graphistry package¶

Submodules¶

graphistry.plotter module¶

class graphistry.plotter.Plotter¶

Bases: object

Graph plotting class.

Created using Graphistry.bind().

Chained calls successively add data and visual encodings, and end with a plot call.

To streamline reuse and replayable notebooks, Plotter manipulations are immutable. Each chained call returns a new instance that derives from the previous one. The old plotter or the new one can then be used to create different graphs.

When using memoization, for .register(api=3) sessions with .plot(memoize=True), Pandas/cudf arrow coercions are memoized, and file uploads are skipped on same-hash dataframes.

The class supports convenience methods for mixing calls across Pandas, NetworkX, and IGraph.

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

Set general visual styles

See .bind() and .settings(url_params={}) for additional styling options, and style() for another way to set the same attributes.

To facilitate reuse and replayable notebooks, the addStyle() call is chainable. Invocation does not effect the old style: it instead returns a new Plotter instance with the new styles added to the existing ones. Both the old and new styles can then be used for different graphs.

addStyle() will extend the existing style settings, while style() will replace any in the same group

Parameters

fg (dict) – Dictionary {‘blendMode’: str} of any valid CSS blend mode
bg (dict) – Nested dictionary of page background properties. {‘color’: str, ‘gradient’: {‘kind’: str, ‘position’: str, ‘stops’: list }, ‘image’: { ‘url’: str, ‘width’: int, ‘height’: int, ‘blendMode’: str }
logo (dict) – Nested dictionary of logo properties. { ‘url’: str, ‘autoInvert’: bool, ‘position’: str, ‘dimensions’: { ‘maxWidth’: int, ‘maxHeight’: int }, ‘crop’: { ‘top’: int, ‘left’: int, ‘bottom’: int, ‘right’: int }, ‘padding’: { ‘top’: int, ‘left’: int, ‘bottom’: int, ‘right’: int}, ‘style’: str}
page (dict) – Dictionary of page metadata settings. { ‘favicon’: str, ‘title’: str }

Returns

Plotter

Return type

Plotter

Example: Chained merge - results in color, blendMode, and url being set

g2 =  g.addStyle(bg={'color': 'black'}, fg={'blendMode': 'screen'})
g3 = g2.addStyle(bg={'image': {'url': 'http://site.com/watermark.png'}})

Example: Overwrite - results in blendMode multiply

g2 =  g.addStyle(fg={'blendMode': 'screen'})
g3 = g2.addStyle(fg={'blendMode': 'multiply'})

Example: Gradient background

g.addStyle(bg={'gradient': {'kind': 'linear', 'position': 45, 'stops': [['rgb(0,0,0)', '0%'], ['rgb(255,255,255)', '100%']]}})

Example: Page settings

g.addStyle(page={'title': 'Site - {{ name }}', 'favicon': 'http://site.com/logo.ico'})

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

Relate data attributes to graph structure and visual representation.

To facilitate reuse and replayable notebooks, the binding call is chainable. Invocation does not effect the old binding: it instead returns a new Plotter instance with the new bindings added to the existing ones. Both the old and new bindings can then be used for different graphs.

Parameters

source (str) – Attribute containing an edge’s source ID
destination (str) – Attribute containing an edge’s destination ID
node (str) – Attribute containing a node’s ID
edge_title (str) – Attribute overriding edge’s minimized label text. By default, the edge source and destination is used.
edge_label (str) – Attribute overriding edge’s expanded label text. By default, scrollable list of attribute/value mappings.
edge_color (str) – Attribute overriding edge’s color. rgba (int64) or int32 palette index, see palette definitions <https://graphistry.github.io/docs/legacy/api/0.9.2/api.html#extendedpalette>`_ for values. Based on Color Brewer.
edge_source_color (str) – Attribute overriding edge’s source color if no edge_color, as an rgba int64 value.
edge_destination_color (str) – Attribute overriding edge’s destination color if no edge_color, as an rgba int64 value.
edge_weight (str) – Attribute overriding edge weight. Default is 1. Advanced layout controls will relayout edges based on this value.
point_title (str) – Attribute overriding node’s minimized label text. By default, the node ID is used.
point_label (str) – Attribute overriding node’s expanded label text. By default, scrollable list of attribute/value mappings.
point_color (str) – Attribute overriding node’s color.rgba (int64) or int32 palette index, see palette definitions <https://graphistry.github.io/docs/legacy/api/0.9.2/api.html#extendedpalette>`_ for values. Based on Color Brewer.
point_size (str) – Attribute overriding node’s size. By default, uses the node degree. The visualization will normalize point sizes and adjust dynamically using semantic zoom.
point_x (str) – Attribute overriding node’s initial x position. Combine with “.settings(url_params={‘play’: 0}))” to create a custom layout
point_y (str) – Attribute overriding node’s initial y position. Combine with “.settings(url_params={‘play’: 0}))” to create a custom layout

Returns

Plotter

Return type

Plotter

Example: Minimal

import graphistry
g = graphistry.bind()
g = g.bind(source='src', destination='dst')

Example: Node colors

import graphistry
g = graphistry.bind()
g = g.bind(source='src', destination='dst',
           node='id', point_color='color')

Example: Chaining

import graphistry
g = graphistry.bind(source='src', destination='dst', node='id')

g1 = g.bind(point_color='color1', point_size='size1')

g.bind(point_color='color1b')

g2a = g1.bind(point_color='color2a')
g2b = g1.bind(point_color='color2b', point_size='size2b')

g3a = g2a.bind(point_size='size3a')
g3b = g2b.bind(point_size='size3b')

In the above Chaining example, all bindings use src/dst/id. Colors and sizes bind to:

g: default/default
g1: color1/size1
g2a: color2a/size1
g2b: color2b/size2b
g3a: color2a/size3a
g3b: color2b/size3b

bolt(driver)¶

cypher(query, params={})¶

description(description)¶

Upload description

Parameters: description (str) – Upload description

edges(edges, source=None, destination=None)¶

Specify edge list data and associated edge attribute values.

Parameters: edges – Edges and their attributes.
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()

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)¶

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

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/ , image URLs (http://…), and data URIs (data:…). When as_text=True is enabled, values are instead interpreted as raw strings.

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.)

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', as_text=True, 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'})

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)¶

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')

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/ , image URLs (http://…), and data URIs (data:…). When as_text=True is enabled, values are instead interpreted as raw strings.

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'})

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)

graph(ig)¶

Specify the node and edge data.

Parameters: ig (Any) – NetworkX graph or an IGraph graph with node and edge attributes.
Returns: Plotter
Return type: Plotter

gsql(query, bindings={}, 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()

gsql_endpoint(method_name, args={}, bindings={}, 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)

igraph2pandas(ig)¶

Under current bindings, transform an IGraph into a pandas edges dataframe and a nodes dataframe.

Example

import graphistry
g = graphistry.bind()

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

ig = g.pandas2igraph(es)
ig.vs['community'] = ig.community_infomap().membership

(es2, vs2) = g.igraph2pandas(ig)
g.nodes(vs2).bind(point_color='community').plot()

name(name)¶

Upload name

Parameters: name (str) – Upload name

networkx2pandas(g)¶

networkx_checkoverlap(g)¶

nodes(nodes, node=None)¶

Specify the set of nodes and associated data.

Must include any nodes referenced in the edge list.

Parameters: nodes – 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()

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

pandas2igraph(edges, directed=True)¶

Convert a pandas edge dataframe to an IGraph graph.

Uses current bindings. Defaults to treating edges as directed.

Example

import graphistry
g = graphistry.bind()

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

ig = g.pandas2igraph(es)
ig.vs['community'] = ig.community_infomap().membership
g.bind(point_color='community').plot(ig)

plot(graph=None, nodes=None, name=None, description=None, render=None, skip_upload=False, as_files=False, memoize=True)¶

Upload data to the Graphistry server and show as an iframe of it.

Uses the currently bound schema structure and visual encodings. Optional parameters override the current bindings.

When used in a notebook environment, will also show an iframe of the visualization.

Parameters

graph (Any) – Edge table (pandas, arrow, cudf) or graph (NetworkX, IGraph).
nodes (Any) – Nodes table (pandas, arrow, cudf)
name (str) – Upload name.
description (str) – Upload description.
render (bool) – Whether to render the visualization using the native notebook environment (default True), or return the visualization URL
skip_upload (bool) – Return node/edge/bindings that would have been uploaded. By default, upload happens.
as_files (bool) – Upload distinct node/edge files under the managed Files PI. Default off, will switch to default-on when stable.
memoize (bool) – Tries to memoize pandas/cudf->arrow conversion, including skipping upload. Default on.

Example: Simple

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

Example: Shorthand

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

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

Specify iframe height and add URL parameter dictionary.

The library takes care of URI component encoding for the dictionary.

Parameters

height (int) – Height in pixels.
url_params (dict) – Dictionary of querystring parameters to append to the URL.
render (bool) – Whether to render the visualization using the native notebook environment (default True), or return the visualization URL

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

Set general visual styles

See .bind() and .settings(url_params={}) for additional styling options, and addStyle() for another way to set the same attributes.

To facilitate reuse and replayable notebooks, the style() call is chainable. Invocation does not effect the old style: it instead returns a new Plotter instance with the new styles added to the existing ones. Both the old and new styles can then be used for different graphs.

style() will fully replace any defined parameter in the existing style settings, while addStyle() will merge over previous values

Parameters

fg (dict) – Dictionary {‘blendMode’: str} of any valid CSS blend mode
bg (dict) – Nested dictionary of page background properties. {‘color’: str, ‘gradient’: {‘kind’: str, ‘position’: str, ‘stops’: list }, ‘image’: { ‘url’: str, ‘width’: int, ‘height’: int, ‘blendMode’: str }
logo (dict) – Nested dictionary of logo properties. { ‘url’: str, ‘autoInvert’: bool, ‘position’: str, ‘dimensions’: { ‘maxWidth’: int, ‘maxHeight’: int }, ‘crop’: { ‘top’: int, ‘left’: int, ‘bottom’: int, ‘right’: int }, ‘padding’: { ‘top’: int, ‘left’: int, ‘bottom’: int, ‘right’: int}, ‘style’: str}
page (dict) – Dictionary of page metadata settings. { ‘favicon’: str, ‘title’: str }

Returns

Plotter

Return type

Plotter

Example: Chained merge - results in url and blendMode being set, while color is dropped

::: g2 = g.style(bg={‘color’: ‘black’}, fg={‘blendMode’: ‘screen’}) g3 = g2.style(bg={‘image’: {‘url’: ‘http://site.com/watermark.png’}})

Example: Gradient background

::: g.style(bg={‘gradient’: {‘kind’: ‘linear’, ‘position’: 45, ‘stops’: [[‘rgb(0,0,0)’, ‘0%’], [‘rgb(255,255,255)’, ‘100%’]]}})

Example: Page settings

::: g.style(page={‘title’: ‘Site - {{ name }}’, ‘favicon’: ‘http://site.com/logo.ico’})

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')                    

class graphistry.plotter.WeakValueWrapper(v)¶: Bases: object

graphistry.plotter.cache_coercion(k, v)¶: Holds references to last 100 used coercions Use with weak key/value dictionaries for actual lookups

graphistry.plotter.cache_coercion_helper(k)¶

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 or 2 for 1.0 (deprecated), 3 for 2.0 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 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 edges(edges, source=None, destination=None)¶

Specify edge list data and associated edge attribute values.

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

Example

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

Example

import graphistry
df = pd.DataFrame({'src': [0,1,2], 'dst': [1,2,0]})
graphistry
    .edges(df, 'src', 'dst')
    .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 graph(ig)¶

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 login(username, password, 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 nodes(nodes, node=None)¶

Specify the set of nodes and associated data.

Must include any nodes referenced in the edge list.

Parameters: nodes – Nodes and their attributes.
Returns: Plotter
Return type: Plotter

Example

import graphistry

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

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

g.plot()

Example

import graphistry

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

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

g.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 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)¶

API key registration and server selection

Changing the key effects all derived Plotter instances.

Provide one of key (api=1,2) or username/password (api=3) or token (api=3).

Parameters

key (Optional[str]) – API key (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.
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 (Optional[str]) – 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.

Returns

None.

Return type

None

Example: Standard (2.0 api by username/password)

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 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.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.edges(edges, source=None, destination=None)¶

Specify edge list data and associated edge attribute values.

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

Example

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

Example

import graphistry
df = pd.DataFrame({'src': [0,1,2], 'dst': [1,2,0]})
graphistry
    .edges(df, 'src', 'dst')
    .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.graph(ig)¶

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.login(username, password, 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.nodes(nodes, node=None)¶

Specify the set of nodes and associated data.

Must include any nodes referenced in the edge list.

Parameters: nodes – Nodes and their attributes.
Returns: Plotter
Return type: Plotter

Example

import graphistry

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

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

g.plot()

Example

import graphistry

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

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

g.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.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)¶

API key registration and server selection

Changing the key effects all derived Plotter instances.

Provide one of key (api=1,2) or username/password (api=3) or token (api=3).

Parameters

key (Optional[str]) – API key (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.
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 (Optional[str]) – 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.

Returns

None.

Return type

None

Example: Standard (2.0 api by username/password)

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.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.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)¶

Bases: object

arrow_to_buffer(table)¶

Parameters: table (Table) –

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)¶

maybe_bindings(g, bindings, base={})¶

property metadata¶

property name¶

Return type: str

property node_encodings¶

property nodes¶

Return type: Table

post(as_files=True, memoize=True)¶

as_files deprecation plan:: Graphistry 2.34: Introduced Graphistry 2.35: Does nothing (runtime warning); all uploads are Files Graphistry 2.36: Remove flag

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)¶

post_nodes_arrow(arr=None, opts='')¶

post_nodes_file(file_path, file_type='csv')¶

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