Calculate min, max, average, median, and more for CSV data
How spread out is your data? Where do the outliers live? Upload a CSV and this tool calculates descriptive statistics for every numeric column, mean, median, mode, standard deviation, min, max, quartiles, and more. Categorical columns get frequency counts and unique value summaries. No formulas to write, no code to run.
Initializing in your browser…
Before modelling, you want a quick statistical profile of a numeric column without writing pandas.
Column
"price" across 5,000 rows
Summary
count 5000 mean 42.18 median 39.00 min 4.99 max 499.00 std 27.6 Q1 24.00 Q3 58.00 variance 761.8
You get the full five-number summary (min, Q1, median, Q3, max) plus mean, mode, variance, and standard deviation per column, enough to spot a skew or a data-entry error before it poisons a model. Everything is computed locally on your file.
How spread out is your data? Where do the outliers live? Upload a CSV and this tool calculates descriptive statistics for every numeric column, mean, median, mode, standard deviation, min, max, quartiles, and more. Categorical columns get frequency counts and unique value summaries. No formulas to write, no code to run.
Get a quick statistical overview of a new dataset before committing to a full analysis workflow.
Spot columns with unexpected ranges, high null rates, or unusual distributions that signal data problems.
Generate summary statistics to include in reports, presentations, or documentation.
Identify values that fall far outside expected ranges using quartile and standard deviation metrics.
Getting a statistical snapshot of a dataset is usually the first step before any deeper analysis. This tool automates that step entirely. It scans each column, determines whether it holds numeric or categorical data, and generates the appropriate summary. For numbers you get central tendency measures (mean, median, mode), dispersion measures (standard deviation, variance, range, IQR), and distribution markers (quartiles, skewness, kurtosis). For text columns you get value frequencies, unique counts, and most/least common entries.
Results are presented both as tables and inline visualizations so you can spot patterns at a glance, a skewed distribution, an unusual number of nulls, or an outlier that needs investigation.
Yes. The tool detects the dominant type in each column and reports statistics accordingly, noting any mixed-type values it encounters.
Absolutely. You can select individual columns or analyze all columns at once.
Missing and empty values are excluded from numeric calculations. The tool reports the count and percentage of missing values separately for each column.
Rows and columns are parsed and transformed in memory in your browser. No record ever reaches a server.