`seaborn.heatmap()` creates a heatmap to visualize matrix-like data (such as correlation matrices or 2D data arrays) with color intensity representing values.
`seaborn.pairplot()` takes a Pandas DataFrame or similar data structure as input and plots pairwise relationships between the columns.
`seaborn.boxplot()` is used to create box plots that visualize the distribution of a dataset across different categories, displaying the median, quartiles, and outliers.
`seaborn.distplot()` creates a histogram along with a kernel density estimate (KDE) to visualize the distribution of a dataset.
`seaborn.set(style='dark')` changes the default theme of seaborn plots to a dark background, making the visual elements like gridlines and axes more prominent.
`sns.scatterplot(x=np.array([1, 2, 3]), y=np.array([4, 5, 6]))` uses NumPy arrays for the x and y values to create a scatter plot.
`sns.regplot()` allows you to plot a regression line along with scatter plot points, fitting a linear model to the data.
`seaborn.violinplot()` shows the distribution of a dataset across different categories by combining aspects of a box plot and a kernel density estimate, using violin-shaped distributions.
`seaborn.heatmap()` is commonly used to visualize correlation matrices and other matrix-like data in a heatmap format.
`seaborn.lmplot()` creates a scatter plot with a linear regression line, helping visualize the relationship between two continuous variables.