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Merged
srittau merged 7 commits into from
Jul 8, 2025
Merged

networkx: All return types from python-type-stubs #14256

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4a6f83d
Finished migrating all return types
Avasam Jun 10, 2025
99c280b
Merge branch 'main' of https://github.com/python/typeshed into networ…
Avasam Jun 10, 2025
359d750
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Jun 10, 2025
b891c71
Fixes post-merge
Avasam Jun 10, 2025
5b3674f
Merge branch 'networkx-return-type-annotations-from-python-type-stubs…
Avasam Jun 10, 2025
d5c889e
Update stubs/networkx/networkx/algorithms/bipartite/edgelist.pyi
Avasam Jul 8, 2025
f7fcb64
Merge branch 'main' into networkx-return-type-annotations-from-python…
Avasam Jul 8, 2025
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6 changes: 4 additions & 2 deletions stubs/networkx/networkx/algorithms/approximation/clique.pyi
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@@ -1,12 +1,14 @@
from _typeshed import Incomplete

from networkx.classes.graph import Graph, _Node
from networkx.utils.backends import _dispatchable

__all__ = ["clique_removal", "max_clique", "large_clique_size", "maximum_independent_set"]

@_dispatchable
def maximum_independent_set(G: Graph[_Node]): ...
def maximum_independent_set(G: Graph[_Node]) -> set[Incomplete]: ...
@_dispatchable
def max_clique(G: Graph[_Node]): ...
def max_clique(G: Graph[_Node]) -> set[Incomplete]: ...
@_dispatchable
def clique_removal(G: Graph[_Node]): ...
@_dispatchable
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2 changes: 1 addition & 1 deletion stubs/networkx/networkx/algorithms/approximation/clustering_coefficient.pyi
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Expand Up @@ -5,4 +5,4 @@ from numpy.random import RandomState
__all__ = ["average_clustering"]

@_dispatchable
def average_clustering(G: Graph[_Node], trials: int = 1000, seed: int | RandomState | None = None): ...
def average_clustering(G: Graph[_Node], trials: int = 1000, seed: int | RandomState | None = None) -> float: ...
4 changes: 3 additions & 1 deletion stubs/networkx/networkx/algorithms/approximation/connectivity.pyi
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Expand Up @@ -11,4 +11,6 @@ def local_node_connectivity(G: Graph[_Node], source: _Node, target: _Node, cutof
@_dispatchable
def node_connectivity(G: Graph[_Node], s: _Node | None = None, t: _Node | None = None): ...
@_dispatchable
def all_pairs_node_connectivity(G: Graph[_Node], nbunch: Iterable[Incomplete] | None = None, cutoff: int | None = None): ...
def all_pairs_node_connectivity(
G: Graph[_Node], nbunch: Iterable[Incomplete] | None = None, cutoff: int | None = None
) -> dict[Incomplete, dict[Incomplete, Incomplete]]: ...
6 changes: 4 additions & 2 deletions stubs/networkx/networkx/algorithms/approximation/dominating_set.pyi
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@@ -1,9 +1,11 @@
from _typeshed import Incomplete

from networkx.classes.graph import Graph, _Node
from networkx.utils.backends import _dispatchable

__all__ = ["min_weighted_dominating_set", "min_edge_dominating_set"]

@_dispatchable
def min_weighted_dominating_set(G: Graph[_Node], weight: str | None = None): ...
def min_weighted_dominating_set(G: Graph[_Node], weight: str | None = None) -> set[Incomplete]: ...
@_dispatchable
def min_edge_dominating_set(G: Graph[_Node]): ...
def min_edge_dominating_set(G: Graph[_Node]) -> set[Incomplete]: ...
5 changes: 4 additions & 1 deletion stubs/networkx/networkx/algorithms/approximation/kcomponents.pyi
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from _typeshed import Incomplete
from collections import defaultdict

from networkx.classes.graph import Graph, _Node
from networkx.utils.backends import _dispatchable

__all__ = ["k_components"]

@_dispatchable
def k_components(G: Graph[_Node], min_density: float = 0.95): ...
def k_components(G: Graph[_Node], min_density: float = 0.95) -> defaultdict[Incomplete, list[Incomplete]]: ...
4 changes: 3 additions & 1 deletion stubs/networkx/networkx/algorithms/approximation/matching.pyi
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from _typeshed import Incomplete

from networkx.classes.graph import Graph, _Node
from networkx.utils.backends import _dispatchable

__all__ = ["min_maximal_matching"]

@_dispatchable
def min_maximal_matching(G: Graph[_Node]): ...
def min_maximal_matching(G: Graph[_Node]) -> set[Incomplete]: ...
2 changes: 1 addition & 1 deletion stubs/networkx/networkx/algorithms/approximation/traveling_salesman.pyi
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Expand Up @@ -21,7 +21,7 @@ _SupportsLenAndGetItemT = TypeVar("_SupportsLenAndGetItemT", bound=SupportsLenAn
def swap_two_nodes(soln: _SupportsLenAndGetItemT, seed) -> _SupportsLenAndGetItemT: ...
def move_one_node(soln: _SupportsLenAndGetItemT, seed) -> _SupportsLenAndGetItemT: ...
@_dispatchable
def christofides(G: Graph[_Node], weight: str | None = "weight", tree: Graph[_Node] | None = None): ...
def christofides(G: Graph[_Node], weight: str | None = "weight", tree: Graph[_Node] | None = None) -> list[Incomplete]: ...
@_dispatchable
def traveling_salesman_problem(
G: Graph[_Node],
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4 changes: 3 additions & 1 deletion stubs/networkx/networkx/algorithms/approximation/vertex_cover.pyi
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from _typeshed import Incomplete

from networkx.classes.graph import Graph, _Node
from networkx.utils.backends import _dispatchable

__all__ = ["min_weighted_vertex_cover"]

@_dispatchable
def min_weighted_vertex_cover(G: Graph[_Node], weight: str | None = None): ...
def min_weighted_vertex_cover(G: Graph[_Node], weight: str | None = None) -> set[Incomplete]: ...
2 changes: 1 addition & 1 deletion stubs/networkx/networkx/algorithms/assortativity/connectivity.pyi
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Expand Up @@ -9,4 +9,4 @@ __all__ = ["average_degree_connectivity"]
@_dispatchable
def average_degree_connectivity(
G: Graph[_Node], source="in+out", target="in+out", nodes: Iterable[Incomplete] | None = None, weight: str | None = None
): ...
) -> dict[Incomplete, int | float]: ...
8 changes: 4 additions & 4 deletions stubs/networkx/networkx/algorithms/assortativity/correlation.pyi
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Expand Up @@ -14,12 +14,12 @@ __all__ = [
@_dispatchable
def degree_assortativity_coefficient(
G: Graph[_Node], x: str = "out", y: str = "in", weight: str | None = None, nodes: Iterable[Incomplete] | None = None
): ...
) -> float: ...
@_dispatchable
def degree_pearson_correlation_coefficient(
G: Graph[_Node], x: str = "out", y: str = "in", weight: str | None = None, nodes: Iterable[Incomplete] | None = None
): ...
) -> float: ...
@_dispatchable
def attribute_assortativity_coefficient(G: Graph[_Node], attribute: str, nodes: Iterable[Incomplete] | None = None): ...
def attribute_assortativity_coefficient(G: Graph[_Node], attribute: str, nodes: Iterable[Incomplete] | None = None) -> float: ...
@_dispatchable
def numeric_assortativity_coefficient(G: Graph[_Node], attribute: str, nodes: Iterable[Incomplete] | None = None): ...
def numeric_assortativity_coefficient(G: Graph[_Node], attribute: str, nodes: Iterable[Incomplete] | None = None) -> float: ...
6 changes: 3 additions & 3 deletions stubs/networkx/networkx/algorithms/assortativity/mixing.pyi
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Expand Up @@ -9,7 +9,7 @@ __all__ = ["attribute_mixing_matrix", "attribute_mixing_dict", "degree_mixing_ma
@_dispatchable
def attribute_mixing_dict(
G: Graph[_Node], attribute: str, nodes: Iterable[Incomplete] | None = None, normalized: bool = False
): ...
) -> dict[Incomplete, Incomplete]: ...
@_dispatchable
def attribute_mixing_matrix(
G: Graph[_Node],
Expand All @@ -21,7 +21,7 @@ def attribute_mixing_matrix(
@_dispatchable
def degree_mixing_dict(
G: Graph[_Node], x: str = "out", y: str = "in", weight: str | None = None, nodes=None, normalized: bool = False
): ...
) -> dict[Incomplete, Incomplete]: ...
@_dispatchable
def degree_mixing_matrix(
G: Graph[_Node],
Expand All @@ -33,4 +33,4 @@ def degree_mixing_matrix(
mapping: SupportsGetItem[Incomplete, Incomplete] | None = None,
): ...
@_dispatchable
def mixing_dict(xy, normalized: bool = False): ...
def mixing_dict(xy, normalized: bool = False) -> dict[Incomplete, Incomplete]: ...
2 changes: 1 addition & 1 deletion stubs/networkx/networkx/algorithms/assortativity/neighbor_degree.pyi
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Expand Up @@ -13,4 +13,4 @@ def average_neighbor_degree(
target: str | None = "out",
nodes: Iterable[Incomplete] | None = None,
weight: str | None = None,
): ...
) -> dict[Incomplete, Incomplete]: ...
2 changes: 1 addition & 1 deletion stubs/networkx/networkx/algorithms/asteroidal.pyi
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Expand Up @@ -6,7 +6,7 @@ from networkx.utils.backends import _dispatchable
__all__ = ["is_at_free", "find_asteroidal_triple"]

@_dispatchable
def find_asteroidal_triple(G: Graph[_Node]): ...
def find_asteroidal_triple(G: Graph[_Node]) -> list[Incomplete] | None: ...
@_dispatchable
def is_at_free(G: Graph[_Node]) -> bool: ...
@_dispatchable
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8 changes: 4 additions & 4 deletions stubs/networkx/networkx/algorithms/bipartite/basic.pyi
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Expand Up @@ -7,14 +7,14 @@ from networkx.utils.backends import _dispatchable
__all__ = ["is_bipartite", "is_bipartite_node_set", "color", "sets", "density", "degrees"]

@_dispatchable
def color(G: Graph[_Node]): ...
def color(G: Graph[_Node]) -> dict[Incomplete, Incomplete]: ...
@_dispatchable
def is_bipartite(G: Graph[_Node]) -> bool: ...
@_dispatchable
def is_bipartite_node_set(G: Graph[_Node], nodes: Iterable[Incomplete]) -> bool: ...
@_dispatchable
def sets(G: Graph[_Node], top_nodes: Iterable[Incomplete] | None = None): ...
def sets(G: Graph[_Node], top_nodes: Iterable[Incomplete] | None = None) -> tuple[set[Incomplete], set[Incomplete]]: ...
@_dispatchable
def density(B: Graph[_Node], nodes): ...
def density(B: Graph[_Node], nodes) -> float: ...
@_dispatchable
def degrees(B: Graph[_Node], nodes, weight: str | None = None): ...
def degrees(B: Graph[_Node], nodes, weight: str | None = None) -> tuple[Incomplete, Incomplete]: ...
8 changes: 5 additions & 3 deletions stubs/networkx/networkx/algorithms/bipartite/centrality.pyi
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from _typeshed import Incomplete

from networkx.classes.graph import Graph, _Node
from networkx.utils.backends import _dispatchable

__all__ = ["degree_centrality", "betweenness_centrality", "closeness_centrality"]

@_dispatchable
def degree_centrality(G: Graph[_Node], nodes): ...
def degree_centrality(G: Graph[_Node], nodes) -> dict[Incomplete, Incomplete]: ...
@_dispatchable
def betweenness_centrality(G: Graph[_Node], nodes): ...
def betweenness_centrality(G: Graph[_Node], nodes) -> dict[Incomplete, Incomplete]: ...
@_dispatchable
def closeness_centrality(G: Graph[_Node], nodes, normalized: bool | None = True): ...
def closeness_centrality(G: Graph[_Node], nodes, normalized: bool | None = True) -> dict[Incomplete, Incomplete]: ...
8 changes: 5 additions & 3 deletions stubs/networkx/networkx/algorithms/bipartite/cluster.pyi
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Expand Up @@ -13,11 +13,13 @@ def cc_min(nu, nv) -> float: ...
modes: dict[str, Callable[[Incomplete, Incomplete], float]]

@_dispatchable
def latapy_clustering(G: Graph[_Node], nodes: Iterable[Incomplete] | None = None, mode: str = "dot"): ...
def latapy_clustering(
G: Graph[_Node], nodes: Iterable[Incomplete] | None = None, mode: str = "dot"
) -> dict[Incomplete, Incomplete]: ...

clustering = latapy_clustering

@_dispatchable
def average_clustering(G: Graph[_Node], nodes: Iterable[Incomplete] | None = None, mode: str = "dot"): ...
def average_clustering(G: Graph[_Node], nodes: Iterable[Incomplete] | None = None, mode: str = "dot") -> float: ...
@_dispatchable
def robins_alexander_clustering(G: Graph[_Node]): ...
def robins_alexander_clustering(G: Graph[_Node]) -> float: ...
2 changes: 1 addition & 1 deletion stubs/networkx/networkx/algorithms/bipartite/covering.pyi
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Expand Up @@ -7,4 +7,4 @@ from networkx.utils.backends import _dispatchable
__all__ = ["min_edge_cover"]

@_dispatchable
def min_edge_cover(G: Graph[_Node], matching_algorithm: Callable[..., Incomplete] | None = None): ...
def min_edge_cover(G: Graph[_Node], matching_algorithm: Callable[..., Incomplete] | None = None) -> set[Incomplete]: ...
3 changes: 1 addition & 2 deletions stubs/networkx/networkx/algorithms/bipartite/edgelist.pyi
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from _typeshed import Incomplete
from collections.abc import Generator

from networkx.classes.graph import Graph, _Node
Expand All @@ -9,7 +8,7 @@ __all__ = ["generate_edgelist", "write_edgelist", "parse_edgelist", "read_edgeli
@_dispatchable
def write_edgelist(G, path, comments: str = "#", delimiter: str = " ", data: bool = True, encoding: str = "utf-8") -> None: ...
@_dispatchable
def generate_edgelist(G, delimiter: str = " ", data: bool = True) -> Generator[Incomplete, None, None]: ...
def generate_edgelist(G, delimiter: str = " ", data: bool = True) -> Generator[str, None, None]: ...
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@srittau srittau Jul 7, 2025

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Nitty nit:

Suggested change
def generate_edgelist(G, delimiter: str = " ", data: bool = True) -> Generator[str, None, None]: ...
def generate_edgelist(G, delimiter: str = " ", data: bool = True) -> Generator[str]: ...

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@Avasam Avasam Jul 8, 2025 •
edited
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(applied suggestion)
There's a ton of those in networkx stubs. Maybe we should have a lint rule for the new concise Generator format ?

@_dispatchable
def parse_edgelist(
lines,
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6 changes: 3 additions & 3 deletions stubs/networkx/networkx/algorithms/bipartite/matching.pyi
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Expand Up @@ -7,9 +7,9 @@ from networkx.utils.backends import _dispatchable
__all__ = ["maximum_matching", "hopcroft_karp_matching", "eppstein_matching", "to_vertex_cover", "minimum_weight_full_matching"]

@_dispatchable
def hopcroft_karp_matching(G: Graph[_Node], top_nodes: Iterable[_Node] | None = None): ...
def hopcroft_karp_matching(G: Graph[_Node], top_nodes: Iterable[_Node] | None = None) -> dict[Incomplete, Incomplete]: ...
@_dispatchable
def eppstein_matching(G: Graph[_Node], top_nodes: Iterable[Incomplete] | None = None): ...
def eppstein_matching(G: Graph[_Node], top_nodes: Iterable[Incomplete] | None = None) -> dict[Incomplete, Incomplete]: ...
@_dispatchable
def to_vertex_cover(
G: Graph[_Node], matching: SupportsGetItem[Incomplete, Incomplete], top_nodes: Iterable[Incomplete] | None = None
Expand All @@ -20,4 +20,4 @@ maximum_matching = hopcroft_karp_matching
@_dispatchable
def minimum_weight_full_matching(
G: Graph[_Node], top_nodes: Iterable[Incomplete] | None = None, weight: str | None = "weight"
): ...
) -> dict[Incomplete, Incomplete]: ...
2 changes: 1 addition & 1 deletion stubs/networkx/networkx/algorithms/bipartite/matrix.pyi
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Expand Up @@ -14,6 +14,6 @@ def biadjacency_matrix(
dtype=None,
weight: str | None = "weight",
format="csr",
): ...
): ... # Return is a complex union of scipy classes depending on the format param
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@srittau srittau Jul 7, 2025

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So, should we return Any?

Suggested change
): ... # Return is a complex union of scipy classes depending on the format param
) -> Any: ... # Return is a complex union of scipy classes depending on the format param

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@Avasam Avasam Jul 8, 2025

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I think I added this comment initially because I probably added Any as well, but I think you could actually represent the return type with overloads based on the format param. So it's more "incomplete", than "unrepresentable".

@_dispatchable
def from_biadjacency_matrix(A, create_using: Graph[_Node] | None = None, edge_attribute: str = "weight"): ...
2 changes: 1 addition & 1 deletion stubs/networkx/networkx/algorithms/bipartite/redundancy.pyi
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Expand Up @@ -7,4 +7,4 @@ from networkx.utils.backends import _dispatchable
__all__ = ["node_redundancy"]

@_dispatchable
def node_redundancy(G: Graph[_Node], nodes: Iterable[Incomplete] | None = None): ...
def node_redundancy(G: Graph[_Node], nodes: Iterable[Incomplete] | None = None) -> dict[Incomplete, float]: ...
4 changes: 3 additions & 1 deletion stubs/networkx/networkx/algorithms/bipartite/spectral.pyi
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@@ -1,7 +1,9 @@
from _typeshed import Incomplete

from networkx.classes.graph import Graph, _Node
from networkx.utils.backends import _dispatchable

__all__ = ["spectral_bipartivity"]

@_dispatchable
def spectral_bipartivity(G: Graph[_Node], nodes=None, weight: str = "weight"): ...
def spectral_bipartivity(G: Graph[_Node], nodes=None, weight: str = "weight") -> float | dict[Incomplete, Incomplete]: ...
8 changes: 5 additions & 3 deletions stubs/networkx/networkx/algorithms/centrality/current_flow_betweenness.pyi
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@@ -1,3 +1,5 @@
from _typeshed import Incomplete

from networkx.classes.graph import Graph, _Node
from networkx.utils.backends import _dispatchable
from numpy.random import RandomState
Expand All @@ -18,12 +20,12 @@ def approximate_current_flow_betweenness_centrality(
epsilon: float = 0.5,
kmax: int = 10000,
seed: int | RandomState | None = None,
): ...
) -> dict[Incomplete, float]: ...
@_dispatchable
def current_flow_betweenness_centrality(
G: Graph[_Node], normalized: bool | None = True, weight: str | None = None, dtype: type = ..., solver: str = "full"
): ...
) -> dict[Incomplete, Incomplete]: ...
@_dispatchable
def edge_current_flow_betweenness_centrality(
G: Graph[_Node], normalized: bool | None = True, weight: str | None = None, dtype: type = ..., solver: str = "full"
): ...
) -> dict[tuple[Incomplete, Incomplete], float]: ...
5 changes: 3 additions & 2 deletions stubs/networkx/networkx/algorithms/centrality/current_flow_betweenness_subset.pyi
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@@ -1,3 +1,4 @@
from _typeshed import Incomplete
from collections.abc import Iterable

from networkx.classes.graph import Graph, _Node
Expand All @@ -14,7 +15,7 @@ def current_flow_betweenness_centrality_subset(
weight: str | None = None,
dtype: type = ...,
solver: str = "lu",
): ...
) -> dict[Incomplete, float]: ...
@_dispatchable
def edge_current_flow_betweenness_centrality_subset(
G: Graph[_Node],
Expand All @@ -24,4 +25,4 @@ def edge_current_flow_betweenness_centrality_subset(
weight: str | None = None,
dtype: type = ...,
solver: str = "lu",
): ...
) -> dict[tuple[Incomplete, Incomplete], float]: ...
6 changes: 5 additions & 1 deletion stubs/networkx/networkx/algorithms/centrality/current_flow_closeness.pyi
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from _typeshed import Incomplete

from networkx.classes.graph import Graph, _Node
from networkx.utils.backends import _dispatchable

__all__ = ["current_flow_closeness_centrality", "information_centrality"]

@_dispatchable
def current_flow_closeness_centrality(G: Graph[_Node], weight: str | None = None, dtype: type = ..., solver: str = "lu"): ...
def current_flow_closeness_centrality(
G: Graph[_Node], weight: str | None = None, dtype: type = ..., solver: str = "lu"
) -> dict[Incomplete, float]: ...

information_centrality = current_flow_closeness_centrality
4 changes: 2 additions & 2 deletions stubs/networkx/networkx/algorithms/centrality/eigenvector.pyi
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Expand Up @@ -12,8 +12,8 @@ def eigenvector_centrality(
tol: float | None = 1e-06,
nstart: SupportsGetItem[Incomplete, Incomplete] | None = None,
weight: str | None = None,
): ...
) -> dict[Incomplete, float]: ...
@_dispatchable
def eigenvector_centrality_numpy(
G: Graph[_Node], weight: str | None = None, max_iter: int | None = 50, tol: float | None = 0
): ...
) -> dict[Incomplete, Incomplete]: ...
10 changes: 5 additions & 5 deletions stubs/networkx/networkx/algorithms/centrality/group.pyi
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Expand Up @@ -26,12 +26,12 @@ def prominent_group(
endpoints: bool | None = False,
normalized: bool | None = True,
greedy: bool | None = False,
): ...
) -> tuple[float, list[Incomplete]]: ...
@_dispatchable
def group_closeness_centrality(G: Graph[_Node], S: Iterable[Incomplete], weight: str | None = None): ...
def group_closeness_centrality(G: Graph[_Node], S: Iterable[Incomplete], weight: str | None = None) -> float: ...
@_dispatchable
def group_degree_centrality(G: Graph[_Node], S: Iterable[Incomplete]): ...
def group_degree_centrality(G: Graph[_Node], S: Iterable[Incomplete]) -> float: ...
@_dispatchable
def group_in_degree_centrality(G: Graph[_Node], S: Iterable[Incomplete]): ...
def group_in_degree_centrality(G: Graph[_Node], S: Iterable[Incomplete]) -> float: ...
@_dispatchable
def group_out_degree_centrality(G: Graph[_Node], S: Iterable[Incomplete]): ...
def group_out_degree_centrality(G: Graph[_Node], S: Iterable[Incomplete]) -> float: ...
2 changes: 1 addition & 1 deletion stubs/networkx/networkx/algorithms/centrality/harmonic.pyi
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Expand Up @@ -9,4 +9,4 @@ __all__ = ["harmonic_centrality"]
@_dispatchable
def harmonic_centrality(
G: Graph[_Node], nbunch: Iterable[Incomplete] | None = None, distance=None, sources: Iterable[Incomplete] | None = None
): ...
) -> dict[Incomplete, int]: ...
4 changes: 2 additions & 2 deletions stubs/networkx/networkx/algorithms/centrality/katz.pyi
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Expand Up @@ -15,12 +15,12 @@ def katz_centrality(
nstart: SupportsGetItem[Incomplete, Incomplete] | None = None,
normalized: bool | None = True,
weight: str | None = None,
): ...
) -> dict[Incomplete, Incomplete]: ...
@_dispatchable
def katz_centrality_numpy(
G: Graph[_Node],
alpha: float = 0.1,
beta: float | SupportsGetItem[Incomplete, Incomplete] | None = 1.0,
normalized: bool = True,
weight: str | None = None,
): ...
) -> dict[Incomplete, Incomplete]: ...
4 changes: 3 additions & 1 deletion stubs/networkx/networkx/algorithms/centrality/load.pyi
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@@ -1,3 +1,5 @@
from _typeshed import Incomplete

from networkx.classes.graph import Graph, _Node
from networkx.utils.backends import _dispatchable

Expand All @@ -6,7 +8,7 @@ __all__ = ["load_centrality", "edge_load_centrality"]
@_dispatchable
def newman_betweenness_centrality(
G: Graph[_Node], v=None, cutoff: bool | None = None, normalized: bool | None = True, weight: str | None = None
): ...
) -> float | dict[Incomplete, float]: ...

load_centrality = newman_betweenness_centrality

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2 changes: 1 addition & 1 deletion stubs/networkx/networkx/algorithms/centrality/percolation.pyi
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Expand Up @@ -11,4 +11,4 @@ def percolation_centrality(
attribute: str | None = "percolation",
states: SupportsGetItem[Incomplete, Incomplete] | None = None,
weight: str | None = None,
): ...
) -> dict[Incomplete, float]: ...
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