How to Set Color by Temperature in Seaborn: Step-by-Step Tutorial with 3 Visualization Examples

By Jonathan Dough Last updated Feb 21, 2026

Data visualization is not only about presenting numbers—it is about communicating patterns clearly and accurately. In many real-world datasets, temperature plays a critical role in analysis, whether you are evaluating climate trends, industrial sensor readings, or seasonal business performance. Seaborn, a powerful Python visualization library built on top of Matplotlib, makes it straightforward to assign colors based on temperature values. This tutorial provides a structured, step-by-step guide to setting color by temperature in Seaborn, along with three practical visualization examples.

TLDR: In Seaborn, you can set color by temperature using the hue parameter combined with color palettes such as coolwarm, viridis, or custom colormaps. Continuous temperature data works best with gradient palettes, while categorical temperature ranges benefit from discrete palettes. This guide walks you through preparing data, selecting the correct palette, and building three visualization types: scatter plot, line plot, and heatmap.

Understanding Color Mapping in Seaborn

Before diving into examples, it is important to understand how Seaborn handles color assignment:

hue: Maps a variable to different colors.
palette: Defines the color scheme used.
Continuous vs. categorical data: Determines whether a gradient or discrete palette is appropriate.

Temperature is typically a continuous numeric variable. Therefore, gradient-based colormaps such as coolwarm, magma, or viridis are usually the best choice.

Install and import required libraries:

pip install seaborn matplotlib pandas numpy

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

Step 1: Preparing Sample Temperature Data

To demonstrate clearly, we will create a synthetic dataset representing temperature measurements across time and locations.

# Create sample dataset
np.random.seed(42)

data = pd.DataFrame({
    "time": np.arange(0, 100),
    "temperature": np.random.normal(loc=25, scale=5, size=100),
    "humidity": np.random.uniform(40, 80, 100),
    "location": np.random.choice(["North", "South", "East", "West"], 100)
})

data.head()

This dataset includes:

time: Measurement index
temperature: Continuous variable
humidity: Secondary numeric variable
location: Categorical grouping variable

Now let us apply temperature-based color mapping in three visualization scenarios.

Example 1: Scatter Plot Colored by Temperature

A scatter plot is particularly effective when analyzing relationships between two variables, such as humidity and time, while using temperature as a color dimension.

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Step-by-Step Implementation

plt.figure(figsize=(10,6))

sns.scatterplot(
    data=data,
    x="time",
    y="humidity",
    hue="temperature",
    palette="coolwarm"
)

plt.title("Humidity Over Time Colored by Temperature")
plt.legend(title="Temperature")
plt.show()

Explanation

hue=”temperature”: Maps temperature values to color.
palette=”coolwarm”: Blue represents lower temperatures, red represents higher temperatures.
The legend automatically displays a color gradient scale.

Why this works: Continuous palettes like coolwarm intuitively align with common visual expectations—cool values in blue and warm values in red.

Alternative Gradient Options

viridis (colorblind-friendly)
plasma
magma
cividis

Example adjustment:

palette="viridis"

Example 2: Line Plot with Temperature-Based Coloring

Suppose you want to plot temperature changes across time but also emphasize different temperature intensity levels visually.

Because line plots typically use a single color per line, we can bin temperature into ranges to create categorical groups for better clarity.

Step 1: Categorize Temperature

# Create temperature categories
data["temp_category"] = pd.cut(
    data["temperature"],
    bins=[0, 20, 25, 30, 50],
    labels=["Low", "Moderate", "High", "Very High"]
)

Step 2: Plot with Discrete Palette

plt.figure(figsize=(10,6))

sns.lineplot(
    data=data,
    x="time",
    y="temperature",
    hue="temp_category",
    palette="Set1"
)

plt.title("Temperature Trends by Category")
plt.show()

Why Categorization is Useful

Improves interpretability when precise gradients are unnecessary
Useful for reporting dashboards
Allows alignment with business or scientific thresholds

Professional recommendation: Use gradient mapping for exploratory analysis and categorical mapping for executive presentations.

Example 3: Heatmap Based on Temperature Values

A heatmap is one of the most natural ways to visualize temperature distributions, especially across multiple dimensions such as location and time.

Step 1: Pivot the Data

# Create pivot table
pivot_data = data.pivot_table(
    values="temperature",
    index="location",
    columns="time"
)

Step 2: Create Heatmap

plt.figure(figsize=(12,6))

sns.heatmap(
    pivot_data,
    cmap="coolwarm",
    cbar_kws={"label": "Temperature"}
)

plt.title("Temperature Distribution by Location and Time")
plt.show()

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Key Parameters Explained

cmap: Sets the color gradient
cbar_kws: Customizes the color bar label
annot=True (optional): Displays numeric values inside cells

Heatmaps excel in identifying clusters, anomalies, or persistent regional temperature patterns.

Custom Colormaps for Greater Control

In some scientific or regulatory contexts, standard palettes may not meet requirements. You can create a custom colormap:

from matplotlib.colors import LinearSegmentedColormap

custom_palette = LinearSegmentedColormap.from_list(
    "custom_temp",
    ["navy", "blue", "yellow", "red"]
)

sns.scatterplot(
    data=data,
    x="time",
    y="humidity",
    hue="temperature",
    palette=custom_palette
)

This approach gives you precise control over:

Color transitions
Brand alignment
Emphasis on critical thresholds

Best Practices for Temperature-Based Coloring

To maintain clarity, professionalism, and scientific accuracy, follow these guidelines:

1. Match Palette to Meaning

Use coolwarm for intuitive temperature representations.
Avoid misleading color choices (e.g., green for high heat unless justified).

2. Ensure Accessibility

Prefer colorblind-friendly palettes like viridis or cividis.
Test visualizations in grayscale when possible.

3. Avoid Overcrowding

Combine color with size or shape only when necessary.
Too many dimensions may reduce interpretability.

4. Always Label Color Scales Clearly

Include a color bar or legend title.
Specify measurement units (°C or °F).

Common Mistakes to Avoid

Using categorical palettes for continuous data
Failing to include a legend or color bar
Over-saturating colors, making small differences difficult to perceive
Not normalizing extreme values, which can distort gradients

If your dataset has outliers, consider:

sns.heatmap(pivot_data, cmap="coolwarm", vmin=15, vmax=35)

Setting vmin and vmax ensures a controlled color range.

Conclusion

Setting color by temperature in Seaborn is both straightforward and powerful. By using the hue parameter for scatter and line plots, and cmap for heatmaps, you can effectively encode temperature as a visual dimension. Continuous colormaps are ideal for raw temperature values, while categorized ranges improve clarity in reporting environments.

When applied correctly, temperature-based color mapping enhances interpretability, highlights patterns, and improves analytical insight without overwhelming the viewer. As with any visualization technique, selecting appropriate palettes, labeling scales clearly, and maintaining accessibility standards are essential for professional, trustworthy results.

With the step-by-step methods outlined above, you are now equipped to incorporate temperature-driven color logic confidently into your Seaborn visualizations.

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