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CSV stats, JSON flattener, data sampler, CSV merger, and data processing utilities.
Upload CSV to see column stats: min, max, mean, median, count, nulls.
Paste nested JSON to flatten to dot-notation keys.
Paste JSON to minify with all whitespace removed. Size before and after.
Paste JSON to sort keys alphabetically. Recursive sorting.
Upload CSV, select columns, and export a subset CSV.
Paste data to detect type: JSON, CSV, XML, YAML, SQL, or HTML.
Paste JSON to render as editable form. Edit values and export updated JSON.
Upload two CSVs and merge by common column with simple join.
Paste JSON or CSV data to get a random sample of N rows.
Paste JSON and a simple key path to extract matching values.
Calculate z-score from value, mean, and standard deviation.
Calculate t-score for hypothesis testing with small samples.
Calculate chi-square statistic from observed and expected values.
Look up p-values for z-scores in standard normal distribution.
Calculate confidence interval from sample mean, size, and std dev.
Calculate required sample size for a given confidence and margin.
Calculate margin of error from sample size and confidence level.
Calculate Pearson correlation coefficient from paired data.
Calculate linear regression slope and intercept from data points.
Perform one-way ANOVA from group means and variances.
Calculate binomial probability from trials, successes, and probability.
Calculate Poisson probabilities from average rate and occurrences.
Calculate probabilities for normal distribution from mean and std dev.
Calculate exponential distribution probabilities from rate parameter.
Calculate the percentile rank of a value within a dataset.
Calculate Q1, Q2, Q3 quartile values from a dataset.
Calculate box plot values: min, Q1, median, Q3, max, and IQR.
Detect outliers in a dataset using IQR or z-score method.
Calculate coefficient of variation from mean and standard deviation.
Calculate standard error of the mean from std dev and sample size.
Calculate mean absolute error between predicted and actual values.
Calculate mean squared error between predicted and actual values.
Calculate RMSE as the standard deviation of prediction errors.
Calculate coefficient of determination for regression model fit.
Calculate adjusted R-squared accounting for number of predictors.
Calculate F-statistic for overall significance of regression models.
Calculate log-likelihood for model comparison and estimation.
Calculate Akaike Information Criterion for model selection.
Calculate Bayesian Information Criterion for model comparison.
Calculate cross-entropy loss between predicted and true distributions.
Calculate Kullback-Leibler divergence between two probability distributions.
Calculate Jaccard similarity index between two sets.
Calculate cosine similarity between two vectors.
Calculate Euclidean distance between two points in n-dimensional space.
Calculate Manhattan distance between two points using absolute differences.
Calculate Hamming distance between two equal-length strings or vectors.
Calculate minimum edit distance between two strings.
Calculate precision and recall from true/false positive and negative counts.
Calculate F1 score as the harmonic mean of precision and recall.
Generate confusion matrix and derived metrics from classification results.
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