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A visual graph analytics library for extracting, transforming, displaying, and sharing big graphs with end-to-end GPU acceleration

Project description

PyGraphistry: Explore Relationships

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PyGraphistry is a Python visual graph AI library to extract, transform, analyze, model, and visualize big graphs, and especially alongside Graphistry end-to-end GPU server sessions. Installing with optional graphistry[ai] dependencies adds graph autoML, including automatic feature engineering, UMAP, and graph neural net support. Combined, PyGraphistry reduces your time to graph for going from raw data to visualizations and AI models down to three lines of code.

Graphistry gets used on problems like visually mapping the behavior of devices and users, investigating fraud, analyzing machine learning results, and starting in graph AI. It provides point-and-click features like timebars, search, filtering, clustering, coloring, sharing, and more. Graphistry is the only tool built ground-up for large graphs. The client's custom WebGL rendering engine renders up to 8MM nodes + edges at a time, and most older client GPUs smoothly support somewhere between 100K and 2MM elements. The serverside GPU analytics engine supports even bigger graphs. It smoothes graph workflows over the PyData ecosystem including Pandas/Spark/Dask dataframes, Nvidia RAPIDS GPU dataframes & GPU graphs, DGL/PyTorch graph neural networks, and various data connectors.

The PyGraphistry Python client helps several kinds of usage modes:

  • Data scientists: Go from data to accelerated visual explorations in a couple lines, share live results, build up more advanced views over time, and do it all from notebook environments like Jupyter and Google Colab
  • Developers: Quickly prototype stunning Python solutions with PyGraphistry, embed in a language-neutral way with the REST APIs, and go deep on customizations like colors, icons, layouts, JavaScript, and more
  • Analysts: Every Graphistry session is a point-and-click environment with interactive search, filters, timebars, histograms, and more
  • Dashboarding: Embed into your favorite framework. Additionally, see our sister project Graph-App-Kit for quickly building interactive graph dashboards by launching a stack built on PyGraphistry, StreamLit, Docker, and ready recipes for integrating with common graph libraries

PyGraphistry is a friendly and optimized PyData-native interface to the language-neutral Graphistry REST APIs. You can use PyGraphistry with traditional Python data sources like CSVs, SQL, Neo4j, Splunk, and more (see below). Wrangle data however you want, and with especially good support for Pandas dataframes, Apache Arrow tables, Nvidia RAPIDS cuDF dataframes & cuGraph graphs, and DGL/PyTorch graph neural networks.

  1. Interactive Demo
  2. Graph Gallery
  3. Install
  4. Tutorial
  5. Next Steps
  6. Resources

Demo of Friendship Communities on Facebook

Click to open interactive version! (For server-backed interactive analytics, use an API key) Source data: SNAP

PyGraphistry is...

  • Fast & gorgeous: Interactively cluster, filter, inspect large amounts of data, and zip through timebars. It clusters large graphs with a descendant of the gorgeous ForceAtlas2 layout algorithm introduced in Gephi. Our data explorer connects to Graphistry's GPU cluster to layout and render hundreds of thousand of nodes+edges in your browser at unparalleled speeds.

  • Easy to install: pip install the client in your notebook or web app, and then connect to a free Graphistry Hub account or launch your own private GPU server

    # pip install --user graphistry              # minimal
    # pip install --user graphistry[bolt,gremlin,nodexl,igraph,networkx]  # data plugins
    # AI modules: Python 3.8+ with scikit-learn 1.0+:
    # pip install --user graphistry[umap-learn]  # Lightweight: UMAP autoML (without text support); scikit-learn 1.0+
    # pip install --user graphistry[ai]          # Heavy: Full UMAP + GNN autoML, including sentence transformers (1GB+)
    import graphistry
    graphistry.register(api=3, username='abc', password='xyz')  # Free:
    #graphistry.register(..., personal_key_id='pkey_id', personal_key_secret='pkey_secret') # Key instead of username+password+org_name
    #graphistry.register(..., is_sso_login=True)  # SSO instead of password
    #graphistry.register(..., org_name='my-org') # Upload into an organization account vs personal
    #graphistry.register(..., protocol='https', server='')  # Use with a self-hosted server
    # ... and if client (browser) URLs are different than python server<> graphistry server uploads
    #graphistry.register(..., client_protocol_hostname='')
  • Notebook-friendly: PyGraphistry plays well with interactive notebooks like Jupyter, Zeppelin, and Databricks. Process, visualize, and drill into with graphs directly within your notebooks:

    graphistry.edges(pd.read_csv('rows.csv'), 'col_a', 'col_b').plot()
  • Great for events, CSVs, and more: Not sure if your data is graph-friendly? PyGraphistry's hypergraph transform helps turn any sample data like CSVs, SQL results, and event data into a graph for pattern analysis:

    rows = pandas.read_csv('transactions.csv')[:1000]
  • Embeddable: Drop live views into your web dashboards and apps (and go further with JS/React):

    iframe_url = g.plot(render=False)
    print(f'<iframe src="{ iframe_url }"></iframe>')
  • Configurable: In-tool or via the declarative APIs, use the powerful encodings systems for tasks like coloring by time, sizing by score, clustering by weight, show icons by type, and more.

  • Shareable: Share live links, configure who has access, and more! (Notebook tutorial)

  • Graph AI that is fast & easy: In oneines of code, turn messy data into feature vectors for modeling, GNNs for training pipelines, lower dimensional embeddings, and visualizations:

    df = pandas.read_csv('accounts.csv')
    # UMAP dimensionality reduction with automatic feature engineering
    g1 = graphistry.nodes(df).umap()
    # Automatically shows top inferred similarity edges g1._edges
    # Optional: Use subset of columns, supervised learning target, & more
    g2.umap(X=['name', 'description', 'amount'], y=['label_col_1']).plot()

Explore any data as a graph

It is easy to turn arbitrary data into insightful graphs. PyGraphistry comes with many built-in connectors, and by supporting Python dataframes (Pandas, Arrow, RAPIDS), it's easy to bring standard Python data libraries. If the data comes as a table instead of a graph, PyGraphistry will help you extract and explore the relationships.

  • Pandas

    edges = pd.read_csv('facebook_combined.txt', sep=' ', names=['src', 'dst'])
    graphistry.edges(edges, 'src', 'dst').plot()
    table_rows = pd.read_csv('honeypot.csv')
    graphistry.hypergraph(table_rows, ['attackerIP', 'victimIP', 'victimPort', 'vulnName'])['graph'].plot()
    graphistry.hypergraph(table_rows, ['attackerIP', 'victimIP', 'victimPort', 'vulnName'],
        opts={'EDGES': {
          'attackerIP': ['victimIP', 'victimPort', 'vulnName'],
          'victimIP': ['victimPort', 'vulnName'],
          'victimPort': ['vulnName']
    ### Override smart defaults with custom settings
    g1 = graphistry.bind(source='src', destination='dst').edges(edges)
    g2 = g1.nodes(nodes).bind(node='col2')
    g3 = g2.bind(point_color='col3')
    g4 = g3.settings(url_params={'edgeInfluence': 1.0, play: 2000})
    url = g4.plot(render=False)
    ### Read back data and create modified variants
    enriched_edges = my_function1(g1._edges)
    enriched_nodes = my_function2(g1._nodes)
    g2 = g1.edges(enriched_edges).nodes(enriched_nodes)
  • Spark/Databricks (ipynb demo, dbc demo)

    #optional but recommended
    spark.conf.set("spark.sql.execution.arrow.enabled", "true")
    edges_df = ('json').
    g = graphistry.edges(edges_df, 'device_name', 'cn')
    #dashboard: pick size of choice
      g.settings(url_params={'splashAfter': 'false'})
          width: 50em;
          height: 50em;
  • GPU cudf

    edges = cudf.read_csv('facebook_combined.txt', sep=' ', names=['src', 'dst'])
    graphistry.edges(edges, 'src', 'dst').plot()
  • GPU cuML

    g = graphistry.nodes(cudf.read_csv('rows.csv'))
    g = graphistry.nodes(G)
  • GPU cugraph (notebook demo)

    g = graphistry.from_cugraph(G)
    g2 = g.compute_cugraph('pagerank')
    g3 = g2.layout_cugraph('force_atlas2')
    G3 = g.to_cugraph()
  • Apache Arrow

     edges = pa.Table.from_pandas(pd.read_csv('facebook_combined.txt', sep=' ', names=['src', 'dst']))
     graphistry.edges(edges, 'src', 'dst').plot()
  • Neo4j (notebook demo)

    NEO4J_CREDS = {'uri': 'bolt://', 'auth': ('neo4j', 'mypwd')}
    graphistry.cypher("MATCH (n1)-[r1]->(n2) RETURN n1, r1, n2 LIMIT 1000").plot()
    graphistry.cypher("CALL db.schema()").plot()
    from neo4j import GraphDatabase, Driver
    g = graphistry.cypher("""
      MATCH (a)-[p:PAYMENT]->(b)
      WHERE p.USD > 7000 AND p.USD < 10000
      RETURN a, p, b
      LIMIT 100000""")
  • Memgraph (notebook demo)

    from neo4j import GraphDatabase
    MEMGRAPH = {
    'uri': "bolt://localhost:7687",
    'auth': (" ", " ")
    driver = GraphDatabase.driver(**MEMGRAPH)
    with driver.session() as session:"""
      CREATE (per1:Person {id: 1, name: "Julie"})
      CREATE (fil2:File {id: 2, name: "welcome_to_memgraph.txt"})
      CREATE (per1)-[:HAS_ACCESS_TO]->(fil2) """)
    g = graphistry.cypher("""
       MATCH (node1)-[connection]-(node2)
       RETURN node1, connection, node2;""")
  • Azure Cosmos DB (Gremlin)

    # pip install --user gremlinpython
    # Options in help(graphistry.cosmos)
    g = graphistry.cosmos(
    g2 = g.gremlin('g.E().sample(10000)').fetch_nodes()
  • Amazon Neptune (Gremlin) (notebook demo, dashboarding demo)

    # pip install --user gremlinpython==3.4.10
    #   - Deploy tips:
    #   - Versioning tips:
    #   - Login options in help(graphistry.neptune)
    g = graphistry.neptune(endpoint='wss://zzz:8182/gremlin')
    g2 = g.gremlin('g.E().limit(100)').fetch_nodes()
  • TigerGraph (notebook demo)

    g = graphistry.tigergraph(protocol='https', ...)
    g2 = g.gsql("...", {'edges': '@@eList'})
    print('# edges', len(g2._edges))
    g.endpoint('my_fn', {'arg': 'val'}, {'edges': '@@eList'}).plot()
  • igraph

    edges = pd.read_csv('facebook_combined.txt', sep=' ', names=['src', 'dst'])
    g_a = graphistry.edges(edges, 'src', 'dst')
    g_b = g_a.layout_igraph('sugiyama', directed=True)  # directed: for to_igraph
    g_b.compute_igraph('pagerank', params={'damping': 0.85}).plot()  #params: for layout
    ig ='facebook_combined.txt', format='edgelist', directed=False)
    g = graphistry.from_igraph(ig)  # full conversion
    ig2 = g.to_igraph()
    ig2.vs['spinglass'] = ig2.community_spinglass(spins=3).membership
    # selective column updates: preserve g._edges; merge 1 attribute from ig into g._nodes
    g2 = g.from_igraph(ig2, load_edges=False, node_attributes=[g._node, 'spinglass'])
  • NetworkX (notebook demo)

    graph = networkx.read_edgelist('facebook_combined.txt')
    graphistry.bind(source='src', destination='dst', node='nodeid').plot(graph)
  • HyperNetX (notebook demo)

  • Splunk (notebook demo)

    df = splunkToPandas("index=netflow bytes > 100000 | head 100000", {})
    graphistry.edges(df, 'src_ip', 'dest_ip').plot()
  • NodeXL (notebook demo)

    graphistry.nodexl('https://file.xls', 'twitter').plot()
    graphistry.nodexl('https://file.xls', verbose=True).plot()
    graphistry.nodexl('https://file.xls', engine='xlsxwriter').plot()

Graph AI in a single line of code

Graph autoML features including:

Generate features from raw data

Automatically and intelligently transform text, numbers, booleans, and other formats to AI-ready representations:

  • Featurization

    g = graphistry.nodes(df).featurize(kind='nodes', X=['col_1', ..., 'col_n'], y=['label', ..., 'other_targets'], ...)
    print('X', g._node_features)
    print('y', g._node_target)
  • Set kind='edges' to featurize edges:

    g = graphistry.edges(df, src, dst).featurize(kind='edges', X=['col_1', ..., 'col_n'], y=['label', ..., 'other_targets'], ...)
  • Use generated features with both Graphistry and external libraries:

    # graphistry
    g = g.umap()  # UMAP, GNNs, use features if already provided, otherwise will compute
    # other pydata libraries
    X = g._node_features  # g._get_feature('nodes') or g.get_matrix()
    y = g._node_target  # g._get_target('nodes') or g.get_matrix(target=True)
    from sklearn.ensemble import RandomForestRegressor
    model = RandomForestRegressor().fit(X, y)  # assumes train/test split
    new_df = pandas.read_csv(...)  # mini batch
    X_new, _ = g.transform(new_df, None, kind='nodes', return_graph=False)
    preds = model.predict(X_new)
  • Encode model definitions and compare models against each other

     # graphistry
     from graphistry.features import search_model, topic_model, ngrams_model, ModelDict, default_featurize_parameters, default_umap_parameters
     g = graphistry.nodes(df)
     g2 = g.umap(X=[..], y=[..], **search_model)  
     # set custom encoding model with any feature/umap/dbscan kwargs
     new_model = ModelDict(message='encoding new model parameters is easy', **default_featurize_parameters)
     g3 = g.umap(X=[..], **new_model)
     # compare g2 vs g3 or add to different pipelines

See help(g.featurize) for more options

sklearn-based UMAP, cuML-based UMAP

  • Reduce dimensionality by plotting a similarity graph from feature vectors:

      # automatic feature engineering, UMAP
      g = graphistry.nodes(df).umap()
      # plot the similarity graph without any explicit edge_dataframe passed in -- it is created during UMAP.
  • Apply a trained model to new data:

      new_df = pd.read_csv(...)
      embeddings, X_new, _ = g.transform_umap(new_df, None, kind='nodes', return_graph=False)
  • Infer a new graph from new data using the old umap coordinates to run inference without having to train a new umap model.

      new_df = pd.read_csv(...)
      g2 = g.transform_umap(new_df, return_graph=True)   # return_graph=True is default
      g2.plot()  # 
      # or if you want the new minibatch to cluster to closest points in previous fit:
      g3 = g.transform_umap(new_df, return_graph=True, merge_policy=True)
      g3.plot()  # useful to see how new data connects to old -- play with `sample` and `n_neighbors` to control how much of old to include
  • UMAP supports many options, such as supervised mode, working on a subset of columns, and passing arguments to underlying featurize() and UMAP implementations (see help(g.umap)):

      g.umap(kind='nodes', X=['col_1', ..., 'col_n'], y=['label', ..., 'other_targets'], ...)
  • umap(engine="...") supports multiple implementations. It defaults to using the GPU-accelerated engine="cuml" when a GPU is available, resulting in orders-of-magnitude speedups, and falls back to CPU processing via engine="umap_learn".:


You can also featurize edges and UMAP them as we did above.

UMAP support is rapidly evolving, please contact the team directly or on Slack for additional discussions

See help(g.umap) for more options

GNN models

  • Graphistry adds bindings and automation to working with popular GNN models, currently focusing on DGL/PyTorch:

    g = (graphistry
        .edges(edf, src, dst)
            X_nodes=['col_1', ..., 'col_n'], #columns from nodes_dataframe
            y_nodes=['label', ..., 'other_targets'],
            X_edges=['col_1_edge', ..., 'col_n_edge'], #columns from edges_dataframe
            y_edges=['label_edge', ..., 'other_targets_edge'],
    G = g.DGL_graph
    from [your_training_pipeline] import train, model
    # Train
    g = graphistry.nodes(df).build_gnn(y_nodes=`target`) 
    G = g.DGL_graph
    train(G, model)
    # predict on new data
    X_new, _ = g.transform(new_df, None, kind='nodes' or 'edges', return_graph=False) # no targets
    predictions = model.predict(G_new, X_new)

Like g.umap(), GNN layers automate feature engineering (.featurize())

See help(g.build_gnn) for options.

GNN support is rapidly evolving, please contact the team directly or on Slack for additional discussions

Semantic Search

  • Search textual data semantically and see the resulting graph:

      ndf = pd.read_csv(nodes.csv)
      edf = pd.read_csv(edges.csv)
      g = graphistry.nodes(ndf, 'node').edges(edf, 'src', 'dst')
      g2 = g.featurize(X = ['text_col_1', .., 'text_col_n'], kind='nodes',
                        min_words = 0,  # forces all named columns as textual ones
                        #encode text as paraphrase embeddings, supports any sbert model
                        model_name = "paraphrase-MiniLM-L6-v2")
      # or use convienence `ModelDict` to store parameters
      from graphistry.features import search_model
      g2 = g.featurize(X = ['text_col_1', .., 'text_col_n'], kind='nodes', **search_model)
      # query using the power of transformers to find richly relevant results                   
      results_df, query_vector ='my natural language query', ...)
      print(results_df[['_distance', 'text_col', ..]])  #sorted by relevancy
      # or see graph of matching entities and original edges
      g2.search_graph('my natural language query', ...).plot()
  • If edges are not given, g.umap(..) will supply them:

      ndf = pd.read_csv(nodes.csv)
      g = graphistry.nodes(ndf)
      g2 = g.umap(X = ['text_col_1', .., 'text_col_n'], min_words=0, ...)
      g2.search_graph('my natural language query', ...).plot()

See help(g.search_graph) for options

Knowledge Graph Embeddings

  • Train a RGCN model and predict:

      edf = pd.read_csv(edges.csv)
      g = graphistry.edges(edf, src, dst)
      g2 = g.embed(relation='relationship_column_of_interest', **kwargs)
      # predict links over all nodes
      g3 = g2.predict_links_all(threshold=0.95)  # score high confidence predicted edges
      # predict over any set of entities and/or relations. 
      # Set any `source`, `destination` or `relation` to `None` to predict over all of them.
      # if all are None, it is better to use `g.predict_links_all` for speed.
      g4 = g2.predict_links(source=['entity_k'], 
                      relation=['relationship_1', 'relationship_4', ..], 
                      destination=['entity_l', 'entity_m', ..], 
                      threshold=0.9,  # score threshold
                      return_dataframe=False)  # set to `True` to return dataframe, or just access via `g4._edges`
  • Detect Anamolous Behavior (example use cases such as Cyber, Fraud, etc)

      # Score anomolous edges by setting the flag `anomalous` to True and set confidence threshold low
      g5 = g.predict_links_all(threshold=0.05, anomalous=True)  # score low confidence predicted edges
      g6 = g.predict_links(source=['ip_address_1', 'user_id_3'], 
                      relation=['attempt_logon', 'phishing', ..], 
                      destination=['user_id_1', 'active_directory', ..], 
  • Train a RGCN model including auto-featurized node embeddings

      edf = pd.read_csv(edges.csv)
      ndf = pd.read_csv(nodes.csv)  # adding node dataframe
      g = graphistry.edges(edf, src, dst).nodes(ndf, node_column)
      # inherets all the featurization `kwargs` from `g.featurize` 
      g2 = g.embed(relation='relationship_column_of_interest', use_feat=True, **kwargs)

See help(g.embed), help(g.predict_links) , or help(g.predict_links_all) for options


  • Enrich UMAP embeddings or featurization dataframe with GPU or CPU DBSCAN

      g = graphistry.edges(edf, 'src', 'dst').nodes(ndf, 'node')
      # cluster by UMAP embeddings
      kind = 'nodes' | 'edges'
      g2 = g.umap(kind=kind).dbscan(kind=kind)
      print(g2._nodes['_dbscan']) | print(g2._edges['_dbscan'])
      # dbscan in `umap` or `featurize` via flag
      g2 = g.umap(dbscan=True, min_dist=0.2, min_samples=1)
      # or via chaining,
      g2 = g.umap().dbscan(min_dist=1.2, min_samples=2, **kwargs)
      # cluster by feature embeddings
      g2 = g.featurize().dbscan(**kwargs)
      # cluster by a given set of feature column attributes, inhereted from `g.get_matrix(cols)`
      g2 = g.featurize().dbscan(cols=['ip_172', 'location', 'alert'], **kwargs)
      # equivalent to above (ie, cols != None and umap=True will still use features dataframe, rather than UMAP embeddings)
      g2 = g.umap().dbscan(cols=['ip_172', 'location', 'alert'], umap=True | False, **kwargs)
      g2.plot() # color by `_dbscan`
      new_df = pd.read_csv(..)
      # transform on new data according to fit dbscan model
      g3 = g2.transform_dbscan(new_df)

See help(g.dbscan) or help(g.transform_dbscan) for options

Quickly configurable

Set visual attributes through quick data bindings and set all sorts of URL options. Check out the tutorials on colors, sizes, icons, badges, weighted clustering and sharing controls:

    .privacy(mode='private', invited_users=[{'email': '', 'action': '10'}], notify=False)
    .edges(df, 'col_a', 'col_b')
    .nodes(df, 'col_c')
    .bind(source='col_a', destination='col_b', node='col_c')
    .encode_edge_color('timestamp', ["blue", "yellow", "red"], as_continuous=True)
    .encode_point_icon('device_type', categorical_mapping={'macbook': 'laptop', ...})
    .encode_point_badge('passport', 'TopRight', categorical_mapping={'Canada': 'flag-icon-ca', ...})
    .encode_point_color('score', ['black', 'white'])
    .addStyle(bg={'color': 'red'}, fg={}, page={'title': 'My Graph'}, logo={})
      'play': 2000,
      'menu': True, 'info': True,
      'showArrows': True,
      'pointSize': 2.0, 'edgeCurvature': 0.5,
      'edgeOpacity': 1.0, 'pointOpacity': 1.0,
      'lockedX': False, 'lockedY': False, 'lockedR': False,
      'linLog': False, 'strongGravity': False, 'dissuadeHubs': False,
      'edgeInfluence': 1.0, 'precisionVsSpeed': 1.0, 'gravity': 1.0, 'scalingRatio': 1.0,
      'showLabels': True, 'showLabelOnHover': True,
      'showPointsOfInterest': True, 'showPointsOfInterestLabel': True, 'showLabelPropertiesOnHover': True,
      'pointsOfInterestMax': 5


Twitter Botnet
Edit Wars on Wikipedia
Source: SNAP
100,000 Bitcoin Transactions
Port Scan Attack
Protein Interactions
Source: BioGRID
Programming Languages
Source: Socio-PLT project



You need to install the PyGraphistry Python client and connect it to a Graphistry GPU server of your choice:

  1. Graphistry server account:

  2. PyGraphistry Python client:


Most users connect to a Graphistry GPU server account via:

  • graphistry.register(api=3, username='abc', password='xyz'): personal account
  • graphistry.register(api=3, username='abc', password='xyz', org_name='optional_org'): team account
  • graphistry.register(api=3, username='abc', password='xyz', org_name='optiona_org', protocol='http', server=''): private server

For more advanced configuration, read on for:

  • Version: Use protocol api=3, which will soon become the default, or a legacy version

  • JWT Tokens: Connect to a GPU server by providing a username='abc'/password='xyz', or for advanced long-running service account software, a refresh loop using 1-hour-only JWT tokens

  • Organizations: Optionally use org_name to set a specific organization

  • Private servers: PyGraphistry defaults to using the free Graphistry Hub public API

    • Connect to a private Graphistry server and provide optional settings specific to it via protocol, server, and in some cases, client_protocol_hostname

Non-Python users may want to explore the underlying language-neutral authentication REST API docs.

Advanced Login

  • For people: Provide your account username/password:
import graphistry
graphistry.register(api=3, username='username', password='your password')
  • For service accounts: Long-running services may prefer to use 1-hour JWT tokens:
import graphistry
graphistry.register(api=3, username='username', password='your password')
initial_one_hour_token = graphistry.api_token()
graphistry.register(api=3, token=initial_one_hour_token)

# must run every 59min
fresh_token = graphistry.api_token()
assert initial_one_hour_token != fresh_token

Refreshes exhaust their limit every day/month. An upcoming Personal Key feature enables non-expiring use.

Alternatively, you can rerun graphistry.register(api=3, username='username', password='your password'), which will also fetch a fresh token.

Advanced: Private servers - server uploads

Specify which Graphistry server to reach for Python uploads:

graphistry.register(protocol='https', server='')

Private Graphistry notebook environments are preconfigured to fill in this data for you:

graphistry.register(protocol='http', server='nginx', client_protocol_hostname='')

Using 'http'/'nginx' ensures uploads stay within the Docker network (vs. going more slowly through an outside network), and client protocol '' ensures the browser URLs do not show http://nginx/, and instead use the server's name. (See immediately following Switch client URL section.)

Advanced: Private servers - switch client URL for browser views

In cases such as when the notebook server is the same as the Graphistry server, you may want your Python code to upload to a known local Graphistry address without going outside the network (e.g., http://nginx or http://localhost), but for web viewing, generate and embed URLs to a different public address (e.g., In this case, explicitly set a client (browser) location different from protocol / server:

    ### fast local notebook<>graphistry upload
    protocol='http', server='nginx',

    ### shareable public URL for browsers

Prebuilt Graphistry servers are already setup to do this out-of-the-box.

Advanced: Sharing controls

Graphistry supports flexible sharing permissions that are similar to Google documents and Dropbox links

By default, visualizations are publicly viewable by anyone with the URL (that is unguessable & unlisted), and only editable by their owner.

  • Private-only: You can globally default uploads to private:
graphistry.privacy()  # graphistry.privacy(mode='private')
  • Organizations: You can login with an organization and share only within it
graphistry.register(api=3, username='...', password='...', org_name='my-org123')
  • Invitees: You can share access to specify users, and optionally, even email them invites
VIEW = "10"
EDIT = "20"
    {"email": "", "action": VIEW},
    {"email": "", "action": EDIT}
  • Per-visualization: You can choose different rules for global defaults vs. for specific visualizations
g = graphistry.hypergraph(pd.read_csv('...'))['graph']

See additional examples in the sharing tutorial

Tutorial: Les Misérables

Let's visualize relationships between the characters in Les Misérables. For this example, we'll choose Pandas to wrangle data and igraph to run a community detection algorithm. You can view the Jupyter notebook containing this example.

Our dataset is a CSV file that looks like this:

source target value
Cravatte Myriel 1
Valjean Mme.Magloire 3
Valjean Mlle.Baptistine 3

Source and target are character names, and the value column counts the number of time they meet. Parsing is a one-liner with Pandas:

import pandas
links = pandas.read_csv('./lesmiserables.csv')

Quick Visualization

If you already have graph-like data, use this step. Otherwise, try the Hypergraph Transform for creating graphs from rows of data (logs, samples, records, ...).

PyGraphistry can plot graphs directly from Pandas data frames, Arrow tables, cuGraph GPU data frames, igraph graphs, or NetworkX graphs. Calling plot uploads the data to our visualization servers and return an URL to an embeddable webpage containing the visualization.

To define the graph, we bind source and destination to the columns indicating the start and end nodes of each edges:

import graphistry
graphistry.register(api=3, username='YOUR_ACCOUNT_HERE', password='YOUR_PASSWORD_HERE')

g = graphistry.bind(source="source", destination="target")

You should see a beautiful graph like this one: Graph of Miserables

Adding Labels

Let's add labels to edges in order to show how many times each pair of characters met. We create a new column called label in edge table links that contains the text of the label and we bind edge_label to it.

links["label"] = v: "#Meetings: %d" % v)
g = g.bind(edge_title="label")

Controlling Node Title, Size, Color, and Position

Let's size nodes based on their PageRank score and color them using their community.

Warmup: igraph for computing statistics

igraph already has these algorithms implemented for us for small graphs. (See our cuGraph examples for big graphs.) If igraph is not already installed, fetch it with pip install igraph.

We start by converting our edge dateframe into an igraph. The plotter can do the conversion for us using the source and destination bindings. Then we compute two new node attributes (pagerank & community).

g = g.compute_igraph('pagerank', directed=True, params={'damping': 0.85}).compute_igraph('community_infomap')

The algorithm names 'pagerank' and 'community_infomap' correspond to method names of igraph.Graph. Likewise, optional params={...} allow specifying additional parameters.

Bind node data to visual node attributes

We can then bind the node community and pagerank columns to visualization attributes:

g.bind(point_color='community', point_size='pagerank').plot()

See the color palette documentation for specifying color values by using built-in ColorBrewer palettes (int32) or custom RGB values (int64).

To control the position, we can add .bind(point_x='colA', point_y='colB').settings(url_params={'play': 0}) (see demos and additional url parameters]). In api=1, you created columns named x and y.

You may also want to bind point_title: .bind(point_title='colA').

For more in-depth examples, check out the tutorials on colors and sizes.

Second Graph of Miserables

Add edge colors and weights

By default, edges get colored as a gradient between their source/destination node colors. You can override this by setting .bind(edge_color='colA'), similar to how node colors function. (See color documentation.)

Similarly, you can bind the edge weight, where higher weights cause nodes to cluster closer together: .bind(edge_weight='colA'). See tutorial.

For more in-depth examples, check out the tutorials on colors and weighted clustering.

More advanced color and size controls

You may want more controls like using gradients or maping specific values:

g.encode_edge_color('int_col')  # int32 or int64
g.encode_edge_color('time_col', ["blue", "red"], as_continuous=True)
g.encode_edge_color('type', as_categorical=True,
  categorical_mapping={"cat": "red", "sheep": "blue"}, default_mapping='#CCC') 
  categorical_mapping={'toyota': 'red', 'ford': 'blue'},
  categorical_mapping={'critical': 200, 'ok': 100},
g.encode_point_color('int_col')  # int32 or int64
g.encode_point_color('time_col', ["blue", "red"], as_continuous=True)
g.encode_point_color('type', as_categorical=True,
  categorical_mapping={"cat": "red", "sheep": "blue"}, default_mapping='#CCC') 

For more in-depth examples, check out the tutorials on colors.

Custom icons and badges

You can add a main icon and multiple peripherary badges to provide more visual information. Use column type for the icon type to appear visually in the legend. The glyph system supports text, icons, flags, and images, as well as multiple mapping and style controls.

Main icon

  shape="circle", #clip excess
      'macbook': 'laptop', #
      'Canada': 'flag-icon-ca', #ISO3611-Alpha-2:
      'embedded_smile': 'data:svg...',
      'external_logo': 'http://..../img.png'
    [20, 'info'],
    [80, 'exclamation-circle'],
    [None, 'exclamation-triangle']
    'Canada': 'CA',
    'United States': 'US'

For more in-depth examples, check out the tutorials on icons.


# see icons examples for mappings and glyphs
g.encode_point_badge('another_column', 'TopRight', categorical_mapping=...)

g.encode_point_badge('another_column', 'TopRight', categorical_mapping=...,
  border={'width': 2, 'color': 'white', 'stroke': 'solid'},
  color={'mapping': {'categorical': {'fixed': {}, 'other': 'white'}}},
  bg={'color': {'mapping': {'continuous': {'bins': [], 'other': 'black'}}}})

For more in-depth examples, check out the tutorials on badges.


Radial axes support three coloring types ('external', 'internal', and 'space') and optional labels:

  {'r': 14, 'external': True, "label": "outermost"},
  {'r': 12, 'external': True},
  {'r': 10, 'space': True},
  {'r': 8, 'space': True},
  {'r': 6, 'internal': True},
  {'r': 4, 'space': True},
  {'r': 2, 'space': True, "label": "innermost"}

Horizontal axis support optional labels and ranges:

  {"label": "a",  "y": 2, "internal": True },
  {"label": "b",  "y": 40, "external": True,
   "width": 20, "bounds": {"min": 40, "max": 400}},

Radial axis are generally used with radial positioning:

g2 = (g
      x = 1 + (g._nodes['ring']) * g._nodes['n'].apply(math.cos),
      y = 1 + (g._nodes['ring']) * g._nodes['n'].apply(math.sin)
  )).settings(url_params={'lockedR': 'true', 'play': 1000})

Horizontal axis are often used with pinned y and free x positions:

g2 = (g
      y = 50 * g._nodes['level'])
  )).settings(url_params={'lockedY': 'true', 'play': 1000})


You can customize several style options to match your theme:

g.addStyle(bg={'color': 'red'})
  'color': '#333',
  'gradient': {
    'kind': 'radial',
    'stops': [ ["rgba(255,255,255, 0.1)", "10%", "rgba(0,0,0,0)", "20%"] ]}})
g.addStyle(bg={'image': {'url': '', 'blendMode': 'multiply'}})
g.addStyle(fg={'blendMode': 'color-burn'})
g.addStyle(page={'title': 'My site'})
g.addStyle(page={'favicon': ''})
g.addStyle(logo={'url': ''})
  'url': '',
  'dimensions': {'maxHeight': 200, 'maxWidth': 200},
  'style': {'opacity': 0.5}


The below methods let you quickly manipulate graphs directly and with dataframe methods: Search, pattern mine, transform, and more:

from graphistry import n, e_forward, e_reverse, e_undirected
g = (graphistry
    's': ['a', 'b'],
    'd': ['b', 'c'],
    'k1': ['x', 'y']
    'n': ['a', 'b', 'c'],
    'k2': [0, 2, 4, 6]

g2 = graphistry.hypergraph(g._edges, ['s', 'd', 'k1'])['graph']
g2.plot() # nodes are values from cols s, d, k1

  .pipe(lambda g2: g2.nodes(g2._nodes.assign(t=x))) # transform
  .filter_edges_by_dict({"k1": "x"})
  .filter_nodes_by_dict({"k2": 4})
  .hop( # filter to subgraph
    #almost all optional
    direction='forward', # 'reverse', 'undirected'
    hops=1, # number or None if to_fixed_point
    source_node_match={"k2": 0},
    edge_match={"k1": "x"},
    destination_node_match={"k2": 2})
  .chain([ # filter to subgraph
    n({'k2': 0}),
    n(name="start"), # add column 'start':bool
    e_forward({'k1': 'x'}, hops=1), # same API as hop()
  .collapse(node='some_id', column='some_col', attribute='some val')

Table to graph

df = pd.read_csv('events.csv')
hg = graphistry.hypergraph(df, ['user', 'email', 'org'], direct=True)
g = hg['graph']  # g._edges: | src, dst, user, email, org, time, ... |

Generate node table

g = graphistry.edges(pd.DataFrame({'s': ['a', 'b'], 'd': ['b', 'c']}))
g2 = g.materialize_nodes()
g2._nodes  # pd.DataFrame({'id': ['a', 'b', 'c']})

Compute degrees

g = graphistry.edges(pd.DataFrame({'s': ['a', 'b'], 'd': ['b', 'c']}))
g2 = g.get_degrees()
g2._nodes  # pd.DataFrame({
           #  'id': ['a', 'b', 'c'],
           #  'degree_in': [0, 1, 1],
           #  'degree_out': [1, 1, 0],
           #  'degree': [1, 1, 1]

See also get_indegrees() and get_outdegrees()

Use igraph (CPU) and cugraph (GPU) compute

Install the plugin of choice and then:

g2 =  g.compute_igraph('pagerank')
assert 'pagerank' in g2._nodes.columns

g3 = g.compute_cugraph('pagerank')
assert 'pagerank' in g2._nodes.columns

Graph pattern matching

Traverse within a graph, or expand one graph against another

Simple node and edge filtering via filter_edges_by_dict() and filter_nodes_by_dict():

g = graphistry.edges(pd.read_csv('data.csv'), 's', 'd')
g2 = g.materialize_nodes()

g3 = g.filter_edges_by_dict({"v": 1, "b": True})
g4 = g.filter_nodes_by_dict({"v2": 1, "b2": True})

Method .hop() enables slightly more complicated edge filters:

# (a)-[{"v": 1, "type": "z"}]->(b) based on g
g2b = g2.hop(
  source_node_match={g2._node: "a"},
  edge_match={"v": 1, "type": "z"},
  destination_node_match={g2._node: "b"})

# (a or b)-[1 to 8 hops]->(anynode), based on graph g2
g3 = g2.hop(pd.DataFrame({g2._node: ['a', 'b']}), hops=8)

# (c)<-[any number of hops]-(any node), based on graph g3
g4 = g3.hop(source_node_match={"node": "c"}, direction='reverse', to_fixed_point=True)

# (c)-[incoming or outgoing edge]-(any node),
# for c in g4 with expansions against nodes/edges in g2
g5 = g2.hop(pd.DataFrame({g4._node: g4[g4._node]}), hops=1, direction='undirected')


Rich compound patterns are enabled via .chain():

from graphistry import n, e_forward, e_reverse, e_undirected

g2.chain([ n() ])
g2.chain([ n({"v": 1, "y": True}) ])
g2.chain([ e_forward({"type": "x"}, hops=2) ]) # simple multi-hop
g3 = g2.chain([
  n(name="start"),  # tag node matches
  e_forward(name="final_edge"), # tag edge matches
g2.chain(n(), e_forward(), n(), e_reverse(), n()])  # rich shapes
print('# end nodes: ', len(g3._nodes[ g3._nodes.end ]))
print('# end edges: ', len(g3._edges[ g3._edges.final_edge ]))


def capitalize(df, col):
  df2 = df.copy()
  df2[col] df[col].str.capitalize()
  return df2

  .cypher('MATCH (a)-[e]->(b) RETURN a, e, b')
  .nodes(lambda g: capitalize(g._nodes, 'nTitle'))
  .edges(capitalize, None, None, 'eTitle'),
  .pipe(lambda g: g.nodes(g._nodes.pipe(capitalize, 'nTitle')))

Removing nodes

g = graphistry.edges(pd.DataFrame({'s': ['a', 'b', 'c'], 'd': ['b', 'c', 'a']}))
g2 = g.drop_nodes(['c'])  # drops node c, edge c->a, edge b->c,

Keeping nodes

# keep nodes [a,b,c] and edges [(a,b),(b,c)]
g2 = g.keep_nodes(['a, b, c'])  
g2 = g.keep_nodes(pd.Series(['a, b, c']))
g2 = g.keep_nodes(cudf.Series(['a, b, c']))

Collapsing adjacent nodes with specific k=v matches

One col/val pair:

g2 = g.collapse(
  node='root_node_id',  # rooted traversal beginning
  column='some_col',  # column to inspect
  attribute='some val' # value match to collapse on if hit
assert len(g2._nodes) <= len(g._nodes)

Collapse for all possible vals in a column, and assuming a stable root node id:

g3 = g
for v in g._nodes['some_col'].unique():
  g3 = g3.collapse(node='root_node_id', column='some_col', attribute=v)

Control layouts


g = graphistry.edges(pd.DataFrame({'s': ['a', 'b', 'b'], 'd': ['b', 'c', 'd']}))

g2a = g.tree_layout()
g2b = g2.tree_layout(allow_cycles=False, remove_self_loops=False, vertical=False)
g2c = g2.tree_layout(ascending=False, level_align='center')
g2d = g2.tree_layout(level_sort_values_by=['type', 'degree'], level_sort_values_by_ascending=False)

g3a = g2a.layout_settings(locked_r=True, play=1000)
g3b = g2a.layout_settings(locked_y=True, play=0)
g3c = g2a.layout_settings(locked_x=True)

g4 = g2.tree_layout().rotate(90)

Plugin: igraph

With pip install graphistry[igraph], you can also use igraph layouts:

g.layout_igraph('sugiyama', directed=True, params={}).plot()

See list layout_algs

Plugin: cugraph

With Nvidia RAPIDS cuGraph install:


See list layout_algs

Group-in-a-box layout

Group-in-a-box layout with igraph/pandas and cugraph/cudf implementations:

  partition_alg='ecg',  # see igraph/cugraph algs
  #partition_key='some_col',  # use existing col
  #layout_alg='circle',  # see igraph/cugraph algs
  #x, y, w, h
  #colors=['#FFF', '#FF0', ...]

Control render settings

g = graphistry.edges(pd.DataFrame({'s': ['a', 'b', 'b'], 'd': ['b', 'c', 'd']}))
g2 = g.scene_settings(
  #hide menus
  #tweak graph

With pip install graphistry[igraph], you can also use igraph layouts:

g.layout_igraph('sugiyama', directed=True, params={}).plot()

Next Steps

  1. Create a free public data Graphistry Hub account or one-click launch a private Graphistry instance in AWS
  2. Check out the analyst and developer introductions, or try your own CSV
  3. Explore the demos folder for your favorite file format, database, API, use case domain, kind of analysis, and visual analytics feature


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