Data Processing

Paste one or two columns of numbers and get the complete descriptive suite — mean/median/mode, sample and population variance, type-7 quartiles and percentiles, MAD, adjusted skewness and kurtosis, Tukey outliers — plus covariance, Pearson r, tie-corrected Spearman rho and least-squares regression for two columns.

Data Analysis

Import created→started→resolved timestamps from CSV (Jira-style or simple) and get flow metrics the way agile coaches read them: lead/cycle time percentiles, WIP age, throughput per day/week/month, a cumulative flow diagram and a Little's Law consistency check.

Data Processing

Paste a JSON payload from any sports API (ESPN, TheSportsDB, balldontlie, football-data.org, api-sports.io) and get it rendered as a clean scoreboard with winners highlighted and game statuses.

Data Processing

Turn .ipynb into a # %% Python script and detect out-of-order, hidden, and deleted state

Data Processing

Preview JSONata-style queries and transformations, compare results across sample payloads, and export the transformed output as JSON, CSV, YAML, or Markdown

Data Processing

Trace JSON field paths, derived dependencies, and transformation history to build a field-level lineage graph

Data Processing

Query JSON with JSONPath or JMESPath-style expressions, inspect matched paths, and highlight extracted values in the original document

Data Processing

Check if a path exists using lodash _.hasIn

Data Processing

Check if a path exists using lodash _.has

Data Processing

List own and inherited keys using lodash _.keysIn

Data Processing

List own enumerable keys using lodash _.keys

Data Processing

Group items by a derived key using lodash _.groupBy