graphistry package¶

graphistry.plotter module¶

class graphistry.plotter.Plotter(*args, **kwargs)¶: Bases: graphistry.gremlin.CosmosMixin, graphistry.gremlin.NeptuneMixin, graphistry.gremlin.GremlinMixin, graphistry.layouts.LayoutsMixin, graphistry.dgl_utils.DGLGraphMixin, graphistry.umap_utils.UMAPMixin, graphistry.feature_utils.FeatureMixin, graphistry.compute.ComputeMixin.ComputeMixin, graphistry.PlotterBase.PlotterBase, object

graphistry.pygraphistry module¶

class graphistry.pygraphistry.NumpyJSONEncoder(*, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, sort_keys=False, indent=None, separators=None, default=None)¶

Bases: json.encoder.JSONEncoder

default(obj)¶

Implement this method in a subclass such that it returns a serializable object for o, or calls the base implementation (to raise a TypeError).

For example, to support arbitrary iterators, you could implement default like this:

def default(self, o):
    try:
        iterable = iter(o)
    except TypeError:
        pass
    else:
        return list(iterable)
    # Let the base class default method raise the TypeError
    return JSONEncoder.default(self, o)

class graphistry.pygraphistry.PyGraphistry¶

Bases: object

static addStyle(bg=None, fg=None, logo=None, page=None)¶

Creates a base plotter with some style settings.

For parameters, see plotter.addStyle.

Returns: Plotter
Return type: Plotter

Example

import graphistry
graphistry.addStyle(bg={'color': 'black'})

static api_key(value=None)¶: Set or get the API key. Also set via environment variable GRAPHISTRY_API_KEY.

static api_token(value=None)¶: Set or get the API token. Also set via environment variable GRAPHISTRY_API_TOKEN.

static api_token_refresh_ms(value=None)¶: Set or get the API token refresh interval in milliseconds. None and 0 interpreted as no refreshing.

static api_version(value=None)¶: Set or get the API version: 1 for 1.0 (deprecated), 3 for 2.0. Setting api=2 (protobuf) fully deprecated from the PyGraphistry client. Also set via environment variable GRAPHISTRY_API_VERSION.

static authenticate()¶: Authenticate via already provided configuration (api=1,2). This is called once automatically per session when uploading and rendering a visualization. In api=3, if token_refresh_ms > 0 (defaults to 10min), this starts an automatic refresh loop. In that case, note that a manual .login() is still required every 24hr by default.

static bind(node=None, source=None, destination=None, edge_title=None, edge_label=None, edge_color=None, edge_weight=None, edge_icon=None, edge_size=None, edge_opacity=None, edge_source_color=None, edge_destination_color=None, point_title=None, point_label=None, point_color=None, point_weight=None, point_icon=None, point_size=None, point_opacity=None, point_x=None, point_y=None)¶

Create a base plotter.

Typically called at start of a program. For parameters, see plotter.bind() .

Returns: Plotter
Return type: Plotter

Example

import graphistry
g = graphistry.bind()

static bolt(driver=None)¶

Parameters: driver – Neo4j Driver or arguments for GraphDatabase.driver({…})
Returns: Plotter w/neo4j

Call this to create a Plotter with an overridden neo4j driver.

Example

import graphistry
g = graphistry.bolt({ server: 'bolt://...', auth: ('<username>', '<password>') })

import neo4j
import graphistry

driver = neo4j.GraphDatabase.driver(...)

g = graphistry.bolt(driver)

static certificate_validation(value=None)¶: Enable/Disable SSL certificate validation (True, False). Also set via environment variable GRAPHISTRY_CERTIFICATE_VALIDATION.

static client_protocol_hostname(value=None)¶: Get/set the client protocol+hostname for when display urls (distinct from uploading). Also set via environment variable GRAPHISTRY_CLIENT_PROTOCOL_HOSTNAME. Defaults to hostname and no protocol (reusing environment protocol)

static cosmos(COSMOS_ACCOUNT=None, COSMOS_DB=None, COSMOS_CONTAINER=None, COSMOS_PRIMARY_KEY=None, gremlin_client=None)¶

Provide credentials as arguments, as environment variables, or by providing a gremlinpython client Environment variable names are the same as the constructor argument names If no client provided, create (connect)

Parameters

COSMOS_ACCOUNT (Optional[str]) – cosmos account
COSMOS_DB (Optional[str]) – cosmos db name
COSMOS_CONTAINER (Optional[str]) – cosmos container name
COSMOS_PRIMARY_KEY (Optional[str]) – cosmos key
gremlin_client (Optional[Any]) – optional prebuilt client

Return type

Plotter

Returns

Plotter with data from a cypher query. This call binds source, destination, and node.

Example: Login and plot

import graphistry
(graphistry
    .cosmos(
        COSMOS_ACCOUNT='a',
        COSMOS_DB='b',
        COSMOS_CONTAINER='c',
        COSMOS_PRIMARY_KEY='d')
    .gremlin('g.E().sample(10)')
    .fetch_nodes()  # Fetch properties for nodes
    .plot())

static cypher(query, params={})¶

Parameters

query – a cypher query
params – cypher query arguments

Returns

Plotter with data from a cypher query. This call binds source, destination, and node.

Call this to immediately execute a cypher query and store the graph in the resulting Plotter.

import graphistry
g = graphistry.bolt({ query='MATCH (a)-[r:PAYMENT]->(b) WHERE r.USD > 7000 AND r.USD < 10000 RETURN r ORDER BY r.USD DESC', params={ "AccountId": 10 })

static description(description)¶

Upload description

Parameters: description (str) – Upload description

static drop_graph()¶

Remove all graph nodes and edges from the database

Return type: Plotter

static edges(edges, source=None, destination=None, *args, **kwargs)¶

Specify edge list data and associated edge attribute values. If a callable, will be called with current Plotter and whatever positional+named arguments

Parameters: edges (Pandas dataframe, NetworkX graph, or IGraph graph) – Edges and their attributes, or transform from Plotter to edges
Returns: Plotter
Return type: Plotter

Example

import graphistry
df = pandas.DataFrame({'src': [0,1,2], 'dst': [1,2,0]})
graphistry
    .bind(source='src', destination='dst')
    .edges(df)
    .plot()

Example

import graphistry
df = pandas.DataFrame({'src': [0,1,2], 'dst': [1,2,0]})
graphistry
    .edges(df, 'src', 'dst')
    .plot()

Example

::

import graphistry

def sample_edges(g, n):: return g._edges.sample(n)

df = pandas.DataFrame({‘src’: [0,1,2], ‘dst’: [1,2,0]})

graphistry: .edges(df, ‘src’, ‘dst’) .edges(sample_edges, n=2) .edges(sample_edges, None, None, 2) # equivalent .plot()

static encode_edge_badge(column, position='TopRight', categorical_mapping=None, continuous_binning=None, default_mapping=None, comparator=None, color=None, bg=None, fg=None, for_current=False, for_default=True, as_text=None, blend_mode=None, style=None, border=None, shape=None)¶

static encode_edge_color(column, palette=None, as_categorical=None, as_continuous=None, categorical_mapping=None, default_mapping=None, for_default=True, for_current=False)¶

Set edge color with more control than bind()

Parameters

column (str) – Data column name
palette (Optional[list]) – Optional list of color-like strings. Ex: [“black, “#FF0”, “rgb(255,255,255)” ]. Used as a gradient for continuous and round-robin for categorical.
as_categorical (Optional[bool]) – Interpret column values as categorical. Ex: Uses palette via round-robin when more values than palette entries.
as_continuous (Optional[bool]) – Interpret column values as continuous. Ex: Uses palette for an interpolation gradient when more values than palette entries.
categorical_mapping (Optional[dict]) – Mapping from column values to color-like strings. Ex: {“car”: “red”, “truck”: #000”}
default_mapping (Optional[str]) – Augment categorical_mapping with mapping for values not in categorical_mapping. Ex: default_mapping=”gray”.
for_default (Optional[bool]) – Use encoding for when no user override is set. Default on.
for_current (Optional[bool]) – Use encoding as currently active. Clearing the active encoding resets it to default, which may be different. Default on.

Returns

Plotter

Return type

Plotter

Example: See encode_point_color

static encode_edge_icon(column, categorical_mapping=None, continuous_binning=None, default_mapping=None, comparator=None, for_default=True, for_current=False, as_text=False, blend_mode=None, style=None, border=None, shape=None)¶

Set edge icon with more control than bind(). Values from Font Awesome 4 such as “laptop”: https://fontawesome.com/v4.7.0/icons/

Parameters

column (str) – Data column name
categorical_mapping (Optional[dict]) – Mapping from column values to icon name strings. Ex: {“toyota”: ‘car’, “ford”: ‘truck’}
default_mapping (Optional[Union[int,float]]) – Augment categorical_mapping with mapping for values not in categorical_mapping. Ex: default_mapping=50.
for_default (Optional[bool]) – Use encoding for when no user override is set. Default on.
for_current (Optional[bool]) – Use encoding as currently active. Clearing the active encoding resets it to default, which may be different. Default on.
as_text (Optional[bool]) – Values should instead be treated as raw strings, instead of icons and images. (Default False.)
blend_mode (Optional[str]) – CSS blend mode
style (Optional[dict]) – CSS filter properties - opacity, saturation, luminosity, grayscale, and more
border (Optional[dict]) – Border properties - ‘width’, ‘color’, and ‘storke’

Returns

Plotter

Return type

Plotter

Example: Set a string column of icons for the edge icons, same as bind(edge_icon=’my_column’)

g2a = g.encode_edge_icon('my_icons_column')

Example: Map specific values to specific icons, including with a default

g2a = g.encode_edge_icon('brands', categorical_mapping={'toyota': 'car', 'ford': 'truck'})
g2b = g.encode_edge_icon('brands', categorical_mapping={'toyota': 'car', 'ford': 'truck'}, default_mapping='question')

Example: Map countries to abbreviations

g2a = g.encode_edge_icon('country_abbrev', as_text=True)
g2b = g.encode_edge_icon('country', categorical_mapping={'England': 'UK', 'America': 'US'}, default_mapping='')

Example: Border

g2b = g.encode_edge_icon('country', border={'width': 3, color: 'black', 'stroke': 'dashed'}, 'categorical_mapping={'England': 'UK', 'America': 'US'})

static encode_point_badge(column, position='TopRight', categorical_mapping=None, continuous_binning=None, default_mapping=None, comparator=None, color=None, bg=None, fg=None, for_current=False, for_default=True, as_text=None, blend_mode=None, style=None, border=None, shape=None)¶

static encode_point_color(column, palette=None, as_categorical=None, as_continuous=None, categorical_mapping=None, default_mapping=None, for_default=True, for_current=False)¶

Set point color with more control than bind()

Parameters

column (str) – Data column name
palette (Optional[list]) – Optional list of color-like strings. Ex: [“black, “#FF0”, “rgb(255,255,255)” ]. Used as a gradient for continuous and round-robin for categorical.
as_categorical (Optional[bool]) – Interpret column values as categorical. Ex: Uses palette via round-robin when more values than palette entries.
as_continuous (Optional[bool]) – Interpret column values as continuous. Ex: Uses palette for an interpolation gradient when more values than palette entries.
categorical_mapping (Optional[dict]) – Mapping from column values to color-like strings. Ex: {“car”: “red”, “truck”: #000”}
default_mapping (Optional[str]) – Augment categorical_mapping with mapping for values not in categorical_mapping. Ex: default_mapping=”gray”.
for_default (Optional[bool]) – Use encoding for when no user override is set. Default on.
for_current (Optional[bool]) – Use encoding as currently active. Clearing the active encoding resets it to default, which may be different. Default on.

Returns

Plotter

Return type

Plotter

Example: Set a palette-valued column for the color, same as bind(point_color=’my_column’)

g2a = g.encode_point_color('my_int32_palette_column')
g2b = g.encode_point_color('my_int64_rgb_column')

Example: Set a cold-to-hot gradient of along the spectrum blue, yellow, red

g2 = g.encode_point_color('my_numeric_col', palette=["blue", "yellow", "red"], as_continuous=True)

Example: Round-robin sample from 5 colors in hex format

g2 = g.encode_point_color('my_distinctly_valued_col', palette=["#000", "#00F", "#0F0", "#0FF", "#FFF"], as_categorical=True)

Example: Map specific values to specific colors, including with a default

g2a = g.encode_point_color('brands', categorical_mapping={'toyota': 'red', 'ford': 'blue'})
g2a = g.encode_point_color('brands', categorical_mapping={'toyota': 'red', 'ford': 'blue'}, default_mapping='gray')

static encode_point_icon(column, categorical_mapping=None, continuous_binning=None, default_mapping=None, comparator=None, for_default=True, for_current=False, as_text=False, blend_mode=None, style=None, border=None, shape=None)¶

Set node icon with more control than bind(). Values from Font Awesome 4 such as “laptop”: https://fontawesome.com/v4.7.0/icons/

Parameters

column (str) – Data column name
categorical_mapping (Optional[dict]) – Mapping from column values to icon name strings. Ex: {“toyota”: ‘car’, “ford”: ‘truck’}
default_mapping (Optional[Union[int,float]]) – Augment categorical_mapping with mapping for values not in categorical_mapping. Ex: default_mapping=50.
for_default (Optional[bool]) – Use encoding for when no user override is set. Default on.
for_current (Optional[bool]) – Use encoding as currently active. Clearing the active encoding resets it to default, which may be different. Default on.
as_text (Optional[bool]) – Values should instead be treated as raw strings, instead of icons and images. (Default False.)
blend_mode (Optional[str]) – CSS blend mode
style (Optional[dict]) – CSS filter properties - opacity, saturation, luminosity, grayscale, and more
border (Optional[dict]) – Border properties - ‘width’, ‘color’, and ‘storke’

Returns

Plotter

Return type

Plotter

Example: Set a string column of icons for the point icons, same as bind(point_icon=’my_column’)

g2a = g.encode_point_icon('my_icons_column')

Example: Map specific values to specific icons, including with a default

g2a = g.encode_point_icon('brands', categorical_mapping={'toyota': 'car', 'ford': 'truck'})
g2b = g.encode_point_icon('brands', categorical_mapping={'toyota': 'car', 'ford': 'truck'}, default_mapping='question')

Example: Map countries to abbreviations

g2b = g.encode_point_icon('country_abbrev', as_text=True)
g2b = g.encode_point_icon('country', as_text=True, categorical_mapping={'England': 'UK', 'America': 'US'}, default_mapping='')

Example: Border

g2b = g.encode_point_icon('country', border={'width': 3, color: 'black', 'stroke': 'dashed'}, 'categorical_mapping={'England': 'UK', 'America': 'US'})

static encode_point_size(column, categorical_mapping=None, default_mapping=None, for_default=True, for_current=False)¶

Set point size with more control than bind()

Parameters

column (str) – Data column name
categorical_mapping (Optional[dict]) – Mapping from column values to numbers. Ex: {“car”: 100, “truck”: 200}
default_mapping (Optional[Union[int,float]]) – Augment categorical_mapping with mapping for values not in categorical_mapping. Ex: default_mapping=50.
for_default (Optional[bool]) – Use encoding for when no user override is set. Default on.
for_current (Optional[bool]) – Use encoding as currently active. Clearing the active encoding resets it to default, which may be different. Default on.

Returns

Plotter

Return type

Plotter

Example: Set a numerically-valued column for the size, same as bind(point_size=’my_column’)

g2a = g.encode_point_size('my_numeric_column')

Example: Map specific values to specific colors, including with a default

g2a = g.encode_point_size('brands', categorical_mapping={'toyota': 100, 'ford': 200})
g2b = g.encode_point_size('brands', categorical_mapping={'toyota': 100, 'ford': 200}, default_mapping=50)

static from_cugraph(G, node_attributes=None, edge_attributes=None, load_nodes=True, load_edges=True, merge_if_existing=True)¶

Parameters

node_attributes (Optional[List[str]]) –
edge_attributes (Optional[List[str]]) –
load_nodes (bool) –
load_edges (bool) –
merge_if_existing (bool) –

static from_igraph(ig, node_attributes=None, edge_attributes=None, load_nodes=True, load_edges=True)¶

Parameters

node_attributes (Optional[List[str]]) –
edge_attributes (Optional[List[str]]) –

static graph(ig)¶

static gremlin(queries)¶

Run one or more gremlin queries and get back the result as a graph object To support cosmosdb, sends as strings

Example: Login and plot

import graphistry
(graphistry
    .gremlin_client(my_gremlin_client)
    .gremlin('g.E().sample(10)')
    .fetch_nodes()  # Fetch properties for nodes
    .plot())

Parameters: queries (Union[str, Iterable[str]]) –
Return type: Plottable

static gremlin_client(gremlin_client=None)¶

Pass in a generic gremlin python client

Example: Login and plot

import graphistry
from gremlin_python.driver.client import Client

my_gremlin_client = Client(
f'wss://MY_ACCOUNT.gremlin.cosmosdb.azure.com:443/',
'g',
username=f"/dbs/MY_DB/colls/{self.COSMOS_CONTAINER}",
password=self.COSMOS_PRIMARY_KEY,
message_serializer=GraphSONSerializersV2d0())

(graphistry
    .gremlin_client(my_gremlin_client)
    .gremlin('g.E().sample(10)')
    .fetch_nodes()  # Fetch properties for nodes
    .plot())

Parameters: gremlin_client (Optional[Any]) –
Return type: Plotter

static gsql(query, bindings=None, dry_run=False)¶

Run Tigergraph query in interpreted mode and return transformed Plottable

param query

Code to run

type query

str

param bindings

Mapping defining names of returned ‘edges’ and/or ‘nodes’, defaults to @@nodeList and @@edgeList

type bindings

Optional[dict]

param dry_run

Return target URL without running

type dry_run

bool

returns

Plotter

rtype

Plotter
Example: Minimal
import graphistry
tg = graphistry.tigergraph()
tg.gsql("""
INTERPRET QUERY () FOR GRAPH Storage {

    OrAccum<BOOL> @@stop;
    ListAccum<EDGE> @@edgeList;
    SetAccum<vertex> @@set;

    @@set += to_vertex("61921", "Pool");

    Start = @@set;

    while Start.size() > 0 and @@stop == false do

    Start = select t from Start:s-(:e)-:t
    where e.goUpper == TRUE
    accum @@edgeList += e
    having t.type != "Service";
    end;

    print @@edgeList;
}
""").plot()

Example: Full

import graphistry
tg = graphistry.tigergraph()
tg.gsql("""
INTERPRET QUERY () FOR GRAPH Storage {

    OrAccum<BOOL> @@stop;
    ListAccum<EDGE> @@edgeList;
    SetAccum<vertex> @@set;

    @@set += to_vertex("61921", "Pool");

    Start = @@set;

    while Start.size() > 0 and @@stop == false do

    Start = select t from Start:s-(:e)-:t
    where e.goUpper == TRUE
    accum @@edgeList += e
    having t.type != "Service";
    end;

    print @@my_edge_list;
}
""", {'edges': 'my_edge_list'}).plot()

static gsql_endpoint(self, method_name, args={}, bindings=None, db=None, dry_run=False)¶

Invoke Tigergraph stored procedure at a user-definend endpoint and return transformed Plottable

Parameters

method_name (str) – Stored procedure name
args (Optional[dict]) – Named endpoint arguments
bindings (Optional[dict]) – Mapping defining names of returned ‘edges’ and/or ‘nodes’, defaults to @@nodeList and @@edgeList
db (Optional[str]) – Name of the database, defaults to value set in .tigergraph(…)
dry_run (bool) – Return target URL without running

Returns

Plotter

Return type

Plotter

Example: Minimal

import graphistry
tg = graphistry.tigergraph(db='my_db')
tg.gsql_endpoint('neighbors').plot()

Example: Full

import graphistry
tg = graphistry.tigergraph()
tg.gsql_endpoint('neighbors', {'k': 2}, {'edges': 'my_edge_list'}, 'my_db').plot()

Example: Read data

import graphistry
tg = graphistry.tigergraph()
out = tg.gsql_endpoint('neighbors')
(nodes_df, edges_df) = (out._nodes, out._edges)

static hypergraph(raw_events, entity_types=None, opts={}, drop_na=True, drop_edge_attrs=False, verbose=True, direct=False, engine='pandas', npartitions=None, chunksize=None)¶

Transform a dataframe into a hypergraph.

Parameters

raw_events (pandas.DataFrame) – Dataframe to transform (pandas or cudf).
entity_types (Optional[list]) – Columns (strings) to turn into nodes, None signifies all
opts (dict) – See below
drop_edge_attrs (bool) – Whether to include each row’s attributes on its edges, defaults to False (include)
verbose (bool) – Whether to print size information
direct (bool) – Omit hypernode and instead strongly connect nodes in an event
engine (bool) – String (pandas, cudf, …) for engine to use
npartitions (Optional[int]) – For distributed engines, how many coarse-grained pieces to split events into
chunksize (Optional[int]) – For distributed engines, split events after chunksize rows

Create a graph out of the dataframe, and return the graph components as dataframes, and the renderable result Plotter. Hypergraphs reveal relationships between rows and between column values. This transform is useful for lists of events, samples, relationships, and other structured high-dimensional data.

Specify local compute engine by passing engine=’pandas’, ‘cudf’, ‘dask’, ‘dask_cudf’ (default: ‘pandas’). If events are not in that engine’s format, they will be converted into it.

The transform creates a node for every unique value in the entity_types columns (default: all columns). If direct=False (default), every row is also turned into a node. Edges are added to connect every table cell to its originating row’s node, or if direct=True, to the other nodes from the same row. Nodes are given the attribute ‘type’ corresponding to the originating column name, or in the case of a row, ‘EventID’. Options further control the transform, such column category definitions for controlling whether values reocurring in different columns should be treated as one node, or whether to only draw edges between certain column type pairs.

Consider a list of events. Each row represents a distinct event, and each column some metadata about an event. If multiple events have common metadata, they will be transitively connected through those metadata values. The layout algorithm will try to cluster the events together. Conversely, if an event has unique metadata, the unique metadata will turn into nodes that only have connections to the event node, and the clustering algorithm will cause them to form a ring around the event node.

Best practice is to set EVENTID to a row’s unique ID, SKIP to all non-categorical columns (or entity_types to all categorical columns), and CATEGORY to group columns with the same kinds of values.

To prevent creating nodes for null values, set drop_na=True. Some dataframe engines may have undesirable null handling, and recommend replacing None values with np.nan .

The optional opts={...} configuration options are:

‘EVENTID’: Column name to inspect for a row ID. By default, uses the row index.
‘CATEGORIES’: Dictionary mapping a category name to inhabiting columns. E.g., {‘IP’: [‘srcAddress’, ‘dstAddress’]}. If the same IP appears in both columns, this makes the transform generate one node for it, instead of one for each column.
‘DELIM’: When creating node IDs, defines the separator used between the column name and node value
‘SKIP’: List of column names to not turn into nodes. For example, dates and numbers are often skipped.
‘EDGES’: For direct=True, instead of making all edges, pick column pairs. E.g., {‘a’: [‘b’, ‘d’], ‘d’: [‘d’]} creates edges between columns a->b and a->d, and self-edges d->d.

Returns: {‘entities’: DF, ‘events’: DF, ‘edges’: DF, ‘nodes’: DF, ‘graph’: Plotter}
Return type: dict

Example: Connect user<-row->boss

import graphistry
users_df = pd.DataFrame({'user': ['a','b','x'], 'boss': ['x', 'x', 'y']})
h = graphistry.hypergraph(users_df)
g = h['graph'].plot()

Example: Connect user->boss

import graphistry
users_df = pd.DataFrame({'user': ['a','b','x'], 'boss': ['x', 'x', 'y']})
h = graphistry.hypergraph(users_df, direct=True)
g = h['graph'].plot()

Example: Connect user<->boss

import graphistry
users_df = pd.DataFrame({'user': ['a','b','x'], 'boss': ['x', 'x', 'y']})
h = graphistry.hypergraph(users_df, direct=True, opts={'EDGES': {'user': ['boss'], 'boss': ['user']}})
g = h['graph'].plot()

Example: Only consider some columns for nodes

import graphistry
users_df = pd.DataFrame({'user': ['a','b','x'], 'boss': ['x', 'x', 'y']})
h = graphistry.hypergraph(users_df, entity_types=['boss'])
g = h['graph'].plot()

Example: Collapse matching user::<id> and boss::<id> nodes into one person::<id> node

import graphistry
users_df = pd.DataFrame({'user': ['a','b','x'], 'boss': ['x', 'x', 'y']})
h = graphistry.hypergraph(users_df, opts={'CATEGORIES': {'person': ['user', 'boss']}})
g = h['graph'].plot()

Example: Use cudf engine instead of pandas

import cudf, graphistry
users_gdf = cudf.DataFrame({'user': ['a','b','x'], 'boss': ['x', 'x', 'y']})
h = graphistry.hypergraph(users_gdf, engine='cudf')
g = h['graph'].plot()

Parameters

entity_types (Optional[List[str]]) –
opts (dict) –
drop_na (bool) –
drop_edge_attrs (bool) –
verbose (bool) –
direct (bool) –
engine (str) –
npartitions (Optional[int]) –
chunksize (Optional[int]) –

static infer_labels(self)¶

Returns: Plotter w/neo4j

Prefers point_title/point_label if available
Fallback to node id
Raises exception if no nodes available, no likely candidates, and no matching node id fallback

Example

import graphistry
g = graphistry.nodes(pd.read_csv('nodes.csv'), 'id_col').infer_labels()
g.plot()

static layout_settings(play=None, locked_x=None, locked_y=None, locked_r=None, left=None, top=None, right=None, bottom=None, lin_log=None, strong_gravity=None, dissuade_hubs=None, edge_influence=None, precision_vs_speed=None, gravity=None, scaling_ratio=None)¶

Set layout options. Additive over previous settings.

Corresponds to options at https://hub.graphistry.com/docs/api/1/rest/url/#urloptions

Example: Animated radial layout

import graphistry, pandas as pd
edges = pd.DataFrame({'s': ['a','b','c','d'], 'boss': ['c','c','e','e']})
nodes = pd.DataFrame({
    'n': ['a', 'b', 'c', 'd', 'e'],
    'y': [1,   1,   2,   3,   4],
    'x': [1,   1,   0,   0,   0],
})
g = (graphistry
    .edges(edges, 's', 'd')
    .nodes(nodes, 'n')
    .layout_settings(locked_r=True, play=2000)
g.plot()

Parameters

play (Optional[int]) –
locked_x (Optional[bool]) –
locked_y (Optional[bool]) –
locked_r (Optional[bool]) –
left (Optional[float]) –
top (Optional[float]) –
right (Optional[float]) –
bottom (Optional[float]) –
lin_log (Optional[bool]) –
strong_gravity (Optional[bool]) –
dissuade_hubs (Optional[bool]) –
edge_influence (Optional[float]) –
precision_vs_speed (Optional[float]) –
gravity (Optional[float]) –
scaling_ratio (Optional[float]) –

static login(username, password, org_name=None, fail_silent=False)¶: Authenticate and set token for reuse (api=3). If token_refresh_ms (default: 10min), auto-refreshes token. By default, must be reinvoked within 24hr.

static name(name)¶

Upload name

Parameters: name (str) – Upload name

static neptune(NEPTUNE_READER_HOST=None, NEPTUNE_READER_PORT=None, NEPTUNE_READER_PROTOCOL='wss', endpoint=None, gremlin_client=None)¶

Provide credentials as arguments, as environment variables, or by providing a gremlinpython client Environment variable names are the same as the constructor argument names If endpoint provided, do not need host/port/protocol If no client provided, create (connect)

Example: Login and plot via parrams

import graphistry
(graphistry
    .neptune(
        NEPTUNE_READER_PROTOCOL='wss'
        NEPTUNE_READER_HOST='neptunedbcluster-xyz.cluster-ro-abc.us-east-1.neptune.amazonaws.com'
        NEPTUNE_READER_PORT='8182'
    )
    .gremlin('g.E().sample(10)')
    .fetch_nodes()  # Fetch properties for nodes
    .plot())

Example: Login and plot via env vars

import graphistry
(graphistry
    .neptune()
    .gremlin('g.E().sample(10)')
    .fetch_nodes()  # Fetch properties for nodes
    .plot())

Example: Login and plot via endpoint

import graphistry
(graphistry
    .neptune(endpoint='wss://neptunedbcluster-xyz.cluster-ro-abc.us-east-1.neptune.amazonaws.com:8182/gremlin')
    .gremlin('g.E().sample(10)')
    .fetch_nodes()  # Fetch properties for nodes
    .plot())

Example: Login and plot via client

import graphistry
(graphistry
    .neptune(gremlin_client=client)
    .gremlin('g.E().sample(10)')
    .fetch_nodes()  # Fetch properties for nodes
    .plot())

Parameters

NEPTUNE_READER_HOST (Optional[str]) –
NEPTUNE_READER_PORT (Optional[str]) –
NEPTUNE_READER_PROTOCOL (Optional[str]) –
endpoint (Optional[str]) –
gremlin_client (Optional[Any]) –

Return type

Plotter

static nodes(nodes, node=None, *args, **kwargs)¶

Specify the set of nodes and associated data. If a callable, will be called with current Plotter and whatever positional+named arguments

Must include any nodes referenced in the edge list.

Parameters: nodes (Pandas dataframe or Callable) – Nodes and their attributes.
Returns: Plotter
Return type: Plotter

Example

import graphistry

es = pandas.DataFrame({'src': [0,1,2], 'dst': [1,2,0]})
g = graphistry
    .bind(source='src', destination='dst')
    .edges(es)

vs = pandas.DataFrame({'v': [0,1,2], 'lbl': ['a', 'b', 'c']})
g = g.bind(node='v').nodes(vs)

g.plot()

Example

import graphistry

es = pandas.DataFrame({'src': [0,1,2], 'dst': [1,2,0]})
g = graphistry.edges(es, 'src', 'dst')

vs = pandas.DataFrame({'v': [0,1,2], 'lbl': ['a', 'b', 'c']})
g = g.nodes(vs, 'v)

g.plot()

Example

::

import graphistry

def sample_nodes(g, n):: return g._nodes.sample(n)

df = pandas.DataFrame({‘id’: [0,1,2], ‘v’: [1,2,0]})

graphistry: .nodes(df, ‘id’) ..nodes(sample_nodes, n=2) ..nodes(sample_nodes, None, 2) # equivalent .plot()

static nodexl(xls_or_url, source='default', engine=None, verbose=False)¶

Parameters

xls_or_url – file/http path string to a nodexl-generated xls, or a pandas ExcelFile() object
source – optionally activate binding by string name for a known nodexl data source (‘twitter’, ‘wikimedia’)
engine – optionally set a pandas Excel engine
verbose – optionally enable printing progress by overriding to True

static not_implemented_thunk()¶

static org_name(value=None)¶: Set or get the org_name when register/login.

static pipe(graph_transform, *args, **kwargs)¶

Create new Plotter derived from current

Parameters: graph_transform (Callable) –

Example: Simple

import graphistry

def fill_missing_bindings(g, source='src', destination='dst):
    return g.bind(source=source, destination=destination)

graphistry
    .edges(pandas.DataFrame({'src': [0,1,2], 'd': [1,2,0]}))
    .pipe(fill_missing_bindings, destination='d')  # binds 'src'
    .plot()

Return type: Plottable

static privacy(mode=None, notify=None, invited_users=None, mode_action=None, message=None)¶

Set global default sharing mode

Parameters

mode (str) – Either “private” or “public” or “organization”
notify (bool) – Whether to email the recipient(s) upon upload
invited_users (List) – List of recipients, where each is {“email”: str, “action”: str} and action is “10” (view) or “20” (edit)
mode_action (str) – Only used when mode=”organization”, action for sharing within organization, “10” (view) or “20” (edit), default is “20”

Requires an account with sharing capabilities.

Shared datasets will appear in recipients’ galleries.

If mode is set to “private”, only accounts in invited_users list can access. Mode “public” permits viewing by any user with the URL.

Action “10” (view) gives read access, while action “20” (edit) gives edit access, like changing the sharing mode.

When notify is true, uploads will trigger notification emails to invitees. Email will use visualization’s “.name()”

Example: Limit visualizations to current user

import graphistry
graphistry.register(api=3, username='myuser', password='mypassword')
graphistry.privacy()  # default uploads to mode="private"

#Subsequent uploads default to using .privacy() settings
users_df = pd.DataFrame({'user': ['a','b','x'], 'boss': ['x', 'x', 'y']})
h = graphistry.hypergraph(users_df, direct=True)
g = h['graph'].plot()

Example: Default to publicly viewable visualizations

import graphistry
graphistry.register(api=3, username='myuser', password='mypassword')
#graphistry.privacy(mode="public")  # can skip calling .privacy() for this default

#Subsequent uploads default to using .privacy() settings
users_df = pd.DataFrame({'user': ['a','b','x'], 'boss': ['x', 'x', 'y']})
h = graphistry.hypergraph(users_df, direct=True)
g = h['graph'].plot()

Example: Default to sharing with select teammates, and keep notifications opt-in

import graphistry
graphistry.register(api=3, username='myuser', password='mypassword')
graphistry.privacy(
    mode="private",
    invited_users=[
        {"email": "friend1@acme.org", "action": "10"}, # view
        {"email": "friend2@acme.org", "action": "20"}, # edit
    ],
    notify=False)

#Subsequent uploads default to using .privacy() settings
users_df = pd.DataFrame({'user': ['a','b','x'], 'boss': ['x', 'x', 'y']})
h = graphistry.hypergraph(users_df, direct=True)
g = h['graph'].plot()

Example: Keep visualizations public and email notifications upon upload

import graphistry
graphistry.register(api=3, username='myuser', password='mypassword')
graphistry.privacy(
    mode="public",
    invited_users=[
        {"email": "friend1@acme.org", "action": "10"}, # view
        {"email": "friend2@acme.org", "action": "20"}, # edit
    ],
    notify=True)

#Subsequent uploads default to using .privacy() settings
users_df = pd.DataFrame({'user': ['a','b','x'], 'boss': ['x', 'x', 'y']})
h = graphistry.hypergraph(users_df, direct=True)
g = h['graph']
g = g.name('my cool viz')  # For friendlier invitations
g.plot()

Parameters: message (Optional[str]) –

static protocol(value=None)¶: Set or get the protocol (‘http’ or ‘https’). Set automatically when using a server alias. Also set via environment variable GRAPHISTRY_PROTOCOL.

static refresh(token=None, fail_silent=False)¶: Use self or provided JWT token to get a fresher one. If self token, internalize upon refresh.

static register(key=None, username=None, password=None, token=None, server=None, protocol=None, api=None, certificate_validation=None, bolt=None, token_refresh_ms=600000, store_token_creds_in_memory=None, client_protocol_hostname=None, org_name=None)¶

API key registration and server selection

Changing the key effects all derived Plotter instances.

Provide one of key (deprecated api=1), username/password (api=3) or temporary token (api=3).

Parameters

key (Optional[str]) – API key (deprecated 1.0 API)
username (Optional[str]) – Account username (2.0 API).
password (Optional[str]) – Account password (2.0 API).
token (Optional[str]) – Valid Account JWT token (2.0). Provide token, or username/password, but not both.
server (Optional[str]) – URL of the visualization server.
protocol (Optional[str]) – Protocol to use for server uploaders, defaults to “https”.
api (Optional[Literal[1, 3]]) – API version to use, defaults to 1 (deprecated slow json 1.0 API), prefer 3 (2.0 API with Arrow+JWT)
certificate_validation (Optional[bool]) – Override default-on check for valid TLS certificate by setting to True.
bolt (Union[dict, Any]) – Neo4j bolt information. Optional driver or named constructor arguments for instantiating a new one.
protocol – Protocol used to contact visualization server, defaults to “https”.
token_refresh_ms (int) – Ignored for now; JWT token auto-refreshed on plot() calls.
store_token_creds_in_memory (Optional[bool]) – Store username/password in-memory for JWT token refreshes (Token-originated have a hard limit, so always-on requires creds somewhere)
client_protocol_hostname (Optional[str]) – Override protocol and host shown in browser. Defaults to protocol/server or envvar GRAPHISTRY_CLIENT_PROTOCOL_HOSTNAME.
org_name (Optional[str]) – Set login organization’s name(slug). Defaults to user’s personal organization.

Returns

None.

Return type

None

Example: Standard (2.0 api by username/password with org_name)

import graphistry
graphistry.register(api=3, protocol='http', server='200.1.1.1', username='person', password='pwd', org_name="org-name")

Example: Standard (2.0 api by username/password) without org_name

import graphistry
graphistry.register(api=3, protocol='http', server='200.1.1.1', username='person', password='pwd')

Example: Standard (2.0 api by token)

import graphistry
graphistry.register(api=3, protocol='http', server='200.1.1.1', token='abc')

Example: Remote browser to Graphistry-provided notebook server (2.0)

import graphistry
graphistry.register(api=3, protocol='http', server='nginx', client_protocol_hostname='https://my.site.com', token='abc')

Example: Standard (1.0)

import graphistry
graphistry.register(api=1, key="my api key")

relogin()¶

static scene_settings(menu=None, info=None, show_arrows=None, point_size=None, edge_curvature=None, edge_opacity=None, point_opacity=None)¶

Parameters

menu (Optional[bool]) –
info (Optional[bool]) –
show_arrows (Optional[bool]) –
point_size (Optional[float]) –
edge_curvature (Optional[float]) –
edge_opacity (Optional[float]) –
point_opacity (Optional[float]) –

static server(value=None)¶: Get the hostname of the server or set the server using hostname or aliases. Also set via environment variable GRAPHISTRY_HOSTNAME.

static set_bolt_driver(driver=None)¶

static settings(height=None, url_params={}, render=None)¶

static store_token_creds_in_memory(value=None)¶: Cache credentials for JWT token access. Default off due to not being safe.

static style(bg=None, fg=None, logo=None, page=None)¶

Creates a base plotter with some style settings.

For parameters, see plotter.style.

Returns: Plotter
Return type: Plotter

Example

import graphistry
graphistry.style(bg={'color': 'black'})

static tigergraph(protocol='http', server='localhost', web_port=14240, api_port=9000, db=None, user='tigergraph', pwd='tigergraph', verbose=False)¶

Register Tigergraph connection setting defaults

Parameters

protocol (Optional[str]) – Protocol used to contact the database.
server (Optional[str]) – Domain of the database
web_port (Optional[int]) –
api_port (Optional[int]) –
db (Optional[str]) – Name of the database
user (Optional[str]) –
pwd (Optional[str]) –
verbose (Optional[bool]) – Whether to print operations

Returns

Plotter

Return type

Plotter

Example: Standard

import graphistry
tg = graphistry.tigergraph(protocol='https', server='acme.com', db='my_db', user='alice', pwd='tigergraph2')

static verify_token(token=None, fail_silent=False)¶

Return True iff current or provided token is still valid

Return type: bool

graphistry.pygraphistry.addStyle(bg=None, fg=None, logo=None, page=None)¶

Creates a base plotter with some style settings.

For parameters, see plotter.addStyle.

Returns: Plotter
Return type: Plotter

Example

import graphistry
graphistry.addStyle(bg={'color': 'black'})

graphistry.pygraphistry.api_token(value=None)¶: Set or get the API token. Also set via environment variable GRAPHISTRY_API_TOKEN.

graphistry.pygraphistry.bind(node=None, source=None, destination=None, edge_title=None, edge_label=None, edge_color=None, edge_weight=None, edge_icon=None, edge_size=None, edge_opacity=None, edge_source_color=None, edge_destination_color=None, point_title=None, point_label=None, point_color=None, point_weight=None, point_icon=None, point_size=None, point_opacity=None, point_x=None, point_y=None)¶

Create a base plotter.

Typically called at start of a program. For parameters, see plotter.bind() .

Returns: Plotter
Return type: Plotter

Example

import graphistry
g = graphistry.bind()

graphistry.pygraphistry.bolt(driver=None)¶

Parameters: driver – Neo4j Driver or arguments for GraphDatabase.driver({…})
Returns: Plotter w/neo4j

Call this to create a Plotter with an overridden neo4j driver.

Example

import graphistry
g = graphistry.bolt({ server: 'bolt://...', auth: ('<username>', '<password>') })

import neo4j
import graphistry

driver = neo4j.GraphDatabase.driver(...)

g = graphistry.bolt(driver)

graphistry.pygraphistry.client_protocol_hostname(value=None)¶: Get/set the client protocol+hostname for when display urls (distinct from uploading). Also set via environment variable GRAPHISTRY_CLIENT_PROTOCOL_HOSTNAME. Defaults to hostname and no protocol (reusing environment protocol)

graphistry.pygraphistry.cosmos(COSMOS_ACCOUNT=None, COSMOS_DB=None, COSMOS_CONTAINER=None, COSMOS_PRIMARY_KEY=None, gremlin_client=None)¶

Provide credentials as arguments, as environment variables, or by providing a gremlinpython client Environment variable names are the same as the constructor argument names If no client provided, create (connect)

Parameters

COSMOS_ACCOUNT (Optional[str]) – cosmos account
COSMOS_DB (Optional[str]) – cosmos db name
COSMOS_CONTAINER (Optional[str]) – cosmos container name
COSMOS_PRIMARY_KEY (Optional[str]) – cosmos key
gremlin_client (Optional[Any]) – optional prebuilt client

Return type

Plotter

Returns

Plotter with data from a cypher query. This call binds source, destination, and node.

Example: Login and plot

import graphistry
(graphistry
    .cosmos(
        COSMOS_ACCOUNT='a',
        COSMOS_DB='b',
        COSMOS_CONTAINER='c',
        COSMOS_PRIMARY_KEY='d')
    .gremlin('g.E().sample(10)')
    .fetch_nodes()  # Fetch properties for nodes
    .plot())

graphistry.pygraphistry.cypher(query, params={})¶

Parameters

query – a cypher query
params – cypher query arguments

Returns

Plotter with data from a cypher query. This call binds source, destination, and node.

Call this to immediately execute a cypher query and store the graph in the resulting Plotter.

import graphistry
g = graphistry.bolt({ query='MATCH (a)-[r:PAYMENT]->(b) WHERE r.USD > 7000 AND r.USD < 10000 RETURN r ORDER BY r.USD DESC', params={ "AccountId": 10 })

graphistry.pygraphistry.description(description)¶

Upload description

Parameters: description (str) – Upload description

graphistry.pygraphistry.drop_graph()¶

Remove all graph nodes and edges from the database

Return type: Plotter

graphistry.pygraphistry.edges(edges, source=None, destination=None, *args, **kwargs)¶

Specify edge list data and associated edge attribute values. If a callable, will be called with current Plotter and whatever positional+named arguments

Parameters: edges (Pandas dataframe, NetworkX graph, or IGraph graph) – Edges and their attributes, or transform from Plotter to edges
Returns: Plotter
Return type: Plotter

Example

import graphistry
df = pandas.DataFrame({'src': [0,1,2], 'dst': [1,2,0]})
graphistry
    .bind(source='src', destination='dst')
    .edges(df)
    .plot()

Example

import graphistry
df = pandas.DataFrame({'src': [0,1,2], 'dst': [1,2,0]})
graphistry
    .edges(df, 'src', 'dst')
    .plot()

Example

::

import graphistry

def sample_edges(g, n):: return g._edges.sample(n)

df = pandas.DataFrame({‘src’: [0,1,2], ‘dst’: [1,2,0]})

graphistry: .edges(df, ‘src’, ‘dst’) .edges(sample_edges, n=2) .edges(sample_edges, None, None, 2) # equivalent .plot()

graphistry.pygraphistry.encode_edge_badge(column, position='TopRight', categorical_mapping=None, continuous_binning=None, default_mapping=None, comparator=None, color=None, bg=None, fg=None, for_current=False, for_default=True, as_text=None, blend_mode=None, style=None, border=None, shape=None)¶

graphistry.pygraphistry.encode_edge_color(column, palette=None, as_categorical=None, as_continuous=None, categorical_mapping=None, default_mapping=None, for_default=True, for_current=False)¶

Set edge color with more control than bind()

Parameters

column (str) – Data column name
palette (Optional[list]) – Optional list of color-like strings. Ex: [“black, “#FF0”, “rgb(255,255,255)” ]. Used as a gradient for continuous and round-robin for categorical.
as_categorical (Optional[bool]) – Interpret column values as categorical. Ex: Uses palette via round-robin when more values than palette entries.
as_continuous (Optional[bool]) – Interpret column values as continuous. Ex: Uses palette for an interpolation gradient when more values than palette entries.
categorical_mapping (Optional[dict]) – Mapping from column values to color-like strings. Ex: {“car”: “red”, “truck”: #000”}
default_mapping (Optional[str]) – Augment categorical_mapping with mapping for values not in categorical_mapping. Ex: default_mapping=”gray”.
for_default (Optional[bool]) – Use encoding for when no user override is set. Default on.
for_current (Optional[bool]) – Use encoding as currently active. Clearing the active encoding resets it to default, which may be different. Default on.

Returns

Plotter

Return type

Plotter

Example: See encode_point_color

graphistry.pygraphistry.encode_edge_icon(column, categorical_mapping=None, continuous_binning=None, default_mapping=None, comparator=None, for_default=True, for_current=False, as_text=False, blend_mode=None, style=None, border=None, shape=None)¶

Set edge icon with more control than bind(). Values from Font Awesome 4 such as “laptop”: https://fontawesome.com/v4.7.0/icons/

Parameters

column (str) – Data column name
categorical_mapping (Optional[dict]) – Mapping from column values to icon name strings. Ex: {“toyota”: ‘car’, “ford”: ‘truck’}
default_mapping (Optional[Union[int,float]]) – Augment categorical_mapping with mapping for values not in categorical_mapping. Ex: default_mapping=50.
for_default (Optional[bool]) – Use encoding for when no user override is set. Default on.
for_current (Optional[bool]) – Use encoding as currently active. Clearing the active encoding resets it to default, which may be different. Default on.
as_text (Optional[bool]) – Values should instead be treated as raw strings, instead of icons and images. (Default False.)
blend_mode (Optional[str]) – CSS blend mode
style (Optional[dict]) – CSS filter properties - opacity, saturation, luminosity, grayscale, and more
border (Optional[dict]) – Border properties - ‘width’, ‘color’, and ‘storke’

Returns

Plotter

Return type

Plotter

Example: Set a string column of icons for the edge icons, same as bind(edge_icon=’my_column’)

g2a = g.encode_edge_icon('my_icons_column')

Example: Map specific values to specific icons, including with a default

g2a = g.encode_edge_icon('brands', categorical_mapping={'toyota': 'car', 'ford': 'truck'})
g2b = g.encode_edge_icon('brands', categorical_mapping={'toyota': 'car', 'ford': 'truck'}, default_mapping='question')

Example: Map countries to abbreviations

g2a = g.encode_edge_icon('country_abbrev', as_text=True)
g2b = g.encode_edge_icon('country', categorical_mapping={'England': 'UK', 'America': 'US'}, default_mapping='')

Example: Border

g2b = g.encode_edge_icon('country', border={'width': 3, color: 'black', 'stroke': 'dashed'}, 'categorical_mapping={'England': 'UK', 'America': 'US'})

graphistry.pygraphistry.encode_point_badge(column, position='TopRight', categorical_mapping=None, continuous_binning=None, default_mapping=None, comparator=None, color=None, bg=None, fg=None, for_current=False, for_default=True, as_text=None, blend_mode=None, style=None, border=None, shape=None)¶

graphistry.pygraphistry.encode_point_color(column, palette=None, as_categorical=None, as_continuous=None, categorical_mapping=None, default_mapping=None, for_default=True, for_current=False)¶

Set point color with more control than bind()

Parameters

column (str) – Data column name
palette (Optional[list]) – Optional list of color-like strings. Ex: [“black, “#FF0”, “rgb(255,255,255)” ]. Used as a gradient for continuous and round-robin for categorical.
as_categorical (Optional[bool]) – Interpret column values as categorical. Ex: Uses palette via round-robin when more values than palette entries.
as_continuous (Optional[bool]) – Interpret column values as continuous. Ex: Uses palette for an interpolation gradient when more values than palette entries.
categorical_mapping (Optional[dict]) – Mapping from column values to color-like strings. Ex: {“car”: “red”, “truck”: #000”}
default_mapping (Optional[str]) – Augment categorical_mapping with mapping for values not in categorical_mapping. Ex: default_mapping=”gray”.
for_default (Optional[bool]) – Use encoding for when no user override is set. Default on.
for_current (Optional[bool]) – Use encoding as currently active. Clearing the active encoding resets it to default, which may be different. Default on.

Returns

Plotter

Return type

Plotter

Example: Set a palette-valued column for the color, same as bind(point_color=’my_column’)

g2a = g.encode_point_color('my_int32_palette_column')
g2b = g.encode_point_color('my_int64_rgb_column')

Example: Set a cold-to-hot gradient of along the spectrum blue, yellow, red

g2 = g.encode_point_color('my_numeric_col', palette=["blue", "yellow", "red"], as_continuous=True)

Example: Round-robin sample from 5 colors in hex format

g2 = g.encode_point_color('my_distinctly_valued_col', palette=["#000", "#00F", "#0F0", "#0FF", "#FFF"], as_categorical=True)

Example: Map specific values to specific colors, including with a default

g2a = g.encode_point_color('brands', categorical_mapping={'toyota': 'red', 'ford': 'blue'})
g2a = g.encode_point_color('brands', categorical_mapping={'toyota': 'red', 'ford': 'blue'}, default_mapping='gray')

graphistry.pygraphistry.encode_point_icon(column, categorical_mapping=None, continuous_binning=None, default_mapping=None, comparator=None, for_default=True, for_current=False, as_text=False, blend_mode=None, style=None, border=None, shape=None)¶

Set node icon with more control than bind(). Values from Font Awesome 4 such as “laptop”: https://fontawesome.com/v4.7.0/icons/

Parameters

column (str) – Data column name
categorical_mapping (Optional[dict]) – Mapping from column values to icon name strings. Ex: {“toyota”: ‘car’, “ford”: ‘truck’}
default_mapping (Optional[Union[int,float]]) – Augment categorical_mapping with mapping for values not in categorical_mapping. Ex: default_mapping=50.
for_default (Optional[bool]) – Use encoding for when no user override is set. Default on.
for_current (Optional[bool]) – Use encoding as currently active. Clearing the active encoding resets it to default, which may be different. Default on.
as_text (Optional[bool]) – Values should instead be treated as raw strings, instead of icons and images. (Default False.)
blend_mode (Optional[str]) – CSS blend mode
style (Optional[dict]) – CSS filter properties - opacity, saturation, luminosity, grayscale, and more
border (Optional[dict]) – Border properties - ‘width’, ‘color’, and ‘storke’

Returns

Plotter

Return type

Plotter

Example: Set a string column of icons for the point icons, same as bind(point_icon=’my_column’)

g2a = g.encode_point_icon('my_icons_column')

Example: Map specific values to specific icons, including with a default

g2a = g.encode_point_icon('brands', categorical_mapping={'toyota': 'car', 'ford': 'truck'})
g2b = g.encode_point_icon('brands', categorical_mapping={'toyota': 'car', 'ford': 'truck'}, default_mapping='question')

Example: Map countries to abbreviations

g2b = g.encode_point_icon('country_abbrev', as_text=True)
g2b = g.encode_point_icon('country', as_text=True, categorical_mapping={'England': 'UK', 'America': 'US'}, default_mapping='')

Example: Border

g2b = g.encode_point_icon('country', border={'width': 3, color: 'black', 'stroke': 'dashed'}, 'categorical_mapping={'England': 'UK', 'America': 'US'})

graphistry.pygraphistry.encode_point_size(column, categorical_mapping=None, default_mapping=None, for_default=True, for_current=False)¶

Set point size with more control than bind()

Parameters

column (str) – Data column name
categorical_mapping (Optional[dict]) – Mapping from column values to numbers. Ex: {“car”: 100, “truck”: 200}
default_mapping (Optional[Union[int,float]]) – Augment categorical_mapping with mapping for values not in categorical_mapping. Ex: default_mapping=50.
for_default (Optional[bool]) – Use encoding for when no user override is set. Default on.
for_current (Optional[bool]) – Use encoding as currently active. Clearing the active encoding resets it to default, which may be different. Default on.

Returns

Plotter

Return type

Plotter

Example: Set a numerically-valued column for the size, same as bind(point_size=’my_column’)

g2a = g.encode_point_size('my_numeric_column')

Example: Map specific values to specific colors, including with a default

g2a = g.encode_point_size('brands', categorical_mapping={'toyota': 100, 'ford': 200})
g2b = g.encode_point_size('brands', categorical_mapping={'toyota': 100, 'ford': 200}, default_mapping=50)

graphistry.pygraphistry.from_cugraph(G, node_attributes=None, edge_attributes=None, load_nodes=True, load_edges=True, merge_if_existing=True)¶

Parameters

node_attributes (Optional[List[str]]) –
edge_attributes (Optional[List[str]]) –
load_nodes (bool) –
load_edges (bool) –
merge_if_existing (bool) –

graphistry.pygraphistry.from_igraph(ig, node_attributes=None, edge_attributes=None, load_nodes=True, load_edges=True)¶

Parameters

node_attributes (Optional[List[str]]) –
edge_attributes (Optional[List[str]]) –

graphistry.pygraphistry.graph(ig)¶

graphistry.pygraphistry.gremlin(queries)¶

Run one or more gremlin queries and get back the result as a graph object To support cosmosdb, sends as strings

Example: Login and plot

import graphistry
(graphistry
    .gremlin_client(my_gremlin_client)
    .gremlin('g.E().sample(10)')
    .fetch_nodes()  # Fetch properties for nodes
    .plot())

Parameters: queries (Union[str, Iterable[str]]) –
Return type: Plottable

graphistry.pygraphistry.gremlin_client(gremlin_client=None)¶

Pass in a generic gremlin python client

Example: Login and plot

import graphistry
from gremlin_python.driver.client import Client

my_gremlin_client = Client(
f'wss://MY_ACCOUNT.gremlin.cosmosdb.azure.com:443/',
'g',
username=f"/dbs/MY_DB/colls/{self.COSMOS_CONTAINER}",
password=self.COSMOS_PRIMARY_KEY,
message_serializer=GraphSONSerializersV2d0())

(graphistry
    .gremlin_client(my_gremlin_client)
    .gremlin('g.E().sample(10)')
    .fetch_nodes()  # Fetch properties for nodes
    .plot())

Parameters: gremlin_client (Optional[Any]) –
Return type: Plotter

graphistry.pygraphistry.gsql(query, bindings=None, dry_run=False)¶

Run Tigergraph query in interpreted mode and return transformed Plottable

param query

Code to run

type query

str

param bindings

Mapping defining names of returned ‘edges’ and/or ‘nodes’, defaults to @@nodeList and @@edgeList

type bindings

Optional[dict]

param dry_run

Return target URL without running

type dry_run

bool

returns

Plotter

rtype

Plotter
Example: Minimal
import graphistry
tg = graphistry.tigergraph()
tg.gsql("""
INTERPRET QUERY () FOR GRAPH Storage {

    OrAccum<BOOL> @@stop;
    ListAccum<EDGE> @@edgeList;
    SetAccum<vertex> @@set;

    @@set += to_vertex("61921", "Pool");

    Start = @@set;

    while Start.size() > 0 and @@stop == false do

    Start = select t from Start:s-(:e)-:t
    where e.goUpper == TRUE
    accum @@edgeList += e
    having t.type != "Service";
    end;

    print @@edgeList;
}
""").plot()

Example: Full

import graphistry
tg = graphistry.tigergraph()
tg.gsql("""
INTERPRET QUERY () FOR GRAPH Storage {

    OrAccum<BOOL> @@stop;
    ListAccum<EDGE> @@edgeList;
    SetAccum<vertex> @@set;

    @@set += to_vertex("61921", "Pool");

    Start = @@set;

    while Start.size() > 0 and @@stop == false do

    Start = select t from Start:s-(:e)-:t
    where e.goUpper == TRUE
    accum @@edgeList += e
    having t.type != "Service";
    end;

    print @@my_edge_list;
}
""", {'edges': 'my_edge_list'}).plot()

graphistry.pygraphistry.gsql_endpoint(self, method_name, args={}, bindings=None, db=None, dry_run=False)¶

Invoke Tigergraph stored procedure at a user-definend endpoint and return transformed Plottable

Parameters

method_name (str) – Stored procedure name
args (Optional[dict]) – Named endpoint arguments
bindings (Optional[dict]) – Mapping defining names of returned ‘edges’ and/or ‘nodes’, defaults to @@nodeList and @@edgeList
db (Optional[str]) – Name of the database, defaults to value set in .tigergraph(…)
dry_run (bool) – Return target URL without running

Returns

Plotter

Return type

Plotter

Example: Minimal

import graphistry
tg = graphistry.tigergraph(db='my_db')
tg.gsql_endpoint('neighbors').plot()

Example: Full

import graphistry
tg = graphistry.tigergraph()
tg.gsql_endpoint('neighbors', {'k': 2}, {'edges': 'my_edge_list'}, 'my_db').plot()

Example: Read data

import graphistry
tg = graphistry.tigergraph()
out = tg.gsql_endpoint('neighbors')
(nodes_df, edges_df) = (out._nodes, out._edges)

graphistry.pygraphistry.hypergraph(raw_events, entity_types=None, opts={}, drop_na=True, drop_edge_attrs=False, verbose=True, direct=False, engine='pandas', npartitions=None, chunksize=None)¶

Transform a dataframe into a hypergraph.

Parameters

raw_events (pandas.DataFrame) – Dataframe to transform (pandas or cudf).
entity_types (Optional[list]) – Columns (strings) to turn into nodes, None signifies all
opts (dict) – See below
drop_edge_attrs (bool) – Whether to include each row’s attributes on its edges, defaults to False (include)
verbose (bool) – Whether to print size information
direct (bool) – Omit hypernode and instead strongly connect nodes in an event
engine (bool) – String (pandas, cudf, …) for engine to use
npartitions (Optional[int]) – For distributed engines, how many coarse-grained pieces to split events into
chunksize (Optional[int]) – For distributed engines, split events after chunksize rows

Create a graph out of the dataframe, and return the graph components as dataframes, and the renderable result Plotter. Hypergraphs reveal relationships between rows and between column values. This transform is useful for lists of events, samples, relationships, and other structured high-dimensional data.

Specify local compute engine by passing engine=’pandas’, ‘cudf’, ‘dask’, ‘dask_cudf’ (default: ‘pandas’). If events are not in that engine’s format, they will be converted into it.

The transform creates a node for every unique value in the entity_types columns (default: all columns). If direct=False (default), every row is also turned into a node. Edges are added to connect every table cell to its originating row’s node, or if direct=True, to the other nodes from the same row. Nodes are given the attribute ‘type’ corresponding to the originating column name, or in the case of a row, ‘EventID’. Options further control the transform, such column category definitions for controlling whether values reocurring in different columns should be treated as one node, or whether to only draw edges between certain column type pairs.

Consider a list of events. Each row represents a distinct event, and each column some metadata about an event. If multiple events have common metadata, they will be transitively connected through those metadata values. The layout algorithm will try to cluster the events together. Conversely, if an event has unique metadata, the unique metadata will turn into nodes that only have connections to the event node, and the clustering algorithm will cause them to form a ring around the event node.

Best practice is to set EVENTID to a row’s unique ID, SKIP to all non-categorical columns (or entity_types to all categorical columns), and CATEGORY to group columns with the same kinds of values.

To prevent creating nodes for null values, set drop_na=True. Some dataframe engines may have undesirable null handling, and recommend replacing None values with np.nan .

The optional opts={...} configuration options are:

‘EVENTID’: Column name to inspect for a row ID. By default, uses the row index.
‘CATEGORIES’: Dictionary mapping a category name to inhabiting columns. E.g., {‘IP’: [‘srcAddress’, ‘dstAddress’]}. If the same IP appears in both columns, this makes the transform generate one node for it, instead of one for each column.
‘DELIM’: When creating node IDs, defines the separator used between the column name and node value
‘SKIP’: List of column names to not turn into nodes. For example, dates and numbers are often skipped.
‘EDGES’: For direct=True, instead of making all edges, pick column pairs. E.g., {‘a’: [‘b’, ‘d’], ‘d’: [‘d’]} creates edges between columns a->b and a->d, and self-edges d->d.

Returns: {‘entities’: DF, ‘events’: DF, ‘edges’: DF, ‘nodes’: DF, ‘graph’: Plotter}
Return type: dict

Example: Connect user<-row->boss

import graphistry
users_df = pd.DataFrame({'user': ['a','b','x'], 'boss': ['x', 'x', 'y']})
h = graphistry.hypergraph(users_df)
g = h['graph'].plot()

Example: Connect user->boss

import graphistry
users_df = pd.DataFrame({'user': ['a','b','x'], 'boss': ['x', 'x', 'y']})
h = graphistry.hypergraph(users_df, direct=True)
g = h['graph'].plot()

Example: Connect user<->boss

import graphistry
users_df = pd.DataFrame({'user': ['a','b','x'], 'boss': ['x', 'x', 'y']})
h = graphistry.hypergraph(users_df, direct=True, opts={'EDGES': {'user': ['boss'], 'boss': ['user']}})
g = h['graph'].plot()

Example: Only consider some columns for nodes

import graphistry
users_df = pd.DataFrame({'user': ['a','b','x'], 'boss': ['x', 'x', 'y']})
h = graphistry.hypergraph(users_df, entity_types=['boss'])
g = h['graph'].plot()

Example: Collapse matching user::<id> and boss::<id> nodes into one person::<id> node

import graphistry
users_df = pd.DataFrame({'user': ['a','b','x'], 'boss': ['x', 'x', 'y']})
h = graphistry.hypergraph(users_df, opts={'CATEGORIES': {'person': ['user', 'boss']}})
g = h['graph'].plot()

Example: Use cudf engine instead of pandas

import cudf, graphistry
users_gdf = cudf.DataFrame({'user': ['a','b','x'], 'boss': ['x', 'x', 'y']})
h = graphistry.hypergraph(users_gdf, engine='cudf')
g = h['graph'].plot()

Parameters

entity_types (Optional[List[str]]) –
opts (dict) –
drop_na (bool) –
drop_edge_attrs (bool) –
verbose (bool) –
direct (bool) –
engine (str) –
npartitions (Optional[int]) –
chunksize (Optional[int]) –

graphistry.pygraphistry.infer_labels(self)¶

Returns: Plotter w/neo4j

Prefers point_title/point_label if available
Fallback to node id
Raises exception if no nodes available, no likely candidates, and no matching node id fallback

Example

import graphistry
g = graphistry.nodes(pd.read_csv('nodes.csv'), 'id_col').infer_labels()
g.plot()

graphistry.pygraphistry.layout_settings(play=None, locked_x=None, locked_y=None, locked_r=None, left=None, top=None, right=None, bottom=None, lin_log=None, strong_gravity=None, dissuade_hubs=None, edge_influence=None, precision_vs_speed=None, gravity=None, scaling_ratio=None)¶

Set layout options. Additive over previous settings.

Corresponds to options at https://hub.graphistry.com/docs/api/1/rest/url/#urloptions

Example: Animated radial layout

import graphistry, pandas as pd
edges = pd.DataFrame({'s': ['a','b','c','d'], 'boss': ['c','c','e','e']})
nodes = pd.DataFrame({
    'n': ['a', 'b', 'c', 'd', 'e'],
    'y': [1,   1,   2,   3,   4],
    'x': [1,   1,   0,   0,   0],
})
g = (graphistry
    .edges(edges, 's', 'd')
    .nodes(nodes, 'n')
    .layout_settings(locked_r=True, play=2000)
g.plot()

Parameters

play (Optional[int]) –
locked_x (Optional[bool]) –
locked_y (Optional[bool]) –
locked_r (Optional[bool]) –
left (Optional[float]) –
top (Optional[float]) –
right (Optional[float]) –
bottom (Optional[float]) –
lin_log (Optional[bool]) –
strong_gravity (Optional[bool]) –
dissuade_hubs (Optional[bool]) –
edge_influence (Optional[float]) –
precision_vs_speed (Optional[float]) –
gravity (Optional[float]) –
scaling_ratio (Optional[float]) –

graphistry.pygraphistry.login(username, password, org_name=None, fail_silent=False)¶: Authenticate and set token for reuse (api=3). If token_refresh_ms (default: 10min), auto-refreshes token. By default, must be reinvoked within 24hr.

graphistry.pygraphistry.name(name)¶

Upload name

Parameters: name (str) – Upload name

graphistry.pygraphistry.neptune(NEPTUNE_READER_HOST=None, NEPTUNE_READER_PORT=None, NEPTUNE_READER_PROTOCOL='wss', endpoint=None, gremlin_client=None)¶

Provide credentials as arguments, as environment variables, or by providing a gremlinpython client Environment variable names are the same as the constructor argument names If endpoint provided, do not need host/port/protocol If no client provided, create (connect)

Example: Login and plot via parrams

import graphistry
(graphistry
    .neptune(
        NEPTUNE_READER_PROTOCOL='wss'
        NEPTUNE_READER_HOST='neptunedbcluster-xyz.cluster-ro-abc.us-east-1.neptune.amazonaws.com'
        NEPTUNE_READER_PORT='8182'
    )
    .gremlin('g.E().sample(10)')
    .fetch_nodes()  # Fetch properties for nodes
    .plot())

Example: Login and plot via env vars

import graphistry
(graphistry
    .neptune()
    .gremlin('g.E().sample(10)')
    .fetch_nodes()  # Fetch properties for nodes
    .plot())

Example: Login and plot via endpoint

import graphistry
(graphistry
    .neptune(endpoint='wss://neptunedbcluster-xyz.cluster-ro-abc.us-east-1.neptune.amazonaws.com:8182/gremlin')
    .gremlin('g.E().sample(10)')
    .fetch_nodes()  # Fetch properties for nodes
    .plot())

Example: Login and plot via client

import graphistry
(graphistry
    .neptune(gremlin_client=client)
    .gremlin('g.E().sample(10)')
    .fetch_nodes()  # Fetch properties for nodes
    .plot())

Parameters

NEPTUNE_READER_HOST (Optional[str]) –
NEPTUNE_READER_PORT (Optional[str]) –
NEPTUNE_READER_PROTOCOL (Optional[str]) –
endpoint (Optional[str]) –
gremlin_client (Optional[Any]) –

Return type

Plotter

graphistry.pygraphistry.nodes(nodes, node=None, *args, **kwargs)¶

Specify the set of nodes and associated data. If a callable, will be called with current Plotter and whatever positional+named arguments

Must include any nodes referenced in the edge list.

Parameters: nodes (Pandas dataframe or Callable) – Nodes and their attributes.
Returns: Plotter
Return type: Plotter

Example

import graphistry

es = pandas.DataFrame({'src': [0,1,2], 'dst': [1,2,0]})
g = graphistry
    .bind(source='src', destination='dst')
    .edges(es)

vs = pandas.DataFrame({'v': [0,1,2], 'lbl': ['a', 'b', 'c']})
g = g.bind(node='v').nodes(vs)

g.plot()

Example

import graphistry

es = pandas.DataFrame({'src': [0,1,2], 'dst': [1,2,0]})
g = graphistry.edges(es, 'src', 'dst')

vs = pandas.DataFrame({'v': [0,1,2], 'lbl': ['a', 'b', 'c']})
g = g.nodes(vs, 'v)

g.plot()

Example

::

import graphistry

def sample_nodes(g, n):: return g._nodes.sample(n)

df = pandas.DataFrame({‘id’: [0,1,2], ‘v’: [1,2,0]})

graphistry: .nodes(df, ‘id’) ..nodes(sample_nodes, n=2) ..nodes(sample_nodes, None, 2) # equivalent .plot()

graphistry.pygraphistry.nodexl(xls_or_url, source='default', engine=None, verbose=False)¶

Parameters

xls_or_url – file/http path string to a nodexl-generated xls, or a pandas ExcelFile() object
source – optionally activate binding by string name for a known nodexl data source (‘twitter’, ‘wikimedia’)
engine – optionally set a pandas Excel engine
verbose – optionally enable printing progress by overriding to True

graphistry.pygraphistry.org_name(value=None)¶: Set or get the org_name when register/login.

graphistry.pygraphistry.pipe(graph_transform, *args, **kwargs)¶

Create new Plotter derived from current

Parameters: graph_transform (Callable) –

Example: Simple

import graphistry

def fill_missing_bindings(g, source='src', destination='dst):
    return g.bind(source=source, destination=destination)

graphistry
    .edges(pandas.DataFrame({'src': [0,1,2], 'd': [1,2,0]}))
    .pipe(fill_missing_bindings, destination='d')  # binds 'src'
    .plot()

Return type: Plottable

graphistry.pygraphistry.privacy(mode=None, notify=None, invited_users=None, mode_action=None, message=None)¶

Set global default sharing mode

Parameters

mode (str) – Either “private” or “public” or “organization”
notify (bool) – Whether to email the recipient(s) upon upload
invited_users (List) – List of recipients, where each is {“email”: str, “action”: str} and action is “10” (view) or “20” (edit)
mode_action (str) – Only used when mode=”organization”, action for sharing within organization, “10” (view) or “20” (edit), default is “20”

Requires an account with sharing capabilities.

Shared datasets will appear in recipients’ galleries.

If mode is set to “private”, only accounts in invited_users list can access. Mode “public” permits viewing by any user with the URL.

Action “10” (view) gives read access, while action “20” (edit) gives edit access, like changing the sharing mode.

When notify is true, uploads will trigger notification emails to invitees. Email will use visualization’s “.name()”

Example: Limit visualizations to current user

import graphistry
graphistry.register(api=3, username='myuser', password='mypassword')
graphistry.privacy()  # default uploads to mode="private"

#Subsequent uploads default to using .privacy() settings
users_df = pd.DataFrame({'user': ['a','b','x'], 'boss': ['x', 'x', 'y']})
h = graphistry.hypergraph(users_df, direct=True)
g = h['graph'].plot()

Example: Default to publicly viewable visualizations

import graphistry
graphistry.register(api=3, username='myuser', password='mypassword')
#graphistry.privacy(mode="public")  # can skip calling .privacy() for this default

#Subsequent uploads default to using .privacy() settings
users_df = pd.DataFrame({'user': ['a','b','x'], 'boss': ['x', 'x', 'y']})
h = graphistry.hypergraph(users_df, direct=True)
g = h['graph'].plot()

Example: Default to sharing with select teammates, and keep notifications opt-in

import graphistry
graphistry.register(api=3, username='myuser', password='mypassword')
graphistry.privacy(
    mode="private",
    invited_users=[
        {"email": "friend1@acme.org", "action": "10"}, # view
        {"email": "friend2@acme.org", "action": "20"}, # edit
    ],
    notify=False)

#Subsequent uploads default to using .privacy() settings
users_df = pd.DataFrame({'user': ['a','b','x'], 'boss': ['x', 'x', 'y']})
h = graphistry.hypergraph(users_df, direct=True)
g = h['graph'].plot()

Example: Keep visualizations public and email notifications upon upload

import graphistry
graphistry.register(api=3, username='myuser', password='mypassword')
graphistry.privacy(
    mode="public",
    invited_users=[
        {"email": "friend1@acme.org", "action": "10"}, # view
        {"email": "friend2@acme.org", "action": "20"}, # edit
    ],
    notify=True)

#Subsequent uploads default to using .privacy() settings
users_df = pd.DataFrame({'user': ['a','b','x'], 'boss': ['x', 'x', 'y']})
h = graphistry.hypergraph(users_df, direct=True)
g = h['graph']
g = g.name('my cool viz')  # For friendlier invitations
g.plot()

Parameters: message (Optional[str]) –

graphistry.pygraphistry.protocol(value=None)¶: Set or get the protocol (‘http’ or ‘https’). Set automatically when using a server alias. Also set via environment variable GRAPHISTRY_PROTOCOL.

graphistry.pygraphistry.refresh(token=None, fail_silent=False)¶: Use self or provided JWT token to get a fresher one. If self token, internalize upon refresh.

graphistry.pygraphistry.register(key=None, username=None, password=None, token=None, server=None, protocol=None, api=None, certificate_validation=None, bolt=None, token_refresh_ms=600000, store_token_creds_in_memory=None, client_protocol_hostname=None, org_name=None)¶

API key registration and server selection

Changing the key effects all derived Plotter instances.

Provide one of key (deprecated api=1), username/password (api=3) or temporary token (api=3).

Parameters

key (Optional[str]) – API key (deprecated 1.0 API)
username (Optional[str]) – Account username (2.0 API).
password (Optional[str]) – Account password (2.0 API).
token (Optional[str]) – Valid Account JWT token (2.0). Provide token, or username/password, but not both.
server (Optional[str]) – URL of the visualization server.
protocol (Optional[str]) – Protocol to use for server uploaders, defaults to “https”.
api (Optional[Literal[1, 3]]) – API version to use, defaults to 1 (deprecated slow json 1.0 API), prefer 3 (2.0 API with Arrow+JWT)
certificate_validation (Optional[bool]) – Override default-on check for valid TLS certificate by setting to True.
bolt (Union[dict, Any]) – Neo4j bolt information. Optional driver or named constructor arguments for instantiating a new one.
protocol – Protocol used to contact visualization server, defaults to “https”.
token_refresh_ms (int) – Ignored for now; JWT token auto-refreshed on plot() calls.
store_token_creds_in_memory (Optional[bool]) – Store username/password in-memory for JWT token refreshes (Token-originated have a hard limit, so always-on requires creds somewhere)
client_protocol_hostname (Optional[str]) – Override protocol and host shown in browser. Defaults to protocol/server or envvar GRAPHISTRY_CLIENT_PROTOCOL_HOSTNAME.
org_name (Optional[str]) – Set login organization’s name(slug). Defaults to user’s personal organization.

Returns

None.

Return type

None

Example: Standard (2.0 api by username/password with org_name)

import graphistry
graphistry.register(api=3, protocol='http', server='200.1.1.1', username='person', password='pwd', org_name="org-name")

Example: Standard (2.0 api by username/password) without org_name

import graphistry
graphistry.register(api=3, protocol='http', server='200.1.1.1', username='person', password='pwd')

Example: Standard (2.0 api by token)

import graphistry
graphistry.register(api=3, protocol='http', server='200.1.1.1', token='abc')

Example: Remote browser to Graphistry-provided notebook server (2.0)

import graphistry
graphistry.register(api=3, protocol='http', server='nginx', client_protocol_hostname='https://my.site.com', token='abc')

Example: Standard (1.0)

import graphistry
graphistry.register(api=1, key="my api key")

graphistry.pygraphistry.scene_settings(menu=None, info=None, show_arrows=None, point_size=None, edge_curvature=None, edge_opacity=None, point_opacity=None)¶

Parameters

menu (Optional[bool]) –
info (Optional[bool]) –
show_arrows (Optional[bool]) –
point_size (Optional[float]) –
edge_curvature (Optional[float]) –
edge_opacity (Optional[float]) –
point_opacity (Optional[float]) –

graphistry.pygraphistry.server(value=None)¶: Get the hostname of the server or set the server using hostname or aliases. Also set via environment variable GRAPHISTRY_HOSTNAME.

graphistry.pygraphistry.settings(height=None, url_params={}, render=None)¶

graphistry.pygraphistry.store_token_creds_in_memory(value=None)¶: Cache credentials for JWT token access. Default off due to not being safe.

graphistry.pygraphistry.strtobool(val)¶

Parameters: val (Any) –
Return type: bool

graphistry.pygraphistry.style(bg=None, fg=None, logo=None, page=None)¶

Creates a base plotter with some style settings.

For parameters, see plotter.style.

Returns: Plotter
Return type: Plotter

Example

import graphistry
graphistry.style(bg={'color': 'black'})

graphistry.pygraphistry.tigergraph(protocol='http', server='localhost', web_port=14240, api_port=9000, db=None, user='tigergraph', pwd='tigergraph', verbose=False)¶

Register Tigergraph connection setting defaults

Parameters

protocol (Optional[str]) – Protocol used to contact the database.
server (Optional[str]) – Domain of the database
web_port (Optional[int]) –
api_port (Optional[int]) –
db (Optional[str]) – Name of the database
user (Optional[str]) –
pwd (Optional[str]) –
verbose (Optional[bool]) – Whether to print operations

Returns

Plotter

Return type

Plotter

Example: Standard

import graphistry
tg = graphistry.tigergraph(protocol='https', server='acme.com', db='my_db', user='alice', pwd='tigergraph2')

graphistry.pygraphistry.verify_token(token=None, fail_silent=False)¶

Return True iff current or provided token is still valid

Return type: bool

graphistry.arrow_uploader module¶

class graphistry.arrow_uploader.ArrowUploader(server_base_path='http://nginx', view_base_path='http://localhost', name=None, description=None, edges=None, nodes=None, node_encodings=None, edge_encodings=None, token=None, dataset_id=None, metadata=None, certificate_validation=True, org_name=None)¶

Bases: object

Parameters: org_name (Optional[str]) –

arrow_to_buffer(table)¶

Parameters: table (Table) –

cascade_privacy_settings(mode=None, notify=None, invited_users=None, mode_action=None, message=None)¶

Cascade:

local (passed in)
global
hard-coded

Parameters

mode (Optional[str]) –
notify (Optional[bool]) –
invited_users (Optional[List]) –
mode_action (Optional[str]) –
message (Optional[str]) –

property certificate_validation¶

create_dataset(json)¶

property dataset_id¶

Return type: str

property description¶

Return type: str

property edge_encodings¶

property edges¶

Return type: Table

g_to_edge_bindings(g)¶

g_to_edge_encodings(g)¶

g_to_node_bindings(g)¶

g_to_node_encodings(g)¶

login(username, password, org_name=None)¶

maybe_bindings(g, bindings, base={})¶

maybe_post_share_link(g)¶

Skip if never called .privacy() Return True/False based on whether called

Return type: bool

property metadata¶

property name¶

Return type: str

property node_encodings¶

property nodes¶

Return type: Table

property org_name¶

Return type: str

post(as_files=True, memoize=True)¶

Note: likely want to pair with self.maybe_post_share_link(g)

Parameters

as_files (bool) –
memoize (bool) –

post_arrow(arr, graph_type, opts='')¶

Parameters

arr (Table) –
graph_type (str) –
opts (str) –

post_arrow_generic(sub_path, tok, arr, opts='')¶

Parameters

sub_path (str) –
tok (str) –
arr (Table) –

Return type

Response

post_edges_arrow(arr=None, opts='')¶

post_edges_file(file_path, file_type='csv')¶

post_file(file_path, graph_type='edges', file_type='csv')¶

post_g(g, name=None, description=None)¶: Warning: main post() does not call this

post_nodes_arrow(arr=None, opts='')¶

post_nodes_file(file_path, file_type='csv')¶

post_share_link(obj_pk, obj_type='dataset', privacy=None)¶

Set sharing settings. Any settings not passed here will cascade from PyGraphistry or defaults

Parameters

obj_pk (str) –
obj_type (str) –
privacy (Optional[dict]) –

refresh(token=None)¶

property server_base_path¶

Return type: str

property token¶

Return type: str

verify(token=None)¶

Return type: bool

property view_base_path¶

Return type: str

graphistry.ArrowFileUploader module¶

class graphistry.ArrowFileUploader.ArrowFileUploader(uploader)¶

Bases: object

Implement file API with focus on Arrow support

Memoization in this class is based on reference equality, while plotter is based on hash. That means the plotter resolves different-identity value matches, so by the time ArrowFileUploader compares, identities are unified for faster reference-based checks.

Example: Upload files with per-session memoization

uploader : ArrowUploader arr : pa.Table afu = ArrowFileUploader(uploader)

file1_id = afu.create_and_post_file(arr)[0] file2_id = afu.create_and_post_file(arr)[0]

assert file1_id == file2_id # memoizes by default (memory-safe: weak refs)

Example: Explicitly create a file and upload data for it

uploader : ArrowUploader arr : pa.Table afu = ArrowFileUploader(uploader)

file1_id = afu.create_file() afu.post_arrow(arr, file_id)

file2_id = afu.create_file() afu.post_arrow(arr, file_id)

assert file1_id != file2_id

create_and_post_file(arr, file_id=None, file_opts={}, upload_url_opts='erase=true', memoize=True)¶

Create file and upload data for it.

Default upload_url_opts=’erase=true’ throws exceptions on parse errors and deletes upload.

Default memoize=True skips uploading ‘arr’ when previously uploaded in current session

See File REST API for file_opts (file create) and upload_url_opts (file upload)

Parameters

arr (Table) –
file_id (Optional[str]) –
file_opts (dict) –
upload_url_opts (str) –
memoize (bool) –

Return type

Tuple[str, dict]

create_file(file_opts={})¶

Creates File and returns file_id str.

Defauls:

file_type: ‘arrow’

See File REST API for file_opts

Parameters: file_opts (dict) –
Return type: str

post_arrow(arr, file_id, url_opts='erase=true')¶

Upload new data to existing file id

Default url_opts=’erase=true’ throws exceptions on parse errors and deletes upload.

See File REST API for url_opts (file upload)

Parameters

arr (Table) –
file_id (str) –
url_opts (str) –

Return type

dict

uploader: Any = None¶

graphistry.ArrowFileUploader.DF_TO_FILE_ID_CACHE: weakref.WeakKeyDictionary = <WeakKeyDictionary>¶

NOTE: Will switch to pa.Table -> … when RAPIDS upgrades from pyarrow,: which adds weakref support

class graphistry.ArrowFileUploader.MemoizedFileUpload(file_id, output)¶

Bases: object

Parameters

file_id (str) –
output (dict) –

file_id: str¶

output: dict¶

class graphistry.ArrowFileUploader.WrappedTable(arr)¶

Bases: object

Parameters: arr (Table) –

arr: pyarrow.lib.Table¶

graphistry.ArrowFileUploader.cache_arr(arr)¶: Hold reference to most recent memoization entries Hack until RAPIDS supports Arrow 2.0, when pa.Table becomes weakly referenceable