Seaboarn Library in python Welcome to your Seaboarn Library in python 1. What is Seaborn's primary advantage over Matplotlib when it comes to data visualization? Seaborn is faster in rendering plots. Seaborn provides a higher level of abstraction and an easier-to-use interface. Seaborn offers more customization options for plots. 2. Which Seaborn function is used for creating a simple scatter plot? sns.lineplot() sns.relplot() sns.scatterplot() sns.barplot() 3. Which Seaborn function is used to create a line plot? sns.lineplot() sns.relplot() sns.barplot() 4. What is one of the attractive improvements provided by Seaborn over Matplotlib in terms of aesthetics? Seaborn allows for 3D plotting. Seaborn automatically applies color palettes to plots. Seaborn provides more advanced statistical models for plotting. Seaborn offers better support for pie charts. 5. What type of data is NOT well-suited for Seaborn visualizations? Time series data Categorical data Relational data Numeric data 6. How does Seaborn integrate with Pandas? Seaborn provides its own data structures that are compatible with Pandas. Seaborn can automatically import data from Pandas DataFrames Seaborn allows Pandas DataFrames to be directly used as input for plotting functions. Seaborn requires a separate installation to work with Pandas. Time's up
