NodesTable API Reference¶

Type: groggy.NodesTable

Overview¶

Tabular view of node data with columns for node attributes.

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

Related Objects: - GraphTable - BaseTable - NodesAccessor

Complete Method Reference¶

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

Method	Returns	Status
`base_table()`	`BaseTable`	✓
`drop_columns()`	`NodesTable`	✓
`filter()`	`?`	✗
`filter_by_attr()`	`?`	✗
`from_csv()`	`?`	✗
`from_dict()`	`?`	✗
`from_json()`	`?`	✗
`from_parquet()`	`?`	✗
`group_by()`	`NodesTableArray`	✓
`head()`	`NodesTable`	✓
`interactive()`	`str`	✓
`interactive_embed()`	`str`	✓
`interactive_viz()`	`VizAccessor`	✓
`into_base_table()`	`BaseTable`	✓
`is_empty()`	`bool`	✓
`iter()`	`NodesTableRowIterator`	✓
`ncols()`	`int`	✓
`node_ids()`	`NumArray`	✓
`nrows()`	`int`	✓
`rich_display()`	`str`	✓
`select()`	`NodesTable`	✓
`shape()`	`tuple`	✓
`slice()`	`?`	✗
`sort_by()`	`NodesTable`	✓
`sort_values()`	`NodesTable`	✓
`tail()`	`NodesTable`	✓
`to_csv()`	`?`	✗
`to_json()`	`?`	✗
`to_pandas()`	`DataFrame`	✓
`to_parquet()`	`?`	✗
`unique_attr_values()`	`?`	✗
`viz()`	`VizAccessor`	✓
`with_attributes()`	`?`	✗

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

Detailed Method Reference¶

Creating NodesTable¶

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

import groggy as gr

g = gr.generators.karate_club()

# From graph
nodes_table = g.nodes.table()

# From GraphTable
table = g.table()
nodes_table = table.nodes

# From subgraph
young = g.nodes[g.nodes["age"] < 30]
young_nodes = young.table().nodes

Core Methods¶

`node_ids()`¶

Get array of node IDs.

Returns: - NumArray: Node IDs

Example:

nodes_table = g.nodes.table()
ids = nodes_table.node_ids()
print(ids.to_list())

`shape()`¶

Get table dimensions.

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

Example:

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

`nrows()` / `ncols()`¶

Get number of rows or columns.

Returns: - int: Row or column count

Example:

print(f"{nodes_table.nrows()} nodes")
print(f"{nodes_table.ncols()} columns")

`is_empty()`¶

Check if table is empty.

Returns: - bool: True if no rows

Example:

if nodes_table.is_empty():
    print("No nodes")

Display & Inspection¶

`head(n=5)` / `tail(n=5)`¶

Show first/last n rows.

Returns: - NodesTable: Subset table

Example:

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

`iter()`¶

Iterate over rows.

Returns: - Iterator over rows

Example:

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

Selection & Filtering¶

`select(columns)`¶

Select specific columns.

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

Returns: - NodesTable: Table with selected columns

Example:

# Select subset of attributes
age_name = nodes_table.select(["age", "name"])
print(age_name.head())

`drop_columns(columns)`¶

Remove specific columns.

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

Returns: - NodesTable: Table without specified columns

Example:

# Remove sensitive attributes
public = nodes_table.drop_columns(["ssn", "password"])

Sorting¶

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

Sort by column values.

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

Returns: - NodesTable: Sorted table

Example:

# Sort by age
by_age = nodes_table.sort_by("age")
print(by_age.head())

# Descending (if supported)
# by_age_desc = nodes_table.sort_by("age", ascending=False)

Notes: Both methods are aliases

Grouping¶

`group_by(column)`¶

Group rows by column value.

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

Returns: - NodesTableArray: Array of grouped tables

Example:

# Group by city
by_city = nodes_table.group_by("city")

for city_table in by_city:
    print(f"City: {city_table.nrows()} nodes")

Export¶

`to_pandas()`¶

Convert to pandas DataFrame.

Returns: - pandas.DataFrame: DataFrame

Example:

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

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

Export to file formats.

Parameters: - path (str): File path

Example:

nodes_table.to_csv("nodes.csv")
nodes_table.to_parquet("nodes.parquet")
nodes_table.to_json("nodes.json")

Conversion¶

`base_table()` / `into_base_table()`¶

Convert to BaseTable.

Returns: - BaseTable: Generic table

Example:

base = nodes_table.base_table()

Notes: Both methods are aliases

Usage Patterns¶

Pattern 1: Basic Export¶

nodes_table = g.nodes.table()

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

# To CSV
nodes_table.to_csv("nodes.csv", index=False)

Pattern 2: Column Selection¶

# Select specific attributes
subset = nodes_table.select(["id", "name", "age"])

# Export subset
subset.to_csv("nodes_basic.csv")

Pattern 3: Sorting & Display¶

# Sort by attribute
by_age = nodes_table.sort_by("age")

# Show oldest/youngest
print("Youngest:")
by_age.head(5)

print("\nOldest:")
by_age.tail(5)

Pattern 4: Grouping Analysis¶

# Group by category
by_dept = nodes_table.group_by("department")

# Analyze each group
for dept_table in by_dept:
    df = dept_table.to_pandas()
    print(f"Dept size: {len(df)}")
    print(f"Avg age: {df['age'].mean():.1f}")

Quick Reference¶

Method	Returns	Description
`node_ids()`	`NumArray`	Get node IDs
`shape()`	`tuple`	(rows, cols)
`nrows()`	`int`	Number of rows
`ncols()`	`int`	Number of columns
`head(n)`	`NodesTable`	First n rows
`tail(n)`	`NodesTable`	Last n rows
`select(cols)`	`NodesTable`	Select columns
`drop_columns(cols)`	`NodesTable`	Remove columns
`sort_by(col)`	`NodesTable`	Sort by column
`group_by(col)`	`NodesTableArray`	Group by column
`to_pandas()`	`DataFrame`	Export to pandas
`to_csv(path)`	None	Export to CSV

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=...)

`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: - NodesTable: Return value

Example:

obj.sort_values()

`tail()`¶

Tail.

Returns: - NodesTable: 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()

`with_attributes(attributes)`¶

With Attributes.

Parameters: - attributes: attributes

Returns: - None: Return value

Example:

obj.with_attributes(attributes=...)

NodesTable API Reference¶

Overview¶

Complete Method Reference¶

Detailed Method Reference¶

Creating NodesTable¶

Core Methods¶

node_ids()¶

shape()¶

nrows() / ncols()¶

is_empty()¶

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)¶

Conversion¶

base_table() / into_base_table()¶

Usage Patterns¶

Pattern 1: Basic Export¶

Pattern 2: Column Selection¶

Pattern 3: Sorting & Display¶

Pattern 4: Grouping Analysis¶

Quick Reference¶

See Also¶

Additional Methods¶

filter(predicate)¶

filter_by_attr(value)¶

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()¶

with_attributes(attributes)¶

`node_ids()`¶

`shape()`¶

`nrows()` / `ncols()`¶

`is_empty()`¶

`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)`¶

`base_table()` / `into_base_table()`¶

`filter(predicate)`¶

`filter_by_attr(value)`¶

`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()`¶

`with_attributes(attributes)`¶