Visualizing Multiple Distributions: Combining Histograms with Seaborn's displot
Seaborn's displot function is a powerful tool for creating aesthetically pleasing and informative visualizations of distributions. While it excels at showcasing single distributions, you might encounter scenarios where you need to compare multiple distributions side-by-side. This article will guide you through the process of creating multiple histograms on the same graph using displot, showcasing the versatility and clarity it offers.
The Challenge: Comparing Distributions Side-by-Side
Imagine you have a dataset containing information about customer spending habits across different product categories. You want to visualize the distribution of spending for each category to understand their differences. Using separate displot calls for each category would result in individual plots, making comparison cumbersome.
Let's use a simplified example for illustration. Consider the following code:
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
# Sample data
data = {'Category': ['A', 'B', 'A', 'C', 'B', 'C', 'A', 'B'],
'Spending': [10, 25, 15, 30, 20, 35, 12, 28]}
df = pd.DataFrame(data)
# Separate displot calls for each category
sns.displot(data=df[df['Category'] == 'A'], x='Spending', kind='hist', kde=True)
sns.displot(data=df[df['Category'] == 'B'], x='Spending', kind='hist', kde=True)
sns.displot(data=df[df['Category'] == 'C'], x='Spending', kind='hist', kde=True)
plt.show()
This code creates three separate plots, each displaying the distribution of spending for a single category. However, it's not ideal for direct comparison. We need a way to display all distributions on the same graph for easier analysis.
The displot Solution: Visualizing Multiple Distributions Together
Seaborn's displot offers a flexible approach to visualizing multiple distributions within a single plot. We can achieve this by leveraging its col parameter.
Here's how to modify our code:
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
# Sample data
data = {'Category': ['A', 'B', 'A', 'C', 'B', 'C', 'A', 'B'],
'Spending': [10, 25, 15, 30, 20, 35, 12, 28]}
df = pd.DataFrame(data)
# Combining distributions with 'displot'
sns.displot(data=df, x='Spending', col='Category', kind='hist', kde=True)
plt.show()
By specifying the col parameter as 'Category', we instruct displot to create separate histograms for each unique category value within the 'Category' column. This produces a single figure with multiple histograms arranged side-by-side, facilitating direct visual comparison.
Additional Customization and Insights
The displot function offers several options for customizing the appearance and content of your plot. You can:
- Adjust the number of bins: Use the
binsparameter to control the number of bins used in the histogram. - Include a kernel density estimate (KDE): Set the
kdeparameter toTrueto overlay a smooth KDE on the histogram, providing a visual representation of the underlying probability density function. - Customize the appearance: Modify the
color,hue, andpaletteparameters to change the color of the histograms and the color scheme of the KDE.
Conclusion: A Powerful Tool for Data Exploration
Seaborn's displot function empowers you to create insightful visualizations of multiple distributions. The col parameter simplifies the process of comparing distributions side-by-side, enhancing data exploration and analysis. By adjusting parameters and exploring customization options, you can tailor the plots to effectively communicate patterns and relationships within your data.
Remember: For more complex visualizations and customizations, explore additional Seaborn features, such as jointplot and pairplot. These functions offer even greater flexibility in exploring and visualizing your data.
