Exercise Dataset
The Exercise dataset contains information about individuals’ pulse measurements after different types of exercise. It includes features such as participant ID, type of diet, pulse rate, time after exercise, and type of exercise.
Advantages: Good for time series analysis, simple and easy to understand.
Disadvantages: Limited to pulse measurements, small dataset.
Features and Characteristics
- id: Participant ID (numerical)
- diet: Type of diet (Low Fat or High Fat) (categorical)
- pulse: Pulse rate (numerical)
- time: Time after exercise (numerical)
- kind: Type of exercise (categorical)
How to load Exercise Dataset?
exercise = sns.load_dataset("exercise")
print(exercise.head())
id | diet | pulse | time | kind |
---|---|---|---|---|
1 | low | 85 | 1 | rest |
1 | low | 85 | 15 | rest |
1 | low | 88 | 30 | rest |
1 | low | 90 | 45 | rest |
1 | low | 92 | 60 | rest |
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