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plotlars

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Plotlars is a Rust library designed to facilitate the integration between the Polars data analysis library and Plotly library.

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최종 버전 다운로드 (.zip)

- - rust.yml
- - Cargo.toml
- - Cargo.toml
- - Cargo.toml
- - Cargo.toml
- animal_statistics.csv
- cities.csv
- contour_surface.csv
- debilt_2023_temps.csv
- employee_data.csv
- energy_transition.csv
- financial_timeseries.csv
- flight_path.csv
- heatmap.csv
- image.png
- industry_region.csv
- penguins.csv
- product_comparison_polar.csv
- regional_sales.csv
- revenue_and_cost.csv
- sankey_flow.csv
- stock_prices.csv
- temperature_seasonal.csv
- us_cities_regions.csv
- us_city_density.csv
- wind_patterns.csv
- world_cities.csv
.gitignore
.pre-commit-config.yaml
Cargo.lock
Cargo.toml
CHANGELOG.md
LICENSE.txt
README.md

// repository documentation

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# Plotlars

<p align="center">
    <a href="https://crates.io/crates/plotlars">
    <img alt="Crates.io" src="https://img.shields.io/crates/v/plotlars.svg"></a>
    <a href="https://docs.rs/plotlars">
    <img alt="docs.rs" src="https://img.shields.io/docsrs/plotlars">
    </a>
    <a href="https://crates.io/crates/plotlars">
    <img alt="Downloads" src="https://img.shields.io/crates/d/plotlars">
    </a>
    <a href="https://github.com/alceal/plotlars/blob/main/LICENSE">
    <img alt="License" src="https://img.shields.io/badge/license-MIT-blue.svg">
    </a>
</p>

Plotlars is a versatile Rust library that bridges the gap between the powerful
Polars data analysis library and visualization backends. It supports two
rendering backends: [Plotly](https://github.com/plotly/plotly.rs) for
interactive HTML-based charts and [Plotters](https://github.com/plotters-rs/plotters)
for static image output (PNG/SVG). Plotlars simplifies the process of creating
visualizations from data frames, allowing developers to focus on data insights
rather than the intricacies of plot creation.

## Implemented Plots Overview

<table>
  <tr>
    <td align="center">
      <img src="https://imgur.com/LMrqAaT.png" width="120" height="120"><br>
      <a href="https://docs.rs/plotlars/latest/plotlars/struct.Array2dPlot.html">Array 2D</a>
    </td>
    <td align="center">
      <img src="https://imgur.com/HQQvQey.png" width="120" height="120"><br>
      <a href="https://docs.rs/plotlars/latest/plotlars/struct.BarPlot.html">Bar Plot</a>
    </td>
    <td align="center">
      <img src="https://imgur.com/jdA3g9r.png" width="120" height="120"><br>
      <a href="https://docs.rs/plotlars/latest/plotlars/struct.BoxPlot.html">Box Plot</a>
    </td>
    <td align="center">
      <img src="https://imgur.com/91y2Kis.png" width="120" height="120"><br>
      <a href="https://docs.rs/plotlars/latest/plotlars/struct.CandlestickPlot.html">Candlestick</a>
    </td>
  </tr>
  <tr>
    <td align="center">
      <img src="https://imgur.com/VWgxHC8.png" width="120" height="120"><br>
      <a href="https://docs.rs/plotlars/latest/plotlars/struct.ContourPlot.html">Contour Plot</a>
    </td>
    <td align="center">
      <img src="https://imgur.com/82eLyBm.png" width="120" height="120"><br>
      <a href="https://docs.rs/plotlars/latest/plotlars/struct.DensityMapbox.html">Density Mapbox</a>
    </td>
    <td align="center">
      <img src="https://imgur.com/5uFih4M.png" width="120" height="120"><br>
      <a href="https://docs.rs/plotlars/latest/plotlars/struct.HeatMap.html">Heat Map</a>
    </td>
    <td align="center">
      <img src="https://imgur.com/w2oiuIo.png" width="120" height="120"><br>
      <a href="https://docs.rs/plotlars/latest/plotlars/struct.Histogram.html">Histogram</a>
    </td>
  </tr>
  <tr>
    <td align="center">
      <img src="https://imgur.com/PAtdaHj.png" width="120" height="120"><br>
      <a href="https://docs.rs/plotlars/latest/plotlars/struct.Image.html">Image</a>
    </td>
    <td align="center">
      <img src="https://imgur.com/PaXG300.png" width="120" height="120"><br>
      <a href="https://docs.rs/plotlars/latest/plotlars/struct.LinePlot.html">Line Plot</a>
    </td>
    <td align="center">
      <img src="https://imgur.com/bljzmw5.png" width="120" height="120"><br>
      <a href="https://docs.rs/plotlars/latest/plotlars/struct.Mesh3D.html">Mesh3D</a>
    </td>
    <td align="center">
      <img src="https://imgur.com/Sv8r9VN.png" width="120" height="120"><br>
      <a href="https://docs.rs/plotlars/latest/plotlars/struct.OhlcPlot.html">OHLC</a>
    </td>
  </tr>
  <tr>
    <td align="center">
      <img src="https://imgur.com/q44HDwT.png" width="120" height="120"><br>
      <a href="https://docs.rs/plotlars/latest/plotlars/struct.PieChart.html">Pie Chart</a>
    </td>
    <td align="center">
      <img src="https://imgur.com/jvAew8u.png" width="120" height="120"><br>
      <a href="https://docs.rs/plotlars/latest/plotlars/struct.SankeyDiagram.html">Sankey Diagram</a>
    </td>
    <td align="center">
      <img src="https://imgur.com/WYTQxHA.png" width="120" height="120"><br>
      <a href="https://docs.rs/plotlars/latest/plotlars/struct.Scatter3dPlot.html">Scatter 3D Plot</a>
    </td>
    <td align="center">
      <img src="https://imgur.com/8PCEbhN.png" width="120" height="120"><br>
      <a href="https://docs.rs/plotlars/latest/plotlars/struct.ScatterGeo.html">Scatter Geo</a>
    </td>
  </tr>
  <tr>
    <td align="center">
      <img src="https://imgur.com/8MCjVOd.png" width="120" height="120"><br>
      <a href="https://docs.rs/plotlars/latest/plotlars/struct.ScatterMap.html">Scatter Map</a>
    </td>
    <td align="center">
      <img src="https://imgur.com/9jfO8RU.png" width="120" height="120"><br>
      <a href="https://docs.rs/plotlars/latest/plotlars/struct.ScatterPlot.html">Scatter Plot</a>
    </td>
    <td align="center">
      <img src="https://imgur.com/kl1pY9c.png" width="120" height="120"><br>
      <a href="https://docs.rs/plotlars/latest/plotlars/struct.ScatterPolar.html">Scatter Polar</a>
    </td>
    <td align="center">
      <img src="https://imgur.com/RvZwv3O.png" width="120" height="120"><br>
      <a href="https://docs.rs/plotlars/latest/plotlars/struct.SubplotGrid.html">Subplot Grid Irregular</a>
    </td>
  </tr>
  <tr>
    <td align="center">
      <img src="https://imgur.com/q0K7cyP.png" width="120" height="120"><br>
      <a href="https://docs.rs/plotlars/latest/plotlars/struct.SubplotGrid.html">Subplot Grid Regular</a>
    </td>
    <td align="center">
      <img src="https://imgur.com/tdVte4l.png" width="120" height="120"><br>
      <a href="https://docs.rs/plotlars/latest/plotlars/struct.SurfacePlot.html">Surface Plot</a>
    </td>
    <td align="center">
      <img src="https://imgur.com/QDKTeFX.png" width="120" height="120"><br>
      <a href="https://docs.rs/plotlars/latest/plotlars/struct.Table.html">Table</a>
    </td>
    <td align="center">
      <img src="https://imgur.com/hL27Xcn.png" width="120" height="120"><br>
      <a href="https://docs.rs/plotlars/latest/plotlars/struct.TimeSeriesPlot.html">Time Series</a>
    </td>
  </tr>
</table>

### Plot Types Reference

| Plot Type | Required Params | Facet | Group | Plotly | Plotters |
|---|---|---|---|---|---|
| [Array2dPlot](https://docs.rs/plotlars/latest/plotlars/struct.Array2dPlot.html) | data | -- | -- | Yes | -- |
| [BarPlot](https://docs.rs/plotlars/latest/plotlars/struct.BarPlot.html) | data, labels, values | Yes | Yes | Yes | Yes |
| [BoxPlot](https://docs.rs/plotlars/latest/plotlars/struct.BoxPlot.html) | data, labels, values | Yes | Yes | Yes | Yes |
| [CandlestickPlot](https://docs.rs/plotlars/latest/plotlars/struct.CandlestickPlot.html) | data, dates, open, high, low, close | -- | -- | Yes | Yes |
| [ContourPlot](https://docs.rs/plotlars/latest/plotlars/struct.ContourPlot.html) | data, x, y, z | Yes | -- | Yes | -- |
| [DensityMapbox](https://docs.rs/plotlars/latest/plotlars/struct.DensityMapbox.html) | data, lat, lon, z | -- | -- | Yes | -- |
| [HeatMap](https://docs.rs/plotlars/latest/plotlars/struct.HeatMap.html) | data, x, y, z | Yes | -- | Yes | Yes |
| [Histogram](https://docs.rs/plotlars/latest/plotlars/struct.Histogram.html) | data, x | Yes | Yes | Yes | Yes |
| [Image](https://docs.rs/plotlars/latest/plotlars/struct.Image.html) | path | -- | -- | Yes | -- |
| [LinePlot](https://docs.rs/plotlars/latest/plotlars/struct.LinePlot.html) | data, x, y | Yes | -- | Yes | Yes |
| [Mesh3D](https://docs.rs/plotlars/latest/plotlars/struct.Mesh3D.html) | data, x, y, z | Yes | -- | Yes | -- |
| [OhlcPlot](https://docs.rs/plotlars/latest/plotlars/struct.OhlcPlot.html) | data, dates, open, high, low, close | -- | -- | Yes | -- |
| [PieChart](https://docs.rs/plotlars/latest/plotlars/struct.PieChart.html) | data, labels | Yes | -- | Yes | -- |
| [SankeyDiagram](https://docs.rs/plotlars/latest/plotlars/struct.SankeyDiagram.html) | data, sources, targets, values | Yes | -- | Yes | -- |
| [Scatter3dPlot](https://docs.rs/plotlars/latest/plotlars/struct.Scatter3dPlot.html) | data, x, y, z | Yes | Yes | Yes | -- |
| [ScatterGeo](https://docs.rs/plotlars/latest/plotlars/struct.ScatterGeo.html) | data, lat, lon | -- | Yes | Yes | -- |
| [ScatterMap](https://docs.rs/plotlars/latest/plotlars/struct.ScatterMap.html) | data, latitude, longitude | -- | Yes | Yes | -- |
| [ScatterPlot](https://docs.rs/plotlars/latest/plotlars/struct.ScatterPlot.html) | data, x, y | Yes | Yes | Yes | Yes |
| [ScatterPolar](https://docs.rs/plotlars/latest/plotlars/struct.ScatterPolar.html) | data, theta, r | Yes | Yes | Yes | -- |
| [SubplotGrid](https://docs.rs/plotlars/latest/plotlars/struct.SubplotGrid.html) | plots | -- | -- | Yes | -- |
| [SurfacePlot](https://docs.rs/plotlars/latest/plotlars/struct.SurfacePlot.html) | data, x, y, z | Yes | -- | Yes | -- |
| [Table](https://docs.rs/plotlars/latest/plotlars/struct.Table.html) | data, columns | -- | -- | Yes | -- |
| [TimeSeriesPlot](https://docs.rs/plotlars/latest/plotlars/struct.TimeSeriesPlot.html) | data, x, y | Yes | -- | Yes | Yes |

## Motivation

The creation of Plotlars was driven by the need to simplify the process of
creating complex plots in Rust, particularly when working with the powerful
Polars data manipulation library. Generating visualizations often requires
extensive boilerplate code and deep knowledge of both the plotting library and
the data structure. This complexity can be a significant hurdle, especially for
users who need to focus on analyzing and interpreting data rather than wrestling
with intricate plotting logic.

To illustrate this, consider the following example where a scatter plot is
created **without Plotlars**:

```rust
use plotly::{
    common::*,
    layout::*,
    Plot,
    Scatter,
};

use polars::prelude::*;

fn main() {
    let dataset = LazyCsvReader::new(PlRefPath::new("data/penguins.csv"))
        .finish().unwrap()
        .select([
            col("species"),
            col("flipper_length_mm").cast(DataType::Int16),
            col("body_mass_g").cast(DataType::Int16),
        ])
        .collect().unwrap();

let group_column = "species";
    let x = "body_mass_g";
    let y = "flipper_length_mm";

let groups = dataset
        .column(group_column).unwrap()
        .unique().unwrap();

let layout = Layout::new()
        .title(Title::with_text("Penguin Flipper Length vs Body Mass"))
        .x_axis(Axis::new().title(Title::with_text("Body Mass (g)")))
        .y_axis(Axis::new().title(Title::with_text("Flipper Length (mm)")))
        .legend(Legend::new().title(Title::with_text("Species")));

let mut plot = Plot::new();
    plot.set_layout(layout);

let groups_str = groups.str().unwrap();

for group in groups_str.into_iter() {
        let group = group.unwrap();

let data = dataset
            .clone()
            .lazy()
            .filter(col(group_column).eq(lit(group)))
            .collect().unwrap();

let x = data
            .column(x).unwrap()
            .i16().unwrap()
            .to_vec();

let y = data
            .column(y).unwrap()
            .i16().unwrap()
            .to_vec();

let trace = Scatter::default()
            .x(x)
            .y(y)
            .name(group)
            .mode(Mode::Markers)
            .marker(Marker::new().size(10).opacity(0.5));

plot.add_trace(trace);
    }

plot.show();
}
```

In this example, creating a scatter plot involves writing substantial code to
manually handle the data and configure the plot, including grouping the data by
category and setting up the plot layout.

Now, compare that to the same plot created **using Plotlars**:

```rust
use plotlars::{
    CsvReader,
    ScatterPlot,
    Plot,
    Rgb,
    polars::prelude::*,
};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let dataset = CsvReader::new("data/penguins.csv")
        .finish()?
        .lazy()
        .select([
            col("species"),
            col("flipper_length_mm").cast(DataType::Int16),
            col("body_mass_g").cast(DataType::Int16),
        ])
        .collect()?;

ScatterPlot::builder()
        .data(&dataset)
        .x("body_mass_g")
        .y("flipper_length_mm")
        .group("species")
        .opacity(0.5)
        .size(12)
        .colors(vec![
            Rgb(178, 34, 34),
            Rgb(65, 105, 225),
            Rgb(255, 140, 0),
        ])
        .plot_title("Penguin Flipper Length vs Body Mass")
        .x_title("Body Mass (g)")
        .y_title("Flipper Length (mm)")
        .legend_title("Species")
        .build()
        .plot();

Ok(())
}
```

This is the output:

![Plot example](https://imgur.com/QMkmhNh.png)

With Plotlars, the same scatter plot is created with significantly less code.
The library abstracts away the complexities of dealing with individual plot
components and allows the user to specify high-level plot characteristics. This
streamlined approach not only saves time but also reduces the potential for
errors and makes the code more readable and maintainable.

## Installation

Plotlars requires exactly one backend feature to be enabled:

```bash
# Interactive HTML charts (Plotly)
cargo add plotlars --features plotly

# Static image output (Plotters)
cargo add plotlars --features plotters
```

Optional features for file loading:

```bash
# JSON file support
cargo add plotlars --features plotly,format-json

# Excel file support
cargo add plotlars --features plotly,format-excel
```

> **Tip:** You don't need to add `polars` to your own `Cargo.toml` — plotlars
> re-exports a matching version, so you can import it via
> `use plotlars::polars::prelude::*;`. If you do depend on `polars` directly,
> pin it to the same version plotlars uses (currently `0.54`) to avoid
> dependency-resolution conflicts.

## Running the examples

Plotlars comes with several ready-to-use demo programs. Examples are prefixed by
backend (`plotly_` or `plotters_`) and require the corresponding feature:

```bash
# Plotly examples
cargo run --features plotly --example plotly_barplot

# Plotters examples
cargo run --features plotters --example plotters_barplot
```

Replace `plotly_barplot` with the example name (without the `.rs` extension) you want to run.

## Features

- **Dual backends**: Choose between Plotly (interactive HTML) and Plotters (static PNG/SVG)
- **Seamless Polars integration**: Build plots directly from DataFrames with no manual extraction
- **22 plot types**: Bar, line, scatter, box, histogram, heatmap, 3D, geo, polar, and more
- **Faceting and subplots**: Split data by category or compose multi-plot grids
- **File loaders**: Read CSV, Parquet, JSON, and Excel files directly into DataFrames
- **Error handling**: Use `try_build` for fallible construction with `PlotlarsError`
- **Polars re-export**: Access polars via `plotlars::polars` without adding it to your `Cargo.toml`

## Exporting Plots

### Plotters backend

The plotters backend renders directly to static images. Use `save` to write
PNG or SVG files (format is inferred from the extension), or `to_svg` for an
in-memory SVG string:

```rust
use plotlars::{CsvReader, ScatterPlot, Plot};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let dataset = CsvReader::new("data/penguins.csv").finish()?;

let plot = ScatterPlot::builder()
        .data(&dataset)
        .x("body_mass_g")
        .y("flipper_length_mm")
        .plot_title("Penguin Data")
        .build();

// Save as PNG or SVG (format inferred from extension)
    plot.save("output.png");
    plot.save("output.svg");

// Or get an SVG string directly
    let svg = plot.to_svg();

Ok(())
}
```

### Plotly backend

The plotly backend produces interactive HTML charts. To export them as static
images (PNG, JPG, WEBP, SVG) via the `write_image` method, enable one of the
export features and install the corresponding WebDriver:

- **Default** (recommended - uses any available driver):
  ```bash
  cargo add plotlars --features export-default
  ```

- **Chrome/Chromium**:
  ```bash
  cargo add plotlars --features export-chrome
  ```
  Install ChromeDriver: <https://chromedriver.chromium.org/>

- **Firefox**:
  ```bash
  cargo add plotlars --features export-firefox
  ```
  Install GeckoDriver: <https://github.com/mozilla/geckodriver/releases>

```rust
use plotlars::{CsvReader, ScatterPlot, Plot};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let dataset = CsvReader::new("data/penguins.csv").finish()?;

let plot = ScatterPlot::builder()
        .data(&dataset)
        .x("body_mass_g")
        .y("flipper_length_mm")
        .plot_title("Penguin Data")
        .build();

// Export as PNG (1200x800 pixels, 2x scale for high DPI)
    plot.write_image("output.png", 1200, 800, 2.0)?;

Ok(())
}
```

## Plotlars in Jupyter Notebooks

Plotlars seamlessly integrates with Jupyter Notebooks, allowing you to leverage
the power of interactive data visualization directly within your notebook
environment. This integration is made possible through the use of the
[evcxr project](https://github.com/evcxr/evcxr), which provides a Jupyter kernel
for the Rust programming language.

![Jupyter notebook](https://imgur.com/zvFDzjj.png)

**With Polars, evcxr, and Plotlars, data science in Rust leaps to the next level
, making powerful data analysis and visualization more accessible and efficient
than ever before.**

## License

This project is licensed under the MIT License. See the LICENSE.txt file for details.

## Acknowledgements

- [Polars](https://github.com/pola-rs/polars): For providing a fast and
  efficient data manipulation library.
- [Plotly](https://github.com/plotly/plotly.rs): For interactive visualization
  capabilities.
- [Plotters](https://github.com/plotters-rs/plotters): For static rendering
  capabilities.
- [Evcxr](https://github.com/evcxr/evcxr): For enabling the use of Rust in
  Jupyter Notebooks.
- Rust Community: For the support and development of an amazing programming
  language.