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This is the API documentation for the gdtest_nav_icons library.
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## Classes

Core classes


Pipeline(name: str)

An ordered sequence of analysis stages.

Construct a pipeline from callables, then run data through
each stage in sequence.

Args:
    name: Human-readable pipeline identifier.

Attributes:
    name: Pipeline name.
    stages: Ordered list of stage callables.

Examples:
    >>> p = Pipeline("demo")
    >>> p.add_stage(lambda x: [v * 2 for v in x])
    >>> p.run([1, 2, 3])
    [2, 4, 6]

BarChart(title: 'str' = 'Chart', width: 'int' = 40)

A horizontal bar chart renderer.

Args:
    title: Chart title displayed above the bars.
    width: Maximum bar width in characters.

Attributes:
    title: Chart title.
    width: Bar width cap.
    data: Mapping of labels to values.

LineChart(title: 'str' = 'Line', height: 'int' = 10)

A simple line chart renderer.

Args:
    title: Chart title.
    height: Vertical resolution in rows.

Attributes:
    title: Chart title.
    height: Row count for rendering.
    series: Stored data series.


## Functions

Public functions


analyze(data: list[float]) -> dict[str, float]

Run a basic statistical analysis on numeric data.

Args:
    data: A list of numeric values to analyze.

Returns:
    A dict with keys ``mean``, ``min``, ``max``, and ``count``.

Examples:
    >>> analyze([1.0, 2.0, 3.0])
    {'mean': 2.0, 'min': 1.0, 'max': 3.0, 'count': 3}

summarize(data: list[float], label: str = 'result') -> str

Produce a one-line summary string for a dataset.

Args:
    data: Numeric values.
    label: Human-readable label for the summary.

Returns:
    A formatted summary string.

transform(data: list[float], *, scale: float = 1.0, offset: float = 0.0) -> list[float]

Apply a linear transform to every element.

Args:
    data: Input values.
    scale: Multiplicative factor.
    offset: Additive constant applied after scaling.

Returns:
    A new list of transformed values.

plot(x: 'list[float]', y: 'list[float]', *, kind: 'str' = 'scatter') -> 'str'

Produce a quick text-based plot.

Args:
    x: X-axis values.
    y: Y-axis values.
    kind: Plot type — ``"scatter"`` or ``"line"``.

Returns:
    A string representation of the plot.

Raises:
    ValueError: If ``x`` and ``y`` have different lengths.

load_csv(path: 'str', *, delimiter: 'str' = ',', header: 'bool' = True) -> 'list[dict]'

Load a CSV file into a list of row dicts.

Args:
    path: File path to read.
    delimiter: Column separator character.
    header: Whether the first row contains column names.

Returns:
    A list of dicts, one per data row.

save_csv(data: 'list[dict]', path: 'str', *, delimiter: 'str' = ',') -> 'None'

Save a list of row dicts to a CSV file.

Args:
    data: Rows to write.
    path: Destination file path.
    delimiter: Column separator character.

export_json(data: 'list[dict]', path: 'str', *, indent: 'int' = 2) -> 'None'

Export data as a formatted JSON file.

Args:
    data: Data to serialize.
    path: Destination file path.
    indent: Number of spaces for pretty-printing.


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This is the User Guide documentation for the package.
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# Getting Started

Welcome to **NavIcons Demo** — a data-analysis toolkit that
showcases Lucide navigation icons in every corner of the site.

## Installation

```bash
pip install gdtest-nav-icons
```

## Quick Example

```python
from gdtest_nav_icons import analyze

result = analyze([10, 20, 30, 40])
print(result)
# {'mean': 25.0, 'min': 10, 'max': 40, 'count': 4}
```

## What's Next?

- Learn about [Configuration](configuration.qmd) options
- Explore the [Visualization](visualization.qmd) guide
- Dive into [Advanced Topics](advanced-topics.qmd)


# Configuration

Configure NavIcons Demo through Python or YAML.

## Pipeline Setup

```python
from gdtest_nav_icons import Pipeline, transform

pipe = Pipeline("preprocess")
pipe.add_stage(lambda data: transform(data, scale=2.0))
pipe.add_stage(lambda data: transform(data, offset=-1.0))

result = pipe.run([1.0, 2.0, 3.0])
```

## CSV Options

Use `load_csv` with custom delimiters:

```python
from gdtest_nav_icons import load_csv

data = load_csv("data.tsv", delimiter="\t")
```


# Visualization

Create quick text-based charts for terminal output.

## Bar Charts

```python
from gdtest_nav_icons import BarChart

chart = BarChart("Sales by Region")
chart.add("North", 120)
chart.add("South", 85)
chart.add("East", 200)
chart.add("West", 150)
print(chart.render())
```

## Line Charts

```python
from gdtest_nav_icons import LineChart

lc = LineChart("Temperature")
lc.add_series([20, 22, 19, 25, 28, 26])
print(lc.render())
```

## Quick Plots

```python
from gdtest_nav_icons import plot

output = plot([1, 2, 3, 4], [10, 20, 15, 30], kind="line")
print(output)
```


# Advanced Topics

## Custom Pipeline Stages

Build complex analysis workflows by chaining stages:

```python
from gdtest_nav_icons import Pipeline, analyze, summarize

pipe = Pipeline("full-analysis")
pipe.add_stage(lambda d: [x for x in d if x > 0])  # filter
pipe.add_stage(lambda d: [x ** 0.5 for x in d])    # sqrt

clean_data = pipe.run([-1, 4, 9, -2, 16, 25])
print(analyze(clean_data))
```

## Exporting Results

```python
from gdtest_nav_icons import export_json

results = [{"metric": "accuracy", "value": 0.95}]
export_json(results, "output.json", indent=4)
```

## Summary Reports

```python
from gdtest_nav_icons import summarize

print(summarize([88, 92, 79, 95, 100], label="Exam scores"))
```