Stacking Bars in Seaborn: Understanding the Issue and Solutions

Seaborn is a popular Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. One of its most useful tools for visualizing categorical data is the catplot function, which can create a variety of bar plots, including stacked bars.

In this article, we will delve into the world of seaborn’s catplot function and explore how to adjust the order of stacked bars for better visibility.

Problem: Stacked Bars Not Visible

The given code snippet demonstrates a common issue when using seaborn’s catplot function:

graph = seaborn.catplot(
    data=data, kind="bar",
    x="year", y="count", hue="name", height=20, palette=customPalette, 
    dodge = False
)

When we run this code with dodge=False, some stacked bars do not appear because they are overlapping and hidden. This is the issue we want to address in this article.

Understanding Seaborn’s Dodge Parameter

The dodge parameter controls how seaborn arranges bars when multiple groups have similar values. When set to True, seaborn tries to “dodge” (or move aside) overlapping bars, so they don’t overlap.

dodge = True  # moves each bar slightly to the right if overlaps with another group's bar

However, in our case, we want all groups’ bars visible and stacked on top of each other without any gaps. We need a different approach.

Solution: Sorting Groups by Value

One way to achieve this is by sorting the data before passing it to seaborn. Since we are trying to stack bars for comparison, it doesn’t matter which group comes first or last; what matters most is the relative order of their values in descending or ascending order.

We will use Python’s built-in sorted function and provide a custom key as an argument. This allows us to sort our data based on specific values without having to explicitly define how they should be ordered.

# Assuming your data looks something like this:
import pandas as pd

data = {
    'name': ['Anne', 'Amy', 'Ava', 'Bill', 'Bo'],
    'decade': [1920, 1920, 1920, 1930, 1930],
    'count': [70000, 60000, 50000, 65000, 55000]
}

df = pd.DataFrame(data)

# Sort the data in descending order by 'count'
sorted_df = df.sort_values(by='count', ascending=False)

graph = seaborn.catplot(
    data=sorted_df, kind="bar",
    x="decade", y="count", hue="name", height=20, palette=customPalette
)

In this code example:

• We first import pandas for handling our DataFrame.
• Then we define the structure of our data using a dictionary and create a DataFrame with it.
• Next, we sort the ‘count’ column in descending order to prioritize larger values. This will place the bar representing the highest count at the top, followed by smaller counts.
• Finally, we pass sorted_df to seaborn’s catplot function instead of the original DataFrame.

By making this change, all groups are now visible as stacked bars without any gaps or overlaps.

Additional Considerations

When you’re working with real data and want your visualization to be accurate and clear, it might not always be practical to manually intervene in the ordering. For instance, if you want to represent a particular group’s count above another even though their actual values are lower. In such scenarios, consider sorting your bar groups by ‘count’ in ascending order.

# Sort in ascending order for smaller counts
graph = seaborn.catplot(
    data=sorted_df, kind="bar",
    x="decade", y="count", hue="name", height=20, palette=customPalette,
    sort=True  # sets whether groups should be sorted by value before plotting
)

The sort=True argument tells seaborn to order the bars according to their counts in ascending order.

Conclusion

In this article, we explored a common issue with stacked bars in seaborn’s catplot function. By sorting your data based on specific values using Python’s built-in functions and passing sorted DataFrames to seaborn, you can achieve clearer bar stacks without gaps or overlaps. The technique is applicable when you want all groups visible regardless of their relative size.

Last modified on 2024-03-08