Pure python module for (de)serialization to and from VDF that works just like json.
Tested and works on py2.7, py3.3+, pypy and pypy3.
VDF is Valve's KeyValue text file format
https://developer.valvesoftware.com/wiki/KeyValues
Supported versions:
kv1Unsupported:
kv2 and kv3You can grab the latest release from https://pypi.org/project/vdf/ or via pip
pip install vdfInstall the current dev version from github
pip install git+https://github.com/ValvePython/vdf- There are known files that contain duplicate keys. This is supported the format and
makes mapping to
dictimpossible. For this case the module providesvdf.VDFDictthat can be used as mapper instead ofdict. See the example section for details. - By default de-serialization will return a
dict, which doesn't preserve nor guarantee key order on Python versions prior to 3.6, due to hash randomization. If key order is important on old Pythons, I suggest usingcollections.OrderedDict, orvdf.VDFDict.
For text representation
import vdf
# parsing vdf from file or string
d = vdf.load(open('file.txt'))
d = vdf.loads(vdf_text)
d = vdf.parse(open('file.txt'))
d = vdf.parse(vdf_text)
# dumping dict as vdf to string
vdf_text = vdf.dumps(d)
indented_vdf = vdf.dumps(d, pretty=True)
# dumping dict as vdf to file
vdf.dump(d, open('file2.txt','w'), pretty=True)For binary representation
d = vdf.binary_loads(vdf_bytes)
b = vdf.binary_dumps(d)
# alternative format - VBKV
d = vdf.binary_loads(vdf_bytes, alt_format=True)
b = vdf.binary_dumps(d, alt_format=True)
# VBKV with header and CRC checking
d = vdf.vbkv_loads(vbkv_bytes)
b = vdf.vbkv_dumps(d)Using an alternative mapper
d = vdf.loads(vdf_string, mapper=collections.OrderedDict)
d = vdf.loads(vdf_string, mapper=vdf.VDFDict)VDFDict works much like the regular dict, except it handles duplicates and remembers
insert order. Additionally, keys can only be of type str. The most important difference
is that when trying to assigning a key that already exist it will create a duplicate instead
of reassign the value to the existing key.
>>> d = vdf.VDFDict()
>>> d['key'] = 111
>>> d['key'] = 222
>>> d
VDFDict([('key', 111), ('key', 222)])
>>> d.items()
[('key', 111), ('key', 222)]
>>> d['key']
111
>>> d[(0, 'key')] # get the first duplicate
111
>>> d[(1, 'key')] # get the second duplicate
222
>>> d.get_all_for('key')
[111, 222]
>>> d[(1, 'key')] = 123 # reassign specific duplicate
>>> d.get_all_for('key')
[111, 123]
>>> d['key'] = 333
>>> d.get_all_for('key')
[111, 123, 333]
>>> del d[(1, 'key')]
>>> d.get_all_for('key')
[111, 333]
>>> d[(1, 'key')]
333
>>> print vdf.dumps(d)
"key" "111"
"key" "333"
>>> d.has_duplicates()
True
>>> d.remove_all_for('key')
>>> len(d)
0
>>> d.has_duplicates()
False