EdgesTable API Reference¶

Type: groggy.EdgesTable

Overview¶

Tabular view of edge data with columns for edge attributes.

Primary Use Cases: - Analyzing edge attributes in tabular form - Aggregating edge data - Exporting edge information

Related Objects: - GraphTable - BaseTable - EdgesAccessor

Complete Method Reference¶

The following methods are available on EdgesTable objects. This reference is generated from comprehensive API testing and shows all empirically validated methods.

Method	Returns	Status
`as_tuples()`	`list`	✓
`auto_assign_edge_ids()`	`EdgesTable`	✓
`base_table()`	`BaseTable`	✓
`drop_columns()`	`EdgesTable`	✓
`edge_ids()`	`NumArray`	✓
`filter()`	`?`	✗
`filter_by_attr()`	`?`	✗
`filter_by_sources()`	`?`	✗
`filter_by_targets()`	`?`	✗
`from_csv()`	`?`	✗
`from_dict()`	`?`	✗
`from_json()`	`?`	✗
`from_parquet()`	`?`	✗
`group_by()`	`EdgesTableArray`	✓
`head()`	`EdgesTable`	✓
`interactive()`	`str`	✓
`interactive_embed()`	`str`	✓
`interactive_viz()`	`VizAccessor`	✓
`into_base_table()`	`BaseTable`	✓
`iter()`	`EdgesTableRowIterator`	✓
`ncols()`	`int`	✓
`nrows()`	`int`	✓
`rich_display()`	`str`	✓
`select()`	`EdgesTable`	✓
`shape()`	`tuple`	✓
`slice()`	`?`	✗
`sort_by()`	`EdgesTable`	✓
`sort_values()`	`EdgesTable`	✓
`sources()`	`NumArray`	✓
`tail()`	`EdgesTable`	✓
`targets()`	`NumArray`	✓
`to_csv()`	`?`	✗
`to_json()`	`?`	✗
`to_pandas()`	`DataFrame`	✓
`to_parquet()`	`?`	✗
`unique_attr_values()`	`?`	✗
`viz()`	`VizAccessor`	✓

Legend: - ✓ = Method tested and working - ✗ = Method failed in testing or not yet validated - ? = Return type not yet determined

Detailed Method Reference¶

Creating EdgesTable¶

EdgesTable is accessed from GraphTable or directly from graph/subgraph:

import groggy as gr

g = gr.generators.karate_club()

# From graph
edges_table = g.edges.table()

# From GraphTable
table = g.table()
edges_table = table.edges

# From subgraph
strong = g.edges[g.edges["weight"] > 5.0]
strong_edges = strong.table().edges

Core Methods¶

`edge_ids()`¶

Get array of edge IDs.

Returns: - NumArray: Edge IDs

Example:

edges_table = g.edges.table()
ids = edges_table.edge_ids()
print(ids.to_list())

`sources()`¶

Get source node IDs for all edges.

Returns: - NumArray: Source node IDs

Example:

edges_table = g.edges.table()
srcs = edges_table.sources()
print(srcs.to_list())

Notes: - For directed graphs: actual source nodes - For undirected: arbitrary endpoint designation

`targets()`¶

Get target node IDs for all edges.

Returns: - NumArray: Target node IDs

Example:

edges_table = g.edges.table()
tgts = edges_table.targets()

# Build edge list
import pandas as pd
edge_list = pd.DataFrame({
    'source': edges_table.sources().to_list(),
    'target': edges_table.targets().to_list()
})

`shape()`¶

Get table dimensions.

Returns: - tuple[int, int]: (rows, columns)

Example:

edges_table = g.edges.table()
rows, cols = edges_table.shape()
print(f"{rows} edges, {cols} attributes")

`nrows()` / `ncols()`¶

Get number of rows or columns.

Returns: - int: Row or column count

Example:

print(f"{edges_table.nrows()} edges")
print(f"{edges_table.ncols()} columns")

`as_tuples()`¶

Get edges as list of tuples.

Returns: - list[tuple]: List of (source, target) tuples

Example:

edges_table = g.edges.table()
edge_list = edges_table.as_tuples()
print(edge_list[:5])  # [(0, 1), (0, 2), (1, 3), ...]

Performance: O(m) where m is edge count

Display & Inspection¶

`head(n=5)` / `tail(n=5)`¶

Show first/last n rows.

Returns: - EdgesTable: Subset table

Example:

edges_table.head()     # First 5
edges_table.head(10)   # First 10
edges_table.tail(5)    # Last 5

`iter()`¶

Iterate over rows.

Returns: - Iterator over rows

Example:

for row in edges_table.iter():
    print(row)

Selection & Filtering¶

`select(columns)`¶

Select specific columns.

Parameters: - columns (list[str]): Column names to keep

Returns: - EdgesTable: Table with selected columns

Example:

# Select subset of attributes
weight_type = edges_table.select(["weight", "type"])
print(weight_type.head())

# Just topology
topology = edges_table.select(["source", "target"])

`drop_columns(columns)`¶

Remove specific columns.

Parameters: - columns (list[str]): Columns to drop

Returns: - EdgesTable: Table without specified columns

Example:

# Remove metadata
clean = edges_table.drop_columns(["timestamp", "internal_id"])

Sorting¶

`sort_by(column)` / `sort_values(column)`¶

Sort by column values.

Parameters: - column (str): Column name to sort by

Returns: - EdgesTable: Sorted table

Example:

# Sort by weight
by_weight = edges_table.sort_by("weight")
print(by_weight.head())

# Heaviest edges first (if descending supported)
# by_weight_desc = edges_table.sort_by("weight", ascending=False)

Notes: Both methods are aliases

Grouping¶

`group_by(column)`¶

Group rows by column value.

Parameters: - column (str): Column to group by

Returns: - EdgesTableArray: Array of grouped tables

Example:

# Group by edge type
by_type = edges_table.group_by("type")

for type_table in by_type:
    print(f"Type: {type_table.nrows()} edges")

# Group by weight threshold
# (First create categorical column)

Export¶

`to_pandas()`¶

Convert to pandas DataFrame.

Returns: - pandas.DataFrame: DataFrame

Example:

df = edges_table.to_pandas()
print(df.head())
print(df.describe())

# Edge weight distribution
import matplotlib.pyplot as plt
df['weight'].hist()
plt.show()

`to_csv(path)` / `to_parquet(path)` / `to_json(path)`¶

Export to file formats.

Parameters: - path (str): File path

Example:

edges_table.to_csv("edges.csv")
edges_table.to_parquet("edges.parquet")
edges_table.to_json("edges.json")

Utility Methods¶

`auto_assign_edge_ids()`¶

Automatically assign IDs to edges if missing.

Returns: - EdgesTable: Table with edge IDs assigned

Example:

# If edges lack IDs
edges_with_ids = edges_table.auto_assign_edge_ids()

Conversion¶

`base_table()` / `into_base_table()`¶

Convert to BaseTable.

Returns: - BaseTable: Generic table

Example:

base = edges_table.base_table()

Notes: Both methods are aliases

Usage Patterns¶

Pattern 1: Basic Edge Export¶

edges_table = g.edges.table()

# To pandas
df = edges_table.to_pandas()
print(df.info())

# To CSV with edge list
edges_table.to_csv("edges.csv", index=False)

Pattern 2: Edge List Construction¶

edges_table = g.edges.table()

# Get edge list with attributes
edge_data = []
for src, tgt, eid in zip(
    edges_table.sources().to_list(),
    edges_table.targets().to_list(),
    edges_table.edge_ids().to_list()
):
    edge_data.append((src, tgt, eid))

# Or use as_tuples for basic edge list
edge_list = edges_table.as_tuples()

Pattern 3: Weight Analysis¶

# Sort by weight
by_weight = edges_table.sort_by("weight")

# Heaviest edges
print("Heaviest edges:")
heavy_df = by_weight.tail(10).to_pandas()
print(heavy_df[['source', 'target', 'weight']])

# Lightest edges
print("\nLightest edges:")
light_df = by_weight.head(10).to_pandas()
print(light_df[['source', 'target', 'weight']])

Pattern 4: Edge Type Grouping¶

# Group by type
by_type = edges_table.group_by("type")

# Analyze each type
for type_table in by_type:
    df = type_table.to_pandas()
    print(f"Edge type: {df['type'].iloc[0]}")
    print(f"  Count: {len(df)}")
    print(f"  Avg weight: {df['weight'].mean():.2f}")
    print(f"  Total weight: {df['weight'].sum():.2f}")

Pattern 5: Network Flow Analysis¶

import pandas as pd

# Get edge data
df = edges_table.to_pandas()

# Out-flow by source
out_flow = df.groupby('source')['weight'].agg(['sum', 'count', 'mean'])
print("Out-flow by node:")
print(out_flow.sort_values('sum', ascending=False).head())

# In-flow by target
in_flow = df.groupby('target')['weight'].agg(['sum', 'count', 'mean'])
print("\nIn-flow by node:")
print(in_flow.sort_values('sum', ascending=False).head())

Pattern 6: Selective Export¶

# Export only specific attributes
subset = edges_table.select(["source", "target", "weight"])
subset.to_csv("edge_list.csv")

# Remove metadata before export
public = edges_table.drop_columns(["internal_id", "timestamp"])
public.to_parquet("edges_public.parquet")

Quick Reference¶

Method	Returns	Description
`edge_ids()`	`NumArray`	Get edge IDs
`sources()`	`NumArray`	Source node IDs
`targets()`	`NumArray`	Target node IDs
`as_tuples()`	`list`	(source, target) pairs
`shape()`	`tuple`	(rows, cols)
`nrows()`	`int`	Number of rows
`ncols()`	`int`	Number of columns
`head(n)`	`EdgesTable`	First n rows
`tail(n)`	`EdgesTable`	Last n rows
`select(cols)`	`EdgesTable`	Select columns
`drop_columns(cols)`	`EdgesTable`	Remove columns
`sort_by(col)`	`EdgesTable`	Sort by column
`group_by(col)`	`EdgesTableArray`	Group by column
`to_pandas()`	`DataFrame`	Export to pandas
`to_csv(path)`	None	Export to CSV

Object Transformations¶

EdgesTable can transform into:

EdgesTable → BaseArray: edges_table["column"]
EdgesTable → DataFrame: edges_table.to_pandas()
EdgesTable → AggregationResult: edges_table.agg({"weight": "sum"})

See Object Transformation Graph for complete delegation chains.

Additional Methods¶

`filter(predicate)`¶

Filter.

Parameters: - predicate: predicate

Returns: - None: Return value

Example:

obj.filter(predicate=...)

`filter_by_attr(value)`¶

Filter By Attr.

Parameters: - value: value

Returns: - None: Return value

Example:

obj.filter_by_attr(value=...)

`filter_by_sources(source_nodes)`¶

Filter By Sources.

Parameters: - source_nodes: source nodes

Returns: - None: Return value

Example:

obj.filter_by_sources(source_nodes=...)

`filter_by_targets(target_nodes)`¶

Filter By Targets.

Parameters: - target_nodes: target nodes

Returns: - None: Return value

Example:

obj.filter_by_targets(target_nodes=...)

`from_csv(path)`¶

From Csv.

Parameters: - path: path

Returns: - None: Return value

Example:

obj.from_csv(path=...)

`from_dict(data)`¶

From Dict.

Parameters: - data: data

Returns: - None: Return value

Example:

obj.from_dict(data=...)

`from_json(path)`¶

From Json.

Parameters: - path: path

Returns: - None: Return value

Example:

obj.from_json(path=...)

`from_parquet(path)`¶

From Parquet.

Parameters: - path: path

Returns: - None: Return value

Example:

obj.from_parquet(path=...)

`interactive()`¶

Interactive.

Returns: - str: Return value

Example:

obj.interactive()

`interactive_embed()`¶

Interactive Embed.

Returns: - str: Return value

Example:

obj.interactive_embed()

`interactive_viz()`¶

Interactive Viz.

Returns: - VizAccessor: Return value

Example:

obj.interactive_viz()

`into_base_table()`¶

Into Base Table.

Returns: - BaseTable: Return value

Example:

obj.into_base_table()

`ncols()`¶

Ncols.

Returns: - int: Return value

Example:

obj.ncols()

`rich_display()`¶

Rich Display.

Returns: - str: Return value

Example:

obj.rich_display()

`slice(start, end)`¶

Slice.

Parameters: - start: start - end: end

Returns: - None: Return value

Example:

obj.slice(start=..., end=...)

`sort_values()`¶

Sort Values.

Returns: - EdgesTable: Return value

Example:

obj.sort_values()

`tail()`¶

Tail.

Returns: - EdgesTable: Return value

Example:

obj.tail()

`to_json(path)`¶

To Json.

Parameters: - path: path

Returns: - None: Return value

Example:

obj.to_json(path=...)

`to_parquet(path)`¶

To Parquet.

Parameters: - path: path

Returns: - None: Return value

Example:

obj.to_parquet(path=...)

`unique_attr_values()`¶

Unique Attr Values.

Returns: - None: Return value

Example:

obj.unique_attr_values()

`viz()`¶

Viz.

Returns: - VizAccessor: Return value

Example:

obj.viz()

EdgesTable API Reference¶

Overview¶

Complete Method Reference¶

Detailed Method Reference¶

Creating EdgesTable¶

Core Methods¶

edge_ids()¶

sources()¶

targets()¶

shape()¶

nrows() / ncols()¶

as_tuples()¶

Display & Inspection¶

head(n=5) / tail(n=5)¶

iter()¶

Selection & Filtering¶

select(columns)¶

drop_columns(columns)¶

Sorting¶

sort_by(column) / sort_values(column)¶

Grouping¶

group_by(column)¶

Export¶

to_pandas()¶

to_csv(path) / to_parquet(path) / to_json(path)¶

Utility Methods¶

auto_assign_edge_ids()¶

Conversion¶

base_table() / into_base_table()¶

Usage Patterns¶

Pattern 1: Basic Edge Export¶

Pattern 2: Edge List Construction¶

Pattern 3: Weight Analysis¶

Pattern 4: Edge Type Grouping¶

Pattern 5: Network Flow Analysis¶

Pattern 6: Selective Export¶

Quick Reference¶

Object Transformations¶

See Also¶

Additional Methods¶

filter(predicate)¶

filter_by_attr(value)¶

filter_by_sources(source_nodes)¶

filter_by_targets(target_nodes)¶

from_csv(path)¶

from_dict(data)¶

from_json(path)¶

from_parquet(path)¶

interactive()¶

interactive_embed()¶

interactive_viz()¶

into_base_table()¶

ncols()¶

rich_display()¶

slice(start, end)¶

sort_values()¶

tail()¶

to_json(path)¶

to_parquet(path)¶

unique_attr_values()¶

viz()¶

`edge_ids()`¶

`sources()`¶

`targets()`¶

`shape()`¶

`nrows()` / `ncols()`¶

`as_tuples()`¶

`head(n=5)` / `tail(n=5)`¶

`iter()`¶

`select(columns)`¶

`drop_columns(columns)`¶

`sort_by(column)` / `sort_values(column)`¶

`group_by(column)`¶

`to_pandas()`¶

`to_csv(path)` / `to_parquet(path)` / `to_json(path)`¶

`auto_assign_edge_ids()`¶

`base_table()` / `into_base_table()`¶

`filter(predicate)`¶

`filter_by_attr(value)`¶

`filter_by_sources(source_nodes)`¶

`filter_by_targets(target_nodes)`¶

`from_csv(path)`¶

`from_dict(data)`¶

`from_json(path)`¶

`from_parquet(path)`¶

`interactive()`¶

`interactive_embed()`¶

`interactive_viz()`¶

`into_base_table()`¶

`ncols()`¶

`rich_display()`¶

`slice(start, end)`¶

`sort_values()`¶

`tail()`¶

`to_json(path)`¶

`to_parquet(path)`¶

`unique_attr_values()`¶

`viz()`¶