> ## Documentation Index > Fetch the complete documentation index at: https://docs.summand.com/llms.txt > Use this file to discover all available pages before exploring further. # Distribution profile > Characterises the shape of each column's data distribution: skewness, kurtosis, best-fit parametric distribution, modality, and density plots. This component is currently in beta and may be subject to changes or instability. The **Distribution Profile** component answers the question: *what is the shape of each column's data distribution?* ## Use cases * **Fraud detection preprocessing** — Is the transaction amount column heavy-tailed enough to warrant a log transformation, or is a Normal assumption reasonable? * **Sensor data validation** — Are the readings from a temperature sensor unimodal as expected, or does a second peak suggest a recurring calibration fault? * **Feature engineering** — Which columns are highly skewed and would benefit from normalisation before being fed into a model? ## What it computes * **Skewness and kurtosis**: measures of distributional shape — how lopsided and how heavy-tailed each numeric column is. * **Best-fit parametric distribution**: the best-fitting distribution from a candidate set, along with its fitted parameters. * **Shapiro-Wilk normality test**: a $p$-value testing whether each numeric column is normally distributed. * **Modality**: whether each numeric column's distribution is unimodal, bimodal, or multimodal. * **Kernel density estimate (KDE)**: a smooth estimate of each numeric column's probability density. * **Histograms**: a 30-bin histogram for each numeric column, and a proportion histogram for categorical columns with 10 or fewer unique categories. ## Display The Distribution Profile component displays the following in the Catalog. * **Distribution profiles table**: skewness, kurtosis, best-fit distribution and its parameters, Shapiro-Wilk $p$-value, modality, and top peaks — one row per numeric column. * **Fitted distributions**: all candidate parametric distributions ranked by BIC, with fitted parameters and goodness-of-fit $p$-value — one table per numeric column. * **Numeric histograms**: a 30-bin histogram per numeric column. * **Categorical histograms**: a proportion histogram per low-cardinality categorical column (10 or fewer unique categories). * **Kernel density estimates**: a smooth density curve per numeric column. ## Inputs | Name | Type | Required | Default | Description | | ------------------------ | ------ | -------- | ------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- | | `peak_prominence_cutoff` | number | No | `0.1` | Controls how pronounced a peak in the distribution must be to count toward modality. Increase to ignore minor bumps; decrease to detect subtler ones. | ## Output shape ```json theme={null} { "distribution_profiles": [ { "column": str, "sample_size": int, "skewness": float, "kurtosis": float, "best_fit_distribution": str | None, "best_fit_distribution_parameters": [{"name": str, "value": float}] | None, "best_fit_sample_size": int, "shapiro_wilk_p_value": float, # present when Shapiro-Wilk was computed "shapiro_wilk_reason_not_computed": str, # present instead when Shapiro-Wilk was not computed "shapiro_wilk_sample_size": int, "modality": "unimodal" | "bimodal" | "multimodal", "peaks": [ {"location": float, "prominence": float}, # ... top 20 most prominent peaks, ordered by descending prominence ], }, # ... numeric columns only ], "fitted_distributions": [ { "column": str, "sample_size": int, "reason_not_computed": str, # present when column has fewer than 2 observed values "distributions": [ { "distribution_name": str, "bayesian_information_criterion": float, # absent when fitting failed for this distribution "parameters": [{"name": str, "value": float}], # absent when fitting failed for this distribution "goodness_of_fit_p_value": float, # absent when fitting failed for this distribution "reason_not_computed": str, # present when fitting failed for this distribution }, # ... one entry per candidate distribution, ranked by descending BIC ], }, # ... numeric columns only ], "numeric_histograms": [ { "column": str, "plot_data": [ { "left": float, "center": float, "right": float, "percentage": float, }, # ... 30 entries ], }, # ... numeric columns only ], "categorical_histograms": [ { "column": str, "plot_data": [ { "category": str, "percentage": float, }, # ... one entry per unique category, ordered by descending percentage ], }, # ... categorical columns with ≤ 10 unique categories only ], "kernel_density_estimates": [ { "column": str, "bandwidth": float, "plot_data": [ { "sample_point": float, "density_estimate": float, }, # ... 100 entries ], }, # ... numeric columns only ], } ``` ## Filtering from chat Summand can query the distribution profile artifact in a variety of ways: ```text theme={null} # Skewness, kurtosis, best-fit distribution, Shapiro-Wilk p-value, modality, and peaks for a specific column analyze({ component: "distribution_profile", params: { distribution_shape_for_column: "transaction_amount" } }) # All candidate distributions ranked by BIC, with fitted parameters and goodness-of-fit p-value analyze({ component: "distribution_profile", params: { list_of_fitted_distributions_for_column: "transaction_amount" } }) # 30-bin histogram for a specific numeric column analyze({ component: "distribution_profile", params: { numeric_histogram_for_column: "transaction_amount" } }) # Proportion histogram for a specific categorical column analyze({ component: "distribution_profile", params: { categorical_histogram_for_column: "product_category" } }) # KDE plot data and bandwidth for a specific numeric column analyze({ component: "distribution_profile", params: { kernel_density_estimate_for_column: "transaction_amount" } }) ``` ## Compute profile | Profile | Value | | ------- | ----------- | | Fargate | 2 CPU, 8 GB |