diff options
| author | Ross Barnowski <rossbar@berkeley.edu> | 2020-08-02 09:20:16 -0700 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2020-08-02 09:20:16 -0700 |
| commit | 549d63c17b0bc540dc92d7ead99b17f1c5967454 (patch) | |
| tree | 1d6fa6e0e0db278d82a758c976b0010a1f027b4c /doc/reference | |
| parent | b95905fae15d2a745909e11c60d9603db2662c49 (diff) | |
| download | networkx-549d63c17b0bc540dc92d7ead99b17f1c5967454.tar.gz | |
DOC: Suggestions and improvments from tutorial readthrough (#4121)
* DOC: rm windows-specific note/instructions.
Assume user knows how to install scientific Python software on their
chosen platform.
* DOC: rely on intersphinx for hashable def
* Removes the hashable term from the nx glossary and instead refer to the
Python glossary via intersphinx.
* Wording was from the Python2 docs and had become out-of-sync with the
Python glossary entry
* DOC: minor fixup to glossary
* DOC: Add node attr example and links.
* Add a few links to glossary terms and forward-refs to the tutorial
section on attributes.
* Add an example of add_nodes_from with attribute dicts.
* Minor rewordings.
* DOC: Add a few more section headings.
* Adds three new section headings to the tutorial to better match
the natural organization of the material.
* Adds additional sentence contextualizing constructors.
* DOC: add link to nbunch glossary entry
* DOC: Wording change.
* DOC: change property links from meth to attr roles.
Prevents the () from being added to properties in the
rendered docs
* DOC: beef up indexing example.
Add edge attribute so that the result is clearer than returning an
empty dictionary
* STY: code style fixup in nbplot
* DOC: More precise wording in comment
* DOC: switch to autosummary for graph creation.
Adds autosummary-generated table for graph creation methods to
tutorial. Nice because the methods are linkable, but may be overkill
* DOC: Remove pylab suggestion.
Use of --pylab is discouraged, so remove the suggestion from tutorial
Diffstat (limited to 'doc/reference')
| -rw-r--r-- | doc/reference/glossary.rst | 20 |
1 files changed, 1 insertions, 19 deletions
diff --git a/doc/reference/glossary.rst b/doc/reference/glossary.rst index 798da320..d2cdda9c 100644 --- a/doc/reference/glossary.rst +++ b/doc/reference/glossary.rst @@ -24,26 +24,8 @@ Glossary assigning to the `G.edges[u][v]` attribute dictionary for the specified edge *u*-*v*. - hashable - An object is hashable if it has a hash value which never changes - during its lifetime (it needs a :meth:`__hash__` method), and can be - compared to other objects (it needs an :meth:`__eq__` or :meth:`__cmp__` - method). Hashable objects which compare equal must have the same - hash value. - - Hashability makes an object usable as a dictionary key and a set - member, because these data structures use the hash value internally. - - All of Python's immutable built-in objects are hashable, while no - mutable containers (such as lists or dictionaries) are. Objects - which are instances of user-defined classes are hashable by - default; they all compare unequal, and their hash value is their - :func:`id`. - - Definition from https://docs.python.org/2/glossary.html - nbunch - An nbunch is a single node, container of nodes or None (representing + An nbunch is a single node, container of nodes or `None` (representing all nodes). It can be a list, set, graph, etc.. To filter an nbunch so that only nodes actually in `G` appear, use `G.nbunch_iter(nbunch)`. |