> For clean Markdown of any page, append .md to the page URL. > For a complete documentation index, see https://docs.umnai.com/llms.txt. > For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.umnai.com/_mcp/server. # Dataset histogram The Dataset histogram view describes the distribution of values in a dataset. Use it to inspect categorical frequencies, numeric bins, target statistics, and masked or infrequent values before training, model review, or explanation work. ## When to use | Use case | Description | | ---------------------------- | --------------------------------------------------------------------------------------------- | | Inspect data distribution | Review how feature and target values are distributed across the dataset. | | Check categorical balance | Compare category frequencies and frequency fractions for categorical fields. | | Review numeric bins | Inspect bin ranges and frequencies for numeric fields. | | Investigate target behaviour | Use target summary statistics to understand how target values vary across bins or categories. | | Focus on selected fields | Use `fields` to return histogram data for specific dataset fields. | ## Generate the view Generate the Dataset histogram view from a dataset. ### Request POST [https://api.hi.umnai.com/datasets/\{datasetId}/views](https://api.hi.umnai.com/datasets/\{datasetId}/views) ```curl Histogram View with default values curl -X POST https://api.hi.umnai.com/datasets/datasetId/views \ -H "Authorization: Bearer " \ -H "Content-Type: application/json" \ -d '{ "data": [ { "view_type": "HISTOGRAM" } ] }' ``` ```python Histogram View with default values import requests url = "https://api.hi.umnai.com/datasets/:datasetId/views" payload = { "data": [{ "view_type": "HISTOGRAM" }] } headers = { "Authorization": "Bearer ", "Content-Type": "application/json" } response = requests.post(url, json=payload, headers=headers) print(response.json()) ``` ```javascript Histogram View with default values const url = 'https://api.hi.umnai.com/datasets/:datasetId/views'; const options = { method: 'POST', headers: {Authorization: 'Bearer ', 'Content-Type': 'application/json'}, body: '{"data":[{"view_type":"HISTOGRAM"}]}' }; try { const response = await fetch(url, options); const data = await response.json(); console.log(data); } catch (error) { console.error(error); } ``` ```go Histogram View with default values package main import ( "fmt" "strings" "net/http" "io" ) func main() { url := "https://api.hi.umnai.com/datasets/:datasetId/views" payload := strings.NewReader("{\n \"data\": [\n {\n \"view_type\": \"HISTOGRAM\"\n }\n ]\n}") req, _ := http.NewRequest("POST", url, payload) req.Header.Add("Authorization", "Bearer ") req.Header.Add("Content-Type", "application/json") res, _ := http.DefaultClient.Do(req) defer res.Body.Close() body, _ := io.ReadAll(res.Body) fmt.Println(res) fmt.Println(string(body)) } ``` ```ruby Histogram View with default values require 'uri' require 'net/http' url = URI("https://api.hi.umnai.com/datasets/:datasetId/views") http = Net::HTTP.new(url.host, url.port) http.use_ssl = true request = Net::HTTP::Post.new(url) request["Authorization"] = 'Bearer ' request["Content-Type"] = 'application/json' request.body = "{\n \"data\": [\n {\n \"view_type\": \"HISTOGRAM\"\n }\n ]\n}" response = http.request(request) puts response.read_body ``` ```java Histogram View with default values import com.mashape.unirest.http.HttpResponse; import com.mashape.unirest.http.Unirest; HttpResponse response = Unirest.post("https://api.hi.umnai.com/datasets/:datasetId/views") .header("Authorization", "Bearer ") .header("Content-Type", "application/json") .body("{\n \"data\": [\n {\n \"view_type\": \"HISTOGRAM\"\n }\n ]\n}") .asString(); ``` ```php Histogram View with default values request('POST', 'https://api.hi.umnai.com/datasets/:datasetId/views', [ 'body' => '{ "data": [ { "view_type": "HISTOGRAM" } ] }', 'headers' => [ 'Authorization' => 'Bearer ', 'Content-Type' => 'application/json', ], ]); echo $response->getBody(); ``` ```csharp Histogram View with default values using RestSharp; var client = new RestClient("https://api.hi.umnai.com/datasets/:datasetId/views"); var request = new RestRequest(Method.POST); request.AddHeader("Authorization", "Bearer "); request.AddHeader("Content-Type", "application/json"); request.AddParameter("application/json", "{\n \"data\": [\n {\n \"view_type\": \"HISTOGRAM\"\n }\n ]\n}", ParameterType.RequestBody); IRestResponse response = client.Execute(request); ``` ```swift Histogram View with default values import Foundation let headers = [ "Authorization": "Bearer ", "Content-Type": "application/json" ] let parameters = ["data": [["view_type": "HISTOGRAM"]]] as [String : Any] let postData = JSONSerialization.data(withJSONObject: parameters, options: []) let request = NSMutableURLRequest(url: NSURL(string: "https://api.hi.umnai.com/datasets/:datasetId/views")! as URL, cachePolicy: .useProtocolCachePolicy, timeoutInterval: 10.0) request.httpMethod = "POST" request.allHTTPHeaderFields = headers request.httpBody = postData as Data let session = URLSession.shared let dataTask = session.dataTask(with: request as URLRequest, completionHandler: { (data, response, error) -> Void in if (error != nil) { print(error as Any) } else { let httpResponse = response as? HTTPURLResponse print(httpResponse) } }) dataTask.resume() ``` ### Response (200) ```json { "data": [ { "view_type": "HISTOGRAM", "view_data": { "columns": [ "bin_index", "bin_low", "bin_high", "token", "field_type", "is_categorical", "frequency", "target_min", "target_max", "target_mean", "target_sum", "target_var", "target_stddev", "frequency_fraction", "is_masked_token", "is_infrequent", "field" ], "index": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112 ], "data": [ [ null, null, null, "10th", "FEATURE", true, 1950, [ 0 ], [ 1 ], [ 0.0594871795 ], [ 116 ], [ 0.055948455 ], [ 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"data_type": "STRING", "format": null }, { "data_type": "BOOLEAN" }, { "data_type": "NUMBER", "format": "INT64" }, { "data_type": "ARRAY", "format": { "data_type": "NUMBER", "format": "INT64" } }, { "data_type": "ARRAY", "format": { "data_type": "NUMBER", "format": "INT64" } }, { "data_type": "ARRAY", "format": { "data_type": "NUMBER", "format": "FLOAT64" } }, { "data_type": "ARRAY", "format": { "data_type": "NUMBER", "format": "INT64" } }, { "data_type": "ARRAY", "format": { "data_type": "NUMBER", "format": "FLOAT64" } }, { "data_type": "ARRAY", "format": { "data_type": "NUMBER", "format": "FLOAT64" } }, { "data_type": "NUMBER", "format": "FLOAT64" }, { "data_type": "BOOLEAN" }, { "data_type": "BOOLEAN" }, { "data_type": "STRING", "format": null } ] }, "views_version": { "major_version": 0, "minor_version": 5, "patch_version": 0, "build_version": "dev76" }, "output_version": { "major_version": 1, "minor_version": 1, "patch_version": 1, "build_version": null } } ] } ``` The request needs `view_type` set to `HISTOGRAM`. ## Filter by field Use `fields` when you only need histogram rows for specific dataset fields. ### Request POST [https://api.hi.umnai.com/datasets/\{datasetId}/views](https://api.hi.umnai.com/datasets/\{datasetId}/views) ```curl Histogram View with fields gender curl -X POST https://api.hi.umnai.com/datasets/datasetId/views \ -H "Authorization: Bearer " \ -H "Content-Type: application/json" \ -d '{ "data": [ { "view_type": "HISTOGRAM", "fields": [ "gender" ] } ] }' ``` ```python Histogram View with fields gender import requests url = "https://api.hi.umnai.com/datasets/:datasetId/views" payload = { "data": [ { "view_type": "HISTOGRAM", "fields": ["gender"] } ] } headers = { "Authorization": "Bearer ", "Content-Type": "application/json" } response = requests.post(url, json=payload, headers=headers) print(response.json()) ``` ```javascript Histogram View with fields gender const url = 'https://api.hi.umnai.com/datasets/:datasetId/views'; const options = { method: 'POST', headers: {Authorization: 'Bearer ', 'Content-Type': 'application/json'}, body: '{"data":[{"view_type":"HISTOGRAM","fields":["gender"]}]}' }; try { const response = await fetch(url, options); const data = await response.json(); console.log(data); } catch (error) { console.error(error); } ``` ```go Histogram View with fields gender package main import ( "fmt" "strings" "net/http" "io" ) func main() { url := "https://api.hi.umnai.com/datasets/:datasetId/views" payload := strings.NewReader("{\n \"data\": [\n {\n \"view_type\": \"HISTOGRAM\",\n \"fields\": [\n \"gender\"\n ]\n }\n ]\n}") req, _ := http.NewRequest("POST", url, payload) req.Header.Add("Authorization", "Bearer ") req.Header.Add("Content-Type", "application/json") res, _ := http.DefaultClient.Do(req) defer res.Body.Close() body, _ := io.ReadAll(res.Body) fmt.Println(res) fmt.Println(string(body)) } ``` ```ruby Histogram View with fields gender require 'uri' require 'net/http' url = URI("https://api.hi.umnai.com/datasets/:datasetId/views") http = Net::HTTP.new(url.host, url.port) http.use_ssl = true request = Net::HTTP::Post.new(url) request["Authorization"] = 'Bearer ' request["Content-Type"] = 'application/json' request.body = "{\n \"data\": [\n {\n \"view_type\": \"HISTOGRAM\",\n \"fields\": [\n \"gender\"\n ]\n }\n ]\n}" response = http.request(request) puts response.read_body ``` ```java Histogram View with fields gender import com.mashape.unirest.http.HttpResponse; import com.mashape.unirest.http.Unirest; HttpResponse response = Unirest.post("https://api.hi.umnai.com/datasets/:datasetId/views") .header("Authorization", "Bearer ") .header("Content-Type", "application/json") .body("{\n \"data\": [\n {\n \"view_type\": \"HISTOGRAM\",\n \"fields\": [\n \"gender\"\n ]\n }\n ]\n}") .asString(); ``` ```php Histogram View with fields gender request('POST', 'https://api.hi.umnai.com/datasets/:datasetId/views', [ 'body' => '{ "data": [ { "view_type": "HISTOGRAM", "fields": [ "gender" ] } ] }', 'headers' => [ 'Authorization' => 'Bearer ', 'Content-Type' => 'application/json', ], ]); echo $response->getBody(); ``` ```csharp Histogram View with fields gender using RestSharp; var client = new RestClient("https://api.hi.umnai.com/datasets/:datasetId/views"); var request = new RestRequest(Method.POST); request.AddHeader("Authorization", "Bearer "); request.AddHeader("Content-Type", "application/json"); request.AddParameter("application/json", "{\n \"data\": [\n {\n \"view_type\": \"HISTOGRAM\",\n \"fields\": [\n \"gender\"\n ]\n }\n ]\n}", ParameterType.RequestBody); IRestResponse response = client.Execute(request); ``` ```swift Histogram View with fields gender import Foundation let headers = [ "Authorization": "Bearer ", "Content-Type": "application/json" ] let parameters = ["data": [ [ "view_type": "HISTOGRAM", "fields": ["gender"] ] ]] as [String : Any] let postData = JSONSerialization.data(withJSONObject: parameters, options: []) let request = NSMutableURLRequest(url: NSURL(string: "https://api.hi.umnai.com/datasets/:datasetId/views")! as URL, cachePolicy: .useProtocolCachePolicy, timeoutInterval: 10.0) request.httpMethod = "POST" request.allHTTPHeaderFields = headers request.httpBody = postData as Data let session = URLSession.shared let dataTask = session.dataTask(with: request as URLRequest, completionHandler: { (data, response, error) -> Void in if (error != nil) { print(error as Any) } else { let httpResponse = response as? HTTPURLResponse print(httpResponse) } }) dataTask.resume() ``` ### Response (200) ```json { "data": [ { "view_type": "HISTOGRAM", "view_data": { "columns": [ "bin_index", "bin_low", "bin_high", "token", "field_type", "is_categorical", "frequency", "target_min", "target_max", "target_mean", "target_sum", "target_var", "target_stddev", "frequency_fraction", "is_masked_token", "is_infrequent", "field" ], "index": [ 17, 18, 19 ], "data": [ [ null, null, null, "Female", "FEATURE", true, 22198, [ 0 ], [ 1 ], [ 0.1159113434 ], [ 2573 ], [ 0.1024759038 ], [ 0.3201185778 ], 0.3170871068, false, false, "gender" ], [ null, null, null, "Male", "FEATURE", true, 41938, [ 0 ], [ 1 ], [ 0.2931708713 ], [ 12295 ], [ 0.2072217115 ], [ 0.4552161152 ], 0.5990629375, false, false, "gender" ], [ null, null, null, "__MASK__", "FEATURE", true, 5870, [ 0 ], [ 1 ], [ 0.2081771721 ], [ 1222 ], [ 0.1648394371 ], [ 0.4060388203 ], 0.0838499557, true, false, "gender" ] ], "foreign_keys": [], "labels": [], "types": [ { "data_type": "ANY" }, { "data_type": "ANY" }, { "data_type": "ANY" }, { "data_type": "STRING", "format": null }, { "data_type": "STRING", "format": null }, { "data_type": "BOOLEAN" }, { "data_type": "NUMBER", "format": "INT64" }, { "data_type": "ARRAY", "format": { "data_type": "NUMBER", "format": "INT64" } }, { "data_type": "ARRAY", "format": { "data_type": "NUMBER", "format": "INT64" } }, { "data_type": "ARRAY", "format": { "data_type": "NUMBER", "format": "FLOAT64" } }, { "data_type": "ARRAY", "format": { "data_type": "NUMBER", "format": "INT64" } }, { "data_type": "ARRAY", "format": { "data_type": "NUMBER", "format": "FLOAT64" } }, { "data_type": "ARRAY", "format": { "data_type": "NUMBER", "format": "FLOAT64" } }, { "data_type": "NUMBER", "format": "FLOAT64" }, { "data_type": "BOOLEAN" }, { "data_type": "BOOLEAN" }, { "data_type": "STRING", "format": null } ] }, "views_version": { "major_version": 0, "minor_version": 5, "patch_version": 0, "build_version": "dev76" }, "output_version": { "major_version": 1, "minor_version": 1, "patch_version": 1, "build_version": null } } ] } ``` This is useful when the dataset has many fields and you only need to inspect a small subset. ## Output The response follows the shared [Response structure](../response-structure) format. The Dataset histogram view returns a single dataframe in `view_data`. | Dataframe | Description | | ----------- | ---------------------------------------------------------------------------------------------------------------------- | | `view_data` | Histogram rows for dataset fields, including bin information, frequency values, target statistics, and field metadata. | ## Interpret the result The Dataset histogram view is easiest to read by selecting a field first, then comparing the bins or categories for that field. ### Start with the field Use `field` to identify the dataset field represented by each row. Use `field_type` to distinguish features from targets. Feature rows describe input distributions. Target rows describe target distributions. ### Separate categorical and numeric fields Use `is_categorical` to understand how to read the row. For categorical fields, `token` identifies the category. For numeric fields, `bin_index`, `bin_low`, and `bin_high` describe the numeric bin. ### Review frequency Use `frequency` to see the number of rows in the category or bin. Use `frequency_fraction` to compare categories or bins on a normalized scale. This is usually the easiest way to spot imbalance across a field. ### Check infrequent or masked values Use `is_infrequent` to identify rare categories. Use `is_masked_token` to identify masked tokens. These rows are useful when reviewing whether sensitive, missing, rare, or protected values are being represented as expected. ### Inspect target statistics Use the `target_*` fields to understand how target values behave within each category or bin. For example, `target_mean` can show whether a category or numeric range is associated with higher or lower target values. `target_min`, `target_max`, `target_sum`, `target_var`, and `target_stddev` provide additional context. ## Options The Dataset histogram view supports one view-specific option. | Option | Use | | -------- | ------------------------------------------------------------ | | `fields` | Restrict the histogram output to one or more dataset fields. | ### Fields Use `fields` to request histogram rows for specific fields. If `fields` is omitted, the view returns histogram rows for the default set of available dataset fields. ## Field reference The Dataset histogram view returns a dataframe, so individual columns are not documented as standalone API schema properties. ### `view_data` The `view_data` dataframe contains the histogram output. | Field | Description | | -------------------- | -------------------------------------------------------------------- | | `bin_index` | Index of the numeric bin, when the row represents a numeric bin. | | `bin_low` | Lower bound of the numeric bin, when available. | | `bin_high` | Upper bound of the numeric bin, when available. | | `token` | Category token, when the row represents a categorical value. | | `field_type` | Type of field represented by the row, such as `FEATURE` or `TARGET`. | | `is_categorical` | Whether the field is categorical. | | `frequency` | Number of dataset rows in the category or bin. | | `target_min` | Minimum target value for rows in the category or bin. | | `target_max` | Maximum target value for rows in the category or bin. | | `target_mean` | Mean target value for rows in the category or bin. | | `target_sum` | Sum of target values for rows in the category or bin. | | `target_var` | Variance of target values for rows in the category or bin. | | `target_stddev` | Standard deviation of target values for rows in the category or bin. | | `frequency_fraction` | Fraction of dataset rows represented by the category or bin. | | `is_masked_token` | Whether the row represents a masked token. | | `is_infrequent` | Whether the row represents an infrequent category or value. | | `field` | Dataset field represented by the row. |