Box Plot (Box and whiskers)

import numpy as np
import lightningchart as lc

# Set the license key
lc.set_license('my-license-key')

# Define categories and their data
categories = [
    'temperature',
    'windspeed',
    'sigheight',
    'humidity',
    'feelslike',
    'swellheight',
]
category_data = {
    'temperature': np.random.normal(20, 5, 100).tolist(),  # Random data for temperature
    'windspeed': np.random.normal(15, 8, 100).tolist(),  # Random data for windspeed
    'sigheight': np.random.normal(2, 0.5, 100).tolist(),  # Random data for sigheight
    'humidity': np.random.normal(70, 15, 100).tolist(),  # Random data for humidity
    'feelslike': np.random.normal(18, 6, 100).tolist(),  # Random data for feelslike
    'swellheight': np.random.normal(
        1, 0.3, 100
    ).tolist(),  # Random data for swellheight
}

# Create a chart
chart = lc.ChartXY(theme=lc.Themes.Light, title='Box Plot')

# Prepare box plot data
dataset = []
x_values_outlier = []
y_values_outlier = []

for i, category in enumerate(categories):
    column_data = category_data[category]

    start = (i * 2) + 1
    end = start + 1
    lowerQuartile = float(np.percentile(column_data, 25))
    upperQuartile = float(np.percentile(column_data, 75))
    median = float(np.median(column_data))
    # Calculate IQR and identify outliers
    iqr = upperQuartile - lowerQuartile
    lower_bound = lowerQuartile - 1.5 * iqr
    upper_bound = upperQuartile + 1.5 * iqr

    # Filter non-outliers for whisker bounds
    non_outliers = [y for y in column_data if lower_bound <= y <= upper_bound]
    lowerExtreme = float(min(non_outliers))
    upperExtreme = float(max(non_outliers))

    dataset.append({
        'start': start,
        'end': end,
        'lowerQuartile': lowerQuartile,
        'upperQuartile': upperQuartile,
        'median': median,
        'lowerExtreme': lowerExtreme,
        'upperExtreme': upperExtreme,
    })

    # Calculate IQR and identify outliers
    iqr = upperQuartile - lowerQuartile
    lower_bound = lowerQuartile - 1.5 * iqr
    upper_bound = upperQuartile + 1.5 * iqr
    outliers = [y for y in column_data if y < lower_bound or y > upper_bound]

    for outlier in outliers:
        x_values_outlier.append(start + 0.5)
        y_values_outlier.append(outlier)

# Add box series to the chart
series = chart.add_box_series()
series.add_multiple(dataset)

# Add outliers to the chart
outlier_series = chart.add_point_series(sizes=True, rotations=True, lookup_values=True)
outlier_series.set_point_color(('red'))
outlier_series.append_samples(
    x_values=x_values_outlier,
    y_values=y_values_outlier,
    sizes=[10] * len(y_values_outlier),
)

# Configure X-axis with category labels
x_axis = chart.get_default_x_axis()
x_axis.set_title("Categories")
x_axis.set_interval(0, len(categories) * 2 + 1)

# Remove default numeric ticks and add custom category labels
x_axis.set_tick_strategy('Empty')

for i, category in enumerate(categories):
    center = (i * 2) + 1.5
    tick = x_axis.add_custom_tick()
    tick.set_value(center)
    tick.set_text(category)

chart.get_default_y_axis().set_title("Value")

chart.open()