# Set Color by Group in Plot in Python Matplotlib & seaborn (2 Examples)

Hi! This tutorial will demonstrate how to determine the color by group in a plot in Matplotlib and seaborn in the Python programming language.

Here is an overview:

Letâ€™s get into the discussion!

## Install & Import Matplotlib & seaborn

To install and import Matplotlib and seaborn, run the lines of code below in your Python programming IDE:

```# install Matplotlib & seaborn
pip install matplotlib seaborn

# import Matplotlib & seaborn
import matplotlib.pyplot as plt

import seaborn as sns```

Having installed and imported Matplotlib and seaborn into our Python programming environment, we can now create the example dataset that we will use in this tutorial.

## Create Example Dataset

We will use the popular iris dataset as our example dataset in this tutorial. The iris dataset is shipped along with the seaborn Python library. Nevertheless, you can make use of any dataset of your choice to follow along with this tutorial.

To load and preview the first 10 rows of the dataset, run the lines of code below:

```df = sns.load_dataset("iris")

# sepal_length	sepal_width	petal_length	petal_width	species
#0	   5.1	        3.5	         1.4	        0.2	setosa
#1	   4.9	        3.0	         1.4	        0.2	setosa
#2	   4.7	        3.2	         1.3	        0.2	setosa
#3	   4.6	        3.1	         1.5	        0.2	setosa
#4	   5.0	        3.6	         1.4	        0.2	setosa
#5	   5.4	        3.9	         1.7	        0.4	setosa
#6	   4.6	        3.4	         1.4	        0.3	setosa
#7	   5.0	        3.4	         1.5	        0.2	setosa
#8	   4.4	        2.9	         1.4	        0.2	setosa
#9	   4.9	        3.1	         1.5	        0.1	setosa```

With the dataset loaded, we can now build visualizations.

## Example 1: Determine Plot Color by Group in Matplotlib

In this first example, we will build a simple scatter plot and color the points by the group:

```colors = {"setosa": "blue", "versicolor": "red", "virginica": "green"}

plt.scatter(df["petal_length"], df["sepal_length"], c = df["species"].map(colors))

plt.show()```

In the above example, we first created a Python dictionary assigning colors to each of the species group in the DataFrame.

Next, we used plt.scatter() to build the scatter plot. In that method, we passed the `petal_length` column to the x axis and the `sepal_length` column to the y axis.

Then, we defined the color argument by mapping the species column of the DataFrame to the colors defined earlier. Finally, we displayed the plot using plt.show().

## Example 2: Determine Plot Color by Group in seaborn

Here we will build a line plot and demonstrate how to color the plot by group:

```sns.lineplot(df, x = "petal_length", y = "sepal_length", hue = "species")

plt.show()```

As you can see, the lines in the line plot are grouped by different colors, with each color representing a species in the dataset.

We used sns.lineplot() to build the line plot, and in it, we passed the DataFrame `df` and `petal_length` to the x axis and `sepal_length` to the y axis. Then we passed `species` to the `hue =` argument in order to group the lines by color. By default, seaborn creates a legend for the plot where there is a color grouping.

Lastly, we displayed the plot with `plt.show()`.

## Video, Further Resources & Summary

Do you need more explanations on how to set the color by group in a plot in Matplotlib and seaborn in Python? Then you should have a look at the following YouTube video of the Statistics Globe YouTube channel.

In the video, we explain in some more detail how to set the color by group in a plot in Matplotlib and seaborn in Python.

So, with that, we have demonstrated how to color a plot by group in both Matplotlib and seaborn in Python. Furthermore, you could have a look at some of the other interesting Matplotlib and seaborn tutorials on Statistics Globe:

This post has shown how to set the color by group in a plot in Matplotlib and seaborn in Python. I hope you found it helpful! In case you have further questions, you may leave a comment below.

This page was created in collaboration with Ifeanyi Idiaye. You might check out Ifeanyiâ€™s personal author page to read more about his academic background and the other articles he has written for the Statistics Globe website.

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