v.db.univar
Calculates univariate statistics on selected table column for a GRASS vector map.
v.db.univar [-eg] map=name [layer=string] column=name [where=sql_query] [percentile=float [,float,...]] [format=string] [--verbose] [--quiet] [--qq] [--ui]
Example:
v.db.univar map=name column=name
grass.script.parse_command("v.db.univar", map, layer="1", column, where=None, percentile=90, format=None, flags=None, verbose=False, quiet=False, superquiet=False)
Example:
gs.parse_command("v.db.univar", map="name", column="name")
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)
Allowed values: 0-100
Default: 90
format=string
Output format
Allowed values: plain, json, shell
plain: Plain text output
json: JSON (JavaScript Object Notation)
shell: Shell script style for Bash eval
-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
--qq
Very quiet module output
--ui
Force launching GUI dialog
map : str, required
Name of vector map
Or data source for direct OGR access
Used as: input, vector, name
layer : str, optional
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.
Used as: input, layer
Default: 1
column : str, required
Name of attribute column on which to calculate statistics (must be numeric)
Used as: input, dbcolumn, name
where : str, optional
WHERE conditions of SQL statement without 'where' keyword
Example: income < 1000 and population >= 10000
Used as: input, sql_query, sql_query
percentile : float | list[float] | str, optional
Percentile to calculate (requires extended statistics flag)
Allowed values: 0-100
Default: 90
format : str, optional
Output format
Allowed values: plain, json, shell
plain: Plain text output
json: JSON (JavaScript Object Notation)
shell: Shell script style for Bash eval
flags : str, optional
Allowed values: e, g
e
Extended statistics (quartiles and 90th percentile)
g
Print stats in shell script style
verbose: bool, optional
Verbose module output
Default: False
quiet: bool, optional
Quiet module output
Default: False
superquiet: bool, optional
Very quiet module output
Default: False
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
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)
Latest change: Monday Mar 10 19:11:25 2025 in commit 9137ecc