# Integration Guide

How to use this package alongside popular data frameworks.

## With plain Python

```python
from gdtest_sec_index_hero import process
result = process([1, 2, 3])
```

## Converting results

The `summarize()` function returns a plain dict, making it easy
to convert to any format you need.

---
title: Custom Validators
description: Build your own validation logic on top of the validate function.
---

# Custom Validators

Extend the built-in `validate()` with custom rules.

```python
from gdtest_sec_index_hero import validate

def validate_positive(record):
    base_ok = validate({'value': int}, record)
    return base_ok and record['value'] > 0

assert validate_positive({'value': 42})
assert not validate_positive({'value': -1})
```

---
title: Real-Time Dashboard
description: A live dashboard that processes streaming data and displays rolling statistics.
image: "data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='600' height='300'%3E%3Crect fill='%23E53935' width='600' height='300'/%3E%3Ctext x='300' y='160' text-anchor='middle' fill='white' font-size='28' font-family='sans-serif'%3EReal-Time%3C/text%3E%3C/svg%3E"
---

# Real-Time Dashboard

This example shows a dashboard that consumes a data stream,
processes each batch, and updates summary statistics in real time.

```python
from gdtest_sec_index_hero import process, summarize

# Simulate a streaming batch
batch = [42, None, 87, 13, None, 65]
clean = process(batch, strict=True)
stats = summarize(clean)
print(stats)
```

---
title: Workflow Automation
description: An automated pipeline that validates, processes, and reports on incoming records.
image: "data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='600' height='300'%3E%3Crect fill='%23009688' width='600' height='300'/%3E%3Ctext x='300' y='160' text-anchor='middle' fill='white' font-size='28' font-family='sans-serif'%3EWorkflow%3C/text%3E%3C/svg%3E"
---

# Workflow Automation

Automate your data quality workflow end to end.

```python
from gdtest_sec_index_hero import validate, process, summarize

schema = {'value': int}
records = [{'value': 10}, {'value': 20}, {'value': 30}]

valid = [r for r in records if validate(schema, r)]
values = [r['value'] for r in valid]
clean = process(values, strict=True)
report = summarize(clean)
print(report)
```

---
title: Data Explorer
description: An interactive data exploration tool that lets you filter, summarize, and drill down.
image: "data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='600' height='300'%3E%3Crect fill='%237C4DFF' width='600' height='300'/%3E%3Ctext x='300' y='160' text-anchor='middle' fill='white' font-size='28' font-family='sans-serif'%3EVisualization%3C/text%3E%3C/svg%3E"
---

# Data Explorer

Explore datasets interactively by processing subsets and reviewing statistics.

```python
from gdtest_sec_index_hero import process, summarize

dataset = [5, 10, 15, 20, 25, 30, 35, 40]

# Filter to values above 15
subset = [x for x in process(dataset) if x > 15]
print(summarize(subset))
```

---
title: Quality Report
description: Generate a comprehensive data quality report with validation summaries.
image: "data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='600' height='300'%3E%3Crect fill='%234A90D9' width='600' height='300'/%3E%3Ctext x='300' y='160' text-anchor='middle' fill='white' font-size='28' font-family='sans-serif'%3EStarter Demo%3C/text%3E%3C/svg%3E"
---

# Quality Report

Produce a data quality report by validating every record.

```python
from gdtest_sec_index_hero import validate

schema = {'name': str, 'score': int}
records = [
    {'name': 'Alice', 'score': 95},
    {'name': 'Bob'},
    {'name': 'Carol', 'score': 88},
]

passed = sum(1 for r in records if validate(schema, r))
print(f"{passed}/{len(records)} records valid")
```

"""Test package for section index hero cards."""

from .core import process, summarize, validate

__all__ = ["process", "summarize", "validate"]

"""Core processing functions."""


def process(data: list, *, strict: bool = False) -> list:
    """Process a list of values.

    Parameters
    ----------
    data : list
        Input values to process.
    strict : bool
        If True, raise on invalid values.

    Returns
    -------
    list
        Processed values.

    Examples
    --------
    >>> process([1, 2, 3])
    [1, 2, 3]
    """
    return [x for x in data if x is not None] if strict else list(data)


def summarize(values: list) -> dict:
    """Summarize a list of numeric values.

    Parameters
    ----------
    values : list
        Numeric values to summarize.

    Returns
    -------
    dict
        Summary with count, sum, and mean.

    Examples
    --------
    >>> summarize([10, 20, 30])
    {'count': 3, 'sum': 60, 'mean': 20.0}
    """
    n = len(values)
    total = sum(values)
    return {"count": n, "sum": total, "mean": total / n if n else 0}


def validate(schema: dict, record: dict) -> bool:
    """Validate a record against a schema.

    Parameters
    ----------
    schema : dict
        Expected field names mapped to types.
    record : dict
        The record to validate.

    Returns
    -------
    bool
        True if the record conforms to the schema.

    Examples
    --------
    >>> validate({"name": str}, {"name": "Alice"})
    True
    """
    for key, expected_type in schema.items():
        if key not in record or not isinstance(record[key], expected_type):
            return False
    return True