Planets Dataset
The Planets dataset includes information about exoplanets, such as their orbital periods and masses. It includes features such as method of detecting the exoplanet, number of planets in the system, orbital period, mass of the planet, and distance from Earth.
Advantages: Unique dataset in the field of astronomy, suitable for exploring exoplanet characteristics and trends.
Disadvantages: Limited to exoplanetary data, small dataset compared to other astronomical datasets.
Features and Characteristics
- method: Method of detecting the exoplanet (categorical)
- number: Number of planets in the system (numerical)
- orbital_period: Orbital period in Earth days (numerical)
- mass: Mass of the planet in Jupiter masses (numerical)
- distance: Distance from Earth in light-years (numerical)
How to load Planets Dataset?
planets = sns.load_dataset("planets")
print(planets.head())
method | number | orbital_period | mass | distance |
---|---|---|---|---|
Radial Velocity | 1 | 269.3 | 7.1 | 77.4 |
Radial Velocity | 1 | 874.8 | 2.2 | 56.95 |
Radial Velocity | 1 | 763.0 | 2.6 | 19.84 |
Radial Velocity | 1 | 326.0 | 19.4 | 110.62 |
Radial Velocity | 1 | 516.0 | 10.5 | 119.47 |
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