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Scatter Plot

Introduction​

A scatter plot is an essential visualization tool for data analysis that helps us represent the relationship between two variables. In this tutorial, we will learn how to create scatter plots using Pandas, a powerful Python library for data manipulation and analysis.

Installing Pandas and Matplotlib​

To make scatter plots using Pandas, we need to install both Pandas and Matplotlib libraries. If these libraries are not installed in your Python environment, you can install them by running the following commands:

!pip install pandas matplotlib

Importing Libraries​

Before we start, let's import the necessary libraries:

import pandas as pd
import matplotlib.pyplot as plt

Understanding Scatter Plots​

A scatter plot uses dots to represent values for two different numeric variables. The position of each dot on the horizontal and vertical axis indicates values for an individual data point. Scatter plots are used to observe relationships between variables.

Loading Data​

We will be using the Iris dataset, which is available in seaborn's data repository. The dataset contains measurements for 150 iris flowers from three different species.

iris_data = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv')

Creating a Scatter Plot​

We can use the plot.scatter method in Pandas DataFrame to create a scatter plot. We need to specify the x and y parameters for the scatter plot.

iris_data.plot.scatter(x='sepal_length', y='sepal_width')
plt.show()

In this plot, each point represents an iris flower. The sepal_length of the flower is plotted on the x-axis, and the sepal_width is plotted on the y-axis.

Enhancing the Scatter Plot​

We can improve the readability and the expressiveness of the plot by adding colors, labels, and a title.

iris_data.plot.scatter(x='sepal_length', y='sepal_width', c='petal_length', colormap='viridis')
plt.xlabel('Sepal Length')
plt.ylabel('Sepal Width')
plt.title('Sepal Length vs Sepal Width')
plt.show()

Summary​

In this tutorial, we learned how to create a scatter plot using Pandas. We started by installing and importing the necessary libraries. Then, we loaded the Iris dataset from seaborn's data repository. Finally, we created a scatter plot to visualize the relationship between the sepal length and sepal width of iris flowers, and enhanced the plot by adding colors, labels, and a title.

Remember, the scatter plot is a powerful tool to understand the relationship between two variables. It's a crucial part of any data analysis process, and Pandas makes it simple and straightforward to create one. Happy analyzing!