Flights Dataset
The Flights dataset includes information about the number of passengers on flights over a period of years. It includes features such as year of the observation, month of the observation, and number of passengers.
Advantages: Suitable for time series analysis, simple and easy to understand.
Disadvantages: Limited to passenger data, data is somewhat outdated.
Features and Characteristics
- year: Year of the observation (numerical)
- month: Month of the observation (categorical)
- passengers: Number of passengers (numerical)
How to load flights dataset?
flights = sns.load_dataset("flights")
print(flights.head())
year | month | passengers |
---|---|---|
1949 | Jan | 112 |
1949 | Feb | 118 |
1949 | Mar | 132 |
1949 | Apr | 129 |
1949 | May | 121 |
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