Iris Dataset
The Iris dataset is one of the most famous datasets used in pattern recognition literature. It contains measurements of iris flowers from three different species. The dataset includes features such as sepal length, sepal width, petal length, petal width, and species of the iris flower.
Advantages: Widely used and well-understood, excellent for classification and clustering demonstrations.
Disadvantages: Small dataset, limited to flower measurements.
Features and Characteristics
- sepal_length: Sepal length in cm (numerical)
- sepal_width: Sepal width in cm (numerical)
- petal_length: Petal length in cm (numerical)
- petal_width: Petal width in cm (numerical)
- species: Species of the iris flower (categorical)
How to load Iris Dataset?
iris = sns.load_dataset("iris")
print(iris.head())
sepal_length | sepal_width | petal_length | petal_width | species |
---|---|---|---|---|
5.1 | 3.5 | 1.4 | 0.2 | setosa |
4.9 | 3.0 | 1.4 | 0.2 | setosa |
4.7 | 3.2 | 1.3 | 0.2 | setosa |
4.6 | 3.1 | 1.5 | 0.2 | setosa |
5.0 | 3.6 | 1.4 | 0.2 | setosa |
Seaborn Datasets For Data Science
Seaborn, a Python data visualization library, offers a range of built-in datasets that are perfect for practicing and demonstrating various data science concepts. These datasets are designed to be simple, intuitive, and easy to work with, making them ideal for beginners and experienced data scientists alike.
In this article, we’ll explore the different datasets available in Seaborn, their characteristics, advantages, and disadvantages, and how they can be used for various data analysis and visualization tasks.
Seaborn Datasets For Data Science
- 1. Tips Dataset
- 2. Iris Dataset
- 3. Penguins Dataset
- 4. Flights Dataset
- 5. Diamonds Dataset
- 6. Titanic Dataset
- 7. Exercise Dataset
- 8. MPG Dataset
- 9. Planets Dataset