How to color a Seaborn boxplot based on DataFrame column name in Matplotlib?

To color a Seaborn boxplot based on DataFrame column names, you need to access the box artists and set their facecolor based on specific column conditions. This approach gives you fine−grained control over individual box colors.

Basic Setup

First, let's create a sample DataFrame and generate a basic boxplot ?

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

# Create sample data
df = pd.DataFrame({
    'col1': [2, 4, 6, 8, 10, 3, 5],
    'col2': [7, 2, 9, 1, 4, 8, 6]
})

print("DataFrame:")
print(df.head())

DataFrame:
   col1  col2
0     2     7
1     4     2
2     6     9
3     8     1
4    10     4

Coloring Boxes by Column Name

Access the box artists and apply colors based on column names ?

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

plt.rcParams["figure.figsize"] = [8.00, 4.00]
plt.rcParams["figure.autolayout"] = True

df = pd.DataFrame({
    'col1': [2, 4, 6, 8, 10, 3, 5],
    'col2': [7, 2, 9, 1, 4, 8, 6]
})

# Create horizontal boxplot
ax = sns.boxplot(data=df, orient='h')

# Get box artists
boxes = ax.artists

# Color boxes based on column names
for i, box in enumerate(boxes):
    if 'col1' in df.columns[i]:
        box.set_facecolor('red')
    else:
        box.set_facecolor('blue')

plt.title('Boxplot Colored by Column Name')
plt.show()

Advanced Coloring with Custom Logic

You can implement more sophisticated coloring logic based on column properties ?

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

# Create DataFrame with descriptive column names
df = pd.DataFrame({
    'sales': [100, 150, 200, 180, 120, 90, 160],
    'profit': [20, 30, 45, 35, 25, 15, 32],
    'cost': [80, 120, 155, 145, 95, 75, 128]
})

fig, ax = plt.subplots(figsize=(10, 5))

# Create boxplot
boxplot = sns.boxplot(data=df, orient='h', ax=ax)

# Define color mapping
color_map = {
    'sales': 'lightgreen',
    'profit': 'gold',
    'cost': 'lightcoral'
}

# Apply colors
boxes = ax.artists
for i, box in enumerate(boxes):
    column_name = df.columns[i]
    box.set_facecolor(color_map.get(column_name, 'gray'))
    box.set_alpha(0.7)

plt.title('Business Metrics Boxplot with Custom Colors')
plt.xlabel('Values')
plt.show()

Key Points

• ax.artists − Returns list of box patch objects
• set_facecolor() − Changes the fill color of boxes
• enumerate() − Provides index to match boxes with column names
• orient='h' − Creates horizontal boxplots for better readability

Alternative Approach Using Hue

For categorical data, you can use the hue parameter for automatic coloring ?

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

# Melt DataFrame for hue-based coloring
df = pd.DataFrame({
    'group_A': [10, 15, 12, 18, 14],
    'group_B': [20, 25, 22, 28, 24]
})

df_melted = df.melt(var_name='group', value_name='value')
print("Melted DataFrame:")
print(df_melted.head())

# Create boxplot with hue
plt.figure(figsize=(8, 5))
sns.boxplot(data=df_melted, x='group', y='value', hue='group')
plt.title('Boxplot Using Hue Parameter')
plt.show()

Melted DataFrame:
     group  value
0  group_A     10
1  group_A     15
2  group_A     12
3  group_A     18
4  group_A     14

Conclusion

Use ax.artists to access individual boxes and set_facecolor() for custom coloring based on column names. For automatic coloring, consider using the hue parameter with melted data.