seaborn pairplot

>>> import seaborn as sns; sns.set(style="ticks", color_codes=True)
>>> iris = sns.load_dataset("iris")
>>> g = sns.pairplot(iris)

Here is what the above code is Doing:
1. Importing the seaborn library
2. Loading the iris dataset from seaborn
3. Creating a pairplot of the dataset

The pairplot shows the relationships between different variables in the dataset.

The diagonal plots show the distribution of the data for each variable.

The off-diagonal plots show the relationships between the different variables.

For example, the plot between “sepal_length” and “sepal_width” shows that there is a positive relationship between these two variables. This means that as sepal_length increases, sepal_width also increases.

The plot between “petal_length” and “petal_width” shows that there is a strong positive relationship between these two variables. This means that as petal_length increases, petal_width also increases.

The plot between “sepal_length” and “petal_length” shows that there is a strong positive relationship between these two variables. This means that as sepal_length increases, petal_length also increases.

The plot between “sepal_width” and “petal_width” shows that there is a weak positive relationship between these two variables. This means that as sepal_width increases, petal_width also increases, but not as strongly as the other relationships.