## NAME

v.db.univar - Calculates univariate statistics on selected table column for a GRASS vector map.

## KEYWORDS

vector, statistics, attribute table

## SYNOPSIS

v.db.univar
v.db.univar --help
v.db.univar [-eg] map=name [layer=string] column=name [where=sql_query] [percentile=float[,float,...]] [format=string] [--help] [--verbose] [--quiet] [--ui]

### Flags:

-e
  Extended statistics (quartiles and 90th percentile)
-g
  Print stats in shell script style
--help
  Print usage summary
--verbose
  Verbose module output
--quiet
  Quiet module output
--ui
  Force launching GUI dialog

### Parameters:

map=name [required]
  Name of vector map
  Or data source for direct OGR access
layer=string
  Layer number or name
  Vector features can have category values in different layers. This number determines which layer to use. When used with direct OGR access this is the layer name.
  Default: 1
column=name [required]
  Name of attribute column on which to calculate statistics (must be numeric)
where=sql_query
  WHERE conditions of SQL statement without 'where' keyword
  Example: income < 1000 and population >= 10000
percentile=float[,float,...]
  Percentile to calculate (requires extended statistics flag)
  Options: 0-100
  Default: 90
format=string
  Output format
  Options: plain, json, shell
  plain: Plain text output
  json: JSON (JavaScript Object Notation)
  shell: Shell script style for Bash eval

## DESCRIPTION

v.db.univar calculates basic univariate statistics for numeric attributes in a vector attribute table. It will calculate minimum, maximum, range, mean, standard deviation, variance, coefficient of variation, quartiles, median, and 90th percentile.

v.db.univar uses db.univar which in turn uses db.select to get the attribute values on which it calculates the statistics. This means that statistics are calculated based on the entries in the attribute table, not based on the features in the map. One attribute value is read from each line in the attribute table, whether there are no, one or several features with the category value referenced by that line, or whether any features have more than one category value. For feature-based, instead of attribute table-based, univariate statistics on attributes see v.univar. NOTES A database connection must be defined for the selected vector layer.

## EXAMPLES

### Univariate statistics on attribute table column

In this example, the 30 years precipitation data table is statistically analysed (North Carolina sample dataset) and univariate statistics performed:

    # show columns of attribute table connected to precipitation map
    v.info -c precip_30ynormals

    # univariate statistics on 30 years annual precipitation in NC
    v.db.univar precip_30ynormals column=annual
     Number of values: 136
     Minimum: 947.42
     Maximum: 2329.18
     Range: 1381.76
     Mean: 1289.31147058823
     [...]

### Univariate statistics on randomly sampled data points

In this example, random points are sampled from the elevation map (North Carolina sample dataset) and univariate statistics performed:

    g.region raster=elevation -p
    v.random output=samples n=100
    v.db.addtable samples column="heights double precision"
    v.what.rast samples raster=elevation column=heights
    v.db.select samples

    v.db.univar samples column=heights

### JSON output

This uses the JSON output of the module which is passed using a pipe (in Bash or other unix-like shell) to the jq tool which selects just the relevant statistic.

    v.db.univar precip_30ynormals column=annual format=json | jq .statistics.mean

## SEE ALSO

db.univar, r.univar, v.univar, db.select, d.vect.thematic, v.random

GRASS SQL interface

## AUTHORS

Michael Barton, Arizona State University

and authors of r.univar.sh (Markus Neteler et al.)

## SOURCE CODE

Available at: v.db.univar source code (history)

Accessed: Friday Apr 04 01:21:13 2025

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