4 ms·

PyPi interactive dependency graph

24 points by alesdotio 13y ago

tlarkworthy 13y ago
setup tools and django win
peterwaller 13y ago
It would be nice to be able to search for a package. Also, the graph currently has a node per "dependency", which may be with a pinned version. Maybe it might be even better if it just showed package dependencies? Looking through the structure you can see "interesting" packages, but because a package counts as different if a version is specified it makes it hard to browse through the interesting connections.
ekns 13y ago
I was curious about what the most common dependencies were so I hacked the IPython notebook a bit: import json, pip, re from base64 import b64decode from itertools import chain from collections import Counter def get_name(x): "This captures most of the base names for dependencies" if x.startswith('#'): return try: return pip.req.InstallRequirement.from_line(re.sub('["\']', '', x)).name.lower() except ValueError: return C = Counter() data = [] with open('pypi-deps.csv', 'r') as file: for line in file: name, version, deps = line.split('\t') deps = filter(None, map(get_name, json.loads(b64decode(deps)))) data+= [(name, version, deps)] for x in deps: C[x] += 1 print '\n'.join('{0}\t{1}'.format(*x) for x in C.most_common(20)) The results: setuptools 1949 django 546 zope.interface 244 requests 241 lxml 204 zope.component 203 simplejson 202 flask 158 zc.buildout 127 argparse 110 zope.schema 109 python-dateutil 108 jinja2 105 distribute 104 pyyaml 103 fanstatic 95 beautifulsoup 93 zope.publisher 91 sqlalchemy 91 numpy 89
hoov 13y ago
My favorite is: [x.strip() for x in open("DEPENDENCES.txt").read().split("\n") if x.strip()]
LyndsySimon 13y ago
It would be interesting if one could trace the dependencies recursively - but knowing how PyPI works, that would be incredibly time- and bandwidth-consuming. Still, if one had access to a mirror, it could be done...

24 points by alesdotio 13y ago

setup tools and django win

It would be nice to be able to search for a package. Also, the graph currently has a node per "dependency", which may be with a pinned version. Maybe it might be even better if it just showed package dependencies? Looking through the structure you can see "interesting" packages, but because a package counts as different if a version is specified it makes it hard to browse through the interesting connections.

ekns 13y ago

I was curious about what the most common dependencies were so I hacked the IPython notebook a bit: import json, pip, re from base64 import b64decode from itertools import chain from collections import Counter def get_name(x): "This captures most of the base names for dependencies" if x.startswith('#'): return try: return pip.req.InstallRequirement.from_line(re.sub('["\']', '', x)).name.lower() except ValueError: return C = Counter() data = [] with open('pypi-deps.csv', 'r') as file: for line in file: name, version, deps = line.split('\t') deps = filter(None, map(get_name, json.loads(b64decode(deps)))) data+= [(name, version, deps)] for x in deps: C[x] += 1 print '\n'.join('{0}\t{1}'.format(*x) for x in C.most_common(20)) The results: setuptools 1949 django 546 zope.interface 244 requests 241 lxml 204 zope.component 203 simplejson 202 flask 158 zc.buildout 127 argparse 110 zope.schema 109 python-dateutil 108 jinja2 105 distribute 104 pyyaml 103 fanstatic 95 beautifulsoup 93 zope.publisher 91 sqlalchemy 91 numpy 89

hoov 13y ago

My favorite is: [x.strip() for x in open("DEPENDENCES.txt").read().split("\n") if x.strip()]

LyndsySimon 13y ago

It would be interesting if one could trace the dependencies recursively - but knowing how PyPI works, that would be incredibly time- and bandwidth-consuming. Still, if one had access to a mirror, it could be done...