Visualizing data is an important step since it helps understand what is going on in the data without actually looking at the numbers and performing complicated computations. Seaborn is a library that helps in visualizing data.
Scatter plot shows the distribution of data as data points that are spread/scattered on the graph. It uses dots to represents values of a dataset, which are numeric in nature. The position of every dot on the horizontal and vertical axis denotes the value for a single data point.
They help understand the relationship between two variables. Let us understand how this can be achieved using Seaborn library in Python −
Example
import seaborn as sb
from matplotlib import pyplot as plt
df = sb.load_dataset('iris')
sb.jointplot(x = 'petal_length',y = 'petal_width',data = df)
plt.show()Output
Explanation
- The required packages are imported.
- The input data is ‘iris_data’ which is loaded from the scikit learn library.
- This data is stored in a dataframe.
- The ‘load_dataset’ function is used to load the iris data.
- This data is visualized using the ‘jointplot’ function.
- Here, the ‘x’ and ‘y’ axis values are supplied as parameters.
- This scatterplot data is displayed on the console.