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Scatter Plot
Scatter plot visualizing the relationship between two quantitative variables using points positioned according to their coordinates. Allows identification of trends, correlations, and outliers in bivariate data.
Box Plot
Box plot showing the statistical distribution of a variable through five indicators: minimum, first quartile, median, third quartile, and maximum. Excellent for comparing distributions between multiple groups.
Heatmap
Heatmap using colors to represent the intensity of values in a two-dimensional matrix. Particularly effective for visualizing correlations and patterns in tabular data.
Violin Plot
Combination of a box plot and a density plot showing the distribution of a variable through its flared shape. The width represents the density of data at different values.
Pair Plot
Matrix of scatter plots simultaneously showing the relationships between all pairs of variables in a dataset. The diagonal often presents the univariate distributions of each variable.
Treemap
Hierarchical visualization using nested rectangles to represent proportions and data structures. The size of each rectangle is proportional to its numerical value.
Density Plot
Smoothed graphical representation of the distribution of a continuous variable using kernel density estimation. An alternative to the histogram offering a continuous view of the distribution.
Bubble Chart
Variation of the scatter plot where the size of the bubbles represents a third quantitative dimension. Allows simultaneous visualization of three variables in a two-dimensional space.
Radar Chart
Star-shaped graph allowing comparison of several quantitative variables on radial axes emanating from a central point. Useful for evaluating multivariate profiles and relative performances.
Violin Plot
Extension of the box plot combining distribution symmetry and density estimation to visualize the complete shape of data. Particularly useful for comparing distributions between categories.
Joint Plot
Combination of a central scatter plot with marginal distributions on the axes. Provides a complete view of the bivariate relationship and individual distributions simultaneously.
Swarm Plot
Categorical graph dispersing individual points along the categorical axis to avoid overlaps. Allows visualization of the actual distribution of observations without aggregation.
Contour Plot
Three-dimensional representation on a 2D plane using contour lines to connect points of equal value. Particularly effective for visualizing response surfaces and density functions.
Hexbin Plot
Alternative to scatter plot for very dense data using hexagons to aggregate points. The color of each hexagon represents the number of points contained in that region.
Parallel Coordinates
Multidimensional visualization technique using parallel axes to represent multiple dimensions. Each line represents an observation crossing all variable axes.
Q-Q Plot
Quantile-quantile plot comparing the distribution of a sample to a theoretical distribution. Points aligned on a diagonal line indicate adequacy between the distributions.