Creating a Seaborn Scatter Plot with Multiple Columns for Y-axis

In this article, we will discuss how to create a seaborn scatter plot with multiple columns for the y-axis using Python's Seaborn library. We will also explore different color palettes and their use cases.

Seaborn is a popular data visualization library built on top of Matplotlib that provides a high-level interface for drawing attractive and informative statistical graphics. It offers a variety of functions to create visualizations such as scatter plots, line plots, histograms, bar plots, and more.

Why Use Seaborn Scatter Plot?

Seaborn scatter plot is used to visualize the relationship between two variables, x and y. It can help us identify patterns, trends, and correlations in the data. By using a seaborn scatter plot with multiple columns for the y-axis, we can create more complex visualizations that show the relationships between multiple variables.

Practical Example

Let's import the necessary libraries and create a sample dataset to demonstrate how to create a seaborn scatter plot with multiple columns for the y-axis.

```python

import seaborn as sns

import matplotlib.pyplot as plt

# Load the tips dataset

tips = sns.load_dataset('tips')

# Create a sample dataset with three features: total_bill, tip, and size

data = pd.DataFrame({'x': range(10), 'y1': '20, 30, 40, 50, 60, 70, 80, 90, 100',

'y2': '100, 120, 140, 160, 180, 200, 220, 240, 260'})

# Create a seaborn scatter plot with multiple columns for the y-axis

plt.figure(figsize=(10,8))

sns.scatterplot(x='x', y=''y1', 'y2'', data=data)

# Add labels and title

plt.xlabel('X-axis')

plt.ylabel('Y-axis')

plt.title('Seaborn Scatter Plot with Multiple Columns for Y-axis')

# Show the plot

plt.show()

```

In this example, we create a sample dataset with three features: `x`, `y1`, and `y2`. We then use the seaborn scatter plot function to create a visualization that shows the relationship between `x` and both `y1` and `y2`.

Color Palettes

Seaborn offers a variety of color palettes that can be used to customize the appearance of our scatter plot. Some popular color palettes include:

* Accent

* Blues

* BrBG

* BuGn

* BuPu

* CMRmap

* Dark2

* GnBu

* Greens

* Greys

* OrRd

* Oranges

* PRGn

* Paired

* Pastel1

* Pastel2

* PiYG

You can use these color palettes by specifying the `palette` parameter in the seaborn scatter plot function. For example:

```python

sns.scatterplot(x='x', y=''y1', 'y2'', data=data, palette='Accent')

```

This will create a scatter plot with an accent color palette.

In this article, we learned how to create a seaborn scatter plot with multiple columns for the y-axis using Python's Seaborn library. We also explored different color palettes and their use cases. By using seaborn scatter plots, we can gain valuable insights from our data by visualizing relationships between multiple variables.

Seaborn scatter plot multiple columns y