Learn KDE Plot | Plotting with Seaborn

Definition

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.

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.


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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):


              123456789101112
            
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.

Note

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

Study More

In case you want to explore more about the KDE plot function, feel free to refer to kdeplot() documentation.

Task

Swipe to start coding

Use the correct function to create a KDE plot.
Use countries_df as the data for the plot (the first argument).
Set 'GDP per capita' as the column to use and the orientation to horizontal via the second argument.
Fill in the area under the curve via the third (rightmost) argument.

Solution

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Section 5. Chapter 4

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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.


              123456789101112
            
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):


              123456789101112
            
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.

Note

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

Study More

In case you want to explore more about the KDE plot function, feel free to refer to kdeplot() documentation.

Task

Swipe to start coding

Use the correct function to create a KDE plot.
Use countries_df as the data for the plot (the first argument).
Set 'GDP per capita' as the column to use and the orientation to horizontal via the second argument.
Fill in the area under the curve via the third (rightmost) argument.

Solution

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Section 5. Chapter 4

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KDE Plot

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KDE Plot

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Second Option

Solution

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