datachart

https://github.com/eriknovak/datachart
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Python data visualization package built on matplotlib: simple chart functions, figure composition...
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================
LIBRARY RULES
================
- Target datachart 1.x. Pre-1.0 names were removed, not deprecated: use AXIS_SCALE (not SCALE), COLOR_NORM (not NORMALIZE), value_format (not valfmt), show_values (not show_heatmap_values)
- Also removed in 1.0: stats.correlation is stats.pearson, update_config/reset_config are config.update/config.reset, and save_figure(format=...) is save_figure(fmt=...)
- Import chart functions from datachart.charts; Panel, Grid, Annotate, save_figure and stats from datachart.utils; config from datachart.config; constants (THEME, FIG_SIZE, SHOW_GRID, LEGEND_LOCATION, ...) from datachart.constants
- Chart data is a list of dicts, one per record, and each chart expects its own keys: LineChart x/y, BarChart label/y, Histogram x, BoxPlot/ViolinPlot/SwarmPlot label/value
- ScatterChart records take x/y plus optional size, hue and annotation. Check the chart's guide for the record keys of every other chart
- A list of record lists draws several series. Name them with a list of subtitles; subplots=True gives each series its own axes
- There is no pandas DataFrame input. Pass df.to_dict("records") with the columns renamed to the chart's keys
- Every argument after data is keyword-only. An unknown parameter or constant value raises an error rather than being ignored
- Set global style with config.set_theme(THEME.X), config.update(...) or `with config.using_theme(...)`. Per-chart overrides go in style={...} with theme keys such as plot_line_color. Prefer these over editing the matplotlib axes
- Every chart returns a DatachartFigure, which is a matplotlib Figure. Panel overlays finished figures on shared axes; {"figure": fig, "y_axis": "right"} puts one on a second y-axis
- Grid arranges finished figures in cells, with nested lists as rows. A Grid can sit inside another Grid but not inside a Panel
- Save with save_figure(fig, path, fmt=[...]). fig.show(interactive=True) needs the datachart[interactive] extra
- Docs index: https://eriknovak.github.io/datachart/latest/llms.txt. Append index.md to any page URL to get it as markdown

================
CODE SNIPPETS
================
### Install datachart with pip

Source: https://github.com/eriknovak/datachart/blob/main/docs/overrides/home.html

Install the datachart package from PyPI using pip. This is the first step to start using the library.

```bash
pip install datachart
```

--------------------------------

### Create example temperature dataset

Source: https://github.com/eriknovak/datachart/blob/main/docs/how-to-guides/charts/ridgelineplot.ipynb

Creates the example dataset: a year of daily mean temperatures in Ljubljana. Each data point is a dictionary with a 'label' (month) and a numeric 'value'. The data is generated with a fixed seed for reproducibility.

```python
import numpy as np

MONTHS = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
# mean monthly temperature in °C (Ljubljana, 1991-2020 normals, rounded)
MONTHLY_MEAN = [0.8, 2.4, 6.8, 11.5, 16.2, 20.1, 22.0, 21.4, 16.6, 11.5, 5.9, 1.3]
# illustrative day-to-day spread in °C, wider in winter
MONTHLY_SPREAD = [4.0, 4.0, 3.5, 3.0, 2.5, 2.5, 2.5, 2.5, 3.0, 3.5, 3.5, 4.0]

rng = np.random.RandomState(11)
temperatures = [
    {"label": month, "value": round(float(celsius), 1)}
    for month, mean, spread in zip(MONTHS, MONTHLY_MEAN, MONTHLY_SPREAD)
    for celsius in rng.normal(mean, spread, 30)
]
```

--------------------------------

### Update overlay_ config and create a Panel

Source: https://github.com/eriknovak/datachart/blob/main/docs/how-to-guides/utility/panel.ipynb

Updates the global overlay configuration to change the auto-threshold and bar transparency, then creates a Panel with those settings. The example resets the configuration afterward to restore defaults for the rest of the guide.

```python
config.update(
    {
        # split the value axes sooner
        "overlay_auto_threshold": 2.0,
        # draw overlaid bars more transparent
        "overlay_bar_alpha": 0.5,
    }
)

Panel(
    [precipitation, temperature],
    title="Climate of Ljubljana",
    xlabel="Month",
    ylabel_left="Precipitation (mm)",
    ylabel_right="Temperature (°C)",
    figsize=FIG_SIZE.FULL_SHORT,
    show_grid=SHOW_GRID.Y,
    show_legend=True,
).show()

# restore the defaults for the rest of the guide
config.reset()
```

```text
Result:
<DatachartFigure size 630x240 with 2 Axes>
```

--------------------------------

### Install datachart with pip or uv

Source: https://github.com/eriknovak/datachart/blob/main/docs/index.md

Install datachart from PyPI using pip or uv. The interactive extra enables zoom, pan, and hover-to-inspect.

```bash
pip install -U datachart
```

```bash
uv add datachart
```

```bash
pip install -U "datachart[interactive]"
```

--------------------------------

### Install datachart with pip or uv

Source: https://github.com/eriknovak/datachart/blob/main/README.md

Install the datachart package using pip or uv. The comment shows the uv alternative.

```bash
pip install datachart   # or: uv add datachart
```

--------------------------------

### Install the interactive extra with pip or uv

Source: https://github.com/eriknovak/datachart/blob/main/docs/how-to-guides/utility/interactive.md

Installs the optional interactive extra, which provides ipympl for the notebook widget canvas and mplcursors for hover annotations. Without these packages, show(interactive=True) raises an ImportError naming the missing package and the extra.

```bash
pip install "datachart[interactive]"   # or: uv add "datachart[interactive]"
```

--------------------------------

### Install datachart with pip

Source: https://github.com/eriknovak/datachart/blob/main/README.md

Install the datachart package from PyPI. Requires Python 3.10 or higher.

```bash
pip install -U datachart
```

--------------------------------

### Apply MINIMAL theme with config.set_theme()

Source: https://github.com/eriknovak/datachart/blob/main/docs/how-to-guides/styling/themes.ipynb

Applies the MINIMAL theme and displays a demo figure. The theme replaces the whole configuration, so set it before building charts.

```python
config.set_theme(THEME.MINIMAL)
demo().show()

```

```text
<DatachartFigure size 900x320 with 2 Axes>
```

--------------------------------

### Create a datetime axis with date objects and DATE_FORMAT

Source: https://github.com/eriknovak/datachart/blob/main/docs/how-to-guides/charts/stackedareachart.ipynb

Use real date objects for the x values to get a time axis instead of a numeric one. Set xmin, xticks, and vlines with dates, and use DATE_FORMAT.YEAR for tick labels. The example starts the axis at 2010, ticks every five years, and marks the Paris Agreement adoption.

```python
from datetime import date

from datachart.constants import DATE_FORMAT

generation_dated = [
    [{"x": date(year, 1, 1), "y": twh} for year, twh in zip(YEARS, GENERATION[source])]
    for source in SOURCES
]

StackedAreaChart(
    data=generation_dated,
    style=SOURCE_STYLE,
    subtitle=SOURCES,
    show_legend=True,
    legend={"title": "Source", "location": LEGEND_LOCATION.UPPER_LEFT, "ncols": 2},
    # the axis reads as time: a datetime limit, one tick per five years, a dated event
    xmin=date(2010, 1, 1),
    xticks=[date(year, 1, 1) for year in range(2010, 2024, 5)],
    xticks_format=DATE_FORMAT.YEAR,
    vlines=[
        {"x": date(2015, 12, 12), "label": "Paris Agreement", "style": {"plot_vline_color": "#1f1f1f", "plot_vline_style": LINE_STYLE.DASHED}}
    ]
    + [None] * (len(SOURCES) - 1),
    title="World electricity generation since 2010",
    xlabel="Year",
    ylabel="Generation (TWh)",
    yticks_format="{x:,.0f}",
    figsize=FIG_SIZE.FULL_MEDIUM,
    ymax=36000,
).show()
```

--------------------------------

### Import BasemapChart, ScatterChart, and Panel

Source: https://github.com/eriknovak/datachart/blob/main/docs/how-to-guides/charts/basemapchart.ipynb

Imports the BasemapChart and ScatterChart classes from datachart.charts and the Panel utility from datachart.utils. Required for the examples in this guide.

```python
from datachart.charts import BasemapChart, ScatterChart
from datachart.utils import Panel
```

--------------------------------

### List available themes with config.list_themes()

Source: https://github.com/eriknovak/datachart/blob/main/docs/how-to-guides/styling/themes.ipynb

Lists all theme names accepted by set_theme, including predefined themes and any registered in the current process. The output shows the available themes.

```python
config.list_themes()

```

```text
['default',
 'greyscale',
 'ink',
 'hatch',
 'minimal',
 'material',
 'sketch',
 'quill',
 'harbor',
 'muted',
 'contrast',
 'mutedhatch',
 'slatehatch',
 'dark']
```

--------------------------------

### Define species and penguin dataset

Source: https://github.com/eriknovak/datachart/blob/main/docs/how-to-guides/charts/histogram.ipynb

Defines the species list and the penguin dataset with flipper length and body mass for each species. This data is used in the examples throughout the guide.

```python
SPECIES = ["Adelie", "Chinstrap", "Gentoo"]

# Palmer penguins (Gorman et al. 2014, CC0): (flipper length in mm, body mass in g) per penguin
PENGUINS = {
    "Adelie": [
        (181, 3750), (186, 3800), (195, 3250), (193, 3450), (190, 3650), (181, 3625),
        (195, 4675), (193, 3475), (190, 4250), (186, 3300), (180, 3700), (182, 3200),
        (191, 3800), (198, 4400), (185, 3700), (195, 3450), (197, 4500), (184, 3325),
        (194, 4200), (174, 3400), (180, 3600), (189, 3800), (185, 3950), (180, 3800),
        (187, 3800), (183, 3550), (187, 3200), (172, 3150), (180, 3950), (178, 3250),
        (178, 3900), (188, 3300), (184, 3900), (195, 3325), (196, 4150), (190, 3950),
        (180, 3550), (181, 3300), (184, 4650), (182, 3150), (195, 3900), (186, 3100),
        (196, 4400), (185, 3000), (190, 4600), (182, 3425), (179, 2975), (190, 3450),
        (191, 4150), (186, 3500), (188, 4300), (190, 3450), (200, 4050), (187, 2900),
        (191, 3700), (186, 3550), (193, 3800), (181, 2850), (194, 3750), (185, 3150),
        (195, 4400), (185, 3600), (192, 4050), (184, 2850), (192, 3950), (195, 3350),
        (188, 4100), (190, 3050), (198, 4450), (190, 3600), (190, 3900), (196, 3550),
        (197, 4150), (190, 3700), (195, 4250), (191, 3700), (184, 3900), (187, 3550),
        (195, 4000), (189, 3200), (196, 4700), (187, 3800), (193, 4200), (191, 3350),
        (194, 3550), (190, 3800), (189, 3500), (189, 3950), (190, 3600), (202, 3550),
        (205, 4300), (185, 3400), (186, 4450), (187, 3300), (208, 4300), (190, 3700),
        (196, 4350), (178, 2900), (192, 4100), (192, 3725), (203, 4725), (183, 3075),
        (190, 4250), (193, 2925), (184, 3550), (199, 3750), (190, 3900), (181, 3175),
        (197, 4775), (198, 3825), (191, 4600), (193, 3200), (197, 4275), (191, 3900),
        (196, 4075), (188, 2900), (199, 3775), (189, 3350), (189, 3325), (187, 3150),
        (198, 3500), (176, 3450), (202, 3875), (186, 3050), (199, 4000), (191, 3275),
        (195, 4300), (191, 3050), (210, 4000), (190, 3325), (197, 3500), (193, 3500),
        (199, 4475), (187, 3425), (190, 3900), (191, 3175), (200, 3975), (185, 3400),
        (193, 4250), (193, 3400), (187, 3475), (188, 3050), (190, 3725), (192, 3000),
        (185, 3650), (190, 4250), (184, 3475), (195, 3450), (193, 3750), (187, 3700),
        (201, 4000),
    ],
    "Chinstrap": [
        (192, 3500), (196, 3900), (193, 3650), (188, 3525), (197, 3725), (198, 3950),
        (178, 3250), (197, 3750), (195, 4150), (198, 3700), (193, 3800), (194, 3775),
        (185, 3700), (201, 4050), (190, 3575), (201, 4050), (197, 3300), (181, 3700),
        (190, 3450), (195, 4400), (181, 3600), (191, 3400), (187, 2900), (193, 3800),
        (195, 3300), (197, 4150), (200, 3400), (200, 3800), (191, 3700), (205, 4550),
        (187, 3200), (201, 4300), (187, 3350), (203, 4100), (195, 3600), (199, 3900),
        (195, 3850), (210, 4800), (192, 2700), (205, 4500), (210, 3950), (187, 3650),
        (196, 3550), (196, 3500), (196, 3675), (201, 4450), (190, 3400), (212, 4300),
        (187, 3250), (198, 3675), (199, 3325), (201, 3950), (193, 3600), (203, 4050),
        (187, 3350), (197, 3450), (191, 3250), (203, 4050), (202, 3800), (194, 3525),
        (206, 3950), (189, 3650), (195, 3650), (207, 4000), (202, 3400), (193, 3775),
        (210, 4100), (198, 3775),
    ],
    "Gentoo": [
        (211, 4500), (230, 5700), (210, 4450), (218, 5700), (215, 5400), (210, 4550),
        (211, 4800), (219, 5200), (209, 4400), (215, 5150), (214, 4650), (216, 5550),

```

--------------------------------

### Setup for theme gallery charts

Source: https://github.com/eriknovak/datachart/blob/main/docs/how-to-guides/styling/theme-gallery.ipynb

Sets up the shared data and helper functions used to render the six signature charts for each theme card. It seeds the random number generator, defines the figure size, an exponential moving average helper, and the grouped bar data used by the gallery.

```python
import math
import random

import matplotlib.colors as mcolors
import matplotlib.pyplot as plt
from IPython.display import HTML, display

from datachart.charts import BarChart, BoxPlot, Heatmap, LineChart, ScatterChart
from datachart.config import config
from datachart.constants import THEME
from datachart.utils import Grid, Panel
from datachart.themes import score_palette
from datachart.themes.score import palette_colors
from datachart.utils._internal.colors import get_discrete_colors

random.seed(42)
FIGSIZE = (4.2, 3.0)


def ema(vals, alpha=0.35):
    out, acc = [], vals[0]
    for v in vals:
        acc = alpha * v + (1 - alpha) * acc
        out.append(acc)
    return out


CATS = ["Bench A", "Bench B", "Bench C", "Bench D"]
GROUPED = [
    [{"label": c, "y": y} for c, y in zip(CATS, [66.7, 58.6, 76.7, 83.2])],
    [{"label": c, "y": y} for c, y in zip(CATS, [65.4, 57.7, 77.8, 82.7])],
    [{"label": c, "y": y} for c, y in zip(CATS, [68.5, 53.4, 76.9, 85.9])],
]
```

--------------------------------

### Create a basic Gantt chart with GanttChart(data=house).show()

Source: https://github.com/eriknovak/datachart/blob/main/docs/how-to-guides/charts/ganttchart.ipynb

Creates a basic Gantt chart from the 'house' data. Only the 'data' argument is required; each task becomes a bar from start to end, groups color the bars and fill the legend, progress fills a darker inner bar, and zero-length tasks become milestone markers.

```python
GanttChart(
    # add the data to the chart
    data=house
).show()
```

```text
Result:
<DatachartFigure size 640x480 with 1 Axes>
```

--------------------------------

### Import datachart charts, constants, and utilities

Source: https://github.com/eriknovak/datachart/blob/main/docs/use-cases/seismology.ipynb

Imports needed for the examples on the page, including chart classes, constants, and utilities. Assumes the datachart package and its dependencies (NumPy) are installed.

```python
import math
from datetime import date, timedelta

import numpy as np

from datachart.charts import (
    CalendarHeatmap,
    ContourChart,
    HexbinChart,
    BasemapChart,
    ImageChart,
    LineChart,
    NetworkChart,
    RadialChart,
)
from datachart.constants import (
    ASPECT_RATIO,
    BASEMAP_FEATURE,
    FIG_SIZE,
    NETWORK_LABEL_POSITION,
    NETWORK_LAYOUT,
    COLOR_NORM,
    RADIAL_TYPE,
    AXIS_SCALE,
    SHOW_GRID,
)
from datachart.utils import Grid, Panel
```

--------------------------------

### Show a LineChart statically and interactively

Source: https://github.com/eriknovak/datachart/blob/main/docs/how-to-guides/utility/interactive.md

Creates a LineChart and shows it twice: once as a static image and once with interactive=True to enable zoom, pan, and hover. Requires the interactive extra (ipympl and mplcursors); otherwise show(interactive=True) raises an ImportError.

```python
from datachart.charts import LineChart

figure = LineChart(
    data=[{"x": i, "y": i**2} for i in range(10)],
    subtitle="growth",
    xlabel="Step",
    ylabel="Value",
)
figure.show()                  # the static image
figure.show(interactive=True)  # zoom, pan, and hover
```

--------------------------------

### Load and apply theme with load_theme and set_theme

Source: https://github.com/eriknovak/datachart/blob/main/docs/how-to-guides/styling/themes.ipynb

Loads the theme from the file, registers it under the name 'neon-shared', applies it with set_theme, and returns the registered name. The result shows the name returned by load_theme.

```python
name = config.load_theme(folder / "neon.json", name="neon-shared")
config.set_theme(name)
config.theme

```

```python
'neon-shared'
```

--------------------------------

### Define RELIEF dataset for image chart examples

Source: https://github.com/eriknovak/datachart/blob/main/docs/how-to-guides/charts/imagechart.ipynb

Defines the shared relief dataset used throughout the guide: latitude and longitude coordinates with elevation values in metres, sourced from NOAA's ETOPO global relief model (public domain).

```python
# NOAA ETOPO global relief (public domain), sampled every quarter degree (metres)
RELIEF = {
    "lat": [34, 34.25, 34.5, 34.75, 35, 35.25, 35.5, 35.75, 36, 36.25, 36.5, 36.75, 37, 37.25, 37.5, 37.75, 38, 38.25, 38.5, 38.75, 39, 39.25, 39.5, 39.75, 40, 40.25, 40.5, 40.75, 41, 41.25, 41.5, 41.75, 42],
    "lon": [19, 19.25, 19.5, 19.75, 20, 20.25, 20.5, 20.75, 21, 21.25, 21.5, 21.75, 22, 22.25, 22.5, 22.75, 23, 23.25, 23.5, 23.75, 24, 24.25, 24.5, 24.75, 25, 25.25, 25.5, 25.75, 26, 26.25, 26.5, 26.75, 27, 27.25, 27.5, 27.75, 28, 28.25, 28.5, 28.75, 29, 29.25, 29.5, 29.75, 30, 30.25, 30.5, 30.75, 31, 31.25, 31.5, 31.75, 32, 32.25, 32.5, 32.75, 33, 33.25, 33.5, 33.75, 34, 34.25, 34.5, 34.75, 35, 35.25, 35.5, 35.75, 36, 36.25, 36.5, 36.75, 37, 37.25, 37.5, 37.75, 38, 38.25, 38.5, 38.75, 39, 39.25, 39.5, 39.75, 40, 40.25, 40.5, 40.75, 41, 41.25, 41.5, 41.75, 42, 42.25, 42.5, 42.75, 43, 43.25, 43.5, 43.75, 44, 44.25, 44.5, 44.75, 45],
    "z": [
        (-3610, -3900, -3961, -3988, -3306, -2917, -2683, -2567, -2474, -2445, -2406, -2299, -2127, -2139, -2350, -2338, -2440, -2561, -2172, -1894, -1728, -1851, -2476, -2925, -2662, -2644, -3004, -2738, -2873, -3291, -3270, -2757, -2682, -2615, -2564, -2484, -2842, -2804, -2630, -2711, -3039, -2889, -3001, -2835, -3023, -2866, -2764, -2746, -2590, -2556, -2616, -2577, -2590, -2709, -2689, -2264, -2082, -2453, -2252, -2238, -2180, -2103, -2029, -1918, -1818, -1537, -1040, 1261, 1303, 1494, 2036, 1270, 1011, 978, 1035, 901, 695, 596, 605, 690, 693, 639, 625, 552, 493, 450, 421, 420, 392, 371, 382, 356, 290, 200, 122, 145, 88, 44, 73, 64, 54, 49, 53, 51, 59),
        (-3833, -3981, -3770, -3368, -3143, -2819, -2655, -2532, -2577, -2476, -2258, -2457, -2275, -2366, -2395, -2664, -2678, -2603, -2586, -2030, -1949, -1909, -2552, -2683, -3519, -3345, -3970, -3337, -2790, -2331, -2901, -3071, -2654, -2670, -2651, -2493, -2521, -2461, -2584, -2699, -2875, -2761, -2935, -3194, -2805, -2662, -2610, -2596, -2481, -2534, -2472, -2489, -2402, -2504, -2575, -2702, -2151, -2038, -1974, -1833, -2060, -2098, -2056, -1981, -1875, -1750, -1799, 355, 1358, 1697, 1311, 1175, 955, 750, 736, 1038, 640, 479, 460, 501, 574, 572, 556, 490, 445, 376, 359, 317, 230, 278, 290, 256, 203, 168, 172, 172, 68, 110, 102, 93, 62, 64, 64, 67, 90),
        (-3861, -3763, -3359, -3118, -2968, -2801, -2704, -2697, -2603, -2606, -2158, -2410, -2234, -2897, -2817, -2807, -2771, -2749, -2693, -2507, -2666, -2879, -1880, -2235, -2068, -1564, -1391, -1192, -3280, -3730, -3510, -1929, -2097, -2242, -2635, -2583, -2512, -2584, -2473, -2547, -2781, -2643, -2698, -2721, -2611, -2436, -2452, -2444, -2422, -2407, -2377, -2372, -2342, -2790, -2273, -1047, -1671, -844, -766, -802, -942, -1535, -1413, -1574, -1882, -1823, -1730, -69, 116, 1500, 522, 707, 814, 703, 581, 510, 493, 380, 393, 403, 389, 419, 402, 406, 373, 327, 270, 257, 197, 211, 197, 160, 189, 223, 238, 177, 71, 131, 119, 78, 71, 85, 93, 131, 136)
    ]
}
```

--------------------------------

### Generate the apartment listings dataset

Source: https://github.com/eriknovak/datachart/blob/main/docs/how-to-guides/charts/hexbinchart.ipynb

Generate the illustrative dataset used throughout the guide: 8,000 apartment listings with floor area (x), monthly rent (y), and days on market (c). Uses a seeded random generator for reproducibility.

```python
import numpy as np

rng = np.random.RandomState(42)
N_LISTINGS = 8000
DISTRICTS = ["Outskirts", "Midtown", "Center"]
# the rent per m² of each district, in € per month
RATES = np.array([13.5, 16.0, 19.5])

district = rng.choice(3, size=N_LISTINGS, p=[0.5, 0.35, 0.15])
area = np.clip(rng.lognormal(np.log(60), 0.35, N_LISTINGS), 18, 220)
rent = np.clip(180 + RATES[district] * area + rng.normal(0, 120, N_LISTINGS), 300, None)
# listings priced above the city-wide rate stay on the market longer
premium = rent - (180 + 16.0 * area)
days = rng.gamma(2.0, np.clip(4 + 0.12 * area + 0.02 * premium, 2, None))

listings = {
    "x": np.round(area, 1).tolist(),
    "y": np.round(rent).tolist(),
    "c": np.round(days).tolist(),
}
points = {"x": listings["x"], "y": listings["y"]}
```

--------------------------------

### Define QUAKES earthquake dataset

Source: https://github.com/eriknovak/datachart/blob/main/docs/how-to-guides/charts/imagechart.ipynb

Defines the QUAKES dataset used in the guide: a list of tuples with hours since 2023-01-01 UTC, latitude, longitude, depth in km, and magnitude for USGS earthquakes of magnitude 5 and above in 2023. This is the input data for the chart examples.

```python
# USGS earthquake catalogue (public domain), magnitude 5 and above, 2023:
# (hours since 2023-01-01 UTC, latitude, longitude, depth in km, magnitude)
QUAKES = [
    (418.137, 38.433, 44.946, 19.1, 5.7),
    (588.618, 35.709, 28.497, 29.6, 5.9),
    (666.246, 38.42, 44.91, 16.0, 5.9),
    (666.282, 38.481, 44.928, 10.0, 5.1),
    (865.293, 37.226, 37.014, 10.0, 7.8),
    (865.447, 37.225, 37.0, 10.0, 5.7),
    (865.471, 37.189, 36.893, 9.8, 6.7),
    (865.608, 36.992, 36.683, 10.0, 5.6),
    (865.973, 37.03, 36.621, 10.0, 5.1),
    (866.06, 37.771, 37.914, 16.9, 5.3),
    (866.107, 37.923, 38.061, 10.0, 5.0),
    (866.387, 37.182, 36.966, 11.4, 5.3),
    (868.313, 38.184, 38.534, 16.4, 5.0),
    (874.414, 38.011, 37.196, 7.4, 7.5),
    (874.446, 38.032, 38.098, 10.0, 6.0),
    (874.536, 37.996, 37.203, 14.1, 5.4),
    (874.599, 38.025, 37.802, 10.0, 5.8),
    (874.859, 38.248, 38.185, 10.0, 5.7),
    (875.093, 38.075, 36.759, 6.0, 5.1),
    (876.036, 38.058, 36.511, 8.5, 6.0),
    (877.129, 38.038, 37.371, 16.9, 5.0),
    (877.657, 38.017, 37.701, 10.0, 5.3),
    (877.748, 37.941, 37.492, 16.9, 5.0),
    (879.559, 38.19, 38.176, 7.4, 5.4),
    (880.725, 38.001, 36.508, 10.0, 5.0),
    (882.065, 37.978, 36.446, 10.0, 5.2),
    (884.631, 37.251, 37.086, 10.0, 5.3),
    (891.22, 37.764, 37.731, 10.0, 5.5),
    (895.188, 38.097, 38.64, 9.1, 5.4),
    (898.304, 38.016, 38.57, 13.0, 5.3),
    (903.815, 37.994, 36.477, 11.1, 5.0),
    (906.167, 37.936, 36.601, 17.9, 5.3),
    (919.809, 38.006, 36.496, 10.0, 5.0),
    (923.198, 37.939, 37.661, 9.6, 5.4),
    (926.34, 37.996, 37.432, 5.4, 5.1),
    (1024.497, 38.824, 38.031, 10.0, 5.0),
    (1123.797, 36.177, 35.797, 10.0, 5.2),
    (1171.525, 38.07, 36.599, 12.2, 5.0),
    (1217.075, 36.162, 36.025, 16.0, 6.3),
    (1217.127, 36.159, 35.935, 10.0, 5.5),
    (1330.453, 37.98, 33.988, 11.8, 5.2),
    (1377.081, 38.214, 38.277, 10.0, 5.2),
    (1466.895, 37.84, 36.7, 7.1, 5.0),
    (1744.6, 35.517, 22.179, 10.0, 5.4),
    (1953.332, 38.011, 36.427, 10.0, 5.3),
    (1971.282, 38.461, 44.85, 11.0, 5.6),
    (2780.01, 36.429, 26.316, 116.5, 5.1),
    (3306.981, 34.992, 24.849, 10.8, 5.1),
    (4925.748, 37.579, 35.948, 13.1, 5.5),
    (5321.8, 38.267, 38.201, 10.0, 5.2),
    (5383.825, 35.279, 23.851, 10.0, 5.1),
    (7350.444, 38.686, 23.558, 10.4, 5.1),
    (7838.769, 38.084, 38.552, 10.0, 5.3),
    (8095.706, 40.437, 28.848, 10.1, 5.1),
]
```

--------------------------------

### Apply THEME.HARBOR and show swatches

Source: https://github.com/eriknovak/datachart/blob/main/docs/how-to-guides/styling/theme-gallery.ipynb

Applies the Harbor theme, shows its swatches, and displays a signature plot with the specified pair of colors.

```python
config.set_theme(THEME.HARBOR)
show_swatches()
signature(pair=("#1F4E79", "#D08C3A")).show()

```

--------------------------------

### Define Palmer penguins dataset with body mass and flipper length

Source: https://github.com/eriknovak/datachart/blob/main/docs/how-to-guides/charts/boxplot.ipynb

Defines the PENGUINS list containing 342 Palmer penguins with recorded body mass and flipper length, grouped by species and sex. This dataset is used throughout the guide for box plot examples.

```python
# the 342 Palmer penguins with a recorded body mass, grouped by species and sex
PENGUINS = [
    {
        "species": "Adelie",
        "sex": "Female",
        "body_mass": [
            3800, 3250, 3450, 3625, 3200, 3700, 3450, 3325, 3400, 3800, 3800, 3200, 3150,
            3250, 3300, 3325, 3550, 3300, 3150, 3100, 3000, 3450, 3500, 3450, 2900, 3550,
            2850, 3150, 3600, 2850, 3350, 3050, 3600, 3550, 3700, 3700, 3550, 3200, 3800,
            3350, 3500, 3600, 3550, 3400, 3300, 3700, 2900, 3725, 3075, 2925, 3750, 3175,
            3825, 3200, 3900, 2900, 3350, 3150, 3450, 3050, 3275, 3050, 3325, 3500, 3425,
            3175, 3400, 3400, 3050, 3000, 3475, 3450, 3700,
        ],
        "flipper_length": [
            186, 195, 193, 181, 182, 185, 195, 184, 174, 189, 187, 187, 172, 178, 188, 195,
            180, 181, 182, 186, 185, 190, 186, 190, 187, 186, 181, 185, 185, 184, 195, 190,
            190, 196, 190, 191, 187, 189, 187, 191, 189, 190, 202, 185, 187, 190, 178, 192,
            183, 193, 199, 181, 198, 193, 191, 188, 189, 187, 176, 186, 191, 191, 190, 193,
            187, 191, 185, 193, 188, 192, 184, 195, 187,
        ],
    },
    {
        "species": "Adelie",
        "sex": "Male",
        "body_mass": [
            3750, 3650, 4675, 3800, 4400, 4500, 4200, 3600, 3950, 3800, 3550, 3950, 3900,
            3900, 4150, 3950, 4650, 3900, 4400, 4600, 3425, 4150, 4300, 4050, 3700, 3800,
            3750, 4400, 4050, 3950, 4100, 4450, 3900, 4150, 4250, 3900, 4000, 4700, 4200,
            3550, 3800, 3950, 4300, 4450, 4300, 4350, 4100, 4725, 4250, 3550, 3900, 4775,
            4600, 4275, 4075, 3775, 3325, 3500, 3875, 4000, 4300, 4000, 3500, 4475, 3900,
            3975, 4250, 3475, 3725, 3650, 4250, 3750, 4000,
        ],
        "flipper_length": [
            181, 190, 195, 191, 198, 197, 194, 180, 185, 180, 183, 180, 178, 184, 196, 190,
            184, 195, 196, 190, 182, 191, 188, 200, 191, 193, 194, 195, 192, 192, 188, 198,
            190, 197, 195, 184, 195, 196, 193, 194, 190, 189, 205, 186, 208, 196, 192, 203,
            190, 184, 190, 197, 191, 197, 196, 199, 189, 198, 202, 199, 195, 210, 197, 199,
            190, 200, 193, 187, 190, 185, 190, 193, 201,
        ],
    },
    {
        "species": "Adelie",
        "sex": None,
        "body_mass": [
            3475, 4250, 3300, 3700, 2975,
        ],
        "flipper_length": [
            193, 190, 186, 180, 179,
        ],
    },
    {
        "species": "Chinstrap",
        "sex": "Female",
        "body_mass": [
            3500, 3525, 3950, 3250, 4150, 3800, 3700, 3575, 3700, 3450, 3600, 2900, 3300,
            3400, 3700, 3200, 3350, 3900, 3850, 2700, 3650, 3500, 3675, 3400, 3675, 3325,
            3600, 3350, 3250, 3525, 3650, 3650, 3400, 3775,
        ],
        "flipper_length": [
            192, 188, 198, 178, 195, 193, 185, 190, 181, 190, 181, 187, 195, 200, 191, 187,
            187, 199, 195, 192, 187, 196, 196, 190, 198, 199, 193, 187, 191, 194, 189, 195,
            202, 198,
        ],
    },
    {
        "species": "Chinstrap",
        "sex": "Male",
        "body_mass": [
            3900, 3650, 3725, 3750, 3700, 3775, 4050, 4050, 3300, 4400, 3400, 3800, 4150,
            3800, 4550, 4300, 4100, 3600, 4800, 4500, 3950, 3550, 4450, 4300, 3250, 3950,
            4050, 3450, 4050, 3800, 3950, 4000, 3775, 4100,
        ],
        "flipper_length": [
            196, 193, 197, 197, 198, 194, 201, 201, 197, 195, 191, 193, 197, 200, 205, 201,

```

--------------------------------

### Import config and theme constants

Source: https://github.com/eriknovak/datachart/blob/main/docs/how-to-guides/styling/themes.ipynb

Imports the global config instance and the FONT_WEIGHT and THEME constants used throughout the theme examples.

```python
from datachart.config import config
from datachart.constants import FONT_WEIGHT, THEME

```

--------------------------------

### Apply THEME.MINIMAL and show swatches

Source: https://github.com/eriknovak/datachart/blob/main/docs/how-to-guides/styling/theme-gallery.ipynb

Applies the Minimal theme, shows its swatches, and displays a signature plot with the specified pair of colors.

```python
config.set_theme(THEME.MINIMAL)
show_swatches()
signature(pair=("#7048E8", "#1B242C")).show()

```
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