Flags:¶

-r

Print minimal region extent of selected vector features instead of attributes

-c

Do not include column names in output

-e

Escape newline and backslash characters

-f

Exclude attributes not linked to features

--overwrite

Allow output files to overwrite existing files

--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

columns=name[,name,...]

Name of attribute column(s)

where=sql_query

WHERE conditions of SQL statement without ’where’ keyword
Example: income < 1000 and population >= 10000

group=string

GROUP BY conditions of SQL statement without ’group by’ keyword

format=string [required]

Output format
Options: plain, csv, json, vertical
Default: plain
plain: Configurable plain text output
csv: CSV (Comma Separated Values)
json: JSON (JavaScript Object Notation)
vertical: Plain text vertical output (instead of horizontal)

separator=character

Field separator
Special characters: pipe, comma, space, tab, newline

vertical_separator=character

Output vertical record separator
Special characters: pipe, comma, space, tab, newline

null_value=string

String representing NULL value

file=name

Name for output file (if omitted or "-" output to stdout)

Output formats¶

Four different formats can be used depending on the circumstances using the format option: plain text, CSV, JSON, and vertical plain text.

Plain text¶

The plain text is the default output which is most suitable for reading by humans, e.g., when working in the command line or obtaining specific values from the attribute table using the v.db.select GUI dialog.

The individual fields (attribute values) are separated by a pipe (|) which can be customized using the separator option. The records (rows) are separated by newlines.

Example with a pipe as a separator (the default):

cat|road_name|multilane|year|length
1|NC-50|no|2001|4825.369405
2|NC-50|no|2002|14392.589058
3|NC-98|no|2003|3212.981242
4|NC-50|no|2004|13391.907552

No quoting or escaping is performed by default, so if these characters are in the output, they look just like the separators. This is usually not a problem for humans looking at the output to get a general idea about query result or attribute table content.

Consequently, this format is not recommended for computers, e.g., for reading attribute data in Python scripts. It works for further parsing in limited cases when the values don’t contain separators or when the separators are set to one of the escaped characters.

CSV¶

CSV (comma-separated values) has many variations. This module by default produces CSV with comma (,) as the field separator (delimiter). All text fields (based on the type) are quoted with double quotes. Double quotes in fields are represented as two double quotes. Newline characters in the fields are present as-is in the output. Header is included by default containing column names.

All full CSV parsers such as the ones in LibreOffice or Python are able to parse this format when configured to the above specification.

Example with default settings:

cat,road_name,multilane,year,length
1,"NC-50","no",2001,4825.369405
2,"NC-50","no",2002,14392.589058
3,"NC-98","no",2003,3212.981242
4,"NC-50","no",2004,13391.907552

If desired, the separator can be customized and escaping can be enabled with the same characters being escaped as for the plain text. Notably, newlines and tabs are escaped, double quotes are not, and the separator is not escaped either (unless it is a tab). However, the format is guaranteed only for the commonly used separators such as comma, semicolon, pipe, and tab.

Note that using multi-character separator is allowed, but not recommended as it is not generally supported by CSV readers.

CSV is the recommended format for further use in another analytical applications, especially for use with spreadsheet applications. For scripting, it is advantageous when tabular data is needed (rather than key-value pairs).

JSON¶

JSON (JavaScript Object Notation) format is produced according to the specification so it is readily readable by JSON parsers. The standard JSON escapes are performed (backslash, carriage return, line feed, tabulator, form feed, backslash, and double quote) for string values. Numbers in the database such as integers and doubles are represented as numbers, while texts (TEXT, VARCHAR, etc.) and dates in the database are represented as strings in JSON. NULL values in database are represented as JSON null. Indentation and newlines in the output are minimal and not guaranteed.

Records which are the result of the query are stored under key records as an array (list) of objects (collections of key-value pairs). The keys for attributes are lowercase or uppercase depending on how the columns were defined in the database.

Example with added indentation (note that booleans are not directly supported; here, an attribute is a string with value no):

{


  "records": [


    {


      "cat": 1,


      "road_name": "NC-50",


      "multilane": "no",


      "year": 2001,


      "length": 4825.369405


    },


    {


      "cat": 2,


      "road_name": "NC-50",


      "multilane": "no",


      "year": 2002,


      "length": 14392.589058


    }


  ]
}

JSON is the recommended format for reading the data in Python and for any uses and environments where convenient access to individual values is desired and JSON parser is available.

Vertical plain text¶

In the vertical plain text format, each value is on a single line and is preceded by the name of the attribute (column) which is separated by separator. The individual records can be separated by the vertical separator (vertical_separator option).

Example with (horizontal) separator = and vertical separator newline:

cat=1
road_name=NC-50
multilane=no
year=2001
length=4825.369405
cat=2
road_name=NC-50
multilane=no
year=2002
length=14392.589058

Select and show entire table¶

v.db.select map=roadsmajor
cat|MAJORRDS_|ROAD_NAME|MULTILANE|PROPYEAR|OBJECTID|SHAPE_LEN
1|1|NC-50|no|0|1|4825.369405
2|2|NC-50|no|0|2|14392.589058
3|3|NC-98|no|0|3|3212.981242
4|4|NC-50|no|0|4|13391.907552
...

Select and show single column from table¶

Note: multiple columns can be specified as comma separated list.

v.db.select map=roadsmajor column=ROAD_NAME
NC-50
NC-50
NC-98
NC-50
NC-98
...

Print region extent of selected vector features¶

v.db.select -r map=roadsmajor where="ROAD_NAME = ’NC-98’"
n=248425.389891
s=245640.640081
w=635906.517653
e=661979.801880

Select empty vector features (no data entries)¶

v.db.select geonames_wake where="ALTERNATEN IS NULL"
cat|GEONAMEID|NAME|ASCIINAME|ALTERNATEN|FEATURECLA|FEATURECOD|...
8|4498303|West Raleigh|West Raleigh||P|PPL|US||NC|338759|123|...
14|4459467|Cary|Cary||P|PPL|US||NC|103945|146|152|America/Iqaluit|...
31|4452808|Apex|Apex||P|PPL|US||NC|30873|167|134|America/Iqaluit|...
...

Select not empty vector features (no data entries)¶

v.db.select geonames_wake where="ALTERNATEN IS NOT NULL"
cat|GEONAMEID|NAME|ASCIINAME|ALTERNATEN|FEATURECLA|FEATURECOD|...
9|4487042|Raleigh|Raleigh|Raleigh,...
31299|4487056|Raleigh-Durham Airport|Raleigh-Durham Airport|...
...

Select features with distinct road names¶

v.db.select map=roadsmajor columns=ROAD_NAME group=ROAD_NAME
ROAD_NAME
I-40
I-440
I-540
NC-231
NC-39
NC-42
...

It is also possible to combine with where option

v.db.select map=roadsmajor columns=ROAD_NAME,MULTILANE group=ROAD_NAME where=’ROAD_NAME is not null’
ROAD_NAME|MULTILANE
I-40|yes
I-440|yes
I-540|yes
NC-231|no
NC-39|no
NC-42|no
NC-50|no
NC-54|no
NC-55|no
NC-96|no
NC-97|no
NC-98|no
US-1|
US-401|no
US-64|yes
US-70|yes

It can also use more columns in group option

v.db.select map=roadsmajor columns=ROAD_NAME,MULTILANE group=ROAD_NAME,MULTILANE where=’ROAD_NAME is not null’
ROAD_NAME|MULTILANE
I-40|yes
I-440|yes
I-540|yes
NC-231|no
NC-39|no
NC-42|no
NC-50|no
NC-54|no
NC-55|no
NC-96|no
NC-97|no
NC-98|no
US-1|
US-1|yes
US-401|no
US-401|yes
US-64|yes
US-70|yes

Read results in Python¶

The json package in the standard Python library can load a JSON string obtained as output from the v.db.select module through the read_command function:

import json
import grass.script as gs
text = gs.read_command("v.db.select", map="roadsmajor", format="json")
data = json.loads(text)
for row in data["records"]:


    print(row["ROAD_NAME"])