Summary  
Demonstrates how to use a plotting library’s boxplot function to compute and visualize data distribution via quartiles, interquartile range, whiskers, and outliers, including creating multiple labeled box plots.

General domain of usage  
Weather data analysis

**Box plot**  is another extremely common plot in statistics used to visualize the central tendency, spread, and potential outliers within the data via their quartiles. 

Definition

**Quartiles** split sorted data into four equal parts:

* **Q1** — the midpoint between the minimum and the median (25% of data below it);
* **Q2** — the median (50% of data below);
* **Q3** — the midpoint between the median and the maximum (75% of data below).

* The **left** side of the box shows **Q1**, the **right** side shows **Q3**;
* **IQR = Q3 − Q1**, shown as the width of the box, with the median marked by a yellow line;
* **Whiskers** extend to (Q1 - 1.5 \cdot IQR) and (Q3 + 1.5 \cdot IQR);
* Points outside the whiskers are **outliers**.

A box plot can be generated using `matplotlib`.

import pandas as pd
import matplotlib.pyplot as plt

# Loading the dataset with the average yearly temperatures in Boston and Seattle
url = 'https://content-media-cdn.codefinity.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/weather_data.csv'
weather_df = pd.read_csv(url, index_col=0)

# Creating a box plot for the Seattle temperatures
plt.boxplot(weather_df['Seattle'])

plt.show()

## Box Plot Data

Use `plt.boxplot(x)`, where `x` can be a 1D array-like object, a 2D array (one box per column), or a sequence of 1D arrays.

## Optional Parameters

`tick_labels` is useful for naming box plots — especially when plotting multiple arrays.

import pandas as pd
import matplotlib.pyplot as plt

# Loading the dataset with the average yearly temperatures in Boston and Seattle
url = 'https://content-media-cdn.codefinity.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/weather_data.csv'
weather_df = pd.read_csv(url, index_col=0)

# Creating two box plots for Boston and Seattle temperatures
plt.boxplot(weather_df, tick_labels=['Boston', 'Seattle'])
plt.show()

Passing a `DataFrame` with two numeric columns to `boxplot()` creates two separate box plots with labels automatically assigned.

There are also quite a bit of optional parameters for customizing the box plot, which you can explore in <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.boxplot.html" target="_blank"><svg width="1em" height="1em" viewBox="0 0 30 32" fill="none" xmlns="http://www.w3.org/2000/svg"><path fill="none" stroke="#098f67" style="stroke: var(--color1, #098f67)" stroke-linejoin="miter" stroke-linecap="round" stroke-miterlimit="4" stroke-width="2.4156" d="M17.289 17.305v0c-1.754 1.754-4.597 1.754-6.351 0l-7.76-7.759c-1.755-1.755-1.755-4.601 0-6.356v0c1.753-1.753 4.595-1.756 6.351-0.005l6.208 6.187"></path><path fill="none" stroke="#098f67" style="stroke: var(--color1, #098f67)" stroke-linejoin="miter" stroke-linecap="round" stroke-miterlimit="4" stroke-width="2.4156" d="M12.504 13.97v0c1.754-1.754 4.597-1.754 6.351 0l7.762 7.762c1.754 1.754 1.754 4.597 0 6.351v0c-1.754 1.754-4.597 1.754-6.351 0l-5.953-5.953"></path></svg> <code>boxplot()</code> documentation</a>, yet in practice you might rarely use them. 

Study More

import unittest
import matplotlib.pyplot as plt
import numpy as np

def _dynamic_test(test_case, condition, success_message, failure_message):
    """
    Custom dynamic test assertion with method name override and detailed messages.
    """
    if condition:
        test_case._testMethodName = success_message
        test_case.assertTrue(True, success_message)
    else:
        test_case._testMethodName = failure_message
        test_case.fail(failure_message)

class TestBoxPlot(unittest.TestCase):
    def setUp(self):
        self.size = 1000
        self.normal_sample_1 = np.random.normal(size=self.size)
        self.normal_sample_2 = np.random.normal(size=self.size)

        self.fig, self.ax = plt.subplots()
        self.box = self.ax.boxplot(
            [self.normal_sample_1, self.normal_sample_2],
            tick_labels=['First sample', 'Second sample']
        )

    def tearDown(self):
        plt.close(self.fig)

    def test_boxplot_box_count(self):
        count = len(self.box['boxes'])
        _dynamic_test(
            self,
            count == 2,
            "Correct number of box plots created.",
            f"Expected 2 box plots, but found {count}."
        )

    def test_tick_labels(self):
        tick_labels = [label.get_text() for label in self.ax.get_xticklabels()]
        expected = ['First sample', 'Second sample']
        _dynamic_test(
            self,
            tick_labels == expected,
            "Tick labels are correct.",
            f"Expected tick labels {expected}, but got {tick_labels}."
        )

if __name__ == '__main__':
    unittest.main()


test_box_plot.py

Learn how to explore and communicate data through visual analysis. The course covers creating common plots, customizing visualizations, and using statistical charts to examine data distributions, relationships, and patterns. Exploratory data analysis techniques are applied to identify trends, anomalies, and insights in real-world datasets.

Box Plot

Quartiles

Box Plot Elements

Box Plot Data

Optional Parameters

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