Seaborn, a powerful data visualization library built on top of Matplotlib, offers a convenient Object Interface for creating stunning visualizations with ease. Using .map() and .map_dataframe() with Seaborn’s object-oriented interface allows for applying custom functions to plot data.

In this article, we will implement these methods and explore how they can be leveraged to enhance data visualization capabilities.

Understanding .map() and .map_dataframe()

Before delving into the practical examples, it’s essential to understand the core concepts behind .map and .map_dataframe methods in Seaborn.

• .map(): This method allows us to apply custom functions to elements of a Seaborn plot. It operates on each element of the plot, facilitating fine-grained customization based on specific data attributes.
• .map_dataframe(): Similar to .map, this method applies custom functions to elements of a Seaborn plot. However, it operates on the entire DataFrame rather than individual elements, enabling comprehensive data-driven customization.

Seaborn’s Object Interface is a more adaptable and versatile method of generating advanced and personalized visualizations in comparison to its traditional  API. It takes use of the object-oriented programming paradigm, enabling users to construct visualizations by manipulating and altering objects directly. The resulting interface gives you more control over the aspects of a plot, making it simpler to construct complex and elaborate representations.

Utilizing .map() and .map_dataframe() for Advanced Visualization

The Key parameters to represent various plot points and provide various kinds of visualizations are:

• Figure: Depicts an entire figure on which plots are produced.
• Axes: Each axis represents a distinct plot or subplot in the illustration.
• Plot: Represents the data being represented and how it is related to the plot’s visual attributes.
• FacetGrid: A grid of charts used to visualize complicated datasets.

Using .map() to Customize Plot Elements

Let’s demonstrate the example for advanced visualizations with seaborn’s in-built tips dataset and understand how to use Seaborn’s FacetGrid along with .map() to customize plot elements.

In this example, FacetGrid is specifying that we want separate columns for different times of day (lunch and dinner), based on the “time” column in the tips DataFrame and following are the customizations made:

• Plotting KDE to plot kernel density estimates.
• Setting shade=True to shade the area under the KDE curve.
• Changed the color to “orange” and adjusted the bandwidth (bw_adjust) for the KDE plot to control its smoothness.

import seaborn as sns
import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")

g = sns.FacetGrid(tips, col="time")
g.map(sns.kdeplot, "total_bill", shade=True, color="orange", bw_adjust=0.5)
g.set_axis_labels("Total Bill", "Density")
g.set_titles("{col_name} Time")
plt.show()

Output:

Using .map_dataframe() with Custom Function

In the example code below, a custom plotting functionn scatterplot is passed with the data argument as the first input to accept the DataFrame directly. The color, marker, and size options are stated for better visualization.

Also, We deleted the explicit ordering of the DataFrame within the scatterplot function and replaced it with data[“total_bill”] and data[“tip”] to access the columns directly. In the .map_dataframe() function, we passed the color, marker, and size arguments to the scatterplot function.

import seaborn as sns
import matplotlib.pyplot as plt
tips = sns.load_dataset("tips")

# Define a custom plotting function with specific arguments
def scatterplot(data,color, marker='o', size=50):
    plt.scatter(data["total_bill"], data["tip"], color=color, marker=marker, s=size)

g = sns.FacetGrid(tips, col="time") # Plot using .map_dataframe()
g.map_dataframe(scatterplot, color="skyblue", marker='o', size=30)
g.set_axis_labels("Total Bill", "Tip")
plt.show()

Output:

Combining .map() and .map_dataframe()

In this example, both .map() and .map_dataframe() are utilized. .map_dataframe() applies a histogram plot to the “age” data for each combination of class and sex, while .map() adds horizontal dashed lines to mark the baseline of each subplot

import seaborn as sns
import matplotlib.pyplot as plt

titanic = sns.load_dataset("titanic")
g = sns.FacetGrid(titanic, col="class", row="sex")

# Use .map_dataframe() with custom function
g.map_dataframe(sns.histplot, x="age", bins=10, kde=True, color="green").set_titles("{row_name} - {col_name}")\
# Use .map() to add titles
g.map(plt.axhline, y=0, color="k", linestyle="--")

Output:

Conclusion

Seaborn’s Object Interface, with its .map() and .map_dataframe() methods, provides a flexible and efficient way to customize plots. By leveraging these methods, users can apply custom functions to plot data with ease, enabling more insightful visualizations tailored to their specific needs.