Summary  
Demonstrates how to use seaborn’s kdeplot function to generate kernel density estimation plots by passing either a sequence or a DataFrame with explicit x/y parameters and customizing the fill of the distribution curve.

General domain of usage  
Statistical data visualization

A **Kernel Density Estimation (KDE) plot** is a type of plot that visualizes the estimated probability density function of a continuous variable. Unlike a histogram, which displays data using discrete bars grouped into intervals, a KDE plot represents the distribution as a smooth, continuous curve based on all data points.


Definition

This example shows a histogram combined with a KDE plot (**orange curve**), providing a clearer approximation of the probability density function than the histogram alone.

In `seaborn`, the `kdeplot()` function makes creating KDE plots easy. Its key parameters—`data`, `x`, and `y`—work just like in `countplot()`.


## First Option

Only one of the parameters can be set by passing a **sequence of values**, allowing for individual customization across elements.



import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# 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 KDE plot setting only the data parameter
sns.kdeplot(data=weather_df['Seattle'], fill=True)

plt.show()

The `data` parameter is set by passing a Series object, and the `fill` parameter is used to fill the area under the curve, which is unfilled by default.


## Second Option

It is also possible to set a **2D object** like a DataFrame for `data` and a column name or a key if the `data` is a dictionary for `x` (**vertical** orientation) or `y` (**horizontal** orientation):


import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# 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 KDE plot setting both the data and x parameters
sns.kdeplot(data=weather_df, x='Seattle', fill=True)

plt.show()

The same results were achieved by passing the entire `DataFrame` as the `data` parameter and specifying the **column name** for the `x` parameter.


The KDE plot created exhibits a characteristic bell curve, closely resembling a normal distribution with a mean around 52°F.


Note

In case you want to explore more about the **KDE plot** function, feel free to refer to <a href="https://seaborn.pydata.org/generated/seaborn.kdeplot.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>kdeplot()</code> documentation</a>.

Study More

import unittest
import ast
import inspect
import user_code  # student's solution module

def _dynamic_test(test_case, condition, success_message, failure_message):
    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 TestKDEPlot(unittest.TestCase):

    @classmethod
    def setUpClass(cls):
        source = inspect.getsource(user_code)
        cls.tree = ast.parse(source)

    def test_kdeplot_call(self):
        calls = [
            node for node in ast.walk(self.tree)
            if isinstance(node, ast.Call) and isinstance(node.func, ast.Attribute)
        ]
        used = any(
            call.func.attr == 'kdeplot' and isinstance(call.func.value, ast.Name) and call.func.value.id == 'sns'
            for call in calls
        )
        _dynamic_test(self, used, "`kdeplot()` function is used", "`kdeplot()` function is not used")

    def test_data_arguments(self):
        calls = [
            node for node in ast.walk(self.tree)
            if isinstance(node, ast.Call) and isinstance(node.func, ast.Attribute) and node.func.attr == 'kdeplot'
        ]
        correct = any(
            any(kw.arg == 'data' and isinstance(kw.value, ast.Name) and kw.value.id == 'countries_df'
                for kw in call.keywords)
            for call in calls
        )
        _dynamic_test(self, correct, "`data` is `countries_df`", "`data` is not `countries_df`")

    def test_y_val(self):
        calls = [
            node for node in ast.walk(self.tree)
            if isinstance(node, ast.Call) and isinstance(node.func, ast.Attribute) and node.func.attr == 'kdeplot'
        ]
        correct = any(
            any(kw.arg == 'y' and isinstance(kw.value, ast.Constant) and kw.value.value == 'GDP per capita'
                for kw in call.keywords)
            for call in calls
        )
        _dynamic_test(self, correct, "`y` is 'GDP per capita'", "`y` is not 'GDP per capita'")

    def test_fill(self):
        calls = [
            node for node in ast.walk(self.tree)
            if isinstance(node, ast.Call) and isinstance(node.func, ast.Attribute) and node.func.attr == 'kdeplot'
        ]
        correct = any(
            any(kw.arg == 'fill' and isinstance(kw.value, ast.Constant) and kw.value.value is True
                for kw in call.keywords)
            for call in calls
        )
        _dynamic_test(self, correct, "`fill` is True", "`fill` is not True")

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


test_kde.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.

KDE Plot

First Option

Second Option

Lösung