automatic import of python-graphistry

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+%global _empty_manifest_terminate_build 0
+Name:		python-graphistry
+Version:	0.29.1
+Release:	1
+Summary:	A visual graph analytics library for extracting, transforming, displaying, and sharing big graphs with end-to-end GPU acceleration
+License:	BSD
+URL:		https://github.com/graphistry/pygraphistry
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/85/a2/7c68be8b319e8fba6ac94c815085bc24289c7c8fe6d36df8391a5a00d8bf/graphistry-0.29.1.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-numpy
+Requires:	python3-palettable
+Requires:	python3-pandas
+Requires:	python3-pyarrow
+Requires:	python3-requests
+Requires:	python3-squarify
+Requires:	python3-typing-extensions
+Requires:	python3-packaging
+Requires:	python3-setuptools
+Requires:	python3-umap-learn
+Requires:	python3-dirty-cat
+Requires:	python3-scikit-learn
+Requires:	python3-scipy
+Requires:	python3-dgl
+Requires:	python3-torch
+Requires:	python3-sentence-transformers
+Requires:	python3-faiss-cpu
+Requires:	python3-joblib
+Requires:	python3-openpyxl
+Requires:	python3-scipy
+Requires:	python3-joblib
+Requires:	python3-neo4j
+Requires:	python3-torch
+Requires:	python3-gremlinpython
+Requires:	python3-dirty-cat
+Requires:	python3-neotime
+Requires:	python3-sentence-transformers
+Requires:	python3-umap-learn
+Requires:	python3-igraph
+Requires:	python3-xlrd
+Requires:	python3-ipython
+Requires:	python3-scikit-learn
+Requires:	python3-faiss-cpu
+Requires:	python3-dgl
+Requires:	python3-networkx
+Requires:	python3-neo4j
+Requires:	python3-neotime
+Requires:	python3-build
+Requires:	python3-openpyxl
+Requires:	python3-neo4j
+Requires:	python3-gremlinpython
+Requires:	python3-neotime
+Requires:	python3-igraph
+Requires:	python3-xlrd
+Requires:	python3-ipython
+Requires:	python3-networkx
+Requires:	python3-types-requests
+Requires:	python3-sphinx-rtd-theme
+Requires:	python3-pytest
+Requires:	python3-sphinx-autodoc-typehints
+Requires:	python3-docutils
+Requires:	python3-mock
+Requires:	python3-Jinja2
+Requires:	python3-flake8
+Requires:	python3-pandas-stubs
+Requires:	python3-mypy
+Requires:	python3-build
+Requires:	python3-sphinx
+Requires:	python3-tqdm-stubs
+Requires:	python3-sphinx
+Requires:	python3-docutils
+Requires:	python3-sphinx-autodoc-typehints
+Requires:	python3-sphinx-rtd-theme
+Requires:	python3-Jinja2
+Requires:	python3-gremlinpython
+Requires:	python3-igraph
+Requires:	python3-ipython
+Requires:	python3-networkx
+Requires:	python3-openpyxl
+Requires:	python3-xlrd
+Requires:	python3-flake8
+Requires:	python3-mock
+Requires:	python3-mypy
+Requires:	python3-pytest
+Requires:	python3-pandas-stubs
+Requires:	python3-types-requests
+Requires:	python3-ipython
+Requires:	python3-tqdm-stubs
+Requires:	python3-umap-learn
+Requires:	python3-dirty-cat
+Requires:	python3-scikit-learn
+
+%description
+# PyGraphistry: Explore Relationships
+
+![Build Status](https://github.com/graphistry/pygraphistry/workflows/CI%20Tests/badge.svg)
+[![CodeQL](https://github.com/graphistry/pygraphistry/workflows/CodeQL/badge.svg)](https://github.com/graphistry/pygraphistry/actions?query=workflow%3ACodeQL)
+[![Documentation Status](https://readthedocs.org/projects/pygraphistry/badge/?version=latest)](https://pygraphistry.readthedocs.io/en/latest/)
+[![Latest Version](https://img.shields.io/pypi/v/graphistry.svg)](https://pypi.python.org/pypi/graphistry)
+[![Latest Version](https://img.shields.io/pypi/pyversions/graphistry.svg)](https://pypi.python.org/pypi/graphistry)
+[![License](https://img.shields.io/pypi/l/graphistry.svg)](https://pypi.python.org/pypi/graphistry)
+![PyPI - Downloads](https://img.shields.io/pypi/dm/graphistry)
+
+[![Uptime Robot status](https://img.shields.io/uptimerobot/status/m787548531-e9c7b7508fc76fea927e2313?label=hub.graphistry.com)](https://status.graphistry.com/) [<img src="https://img.shields.io/badge/slack-Graphistry%20chat-orange.svg?logo=slack">](https://join.slack.com/t/graphistry-community/shared_invite/zt-53ik36w2-fpP0Ibjbk7IJuVFIRSnr6g)
+[![Twitter Follow](https://img.shields.io/twitter/follow/graphistry)](https://twitter.com/graphistry)
+
+**PyGraphistry is a Python visual graph AI library to extract, transform, analyze, model, and visualize big graphs, and especially alongside [Graphistry](https://www.graphistry.com) 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](https://hub.graphistry.com/docs/api/), 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](https://github.com/graphistry/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](https://hub.graphistry.com/docs/api/).
+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](#demo-of-friendship-communities-on-facebook)
+2. [Graph Gallery](#gallery)
+3. [Install](#install)
+4. [Tutorial](#tutorial-les-misérables)
+5. [Next Steps](#next-steps)
+6. [Resources](#resources)
+
+## Demo of Friendship Communities on Facebook
+
+<table style="width:100%;">
+  <tr valign="top">
+    <td align="center">Click to open interactive version! <em>(For server-backed interactive analytics, use an API key)</em><a href="http://hub.graphistry.com/graph/graph.html?dataset=Facebook&splashAfter=true" target="_blank"><img src="http://i.imgur.com/Ows4rK4.png" title="Click to open."></a>
+    <em>Source data: <a href="http://snap.stanford.edu" target="_blank">SNAP</a></em>
+    </td>
+  </tr>
+</table>
+
+## 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](https://www.graphistry.com/get-started) or [launch your own private GPU server](https://www.graphistry.com/get-started)
+
+   ```python
+  # 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: hub.graphistry.com
+  #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='my.site.ngo')  # Use with a self-hosted server
+  # ... and if client (browser) URLs are different than python server<> graphistry server uploads
+  #graphistry.register(..., client_protocol_hostname='https://public.acme.co')
+  ```
+
+* **Notebook-friendly:** PyGraphistry plays well with interactive notebooks like [Jupyter](http://ipython.org), [Zeppelin](https://zeppelin.incubator.apache.org/), and [Databricks](http://databricks.com). Process, visualize, and drill into with graphs directly within your notebooks:
+
+    ```python
+    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:
+
+     ```python
+     rows = pandas.read_csv('transactions.csv')[:1000]
+     graphistry.hypergraph(rows)['graph'].plot()
+     ```
+
+* **Embeddable:** Drop live views into your web dashboards and apps (and go further with [JS/React](https://hub.graphistry.com/docs)):
+
+    ```python
+    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)](https://github.com/graphistry/pygraphistry/blob/master/demos/more_examples/graphistry_features/sharing_tutorial.ipynb)
+
+* **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:
+
+    ```python
+    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
+    g1.plot()
+    
+    # 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](http://pandas.pydata.org)
+
+     ```python
+     edges = pd.read_csv('facebook_combined.txt', sep=' ', names=['src', 'dst'])
+     graphistry.edges(edges, 'src', 'dst').plot()
+     ```
+
+     ```python
+     table_rows = pd.read_csv('honeypot.csv')
+     graphistry.hypergraph(table_rows, ['attackerIP', 'victimIP', 'victimPort', 'vulnName'])['graph'].plot()
+     ```
+
+     ```python
+     graphistry.hypergraph(table_rows, ['attackerIP', 'victimIP', 'victimPort', 'vulnName'],
+         direct=True,
+         opts={'EDGES': {
+           'attackerIP': ['victimIP', 'victimPort', 'vulnName'],
+           'victimIP': ['victimPort', 'vulnName'],
+           'victimPort': ['vulnName']
+   }})['graph'].plot()
+     ```
+
+     ```python
+     ### 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)
+     ```
+
+    ```python
+    ### 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)
+    g2.plot()
+    ```
+
+* [Spark](https://spark.apache.org/)/[Databricks](https://databricks.com/) ([ipynb demo](demos/demos_databases_apis/databricks_pyspark/graphistry-notebook-dashboard.ipynb), [dbc demo](demos/demos_databases_apis/databricks_pyspark/graphistry-notebook-dashboard.dbc))
+
+    ```python
+    #optional but recommended
+    spark.conf.set("spark.sql.execution.arrow.enabled", "true")
+
+    edges_df = (
+        spark.read.format('json').
+          load('/databricks-datasets/iot/iot_devices.json')
+          .sample(fraction=0.1)
+    )
+    g = graphistry.edges(edges_df, 'device_name', 'cn')
+
+    #notebook
+    displayHTML(g.plot())
+
+    #dashboard: pick size of choice
+    displayHTML(
+      g.settings(url_params={'splashAfter': 'false'})
+        .plot(override_html_style="""
+          width: 50em;
+          height: 50em;
+        """)
+    )
+    ```
+
+* GPU [RAPIDS.ai](https://www.rapids.ai) cudf
+
+    ```python
+    edges = cudf.read_csv('facebook_combined.txt', sep=' ', names=['src', 'dst'])
+    graphistry.edges(edges, 'src', 'dst').plot()
+    ```
+
+* GPU [RAPIDS.ai](https://www.rapids.ai) cuML
+
+    ```python
+    g = graphistry.nodes(cudf.read_csv('rows.csv'))
+    g = graphistry.nodes(G)
+    g.umap(engine='cuml',metric='euclidean').plot()
+    ```
+
+* GPU [RAPIDS.ai](https://www.rapids.ai) cugraph ([notebook demo](demos/demos_databases_apis/gpu_rapids/cugraph.ipynb))
+
+    ```python
+    g = graphistry.from_cugraph(G)
+    g2 = g.compute_cugraph('pagerank')
+    g3 = g2.layout_cugraph('force_atlas2')
+    g3.plot()
+    G3 = g.to_cugraph()
+    ```
+
+* [Apache Arrow](https://arrow.apache.org/)
+
+    ```python
+     edges = pa.Table.from_pandas(pd.read_csv('facebook_combined.txt', sep=' ', names=['src', 'dst']))
+     graphistry.edges(edges, 'src', 'dst').plot()
+    ```
+
+* [Neo4j](http://neo4j.com) ([notebook demo](demos/demos_databases_apis/neo4j/official/graphistry_bolt_tutorial_public.ipynb))
+
+    ```python
+    NEO4J_CREDS = {'uri': 'bolt://my.site.ngo:7687', 'auth': ('neo4j', 'mypwd')}
+    graphistry.register(bolt=NEO4J_CREDS)
+    graphistry.cypher("MATCH (n1)-[r1]->(n2) RETURN n1, r1, n2 LIMIT 1000").plot()
+    ```
+
+    ```python
+    graphistry.cypher("CALL db.schema()").plot()
+    ```
+
+    ```python
+    from neo4j import GraphDatabase, Driver
+    graphistry.register(bolt=GraphDatabase.driver(**NEO4J_CREDS))
+    g = graphistry.cypher("""
+      MATCH (a)-[p:PAYMENT]->(b)
+      WHERE p.USD > 7000 AND p.USD < 10000
+      RETURN a, p, b
+      LIMIT 100000""")
+    print(g._edges.columns)
+    g.plot()
+    ```
+
+* [Azure Cosmos DB (Gremlin)](https://azure.microsoft.com/en-us/services/cosmos-db/)
+
+    ```python
+    # pip install --user gremlinpython
+    # Options in help(graphistry.cosmos)
+    g = graphistry.cosmos(
+        COSMOS_ACCOUNT='',
+        COSMOS_DB='',
+        COSMOS_CONTAINER='',
+        COSMOS_PRIMARY_KEY=''
+    )
+    g2 = g.gremlin('g.E().sample(10000)').fetch_nodes()
+    g2.plot()
+    ```
+
+* [Amazon Neptune (Gremlin)](https://aws.amazon.com/neptune/) ([notebook demo](demos/demos_databases_apis/neptune/neptune_tutorial.ipynb), [dashboarding demo](https://aws.amazon.com/blogs/database/enabling-low-code-graph-data-apps-with-amazon-neptune-and-graphistry/))
+
+    ```python
+    # pip install --user gremlinpython==3.4.10
+    #   - Deploy tips: https://github.com/graphistry/graph-app-kit/blob/master/docs/neptune.md
+    #   - Versioning tips: https://gist.github.com/lmeyerov/459f6f0360abea787909c7c8c8f04cee
+    #   - Login options in help(graphistry.neptune)
+    g = graphistry.neptune(endpoint='wss://zzz:8182/gremlin')
+    g2 = g.gremlin('g.E().limit(100)').fetch_nodes()
+    g2.plot()
+    ```
+
+* [TigerGraph](https://tigergraph.com) ([notebook demo](demos/demos_databases_apis/tigergraph/tigergraph_pygraphistry_bindings.ipynb))
+
+    ```python
+    g = graphistry.tigergraph(protocol='https', ...)
+    g2 = g.gsql("...", {'edges': '@@eList'})
+    g2.plot()
+    print('# edges', len(g2._edges))
+    ```
+
+    ```python
+    g.endpoint('my_fn', {'arg': 'val'}, {'edges': '@@eList'}).plot()
+    ```
+
+* [igraph](http://igraph.org)
+
+    ```python
+    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 = igraph.read('facebook_combined.txt', format='edgelist', directed=False)
+    g = graphistry.from_igraph(ig)  # full conversion
+    g.plot()
+
+    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](https://networkx.github.io) ([notebook demo](demos/demos_databases_apis/networkx/networkx.ipynb))
+
+    ```python
+    graph = networkx.read_edgelist('facebook_combined.txt')
+    graphistry.bind(source='src', destination='dst', node='nodeid').plot(graph)
+    ```
+
+* [HyperNetX](https://github.com/pnnl/HyperNetX) ([notebook demo](demos/demos_databases_apis/hypernetx/hypernetx.ipynb))
+
+    ```python
+    hg.hypernetx_to_graphistry_nodes(H).plot()
+    ```
+
+    ```python
+    hg.hypernetx_to_graphistry_bipartite(H.dual()).plot()
+    ```
+
+* [Splunk](https://www.splunk.com) ([notebook demo](demos/demos_databases_apis/splunk/splunk_demo_public.ipynb))
+
+    ```python
+    df = splunkToPandas("index=netflow bytes > 100000 | head 100000", {})
+    graphistry.edges(df, 'src_ip', 'dest_ip').plot()
+    ```
+
+* [NodeXL](https://www.nodexl.com) ([notebook demo](demos/demos_databases_apis/nodexl/official/nodexl_graphistry.ipynb))
+
+    ```python
+    graphistry.nodexl('/my/file.xls').plot()
+    ```
+
+    ```python
+    graphistry.nodexl('https://file.xls').plot()
+    ```
+
+    ```python
+    graphistry.nodexl('https://file.xls', 'twitter').plot()
+    graphistry.nodexl('https://file.xls', verbose=True).plot()
+    graphistry.nodexl('https://file.xls', engine='xlsxwriter').plot()
+    graphistry.nodexl('https://file.xls')._nodes
+    ```
+
+## 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
+
+    ```python
+    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:
+
+    ```python
+    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:
+
+    ```python
+    # 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
+
+   ```python
+    # 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)
+    new_model.update(dict(
+                      y=[...],
+                      kind='edges', 
+                      model_name='sbert/cool_transformer_model', 
+                      use_scaler_target='kbins', 
+                      n_bins=11, 
+                      strategy='normal'))
+    print(new_model)
+
+    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](https://umap-learn.readthedocs.io/en/latest/), [cuML-based UMAP](https://docs.rapids.ai/api/cuml/stable/api.html?highlight=umap#cuml.UMAP)
+
+* Reduce dimensionality by plotting a similarity graph from feature vectors:
+
+    ```python
+      # 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.
+      g.plot()
+    ```
+
+* Apply a trained model to new data:
+
+    ```python
+      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.
+
+    ```python
+      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)`):
+
+    ```python
+      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"`.:
+
+    ```python
+      g.umap(engine='cuml')
+    ```
+
+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](https://docs.dgl.ai/en/0.6.x/index.html)
+
+* Graphistry adds bindings and automation to working with popular GNN models, currently focusing on DGL/PyTorch:
+
+    ```python
+    g = (graphistry
+        .nodes(ndf)
+        .edges(edf, src, dst)
+        .build_gnn(
+            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](https://www.sbert.net/examples/applications/semantic-search/README.html)
+
+* Search textual data semantically and see the resulting graph:
+
+    ```python
+      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 = g2.search('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: 
+
+    ```python
+      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:
+
+    ```python
+      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
+      g3.plot()
+
+      # 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)
+
+    ```python
+      # 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
+      g5.plot()
+
+      g6 = g.predict_links(source=['ip_address_1', 'user_id_3'], 
+                      relation=['attempt_logon', 'phishing', ..], 
+                      destination=['user_id_1', 'active_directory', ..], 
+                      anomalous=True,
+                      threshold=0.05)
+      g6.plot()
+    ```
+
+* Train a RGCN model including auto-featurized node embeddings
+
+    ```python
+      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)
+      g2.predict_links_all(threshold=0.95).plot()
+    ```
+
+See `help(g.embed)`, `help(g.predict_links)` , or `help(g.predict_links_all)` for options
+
+### DBSCAN 
+
+* Enrich UMAP embeddings or featurization dataframe with GPU or CPU DBSCAN
+
+    ```python
+      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](https://hub.graphistry.com/docs/api/2/rest/upload/#createdataset2) and set [all sorts of URL options](https://hub.graphistry.com/docs/api/1/rest/url/). Check out the tutorials on [colors](demos/more_examples/graphistry_features/encodings-colors.ipynb), [sizes](demos/more_examples/graphistry_features/encodings-sizes.ipynb), [icons](demos/more_examples/graphistry_features/encodings-icons.ipynb), [badges](demos/more_examples/graphistry_features/encodings-badges.ipynb), [weighted clustering](demos/more_examples/graphistry_features/edge-weights.ipynb) and [sharing controls](https://github.com/graphistry/pygraphistry/blob/master/demos/more_examples/graphistry_features/sharing_tutorial.ipynb):
+
+  ```python
+    g
+      .privacy(mode='private', invited_users=[{'email': 'friend1@site.ngo', 'action': '10'}], notify=False)
+      .edges(df, 'col_a', 'col_b')
+      .edges(my_transform1(g._edges))
+      .nodes(df, 'col_c')
+      .nodes(my_transform2(g._nodes))
+      .bind(source='col_a', destination='col_b', node='col_c')
+      .bind(
+        point_color='col_a',
+        point_size='col_b',
+        point_title='col_c',
+        point_x='col_d',
+        point_y='col_e')
+      .bind(
+        edge_color='col_m',
+        edge_weight='col_n',
+        edge_title='col_o')
+      .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={})
+      .settings(url_params={
+        '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
+      })
+      .plot()
+  ```
+
+### Gallery
+
+<table>
+    <tr valign="top">
+        <td width="33%" align="center"><a href="http://hub.graphistry.com/graph/graph.html?dataset=Twitter&splashAfter=true" target="_blank">Twitter Botnet<br><img width="266" src="http://i.imgur.com/qm5MCqS.jpg"></a></td>
+        <td width="33%" align="center">Edit Wars on Wikipedia<br><a href="http://i.imgur.com/074zFve.png" target="_blank"><img width="266" src="http://i.imgur.com/074zFve.png"></a><em>Source: <a href="http://snap.stanford.edu" target="_blank">SNAP</a></em></td>
+        <td width="33%" align="center"><a href="https://hub.graphistry.com/graph/graph.html?dataset=bitC&splashAfter=true" target="_blank">100,000 Bitcoin Transactions<br><img width="266" height="266" src="http://imgur.com/download/axIkjfd"></a></td>
+    </tr>
+    <tr valign="top">
+        <td width="33%" align="center">Port Scan Attack<br><a href="http://i.imgur.com/vKUDySw.png" target="_blank"><img width="266" src="http://i.imgur.com/vKUDySw.png"></a></td>
+        <td width="33%" align="center"><a href="http://hub.graphistry.com/graph/graph.html?dataset=PyGraphistry/M9RL4PQFSF&usertag=github&info=true&static=true&contentKey=Biogrid_Github_Demo&play=3000&center=false&menu=true&goLive=false&left=-2.58e+4&right=4.35e+4&top=-1.72e+4&bottom=2.16e+4&legend={%22title%22:%22%3Ch3%3EBioGRID%20Repository%20of%20Protein%20Interactions%3C/h3%3E%22,%22subtitle%22:%22%3Cp%3EEach%20color%20represents%20an%20organism.%20Humans%20are%20in%20light%20blue.%3C/p%3E%22,%22nodes%22:%22Proteins/Genes%22,%22edges%22:%22Interactions%20reported%20in%20scientific%20publications%22}" target="_blank">Protein Interactions <br><img width="266" src="http://i.imgur.com/nrUHLFz.png" target="_blank"></a><em>Source: <a href="http://thebiogrid.org" target="_blank">BioGRID</a></em></td>
+        <td width="33%" align="center"><a href="http://hub.graphistry.com/graph/graph.html?&dataset=PyGraphistry/PC7D53HHS5&info=true&static=true&contentKey=SocioPlt_Github_Demo&play=3000&center=false&menu=true&goLive=false&left=-236&right=265&top=-145&bottom=134&usertag=github&legend=%7B%22nodes%22%3A%20%22%3Cspan%20style%3D%5C%22color%3A%23a6cee3%3B%5C%22%3ELanguages%3C/span%3E%20/%20%3Cspan%20style%3D%5C%22color%3Argb%28106%2C%2061%2C%20154%29%3B%5C%22%3EStatements%3C/span%3E%22%2C%20%22edges%22%3A%20%22Strong%20Correlations%22%2C%20%22subtitle%22%3A%20%22%3Cp%3EFor%20more%20information%2C%20check%20out%20the%20%3Ca%20target%3D%5C%22_blank%5C%22%20href%3D%5C%22https%3A//lmeyerov.github.io/projects/socioplt/viz/index.html%5C%22%3ESocio-PLT%3C/a%3E%20project.%20Make%20your%20own%20visualizations%20with%20%3Ca%20target%3D%5C%22_blank%5C%22%20href%3D%5C%22https%3A//github.com/graphistry/pygraphistry%5C%22%3EPyGraphistry%3C/a%3E.%3C/p%3E%22%2C%20%22title%22%3A%20%22%3Ch3%3ECorrelation%20Between%20Statements%20about%20Programming%20Languages%3C/h3%3E%22%7D" target="_blank">Programming Languages<br><img width="266" src="http://i.imgur.com/0T0EKmD.png"></a><em>Source: <a href="http://lmeyerov.github.io/projects/socioplt/viz/index.html" target="_blank">Socio-PLT project</a></em></td>
+    </tr>
+</table>
+
+## Install
+
+### Get
+
+You need to install the PyGraphistry Python client and connect it to a Graphistry GPU server of your choice:
+
+1. Graphistry server account:
+    * Create a free [Graphistry Hub account](https://www.graphistry.com/get-started) for open data, or [one-click launch your own private AWS/Azure instance](https://www.graphistry.com/get-started)
+    * Later, [setup and manage](https://github.com/graphistry/graphistry-cli) your own private Docker instance ([contact](https://www.graphistry.com/demo-request))
+
+2. PyGraphistry Python client:
+    * `pip install --user graphistry` (Python 3.7+) or [directly call the HTTP API](https://hub.graphistry.com/docs/api/)
+        * Use `pip install --user graphistry[all]` for optional dependencies such as Neo4j drivers
+    * To use from a notebook environment, run your own [Jupyter](https://jupyter.org/) server ([one-click launch your own private AWS/Azure GPU instance](https://www.graphistry.com/get-started)) or another such as [Google Colab](https://colab.research.google.com)
+    * See immediately following `configure` section for how to connect
+
+### Configure
+
+Most users connect to a Graphistry GPU server account via:
+
+* `graphistry.register(api=3, username='abc', password='xyz')`: personal hub.graphistry.com account
+* `graphistry.register(api=3, username='abc', password='xyz', org_name='optional_org')`: team hub.graphistry.com account
+* `graphistry.register(api=3, username='abc', password='xyz', org_name='optiona_org', protocol='http', server='my.private_server.org')`: 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](https://hub.graphistry.com) public API
+
+  * Connect to a [private Graphistry server](https://www.graphistry.com/get-started) 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](https://hub.graphistry.com/docs/api/1/rest/auth/).
+
+#### Advanced Login
+
+* **For people:** Provide your account username/password:
+
+```python
+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:
+
+```python
+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
+graphistry.refresh()
+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:
+
+```python
+graphistry.register(protocol='https', server='hub.graphistry.com')
+```
+
+Private Graphistry notebook environments are preconfigured to fill in this data for you:
+
+```python
+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., `https://graphistry.acme.ngo/`). In this case, explicitly set a  client (browser) location different from `protocol` / `server`:
+
+```python
+graphistry.register(
+    ### fast local notebook<>graphistry upload
+    protocol='http', server='nginx',
+
+    ### shareable public URL for browsers
+    client_protocol_hostname='https://graphistry.acme.ngo'
+)
+```
+
+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:
+
+```python
+graphistry.privacy()  # graphistry.privacy(mode='private')
+```
+
+* Organizations: You can login with an organization and share only within it
+
+```python
+graphistry.register(api=3, username='...', password='...', org_name='my-org123')
+graphistry.privacy(mode='organization')
+```
+
+* Invitees: You can share access to specify users, and optionally, even email them invites
+
+```python
+VIEW = "10"
+EDIT = "20"
+graphistry.privacy(
+  mode='private',
+  invited_users=[
+    {"email": "friend1@site1.com", "action": VIEW},
+    {"email": "friend2@site2.com", "action": EDIT}
+  ],
+  notify=True)
+```
+
+* Per-visualization: You can choose different rules for global defaults vs. for specific visualizations
+
+```python
+graphistry.privacy(invited_users=[...])
+g = graphistry.hypergraph(pd.read_csv('...'))['graph']
+g.privacy(notify=True).plot()
+```
+
+See additional examples in the [sharing tutorial](https://github.com/graphistry/pygraphistry/blob/master/demos/more_examples/graphistry_features/sharing_tutorial.ipynb)
+
+## Tutorial: Les Misérables
+
+Let's visualize relationships between the characters in [Les Misérables](http://en.wikipedia.org/wiki/Les_Misérables).
+For this example, we'll choose [Pandas](http://pandas.pydata.org) to wrangle data and [igraph](http://igraph.org) to run a community detection algorithm. You can [view](demos/more_examples/simple/MarvelTutorial.ipynb) the Jupyter notebook containing this example.
+
+Our [dataset is a CSV file](https://raw.githubusercontent.com/graphistry/pygraphistry/master/demos/data/lesmiserables.csv) 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:
+
+```python
+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](demos/demos_by_use_case/logs/malware-hypergraph/Malware%20Hypergraph.ipynb) 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:
+
+```python
+import graphistry
+graphistry.register(api=3, username='YOUR_ACCOUNT_HERE', password='YOUR_PASSWORD_HERE')
+
+g = graphistry.bind(source="source", destination="target")
+g.edges(links).plot()
+```
+
+You should see a beautiful graph like this one:
+![Graph of Miserables](http://i.imgur.com/dRHHTyK.png)
+
+### 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.
+
+```python
+links["label"] = links.value.map(lambda v: "#Meetings: %d" % v)
+g = g.bind(edge_title="label")
+g.edges(links).plot()
+```
+
+### Controlling Node Title, Size, Color, and Position
+
+Let's size nodes based on their [PageRank](http://en.wikipedia.org/wiki/PageRank) score and color them using their [community](https://en.wikipedia.org/wiki/Community_structure).
+
+#### Warmup: igraph for computing statistics
+
+[igraph](http://igraph.org/python/) 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*).
+
+```python
+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](https://igraph.org/python/api/latest/igraph.Graph.html). 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:
+
+```python
+g.bind(point_color='community', point_size='pagerank').plot()
+```
+
+See the [color palette documentation](https://hub.graphistry.com/docs/api/2/rest/upload/colors/#extendedpalette2) 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](demos/more_examples/graphistry_features/external_layout) and [additional url parameters](https://hub.graphistry.com/docs/api/1/rest/url/#urloptions)]). 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](demos/more_examples/graphistry_features/encodings-colors.ipynb) and [sizes](demos/more_examples/graphistry_features/encodings-sizes.ipynb).
+
+![Second Graph of Miserables](http://i.imgur.com/P7fm5sn.png)
+
+### 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](https://hub.graphistry.com/docs/api/2/rest/upload/colors/#extendedpalette2).)
+
+Similarly, you can bind the edge weight, where higher weights cause nodes to cluster closer together: `.bind(edge_weight='colA')`. [See tutorial](demos/more_examples/graphistry_features/edge-weights.ipynb).
+
+For more in-depth examples, check out the tutorials on [colors](demos/more_examples/graphistry_features/encodings-colors.ipynb) and [weighted clustering](demos/more_examples/graphistry_features/edge-weights.ipynb).
+
+### More advanced color and size controls
+
+You may want more controls like using gradients or maping specific values:
+
+```python
+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') 
+g.encode_edge_color('brand',
+  categorical_mapping={'toyota': 'red', 'ford': 'blue'},
+  default_mapping='#CCC')
+g.encode_point_size('numeric_col')
+g.encode_point_size('criticality',
+  categorical_mapping={'critical': 200, 'ok': 100},
+  default_mapping=50)
+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](demos/more_examples/graphistry_features/encodings-colors.ipynb).
+
+### 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
+
+```python
+g.encode_point_icon(
+  'some_column',
+  shape="circle", #clip excess
+  categorical_mapping={
+      'macbook': 'laptop', #https://fontawesome.com/v4.7.0/icons/
+      'Canada': 'flag-icon-ca', #ISO3611-Alpha-2: https://github.com/datasets/country-codes/blob/master/data/country-codes.csv
+      'embedded_smile': 'data:svg...',
+      'external_logo': 'http://..../img.png'
+  },
+  default_mapping="question")
+g.encode_point_icon(
+  'another_column',
+  continuous_binning=[
+    [20, 'info'],
+    [80, 'exclamation-circle'],
+    [None, 'exclamation-triangle']
+  ]
+)
+g.encode_point_icon(
+  'another_column',
+  as_text=True,
+  categorical_mapping={
+    'Canada': 'CA',
+    'United States': 'US'
+    }
+)
+```
+
+For more in-depth examples, check out the tutorials on [icons](demos/more_examples/graphistry_features/encodings-icons.ipynb).
+
+#### Badges
+
+```python
+# 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=...,
+  shape="circle",
+  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](demos/more_examples/graphistry_features/encodings-badges.ipynb).
+
+#### Axes
+
+Radial axes support three coloring types (`'external'`, `'internal'`, and `'space'`) and optional labels:
+
+```python
+ g.encode_axis([
+  {'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:
+
+```python
+g.encode_axis([
+  {"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:
+
+```python
+g2 = (g
+  .nodes(
+    g._nodes.assign(
+      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:
+
+```python
+g2 = (g
+  .nodes(
+    g._nodes.assign(
+      y = 50 * g._nodes['level'])
+  )).settings(url_params={'lockedY': 'true', 'play': 1000})
+```
+
+### Theming
+
+You can customize several style options to match your theme:
+
+```python
+g.addStyle(bg={'color': 'red'})
+g.addStyle(bg={
+  'color': '#333',
+  'gradient': {
+    'kind': 'radial',
+    'stops': [ ["rgba(255,255,255, 0.1)", "10%", "rgba(0,0,0,0)", "20%"] ]}})
+g.addStyle(bg={'image': {'url': 'http://site.com/cool.png', 'blendMode': 'multiply'}})
+g.addStyle(fg={'blendMode': 'color-burn'})
+g.addStyle(page={'title': 'My site'})
+g.addStyle(page={'favicon': 'http://site.com/favicon.ico'})
+g.addStyle(logo={'url': 'http://www.site.com/transparent_logo.png'})
+g.addStyle(logo={
+  'url': 'http://www.site.com/transparent_logo.png',
+  'dimensions': {'maxHeight': 200, 'maxWidth': 200},
+  'style': {'opacity': 0.5}
+})
+```
+
+### Transforms
+
+The below methods let you quickly manipulate graphs directly and with dataframe methods: Search, pattern mine, transform, and more:
+
+```python
+from graphistry import n, e_forward, e_reverse, e_undirected
+g = (graphistry
+  .edges(pd.DataFrame({
+    's': ['a', 'b'],
+    'd': ['b', 'c'],
+    'k1': ['x', 'y']
+  }))
+  .nodes(pd.DataFrame({
+    '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
+
+(g
+  .materialize_nodes()
+  .get_degrees()
+  .get_indegrees()
+  .get_outdegrees()
+  .pipe(lambda g2: g2.nodes(g2._nodes.assign(t=x))) # transform
+  .filter_edges_by_dict({"k1": "x"})
+  .filter_nodes_by_dict({"k2": 4})
+  .prune_self_edges()
+  .hop( # filter to subgraph
+    #almost all optional
+    direction='forward', # 'reverse', 'undirected'
+    hops=1, # number or None if to_fixed_point
+    to_fixed_point=False, 
+    source_node_match={"k2": 0},
+    edge_match={"k1": "x"},
+    destination_node_match={"k2": 2})
+  .chain([ # filter to subgraph
+    n(),
+    n({'k2': 0}),
+    n(name="start"), # add column 'start':bool
+    e_forward({'k1': 'x'}, hops=1), # same API as hop()
+    e_undirected(name='second_edge'),
+  ])
+  .collapse(node='some_id', column='some_col', attribute='some val')
+```
+
+#### Table to graph
+
+```python
+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, ... |
+g.plot()
+```
+
+#### Generate node table
+
+```python
+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
+
+```python
+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:
+
+```python
+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()`:
+
+```python
+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:
+
+```python
+
+# (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')
+
+g5.plot()
+```
+
+Rich compound patterns are enabled via `.chain()`:
+
+```python
+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(hops=3),
+  e_forward(name="final_edge"), # tag edge matches
+  n(name="end")
+])
+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 ]))
+```
+
+#### Pipelining
+
+```python
+def capitalize(df, col):
+  df2 = df.copy()
+  df2[col] df[col].str.capitalize()
+  return df2
+
+g
+  .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
+
+```python
+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
+
+```python
+# 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:
+
+```python
+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:
+
+```python
+g3 = g
+for v in g._nodes['some_col'].unique():
+  g3 = g3.collapse(node='root_node_id', column='some_col', attribute=v)
+```
+
+### Control layouts
+
+#### Tree
+
+```python
+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](https://igraph.org/python/doc/api/igraph.Graph.html#layout):
+
+```python
+g.layout_igraph('sugiyama').plot()
+g.layout_igraph('sugiyama', directed=True, params={}).plot()
+```
+
+See list [`layout_algs`](https://github.com/graphistry/pygraphistry/blob/master/graphistry/plugins/igraph.py#L365)
+
+### Plugin: cugraph
+
+With [Nvidia RAPIDS cuGraph](https://www.rapids.ai) install:
+
+```python
+g.layout_cugraph('force_atlas2').plot()
+help(g.layout_cugraph)
+```
+
+See list [`layout_algs`](https://github.com/graphistry/pygraphistry/blob/master/graphistry/plugins/cugraph.py#L315)
+
+#### Group-in-a-box layout
+
+[Group-in-a-box layout](https://ieeexplore.ieee.org/document/6113135) with igraph/pandas and cugraph/cudf implementations:
+
+```python
+g.group_in_a_box_layout().plot()
+g.group_in_a_box_layout(
+  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
+  #encode_colors=False,
+  #colors=['#FFF', '#FF0', ...]
+  engine='cudf'
+).plot()
+```
+
+### Control render settings
+
+```python
+g = graphistry.edges(pd.DataFrame({'s': ['a', 'b', 'b'], 'd': ['b', 'c', 'd']}))
+g2 = g.scene_settings(
+  #hide menus
+  menu=False,
+  info=False,
+  #tweak graph
+  show_arrows=False,
+  point_size=1.0,
+  edge_curvature=0.0,
+  edge_opacity=0.5,
+  point_opacity=0.9
+).plot()
+
+```
+
+With `pip install graphistry[igraph]`, you can also use [`igraph` layouts](https://igraph.org/python/doc/api/igraph.Graph.html#layout):
+
+```python
+g.layout_igraph('sugiyama').plot()
+g.layout_igraph('sugiyama', directed=True, params={}).plot()
+```
+
+## Next Steps
+
+1. Create a free public data [Graphistry Hub](https://www.graphistry.com/get-started) account or [one-click launch a private Graphistry instance in AWS](https://www.graphistry.com/get-started)
+2. Check out the [analyst](demos/for_analysis.ipynb) and [developer](demos/for_developers.ipynb) introductions, or [try your own CSV](demos/upload_csv_miniapp.ipynb)
+3. Explore the [demos folder](demos) for your favorite [file format, database, API](demos/demos_databases_apis), use case domain, kind of analysis, and [visual analytics feature](demos/more_examples/graphistry_features)
+
+## Resources
+
+* Graphistry [In-Tool UI Guide](https://hub.graphistry.com/docs/ui/index/)
+* [General and REST API docs](https://hub.graphistry.com/docs/api/):
+  * [URL settings](https://hub.graphistry.com/docs/api/1/rest/url/#urloptions)
+  * [Authentication](https://hub.graphistry.com/docs/api/1/rest/auth/)
+  * [Uploading](https://hub.graphistry.com/docs/api/2/rest/upload/#createdataset2), including multiple file formats and settings
+  * [Color bindings](https://hub.graphistry.com/docs/api/2/rest/upload/colors/#extendedpalette2) and [color palettes](https://hub.graphistry.com/docs/api/api-color-palettes/) (ColorBrewer)
+  * Bindings and colors, REST API, embedding URLs and URL parameters, dynamic JS API, and more
+  * JavaScript and more!
+* Python-specific
+  * [Python API ReadTheDocs](http://pygraphistry.readthedocs.org/en/latest/)
+  * Within a notebook, you can always run `help(graphistry)`, `help(graphistry.hypergraph)`, etc.
+* [Administration docs](https://github.com/graphistry/graphistry-cli) for sizing, installing, configuring, managing, and updating Graphistry servers
+* [Graph-App-Kit Dashboarding](https://github.com/graphistry/graph-app-kit) dashboarding
+
+
+%package -n python3-graphistry
+Summary:	A visual graph analytics library for extracting, transforming, displaying, and sharing big graphs with end-to-end GPU acceleration
+Provides:	python-graphistry
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-graphistry
+# PyGraphistry: Explore Relationships
+
+![Build Status](https://github.com/graphistry/pygraphistry/workflows/CI%20Tests/badge.svg)
+[![CodeQL](https://github.com/graphistry/pygraphistry/workflows/CodeQL/badge.svg)](https://github.com/graphistry/pygraphistry/actions?query=workflow%3ACodeQL)
+[![Documentation Status](https://readthedocs.org/projects/pygraphistry/badge/?version=latest)](https://pygraphistry.readthedocs.io/en/latest/)
+[![Latest Version](https://img.shields.io/pypi/v/graphistry.svg)](https://pypi.python.org/pypi/graphistry)
+[![Latest Version](https://img.shields.io/pypi/pyversions/graphistry.svg)](https://pypi.python.org/pypi/graphistry)
+[![License](https://img.shields.io/pypi/l/graphistry.svg)](https://pypi.python.org/pypi/graphistry)
+![PyPI - Downloads](https://img.shields.io/pypi/dm/graphistry)
+
+[![Uptime Robot status](https://img.shields.io/uptimerobot/status/m787548531-e9c7b7508fc76fea927e2313?label=hub.graphistry.com)](https://status.graphistry.com/) [<img src="https://img.shields.io/badge/slack-Graphistry%20chat-orange.svg?logo=slack">](https://join.slack.com/t/graphistry-community/shared_invite/zt-53ik36w2-fpP0Ibjbk7IJuVFIRSnr6g)
+[![Twitter Follow](https://img.shields.io/twitter/follow/graphistry)](https://twitter.com/graphistry)
+
+**PyGraphistry is a Python visual graph AI library to extract, transform, analyze, model, and visualize big graphs, and especially alongside [Graphistry](https://www.graphistry.com) 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](https://hub.graphistry.com/docs/api/), 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](https://github.com/graphistry/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](https://hub.graphistry.com/docs/api/).
+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](#demo-of-friendship-communities-on-facebook)
+2. [Graph Gallery](#gallery)
+3. [Install](#install)
+4. [Tutorial](#tutorial-les-misérables)
+5. [Next Steps](#next-steps)
+6. [Resources](#resources)
+
+## Demo of Friendship Communities on Facebook
+
+<table style="width:100%;">
+  <tr valign="top">
+    <td align="center">Click to open interactive version! <em>(For server-backed interactive analytics, use an API key)</em><a href="http://hub.graphistry.com/graph/graph.html?dataset=Facebook&splashAfter=true" target="_blank"><img src="http://i.imgur.com/Ows4rK4.png" title="Click to open."></a>
+    <em>Source data: <a href="http://snap.stanford.edu" target="_blank">SNAP</a></em>
+    </td>
+  </tr>
+</table>
+
+## 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](https://www.graphistry.com/get-started) or [launch your own private GPU server](https://www.graphistry.com/get-started)
+
+   ```python
+  # 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: hub.graphistry.com
+  #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='my.site.ngo')  # Use with a self-hosted server
+  # ... and if client (browser) URLs are different than python server<> graphistry server uploads
+  #graphistry.register(..., client_protocol_hostname='https://public.acme.co')
+  ```
+
+* **Notebook-friendly:** PyGraphistry plays well with interactive notebooks like [Jupyter](http://ipython.org), [Zeppelin](https://zeppelin.incubator.apache.org/), and [Databricks](http://databricks.com). Process, visualize, and drill into with graphs directly within your notebooks:
+
+    ```python
+    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:
+
+     ```python
+     rows = pandas.read_csv('transactions.csv')[:1000]
+     graphistry.hypergraph(rows)['graph'].plot()
+     ```
+
+* **Embeddable:** Drop live views into your web dashboards and apps (and go further with [JS/React](https://hub.graphistry.com/docs)):
+
+    ```python
+    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)](https://github.com/graphistry/pygraphistry/blob/master/demos/more_examples/graphistry_features/sharing_tutorial.ipynb)
+
+* **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:
+
+    ```python
+    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
+    g1.plot()
+    
+    # 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](http://pandas.pydata.org)
+
+     ```python
+     edges = pd.read_csv('facebook_combined.txt', sep=' ', names=['src', 'dst'])
+     graphistry.edges(edges, 'src', 'dst').plot()
+     ```
+
+     ```python
+     table_rows = pd.read_csv('honeypot.csv')
+     graphistry.hypergraph(table_rows, ['attackerIP', 'victimIP', 'victimPort', 'vulnName'])['graph'].plot()
+     ```
+
+     ```python
+     graphistry.hypergraph(table_rows, ['attackerIP', 'victimIP', 'victimPort', 'vulnName'],
+         direct=True,
+         opts={'EDGES': {
+           'attackerIP': ['victimIP', 'victimPort', 'vulnName'],
+           'victimIP': ['victimPort', 'vulnName'],
+           'victimPort': ['vulnName']
+   }})['graph'].plot()
+     ```
+
+     ```python
+     ### 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)
+     ```
+
+    ```python
+    ### 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)
+    g2.plot()
+    ```
+
+* [Spark](https://spark.apache.org/)/[Databricks](https://databricks.com/) ([ipynb demo](demos/demos_databases_apis/databricks_pyspark/graphistry-notebook-dashboard.ipynb), [dbc demo](demos/demos_databases_apis/databricks_pyspark/graphistry-notebook-dashboard.dbc))
+
+    ```python
+    #optional but recommended
+    spark.conf.set("spark.sql.execution.arrow.enabled", "true")
+
+    edges_df = (
+        spark.read.format('json').
+          load('/databricks-datasets/iot/iot_devices.json')
+          .sample(fraction=0.1)
+    )
+    g = graphistry.edges(edges_df, 'device_name', 'cn')
+
+    #notebook
+    displayHTML(g.plot())
+
+    #dashboard: pick size of choice
+    displayHTML(
+      g.settings(url_params={'splashAfter': 'false'})
+        .plot(override_html_style="""
+          width: 50em;
+          height: 50em;
+        """)
+    )
+    ```
+
+* GPU [RAPIDS.ai](https://www.rapids.ai) cudf
+
+    ```python
+    edges = cudf.read_csv('facebook_combined.txt', sep=' ', names=['src', 'dst'])
+    graphistry.edges(edges, 'src', 'dst').plot()
+    ```
+
+* GPU [RAPIDS.ai](https://www.rapids.ai) cuML
+
+    ```python
+    g = graphistry.nodes(cudf.read_csv('rows.csv'))
+    g = graphistry.nodes(G)
+    g.umap(engine='cuml',metric='euclidean').plot()
+    ```
+
+* GPU [RAPIDS.ai](https://www.rapids.ai) cugraph ([notebook demo](demos/demos_databases_apis/gpu_rapids/cugraph.ipynb))
+
+    ```python
+    g = graphistry.from_cugraph(G)
+    g2 = g.compute_cugraph('pagerank')
+    g3 = g2.layout_cugraph('force_atlas2')
+    g3.plot()
+    G3 = g.to_cugraph()
+    ```
+
+* [Apache Arrow](https://arrow.apache.org/)
+
+    ```python
+     edges = pa.Table.from_pandas(pd.read_csv('facebook_combined.txt', sep=' ', names=['src', 'dst']))
+     graphistry.edges(edges, 'src', 'dst').plot()
+    ```
+
+* [Neo4j](http://neo4j.com) ([notebook demo](demos/demos_databases_apis/neo4j/official/graphistry_bolt_tutorial_public.ipynb))
+
+    ```python
+    NEO4J_CREDS = {'uri': 'bolt://my.site.ngo:7687', 'auth': ('neo4j', 'mypwd')}
+    graphistry.register(bolt=NEO4J_CREDS)
+    graphistry.cypher("MATCH (n1)-[r1]->(n2) RETURN n1, r1, n2 LIMIT 1000").plot()
+    ```
+
+    ```python
+    graphistry.cypher("CALL db.schema()").plot()
+    ```
+
+    ```python
+    from neo4j import GraphDatabase, Driver
+    graphistry.register(bolt=GraphDatabase.driver(**NEO4J_CREDS))
+    g = graphistry.cypher("""
+      MATCH (a)-[p:PAYMENT]->(b)
+      WHERE p.USD > 7000 AND p.USD < 10000
+      RETURN a, p, b
+      LIMIT 100000""")
+    print(g._edges.columns)
+    g.plot()
+    ```
+
+* [Azure Cosmos DB (Gremlin)](https://azure.microsoft.com/en-us/services/cosmos-db/)
+
+    ```python
+    # pip install --user gremlinpython
+    # Options in help(graphistry.cosmos)
+    g = graphistry.cosmos(
+        COSMOS_ACCOUNT='',
+        COSMOS_DB='',
+        COSMOS_CONTAINER='',
+        COSMOS_PRIMARY_KEY=''
+    )
+    g2 = g.gremlin('g.E().sample(10000)').fetch_nodes()
+    g2.plot()
+    ```
+
+* [Amazon Neptune (Gremlin)](https://aws.amazon.com/neptune/) ([notebook demo](demos/demos_databases_apis/neptune/neptune_tutorial.ipynb), [dashboarding demo](https://aws.amazon.com/blogs/database/enabling-low-code-graph-data-apps-with-amazon-neptune-and-graphistry/))
+
+    ```python
+    # pip install --user gremlinpython==3.4.10
+    #   - Deploy tips: https://github.com/graphistry/graph-app-kit/blob/master/docs/neptune.md
+    #   - Versioning tips: https://gist.github.com/lmeyerov/459f6f0360abea787909c7c8c8f04cee
+    #   - Login options in help(graphistry.neptune)
+    g = graphistry.neptune(endpoint='wss://zzz:8182/gremlin')
+    g2 = g.gremlin('g.E().limit(100)').fetch_nodes()
+    g2.plot()
+    ```
+
+* [TigerGraph](https://tigergraph.com) ([notebook demo](demos/demos_databases_apis/tigergraph/tigergraph_pygraphistry_bindings.ipynb))
+
+    ```python
+    g = graphistry.tigergraph(protocol='https', ...)
+    g2 = g.gsql("...", {'edges': '@@eList'})
+    g2.plot()
+    print('# edges', len(g2._edges))
+    ```
+
+    ```python
+    g.endpoint('my_fn', {'arg': 'val'}, {'edges': '@@eList'}).plot()
+    ```
+
+* [igraph](http://igraph.org)
+
+    ```python
+    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 = igraph.read('facebook_combined.txt', format='edgelist', directed=False)
+    g = graphistry.from_igraph(ig)  # full conversion
+    g.plot()
+
+    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](https://networkx.github.io) ([notebook demo](demos/demos_databases_apis/networkx/networkx.ipynb))
+
+    ```python
+    graph = networkx.read_edgelist('facebook_combined.txt')
+    graphistry.bind(source='src', destination='dst', node='nodeid').plot(graph)
+    ```
+
+* [HyperNetX](https://github.com/pnnl/HyperNetX) ([notebook demo](demos/demos_databases_apis/hypernetx/hypernetx.ipynb))
+
+    ```python
+    hg.hypernetx_to_graphistry_nodes(H).plot()
+    ```
+
+    ```python
+    hg.hypernetx_to_graphistry_bipartite(H.dual()).plot()
+    ```
+
+* [Splunk](https://www.splunk.com) ([notebook demo](demos/demos_databases_apis/splunk/splunk_demo_public.ipynb))
+
+    ```python
+    df = splunkToPandas("index=netflow bytes > 100000 | head 100000", {})
+    graphistry.edges(df, 'src_ip', 'dest_ip').plot()
+    ```
+
+* [NodeXL](https://www.nodexl.com) ([notebook demo](demos/demos_databases_apis/nodexl/official/nodexl_graphistry.ipynb))
+
+    ```python
+    graphistry.nodexl('/my/file.xls').plot()
+    ```
+
+    ```python
+    graphistry.nodexl('https://file.xls').plot()
+    ```
+
+    ```python
+    graphistry.nodexl('https://file.xls', 'twitter').plot()
+    graphistry.nodexl('https://file.xls', verbose=True).plot()
+    graphistry.nodexl('https://file.xls', engine='xlsxwriter').plot()
+    graphistry.nodexl('https://file.xls')._nodes
+    ```
+
+## 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
+
+    ```python
+    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:
+
+    ```python
+    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:
+
+    ```python
+    # 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
+
+   ```python
+    # 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)
+    new_model.update(dict(
+                      y=[...],
+                      kind='edges', 
+                      model_name='sbert/cool_transformer_model', 
+                      use_scaler_target='kbins', 
+                      n_bins=11, 
+                      strategy='normal'))
+    print(new_model)
+
+    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](https://umap-learn.readthedocs.io/en/latest/), [cuML-based UMAP](https://docs.rapids.ai/api/cuml/stable/api.html?highlight=umap#cuml.UMAP)
+
+* Reduce dimensionality by plotting a similarity graph from feature vectors:
+
+    ```python
+      # 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.
+      g.plot()
+    ```
+
+* Apply a trained model to new data:
+
+    ```python
+      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.
+
+    ```python
+      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)`):
+
+    ```python
+      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"`.:
+
+    ```python
+      g.umap(engine='cuml')
+    ```
+
+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](https://docs.dgl.ai/en/0.6.x/index.html)
+
+* Graphistry adds bindings and automation to working with popular GNN models, currently focusing on DGL/PyTorch:
+
+    ```python
+    g = (graphistry
+        .nodes(ndf)
+        .edges(edf, src, dst)
+        .build_gnn(
+            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](https://www.sbert.net/examples/applications/semantic-search/README.html)
+
+* Search textual data semantically and see the resulting graph:
+
+    ```python
+      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 = g2.search('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: 
+
+    ```python
+      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:
+
+    ```python
+      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
+      g3.plot()
+
+      # 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)
+
+    ```python
+      # 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
+      g5.plot()
+
+      g6 = g.predict_links(source=['ip_address_1', 'user_id_3'], 
+                      relation=['attempt_logon', 'phishing', ..], 
+                      destination=['user_id_1', 'active_directory', ..], 
+                      anomalous=True,
+                      threshold=0.05)
+      g6.plot()
+    ```
+
+* Train a RGCN model including auto-featurized node embeddings
+
+    ```python
+      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)
+      g2.predict_links_all(threshold=0.95).plot()
+    ```
+
+See `help(g.embed)`, `help(g.predict_links)` , or `help(g.predict_links_all)` for options
+
+### DBSCAN 
+
+* Enrich UMAP embeddings or featurization dataframe with GPU or CPU DBSCAN
+
+    ```python
+      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](https://hub.graphistry.com/docs/api/2/rest/upload/#createdataset2) and set [all sorts of URL options](https://hub.graphistry.com/docs/api/1/rest/url/). Check out the tutorials on [colors](demos/more_examples/graphistry_features/encodings-colors.ipynb), [sizes](demos/more_examples/graphistry_features/encodings-sizes.ipynb), [icons](demos/more_examples/graphistry_features/encodings-icons.ipynb), [badges](demos/more_examples/graphistry_features/encodings-badges.ipynb), [weighted clustering](demos/more_examples/graphistry_features/edge-weights.ipynb) and [sharing controls](https://github.com/graphistry/pygraphistry/blob/master/demos/more_examples/graphistry_features/sharing_tutorial.ipynb):
+
+  ```python
+    g
+      .privacy(mode='private', invited_users=[{'email': 'friend1@site.ngo', 'action': '10'}], notify=False)
+      .edges(df, 'col_a', 'col_b')
+      .edges(my_transform1(g._edges))
+      .nodes(df, 'col_c')
+      .nodes(my_transform2(g._nodes))
+      .bind(source='col_a', destination='col_b', node='col_c')
+      .bind(
+        point_color='col_a',
+        point_size='col_b',
+        point_title='col_c',
+        point_x='col_d',
+        point_y='col_e')
+      .bind(
+        edge_color='col_m',
+        edge_weight='col_n',
+        edge_title='col_o')
+      .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={})
+      .settings(url_params={
+        '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
+      })
+      .plot()
+  ```
+
+### Gallery
+
+<table>
+    <tr valign="top">
+        <td width="33%" align="center"><a href="http://hub.graphistry.com/graph/graph.html?dataset=Twitter&splashAfter=true" target="_blank">Twitter Botnet<br><img width="266" src="http://i.imgur.com/qm5MCqS.jpg"></a></td>
+        <td width="33%" align="center">Edit Wars on Wikipedia<br><a href="http://i.imgur.com/074zFve.png" target="_blank"><img width="266" src="http://i.imgur.com/074zFve.png"></a><em>Source: <a href="http://snap.stanford.edu" target="_blank">SNAP</a></em></td>
+        <td width="33%" align="center"><a href="https://hub.graphistry.com/graph/graph.html?dataset=bitC&splashAfter=true" target="_blank">100,000 Bitcoin Transactions<br><img width="266" height="266" src="http://imgur.com/download/axIkjfd"></a></td>
+    </tr>
+    <tr valign="top">
+        <td width="33%" align="center">Port Scan Attack<br><a href="http://i.imgur.com/vKUDySw.png" target="_blank"><img width="266" src="http://i.imgur.com/vKUDySw.png"></a></td>
+        <td width="33%" align="center"><a href="http://hub.graphistry.com/graph/graph.html?dataset=PyGraphistry/M9RL4PQFSF&usertag=github&info=true&static=true&contentKey=Biogrid_Github_Demo&play=3000&center=false&menu=true&goLive=false&left=-2.58e+4&right=4.35e+4&top=-1.72e+4&bottom=2.16e+4&legend={%22title%22:%22%3Ch3%3EBioGRID%20Repository%20of%20Protein%20Interactions%3C/h3%3E%22,%22subtitle%22:%22%3Cp%3EEach%20color%20represents%20an%20organism.%20Humans%20are%20in%20light%20blue.%3C/p%3E%22,%22nodes%22:%22Proteins/Genes%22,%22edges%22:%22Interactions%20reported%20in%20scientific%20publications%22}" target="_blank">Protein Interactions <br><img width="266" src="http://i.imgur.com/nrUHLFz.png" target="_blank"></a><em>Source: <a href="http://thebiogrid.org" target="_blank">BioGRID</a></em></td>
+        <td width="33%" align="center"><a href="http://hub.graphistry.com/graph/graph.html?&dataset=PyGraphistry/PC7D53HHS5&info=true&static=true&contentKey=SocioPlt_Github_Demo&play=3000&center=false&menu=true&goLive=false&left=-236&right=265&top=-145&bottom=134&usertag=github&legend=%7B%22nodes%22%3A%20%22%3Cspan%20style%3D%5C%22color%3A%23a6cee3%3B%5C%22%3ELanguages%3C/span%3E%20/%20%3Cspan%20style%3D%5C%22color%3Argb%28106%2C%2061%2C%20154%29%3B%5C%22%3EStatements%3C/span%3E%22%2C%20%22edges%22%3A%20%22Strong%20Correlations%22%2C%20%22subtitle%22%3A%20%22%3Cp%3EFor%20more%20information%2C%20check%20out%20the%20%3Ca%20target%3D%5C%22_blank%5C%22%20href%3D%5C%22https%3A//lmeyerov.github.io/projects/socioplt/viz/index.html%5C%22%3ESocio-PLT%3C/a%3E%20project.%20Make%20your%20own%20visualizations%20with%20%3Ca%20target%3D%5C%22_blank%5C%22%20href%3D%5C%22https%3A//github.com/graphistry/pygraphistry%5C%22%3EPyGraphistry%3C/a%3E.%3C/p%3E%22%2C%20%22title%22%3A%20%22%3Ch3%3ECorrelation%20Between%20Statements%20about%20Programming%20Languages%3C/h3%3E%22%7D" target="_blank">Programming Languages<br><img width="266" src="http://i.imgur.com/0T0EKmD.png"></a><em>Source: <a href="http://lmeyerov.github.io/projects/socioplt/viz/index.html" target="_blank">Socio-PLT project</a></em></td>
+    </tr>
+</table>
+
+## Install
+
+### Get
+
+You need to install the PyGraphistry Python client and connect it to a Graphistry GPU server of your choice:
+
+1. Graphistry server account:
+    * Create a free [Graphistry Hub account](https://www.graphistry.com/get-started) for open data, or [one-click launch your own private AWS/Azure instance](https://www.graphistry.com/get-started)
+    * Later, [setup and manage](https://github.com/graphistry/graphistry-cli) your own private Docker instance ([contact](https://www.graphistry.com/demo-request))
+
+2. PyGraphistry Python client:
+    * `pip install --user graphistry` (Python 3.7+) or [directly call the HTTP API](https://hub.graphistry.com/docs/api/)
+        * Use `pip install --user graphistry[all]` for optional dependencies such as Neo4j drivers
+    * To use from a notebook environment, run your own [Jupyter](https://jupyter.org/) server ([one-click launch your own private AWS/Azure GPU instance](https://www.graphistry.com/get-started)) or another such as [Google Colab](https://colab.research.google.com)
+    * See immediately following `configure` section for how to connect
+
+### Configure
+
+Most users connect to a Graphistry GPU server account via:
+
+* `graphistry.register(api=3, username='abc', password='xyz')`: personal hub.graphistry.com account
+* `graphistry.register(api=3, username='abc', password='xyz', org_name='optional_org')`: team hub.graphistry.com account
+* `graphistry.register(api=3, username='abc', password='xyz', org_name='optiona_org', protocol='http', server='my.private_server.org')`: 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](https://hub.graphistry.com) public API
+
+  * Connect to a [private Graphistry server](https://www.graphistry.com/get-started) 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](https://hub.graphistry.com/docs/api/1/rest/auth/).
+
+#### Advanced Login
+
+* **For people:** Provide your account username/password:
+
+```python
+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:
+
+```python
+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
+graphistry.refresh()
+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:
+
+```python
+graphistry.register(protocol='https', server='hub.graphistry.com')
+```
+
+Private Graphistry notebook environments are preconfigured to fill in this data for you:
+
+```python
+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., `https://graphistry.acme.ngo/`). In this case, explicitly set a  client (browser) location different from `protocol` / `server`:
+
+```python
+graphistry.register(
+    ### fast local notebook<>graphistry upload
+    protocol='http', server='nginx',
+
+    ### shareable public URL for browsers
+    client_protocol_hostname='https://graphistry.acme.ngo'
+)
+```
+
+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:
+
+```python
+graphistry.privacy()  # graphistry.privacy(mode='private')
+```
+
+* Organizations: You can login with an organization and share only within it
+
+```python
+graphistry.register(api=3, username='...', password='...', org_name='my-org123')
+graphistry.privacy(mode='organization')
+```
+
+* Invitees: You can share access to specify users, and optionally, even email them invites
+
+```python
+VIEW = "10"
+EDIT = "20"
+graphistry.privacy(
+  mode='private',
+  invited_users=[
+    {"email": "friend1@site1.com", "action": VIEW},
+    {"email": "friend2@site2.com", "action": EDIT}
+  ],
+  notify=True)
+```
+
+* Per-visualization: You can choose different rules for global defaults vs. for specific visualizations
+
+```python
+graphistry.privacy(invited_users=[...])
+g = graphistry.hypergraph(pd.read_csv('...'))['graph']
+g.privacy(notify=True).plot()
+```
+
+See additional examples in the [sharing tutorial](https://github.com/graphistry/pygraphistry/blob/master/demos/more_examples/graphistry_features/sharing_tutorial.ipynb)
+
+## Tutorial: Les Misérables
+
+Let's visualize relationships between the characters in [Les Misérables](http://en.wikipedia.org/wiki/Les_Misérables).
+For this example, we'll choose [Pandas](http://pandas.pydata.org) to wrangle data and [igraph](http://igraph.org) to run a community detection algorithm. You can [view](demos/more_examples/simple/MarvelTutorial.ipynb) the Jupyter notebook containing this example.
+
+Our [dataset is a CSV file](https://raw.githubusercontent.com/graphistry/pygraphistry/master/demos/data/lesmiserables.csv) 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:
+
+```python
+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](demos/demos_by_use_case/logs/malware-hypergraph/Malware%20Hypergraph.ipynb) 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:
+
+```python
+import graphistry
+graphistry.register(api=3, username='YOUR_ACCOUNT_HERE', password='YOUR_PASSWORD_HERE')
+
+g = graphistry.bind(source="source", destination="target")
+g.edges(links).plot()
+```
+
+You should see a beautiful graph like this one:
+![Graph of Miserables](http://i.imgur.com/dRHHTyK.png)
+
+### 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.
+
+```python
+links["label"] = links.value.map(lambda v: "#Meetings: %d" % v)
+g = g.bind(edge_title="label")
+g.edges(links).plot()
+```
+
+### Controlling Node Title, Size, Color, and Position
+
+Let's size nodes based on their [PageRank](http://en.wikipedia.org/wiki/PageRank) score and color them using their [community](https://en.wikipedia.org/wiki/Community_structure).
+
+#### Warmup: igraph for computing statistics
+
+[igraph](http://igraph.org/python/) 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*).
+
+```python
+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](https://igraph.org/python/api/latest/igraph.Graph.html). 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:
+
+```python
+g.bind(point_color='community', point_size='pagerank').plot()
+```
+
+See the [color palette documentation](https://hub.graphistry.com/docs/api/2/rest/upload/colors/#extendedpalette2) 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](demos/more_examples/graphistry_features/external_layout) and [additional url parameters](https://hub.graphistry.com/docs/api/1/rest/url/#urloptions)]). 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](demos/more_examples/graphistry_features/encodings-colors.ipynb) and [sizes](demos/more_examples/graphistry_features/encodings-sizes.ipynb).
+
+![Second Graph of Miserables](http://i.imgur.com/P7fm5sn.png)
+
+### 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](https://hub.graphistry.com/docs/api/2/rest/upload/colors/#extendedpalette2).)
+
+Similarly, you can bind the edge weight, where higher weights cause nodes to cluster closer together: `.bind(edge_weight='colA')`. [See tutorial](demos/more_examples/graphistry_features/edge-weights.ipynb).
+
+For more in-depth examples, check out the tutorials on [colors](demos/more_examples/graphistry_features/encodings-colors.ipynb) and [weighted clustering](demos/more_examples/graphistry_features/edge-weights.ipynb).
+
+### More advanced color and size controls
+
+You may want more controls like using gradients or maping specific values:
+
+```python
+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') 
+g.encode_edge_color('brand',
+  categorical_mapping={'toyota': 'red', 'ford': 'blue'},
+  default_mapping='#CCC')
+g.encode_point_size('numeric_col')
+g.encode_point_size('criticality',
+  categorical_mapping={'critical': 200, 'ok': 100},
+  default_mapping=50)
+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](demos/more_examples/graphistry_features/encodings-colors.ipynb).
+
+### 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
+
+```python
+g.encode_point_icon(
+  'some_column',
+  shape="circle", #clip excess
+  categorical_mapping={
+      'macbook': 'laptop', #https://fontawesome.com/v4.7.0/icons/
+      'Canada': 'flag-icon-ca', #ISO3611-Alpha-2: https://github.com/datasets/country-codes/blob/master/data/country-codes.csv
+      'embedded_smile': 'data:svg...',
+      'external_logo': 'http://..../img.png'
+  },
+  default_mapping="question")
+g.encode_point_icon(
+  'another_column',
+  continuous_binning=[
+    [20, 'info'],
+    [80, 'exclamation-circle'],
+    [None, 'exclamation-triangle']
+  ]
+)
+g.encode_point_icon(
+  'another_column',
+  as_text=True,
+  categorical_mapping={
+    'Canada': 'CA',
+    'United States': 'US'
+    }
+)
+```
+
+For more in-depth examples, check out the tutorials on [icons](demos/more_examples/graphistry_features/encodings-icons.ipynb).
+
+#### Badges
+
+```python
+# 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=...,
+  shape="circle",
+  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](demos/more_examples/graphistry_features/encodings-badges.ipynb).
+
+#### Axes
+
+Radial axes support three coloring types (`'external'`, `'internal'`, and `'space'`) and optional labels:
+
+```python
+ g.encode_axis([
+  {'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:
+
+```python
+g.encode_axis([
+  {"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:
+
+```python
+g2 = (g
+  .nodes(
+    g._nodes.assign(
+      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:
+
+```python
+g2 = (g
+  .nodes(
+    g._nodes.assign(
+      y = 50 * g._nodes['level'])
+  )).settings(url_params={'lockedY': 'true', 'play': 1000})
+```
+
+### Theming
+
+You can customize several style options to match your theme:
+
+```python
+g.addStyle(bg={'color': 'red'})
+g.addStyle(bg={
+  'color': '#333',
+  'gradient': {
+    'kind': 'radial',
+    'stops': [ ["rgba(255,255,255, 0.1)", "10%", "rgba(0,0,0,0)", "20%"] ]}})
+g.addStyle(bg={'image': {'url': 'http://site.com/cool.png', 'blendMode': 'multiply'}})
+g.addStyle(fg={'blendMode': 'color-burn'})
+g.addStyle(page={'title': 'My site'})
+g.addStyle(page={'favicon': 'http://site.com/favicon.ico'})
+g.addStyle(logo={'url': 'http://www.site.com/transparent_logo.png'})
+g.addStyle(logo={
+  'url': 'http://www.site.com/transparent_logo.png',
+  'dimensions': {'maxHeight': 200, 'maxWidth': 200},
+  'style': {'opacity': 0.5}
+})
+```
+
+### Transforms
+
+The below methods let you quickly manipulate graphs directly and with dataframe methods: Search, pattern mine, transform, and more:
+
+```python
+from graphistry import n, e_forward, e_reverse, e_undirected
+g = (graphistry
+  .edges(pd.DataFrame({
+    's': ['a', 'b'],
+    'd': ['b', 'c'],
+    'k1': ['x', 'y']
+  }))
+  .nodes(pd.DataFrame({
+    '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
+
+(g
+  .materialize_nodes()
+  .get_degrees()
+  .get_indegrees()
+  .get_outdegrees()
+  .pipe(lambda g2: g2.nodes(g2._nodes.assign(t=x))) # transform
+  .filter_edges_by_dict({"k1": "x"})
+  .filter_nodes_by_dict({"k2": 4})
+  .prune_self_edges()
+  .hop( # filter to subgraph
+    #almost all optional
+    direction='forward', # 'reverse', 'undirected'
+    hops=1, # number or None if to_fixed_point
+    to_fixed_point=False, 
+    source_node_match={"k2": 0},
+    edge_match={"k1": "x"},
+    destination_node_match={"k2": 2})
+  .chain([ # filter to subgraph
+    n(),
+    n({'k2': 0}),
+    n(name="start"), # add column 'start':bool
+    e_forward({'k1': 'x'}, hops=1), # same API as hop()
+    e_undirected(name='second_edge'),
+  ])
+  .collapse(node='some_id', column='some_col', attribute='some val')
+```
+
+#### Table to graph
+
+```python
+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, ... |
+g.plot()
+```
+
+#### Generate node table
+
+```python
+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
+
+```python
+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:
+
+```python
+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()`:
+
+```python
+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:
+
+```python
+
+# (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')
+
+g5.plot()
+```
+
+Rich compound patterns are enabled via `.chain()`:
+
+```python
+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(hops=3),
+  e_forward(name="final_edge"), # tag edge matches
+  n(name="end")
+])
+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 ]))
+```
+
+#### Pipelining
+
+```python
+def capitalize(df, col):
+  df2 = df.copy()
+  df2[col] df[col].str.capitalize()
+  return df2
+
+g
+  .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
+
+```python
+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
+
+```python
+# 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:
+
+```python
+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:
+
+```python
+g3 = g
+for v in g._nodes['some_col'].unique():
+  g3 = g3.collapse(node='root_node_id', column='some_col', attribute=v)
+```
+
+### Control layouts
+
+#### Tree
+
+```python
+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](https://igraph.org/python/doc/api/igraph.Graph.html#layout):
+
+```python
+g.layout_igraph('sugiyama').plot()
+g.layout_igraph('sugiyama', directed=True, params={}).plot()
+```
+
+See list [`layout_algs`](https://github.com/graphistry/pygraphistry/blob/master/graphistry/plugins/igraph.py#L365)
+
+### Plugin: cugraph
+
+With [Nvidia RAPIDS cuGraph](https://www.rapids.ai) install:
+
+```python
+g.layout_cugraph('force_atlas2').plot()
+help(g.layout_cugraph)
+```
+
+See list [`layout_algs`](https://github.com/graphistry/pygraphistry/blob/master/graphistry/plugins/cugraph.py#L315)
+
+#### Group-in-a-box layout
+
+[Group-in-a-box layout](https://ieeexplore.ieee.org/document/6113135) with igraph/pandas and cugraph/cudf implementations:
+
+```python
+g.group_in_a_box_layout().plot()
+g.group_in_a_box_layout(
+  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
+  #encode_colors=False,
+  #colors=['#FFF', '#FF0', ...]
+  engine='cudf'
+).plot()
+```
+
+### Control render settings
+
+```python
+g = graphistry.edges(pd.DataFrame({'s': ['a', 'b', 'b'], 'd': ['b', 'c', 'd']}))
+g2 = g.scene_settings(
+  #hide menus
+  menu=False,
+  info=False,
+  #tweak graph
+  show_arrows=False,
+  point_size=1.0,
+  edge_curvature=0.0,
+  edge_opacity=0.5,
+  point_opacity=0.9
+).plot()
+
+```
+
+With `pip install graphistry[igraph]`, you can also use [`igraph` layouts](https://igraph.org/python/doc/api/igraph.Graph.html#layout):
+
+```python
+g.layout_igraph('sugiyama').plot()
+g.layout_igraph('sugiyama', directed=True, params={}).plot()
+```
+
+## Next Steps
+
+1. Create a free public data [Graphistry Hub](https://www.graphistry.com/get-started) account or [one-click launch a private Graphistry instance in AWS](https://www.graphistry.com/get-started)
+2. Check out the [analyst](demos/for_analysis.ipynb) and [developer](demos/for_developers.ipynb) introductions, or [try your own CSV](demos/upload_csv_miniapp.ipynb)
+3. Explore the [demos folder](demos) for your favorite [file format, database, API](demos/demos_databases_apis), use case domain, kind of analysis, and [visual analytics feature](demos/more_examples/graphistry_features)
+
+## Resources
+
+* Graphistry [In-Tool UI Guide](https://hub.graphistry.com/docs/ui/index/)
+* [General and REST API docs](https://hub.graphistry.com/docs/api/):
+  * [URL settings](https://hub.graphistry.com/docs/api/1/rest/url/#urloptions)
+  * [Authentication](https://hub.graphistry.com/docs/api/1/rest/auth/)
+  * [Uploading](https://hub.graphistry.com/docs/api/2/rest/upload/#createdataset2), including multiple file formats and settings
+  * [Color bindings](https://hub.graphistry.com/docs/api/2/rest/upload/colors/#extendedpalette2) and [color palettes](https://hub.graphistry.com/docs/api/api-color-palettes/) (ColorBrewer)
+  * Bindings and colors, REST API, embedding URLs and URL parameters, dynamic JS API, and more
+  * JavaScript and more!
+* Python-specific
+  * [Python API ReadTheDocs](http://pygraphistry.readthedocs.org/en/latest/)
+  * Within a notebook, you can always run `help(graphistry)`, `help(graphistry.hypergraph)`, etc.
+* [Administration docs](https://github.com/graphistry/graphistry-cli) for sizing, installing, configuring, managing, and updating Graphistry servers
+* [Graph-App-Kit Dashboarding](https://github.com/graphistry/graph-app-kit) dashboarding
+
+
+%package help
+Summary:	Development documents and examples for graphistry
+Provides:	python3-graphistry-doc
+%description help
+# PyGraphistry: Explore Relationships
+
+![Build Status](https://github.com/graphistry/pygraphistry/workflows/CI%20Tests/badge.svg)
+[![CodeQL](https://github.com/graphistry/pygraphistry/workflows/CodeQL/badge.svg)](https://github.com/graphistry/pygraphistry/actions?query=workflow%3ACodeQL)
+[![Documentation Status](https://readthedocs.org/projects/pygraphistry/badge/?version=latest)](https://pygraphistry.readthedocs.io/en/latest/)
+[![Latest Version](https://img.shields.io/pypi/v/graphistry.svg)](https://pypi.python.org/pypi/graphistry)
+[![Latest Version](https://img.shields.io/pypi/pyversions/graphistry.svg)](https://pypi.python.org/pypi/graphistry)
+[![License](https://img.shields.io/pypi/l/graphistry.svg)](https://pypi.python.org/pypi/graphistry)
+![PyPI - Downloads](https://img.shields.io/pypi/dm/graphistry)
+
+[![Uptime Robot status](https://img.shields.io/uptimerobot/status/m787548531-e9c7b7508fc76fea927e2313?label=hub.graphistry.com)](https://status.graphistry.com/) [<img src="https://img.shields.io/badge/slack-Graphistry%20chat-orange.svg?logo=slack">](https://join.slack.com/t/graphistry-community/shared_invite/zt-53ik36w2-fpP0Ibjbk7IJuVFIRSnr6g)
+[![Twitter Follow](https://img.shields.io/twitter/follow/graphistry)](https://twitter.com/graphistry)
+
+**PyGraphistry is a Python visual graph AI library to extract, transform, analyze, model, and visualize big graphs, and especially alongside [Graphistry](https://www.graphistry.com) 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](https://hub.graphistry.com/docs/api/), 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](https://github.com/graphistry/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](https://hub.graphistry.com/docs/api/).
+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](#demo-of-friendship-communities-on-facebook)
+2. [Graph Gallery](#gallery)
+3. [Install](#install)
+4. [Tutorial](#tutorial-les-misérables)
+5. [Next Steps](#next-steps)
+6. [Resources](#resources)
+
+## Demo of Friendship Communities on Facebook
+
+<table style="width:100%;">
+  <tr valign="top">
+    <td align="center">Click to open interactive version! <em>(For server-backed interactive analytics, use an API key)</em><a href="http://hub.graphistry.com/graph/graph.html?dataset=Facebook&splashAfter=true" target="_blank"><img src="http://i.imgur.com/Ows4rK4.png" title="Click to open."></a>
+    <em>Source data: <a href="http://snap.stanford.edu" target="_blank">SNAP</a></em>
+    </td>
+  </tr>
+</table>
+
+## 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](https://www.graphistry.com/get-started) or [launch your own private GPU server](https://www.graphistry.com/get-started)
+
+   ```python
+  # 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: hub.graphistry.com
+  #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='my.site.ngo')  # Use with a self-hosted server
+  # ... and if client (browser) URLs are different than python server<> graphistry server uploads
+  #graphistry.register(..., client_protocol_hostname='https://public.acme.co')
+  ```
+
+* **Notebook-friendly:** PyGraphistry plays well with interactive notebooks like [Jupyter](http://ipython.org), [Zeppelin](https://zeppelin.incubator.apache.org/), and [Databricks](http://databricks.com). Process, visualize, and drill into with graphs directly within your notebooks:
+
+    ```python
+    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:
+
+     ```python
+     rows = pandas.read_csv('transactions.csv')[:1000]
+     graphistry.hypergraph(rows)['graph'].plot()
+     ```
+
+* **Embeddable:** Drop live views into your web dashboards and apps (and go further with [JS/React](https://hub.graphistry.com/docs)):
+
+    ```python
+    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)](https://github.com/graphistry/pygraphistry/blob/master/demos/more_examples/graphistry_features/sharing_tutorial.ipynb)
+
+* **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:
+
+    ```python
+    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
+    g1.plot()
+    
+    # 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](http://pandas.pydata.org)
+
+     ```python
+     edges = pd.read_csv('facebook_combined.txt', sep=' ', names=['src', 'dst'])
+     graphistry.edges(edges, 'src', 'dst').plot()
+     ```
+
+     ```python
+     table_rows = pd.read_csv('honeypot.csv')
+     graphistry.hypergraph(table_rows, ['attackerIP', 'victimIP', 'victimPort', 'vulnName'])['graph'].plot()
+     ```
+
+     ```python
+     graphistry.hypergraph(table_rows, ['attackerIP', 'victimIP', 'victimPort', 'vulnName'],
+         direct=True,
+         opts={'EDGES': {
+           'attackerIP': ['victimIP', 'victimPort', 'vulnName'],
+           'victimIP': ['victimPort', 'vulnName'],
+           'victimPort': ['vulnName']
+   }})['graph'].plot()
+     ```
+
+     ```python
+     ### 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)
+     ```
+
+    ```python
+    ### 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)
+    g2.plot()
+    ```
+
+* [Spark](https://spark.apache.org/)/[Databricks](https://databricks.com/) ([ipynb demo](demos/demos_databases_apis/databricks_pyspark/graphistry-notebook-dashboard.ipynb), [dbc demo](demos/demos_databases_apis/databricks_pyspark/graphistry-notebook-dashboard.dbc))
+
+    ```python
+    #optional but recommended
+    spark.conf.set("spark.sql.execution.arrow.enabled", "true")
+
+    edges_df = (
+        spark.read.format('json').
+          load('/databricks-datasets/iot/iot_devices.json')
+          .sample(fraction=0.1)
+    )
+    g = graphistry.edges(edges_df, 'device_name', 'cn')
+
+    #notebook
+    displayHTML(g.plot())
+
+    #dashboard: pick size of choice
+    displayHTML(
+      g.settings(url_params={'splashAfter': 'false'})
+        .plot(override_html_style="""
+          width: 50em;
+          height: 50em;
+        """)
+    )
+    ```
+
+* GPU [RAPIDS.ai](https://www.rapids.ai) cudf
+
+    ```python
+    edges = cudf.read_csv('facebook_combined.txt', sep=' ', names=['src', 'dst'])
+    graphistry.edges(edges, 'src', 'dst').plot()
+    ```
+
+* GPU [RAPIDS.ai](https://www.rapids.ai) cuML
+
+    ```python
+    g = graphistry.nodes(cudf.read_csv('rows.csv'))
+    g = graphistry.nodes(G)
+    g.umap(engine='cuml',metric='euclidean').plot()
+    ```
+
+* GPU [RAPIDS.ai](https://www.rapids.ai) cugraph ([notebook demo](demos/demos_databases_apis/gpu_rapids/cugraph.ipynb))
+
+    ```python
+    g = graphistry.from_cugraph(G)
+    g2 = g.compute_cugraph('pagerank')
+    g3 = g2.layout_cugraph('force_atlas2')
+    g3.plot()
+    G3 = g.to_cugraph()
+    ```
+
+* [Apache Arrow](https://arrow.apache.org/)
+
+    ```python
+     edges = pa.Table.from_pandas(pd.read_csv('facebook_combined.txt', sep=' ', names=['src', 'dst']))
+     graphistry.edges(edges, 'src', 'dst').plot()
+    ```
+
+* [Neo4j](http://neo4j.com) ([notebook demo](demos/demos_databases_apis/neo4j/official/graphistry_bolt_tutorial_public.ipynb))
+
+    ```python
+    NEO4J_CREDS = {'uri': 'bolt://my.site.ngo:7687', 'auth': ('neo4j', 'mypwd')}
+    graphistry.register(bolt=NEO4J_CREDS)
+    graphistry.cypher("MATCH (n1)-[r1]->(n2) RETURN n1, r1, n2 LIMIT 1000").plot()
+    ```
+
+    ```python
+    graphistry.cypher("CALL db.schema()").plot()
+    ```
+
+    ```python
+    from neo4j import GraphDatabase, Driver
+    graphistry.register(bolt=GraphDatabase.driver(**NEO4J_CREDS))
+    g = graphistry.cypher("""
+      MATCH (a)-[p:PAYMENT]->(b)
+      WHERE p.USD > 7000 AND p.USD < 10000
+      RETURN a, p, b
+      LIMIT 100000""")
+    print(g._edges.columns)
+    g.plot()
+    ```
+
+* [Azure Cosmos DB (Gremlin)](https://azure.microsoft.com/en-us/services/cosmos-db/)
+
+    ```python
+    # pip install --user gremlinpython
+    # Options in help(graphistry.cosmos)
+    g = graphistry.cosmos(
+        COSMOS_ACCOUNT='',
+        COSMOS_DB='',
+        COSMOS_CONTAINER='',
+        COSMOS_PRIMARY_KEY=''
+    )
+    g2 = g.gremlin('g.E().sample(10000)').fetch_nodes()
+    g2.plot()
+    ```
+
+* [Amazon Neptune (Gremlin)](https://aws.amazon.com/neptune/) ([notebook demo](demos/demos_databases_apis/neptune/neptune_tutorial.ipynb), [dashboarding demo](https://aws.amazon.com/blogs/database/enabling-low-code-graph-data-apps-with-amazon-neptune-and-graphistry/))
+
+    ```python
+    # pip install --user gremlinpython==3.4.10
+    #   - Deploy tips: https://github.com/graphistry/graph-app-kit/blob/master/docs/neptune.md
+    #   - Versioning tips: https://gist.github.com/lmeyerov/459f6f0360abea787909c7c8c8f04cee
+    #   - Login options in help(graphistry.neptune)
+    g = graphistry.neptune(endpoint='wss://zzz:8182/gremlin')
+    g2 = g.gremlin('g.E().limit(100)').fetch_nodes()
+    g2.plot()
+    ```
+
+* [TigerGraph](https://tigergraph.com) ([notebook demo](demos/demos_databases_apis/tigergraph/tigergraph_pygraphistry_bindings.ipynb))
+
+    ```python
+    g = graphistry.tigergraph(protocol='https', ...)
+    g2 = g.gsql("...", {'edges': '@@eList'})
+    g2.plot()
+    print('# edges', len(g2._edges))
+    ```
+
+    ```python
+    g.endpoint('my_fn', {'arg': 'val'}, {'edges': '@@eList'}).plot()
+    ```
+
+* [igraph](http://igraph.org)
+
+    ```python
+    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 = igraph.read('facebook_combined.txt', format='edgelist', directed=False)
+    g = graphistry.from_igraph(ig)  # full conversion
+    g.plot()
+
+    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](https://networkx.github.io) ([notebook demo](demos/demos_databases_apis/networkx/networkx.ipynb))
+
+    ```python
+    graph = networkx.read_edgelist('facebook_combined.txt')
+    graphistry.bind(source='src', destination='dst', node='nodeid').plot(graph)
+    ```
+
+* [HyperNetX](https://github.com/pnnl/HyperNetX) ([notebook demo](demos/demos_databases_apis/hypernetx/hypernetx.ipynb))
+
+    ```python
+    hg.hypernetx_to_graphistry_nodes(H).plot()
+    ```
+
+    ```python
+    hg.hypernetx_to_graphistry_bipartite(H.dual()).plot()
+    ```
+
+* [Splunk](https://www.splunk.com) ([notebook demo](demos/demos_databases_apis/splunk/splunk_demo_public.ipynb))
+
+    ```python
+    df = splunkToPandas("index=netflow bytes > 100000 | head 100000", {})
+    graphistry.edges(df, 'src_ip', 'dest_ip').plot()
+    ```
+
+* [NodeXL](https://www.nodexl.com) ([notebook demo](demos/demos_databases_apis/nodexl/official/nodexl_graphistry.ipynb))
+
+    ```python
+    graphistry.nodexl('/my/file.xls').plot()
+    ```
+
+    ```python
+    graphistry.nodexl('https://file.xls').plot()
+    ```
+
+    ```python
+    graphistry.nodexl('https://file.xls', 'twitter').plot()
+    graphistry.nodexl('https://file.xls', verbose=True).plot()
+    graphistry.nodexl('https://file.xls', engine='xlsxwriter').plot()
+    graphistry.nodexl('https://file.xls')._nodes
+    ```
+
+## 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
+
+    ```python
+    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:
+
+    ```python
+    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:
+
+    ```python
+    # 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
+
+   ```python
+    # 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)
+    new_model.update(dict(
+                      y=[...],
+                      kind='edges', 
+                      model_name='sbert/cool_transformer_model', 
+                      use_scaler_target='kbins', 
+                      n_bins=11, 
+                      strategy='normal'))
+    print(new_model)
+
+    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](https://umap-learn.readthedocs.io/en/latest/), [cuML-based UMAP](https://docs.rapids.ai/api/cuml/stable/api.html?highlight=umap#cuml.UMAP)
+
+* Reduce dimensionality by plotting a similarity graph from feature vectors:
+
+    ```python
+      # 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.
+      g.plot()
+    ```
+
+* Apply a trained model to new data:
+
+    ```python
+      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.
+
+    ```python
+      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)`):
+
+    ```python
+      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"`.:
+
+    ```python
+      g.umap(engine='cuml')
+    ```
+
+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](https://docs.dgl.ai/en/0.6.x/index.html)
+
+* Graphistry adds bindings and automation to working with popular GNN models, currently focusing on DGL/PyTorch:
+
+    ```python
+    g = (graphistry
+        .nodes(ndf)
+        .edges(edf, src, dst)
+        .build_gnn(
+            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](https://www.sbert.net/examples/applications/semantic-search/README.html)
+
+* Search textual data semantically and see the resulting graph:
+
+    ```python
+      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 = g2.search('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: 
+
+    ```python
+      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:
+
+    ```python
+      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
+      g3.plot()
+
+      # 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)
+
+    ```python
+      # 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
+      g5.plot()
+
+      g6 = g.predict_links(source=['ip_address_1', 'user_id_3'], 
+                      relation=['attempt_logon', 'phishing', ..], 
+                      destination=['user_id_1', 'active_directory', ..], 
+                      anomalous=True,
+                      threshold=0.05)
+      g6.plot()
+    ```
+
+* Train a RGCN model including auto-featurized node embeddings
+
+    ```python
+      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)
+      g2.predict_links_all(threshold=0.95).plot()
+    ```
+
+See `help(g.embed)`, `help(g.predict_links)` , or `help(g.predict_links_all)` for options
+
+### DBSCAN 
+
+* Enrich UMAP embeddings or featurization dataframe with GPU or CPU DBSCAN
+
+    ```python
+      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](https://hub.graphistry.com/docs/api/2/rest/upload/#createdataset2) and set [all sorts of URL options](https://hub.graphistry.com/docs/api/1/rest/url/). Check out the tutorials on [colors](demos/more_examples/graphistry_features/encodings-colors.ipynb), [sizes](demos/more_examples/graphistry_features/encodings-sizes.ipynb), [icons](demos/more_examples/graphistry_features/encodings-icons.ipynb), [badges](demos/more_examples/graphistry_features/encodings-badges.ipynb), [weighted clustering](demos/more_examples/graphistry_features/edge-weights.ipynb) and [sharing controls](https://github.com/graphistry/pygraphistry/blob/master/demos/more_examples/graphistry_features/sharing_tutorial.ipynb):
+
+  ```python
+    g
+      .privacy(mode='private', invited_users=[{'email': 'friend1@site.ngo', 'action': '10'}], notify=False)
+      .edges(df, 'col_a', 'col_b')
+      .edges(my_transform1(g._edges))
+      .nodes(df, 'col_c')
+      .nodes(my_transform2(g._nodes))
+      .bind(source='col_a', destination='col_b', node='col_c')
+      .bind(
+        point_color='col_a',
+        point_size='col_b',
+        point_title='col_c',
+        point_x='col_d',
+        point_y='col_e')
+      .bind(
+        edge_color='col_m',
+        edge_weight='col_n',
+        edge_title='col_o')
+      .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={})
+      .settings(url_params={
+        '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
+      })
+      .plot()
+  ```
+
+### Gallery
+
+<table>
+    <tr valign="top">
+        <td width="33%" align="center"><a href="http://hub.graphistry.com/graph/graph.html?dataset=Twitter&splashAfter=true" target="_blank">Twitter Botnet<br><img width="266" src="http://i.imgur.com/qm5MCqS.jpg"></a></td>
+        <td width="33%" align="center">Edit Wars on Wikipedia<br><a href="http://i.imgur.com/074zFve.png" target="_blank"><img width="266" src="http://i.imgur.com/074zFve.png"></a><em>Source: <a href="http://snap.stanford.edu" target="_blank">SNAP</a></em></td>
+        <td width="33%" align="center"><a href="https://hub.graphistry.com/graph/graph.html?dataset=bitC&splashAfter=true" target="_blank">100,000 Bitcoin Transactions<br><img width="266" height="266" src="http://imgur.com/download/axIkjfd"></a></td>
+    </tr>
+    <tr valign="top">
+        <td width="33%" align="center">Port Scan Attack<br><a href="http://i.imgur.com/vKUDySw.png" target="_blank"><img width="266" src="http://i.imgur.com/vKUDySw.png"></a></td>
+        <td width="33%" align="center"><a href="http://hub.graphistry.com/graph/graph.html?dataset=PyGraphistry/M9RL4PQFSF&usertag=github&info=true&static=true&contentKey=Biogrid_Github_Demo&play=3000&center=false&menu=true&goLive=false&left=-2.58e+4&right=4.35e+4&top=-1.72e+4&bottom=2.16e+4&legend={%22title%22:%22%3Ch3%3EBioGRID%20Repository%20of%20Protein%20Interactions%3C/h3%3E%22,%22subtitle%22:%22%3Cp%3EEach%20color%20represents%20an%20organism.%20Humans%20are%20in%20light%20blue.%3C/p%3E%22,%22nodes%22:%22Proteins/Genes%22,%22edges%22:%22Interactions%20reported%20in%20scientific%20publications%22}" target="_blank">Protein Interactions <br><img width="266" src="http://i.imgur.com/nrUHLFz.png" target="_blank"></a><em>Source: <a href="http://thebiogrid.org" target="_blank">BioGRID</a></em></td>
+        <td width="33%" align="center"><a href="http://hub.graphistry.com/graph/graph.html?&dataset=PyGraphistry/PC7D53HHS5&info=true&static=true&contentKey=SocioPlt_Github_Demo&play=3000&center=false&menu=true&goLive=false&left=-236&right=265&top=-145&bottom=134&usertag=github&legend=%7B%22nodes%22%3A%20%22%3Cspan%20style%3D%5C%22color%3A%23a6cee3%3B%5C%22%3ELanguages%3C/span%3E%20/%20%3Cspan%20style%3D%5C%22color%3Argb%28106%2C%2061%2C%20154%29%3B%5C%22%3EStatements%3C/span%3E%22%2C%20%22edges%22%3A%20%22Strong%20Correlations%22%2C%20%22subtitle%22%3A%20%22%3Cp%3EFor%20more%20information%2C%20check%20out%20the%20%3Ca%20target%3D%5C%22_blank%5C%22%20href%3D%5C%22https%3A//lmeyerov.github.io/projects/socioplt/viz/index.html%5C%22%3ESocio-PLT%3C/a%3E%20project.%20Make%20your%20own%20visualizations%20with%20%3Ca%20target%3D%5C%22_blank%5C%22%20href%3D%5C%22https%3A//github.com/graphistry/pygraphistry%5C%22%3EPyGraphistry%3C/a%3E.%3C/p%3E%22%2C%20%22title%22%3A%20%22%3Ch3%3ECorrelation%20Between%20Statements%20about%20Programming%20Languages%3C/h3%3E%22%7D" target="_blank">Programming Languages<br><img width="266" src="http://i.imgur.com/0T0EKmD.png"></a><em>Source: <a href="http://lmeyerov.github.io/projects/socioplt/viz/index.html" target="_blank">Socio-PLT project</a></em></td>
+    </tr>
+</table>
+
+## Install
+
+### Get
+
+You need to install the PyGraphistry Python client and connect it to a Graphistry GPU server of your choice:
+
+1. Graphistry server account:
+    * Create a free [Graphistry Hub account](https://www.graphistry.com/get-started) for open data, or [one-click launch your own private AWS/Azure instance](https://www.graphistry.com/get-started)
+    * Later, [setup and manage](https://github.com/graphistry/graphistry-cli) your own private Docker instance ([contact](https://www.graphistry.com/demo-request))
+
+2. PyGraphistry Python client:
+    * `pip install --user graphistry` (Python 3.7+) or [directly call the HTTP API](https://hub.graphistry.com/docs/api/)
+        * Use `pip install --user graphistry[all]` for optional dependencies such as Neo4j drivers
+    * To use from a notebook environment, run your own [Jupyter](https://jupyter.org/) server ([one-click launch your own private AWS/Azure GPU instance](https://www.graphistry.com/get-started)) or another such as [Google Colab](https://colab.research.google.com)
+    * See immediately following `configure` section for how to connect
+
+### Configure
+
+Most users connect to a Graphistry GPU server account via:
+
+* `graphistry.register(api=3, username='abc', password='xyz')`: personal hub.graphistry.com account
+* `graphistry.register(api=3, username='abc', password='xyz', org_name='optional_org')`: team hub.graphistry.com account
+* `graphistry.register(api=3, username='abc', password='xyz', org_name='optiona_org', protocol='http', server='my.private_server.org')`: 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](https://hub.graphistry.com) public API
+
+  * Connect to a [private Graphistry server](https://www.graphistry.com/get-started) 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](https://hub.graphistry.com/docs/api/1/rest/auth/).
+
+#### Advanced Login
+
+* **For people:** Provide your account username/password:
+
+```python
+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:
+
+```python
+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
+graphistry.refresh()
+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:
+
+```python
+graphistry.register(protocol='https', server='hub.graphistry.com')
+```
+
+Private Graphistry notebook environments are preconfigured to fill in this data for you:
+
+```python
+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., `https://graphistry.acme.ngo/`). In this case, explicitly set a  client (browser) location different from `protocol` / `server`:
+
+```python
+graphistry.register(
+    ### fast local notebook<>graphistry upload
+    protocol='http', server='nginx',
+
+    ### shareable public URL for browsers
+    client_protocol_hostname='https://graphistry.acme.ngo'
+)
+```
+
+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:
+
+```python
+graphistry.privacy()  # graphistry.privacy(mode='private')
+```
+
+* Organizations: You can login with an organization and share only within it
+
+```python
+graphistry.register(api=3, username='...', password='...', org_name='my-org123')
+graphistry.privacy(mode='organization')
+```
+
+* Invitees: You can share access to specify users, and optionally, even email them invites
+
+```python
+VIEW = "10"
+EDIT = "20"
+graphistry.privacy(
+  mode='private',
+  invited_users=[
+    {"email": "friend1@site1.com", "action": VIEW},
+    {"email": "friend2@site2.com", "action": EDIT}
+  ],
+  notify=True)
+```
+
+* Per-visualization: You can choose different rules for global defaults vs. for specific visualizations
+
+```python
+graphistry.privacy(invited_users=[...])
+g = graphistry.hypergraph(pd.read_csv('...'))['graph']
+g.privacy(notify=True).plot()
+```
+
+See additional examples in the [sharing tutorial](https://github.com/graphistry/pygraphistry/blob/master/demos/more_examples/graphistry_features/sharing_tutorial.ipynb)
+
+## Tutorial: Les Misérables
+
+Let's visualize relationships between the characters in [Les Misérables](http://en.wikipedia.org/wiki/Les_Misérables).
+For this example, we'll choose [Pandas](http://pandas.pydata.org) to wrangle data and [igraph](http://igraph.org) to run a community detection algorithm. You can [view](demos/more_examples/simple/MarvelTutorial.ipynb) the Jupyter notebook containing this example.
+
+Our [dataset is a CSV file](https://raw.githubusercontent.com/graphistry/pygraphistry/master/demos/data/lesmiserables.csv) 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:
+
+```python
+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](demos/demos_by_use_case/logs/malware-hypergraph/Malware%20Hypergraph.ipynb) 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:
+
+```python
+import graphistry
+graphistry.register(api=3, username='YOUR_ACCOUNT_HERE', password='YOUR_PASSWORD_HERE')
+
+g = graphistry.bind(source="source", destination="target")
+g.edges(links).plot()
+```
+
+You should see a beautiful graph like this one:
+![Graph of Miserables](http://i.imgur.com/dRHHTyK.png)
+
+### 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.
+
+```python
+links["label"] = links.value.map(lambda v: "#Meetings: %d" % v)
+g = g.bind(edge_title="label")
+g.edges(links).plot()
+```
+
+### Controlling Node Title, Size, Color, and Position
+
+Let's size nodes based on their [PageRank](http://en.wikipedia.org/wiki/PageRank) score and color them using their [community](https://en.wikipedia.org/wiki/Community_structure).
+
+#### Warmup: igraph for computing statistics
+
+[igraph](http://igraph.org/python/) 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*).
+
+```python
+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](https://igraph.org/python/api/latest/igraph.Graph.html). 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:
+
+```python
+g.bind(point_color='community', point_size='pagerank').plot()
+```
+
+See the [color palette documentation](https://hub.graphistry.com/docs/api/2/rest/upload/colors/#extendedpalette2) 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](demos/more_examples/graphistry_features/external_layout) and [additional url parameters](https://hub.graphistry.com/docs/api/1/rest/url/#urloptions)]). 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](demos/more_examples/graphistry_features/encodings-colors.ipynb) and [sizes](demos/more_examples/graphistry_features/encodings-sizes.ipynb).
+
+![Second Graph of Miserables](http://i.imgur.com/P7fm5sn.png)
+
+### 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](https://hub.graphistry.com/docs/api/2/rest/upload/colors/#extendedpalette2).)
+
+Similarly, you can bind the edge weight, where higher weights cause nodes to cluster closer together: `.bind(edge_weight='colA')`. [See tutorial](demos/more_examples/graphistry_features/edge-weights.ipynb).
+
+For more in-depth examples, check out the tutorials on [colors](demos/more_examples/graphistry_features/encodings-colors.ipynb) and [weighted clustering](demos/more_examples/graphistry_features/edge-weights.ipynb).
+
+### More advanced color and size controls
+
+You may want more controls like using gradients or maping specific values:
+
+```python
+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') 
+g.encode_edge_color('brand',
+  categorical_mapping={'toyota': 'red', 'ford': 'blue'},
+  default_mapping='#CCC')
+g.encode_point_size('numeric_col')
+g.encode_point_size('criticality',
+  categorical_mapping={'critical': 200, 'ok': 100},
+  default_mapping=50)
+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](demos/more_examples/graphistry_features/encodings-colors.ipynb).
+
+### 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
+
+```python
+g.encode_point_icon(
+  'some_column',
+  shape="circle", #clip excess
+  categorical_mapping={
+      'macbook': 'laptop', #https://fontawesome.com/v4.7.0/icons/
+      'Canada': 'flag-icon-ca', #ISO3611-Alpha-2: https://github.com/datasets/country-codes/blob/master/data/country-codes.csv
+      'embedded_smile': 'data:svg...',
+      'external_logo': 'http://..../img.png'
+  },
+  default_mapping="question")
+g.encode_point_icon(
+  'another_column',
+  continuous_binning=[
+    [20, 'info'],
+    [80, 'exclamation-circle'],
+    [None, 'exclamation-triangle']
+  ]
+)
+g.encode_point_icon(
+  'another_column',
+  as_text=True,
+  categorical_mapping={
+    'Canada': 'CA',
+    'United States': 'US'
+    }
+)
+```
+
+For more in-depth examples, check out the tutorials on [icons](demos/more_examples/graphistry_features/encodings-icons.ipynb).
+
+#### Badges
+
+```python
+# 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=...,
+  shape="circle",
+  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](demos/more_examples/graphistry_features/encodings-badges.ipynb).
+
+#### Axes
+
+Radial axes support three coloring types (`'external'`, `'internal'`, and `'space'`) and optional labels:
+
+```python
+ g.encode_axis([
+  {'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:
+
+```python
+g.encode_axis([
+  {"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:
+
+```python
+g2 = (g
+  .nodes(
+    g._nodes.assign(
+      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:
+
+```python
+g2 = (g
+  .nodes(
+    g._nodes.assign(
+      y = 50 * g._nodes['level'])
+  )).settings(url_params={'lockedY': 'true', 'play': 1000})
+```
+
+### Theming
+
+You can customize several style options to match your theme:
+
+```python
+g.addStyle(bg={'color': 'red'})
+g.addStyle(bg={
+  'color': '#333',
+  'gradient': {
+    'kind': 'radial',
+    'stops': [ ["rgba(255,255,255, 0.1)", "10%", "rgba(0,0,0,0)", "20%"] ]}})
+g.addStyle(bg={'image': {'url': 'http://site.com/cool.png', 'blendMode': 'multiply'}})
+g.addStyle(fg={'blendMode': 'color-burn'})
+g.addStyle(page={'title': 'My site'})
+g.addStyle(page={'favicon': 'http://site.com/favicon.ico'})
+g.addStyle(logo={'url': 'http://www.site.com/transparent_logo.png'})
+g.addStyle(logo={
+  'url': 'http://www.site.com/transparent_logo.png',
+  'dimensions': {'maxHeight': 200, 'maxWidth': 200},
+  'style': {'opacity': 0.5}
+})
+```
+
+### Transforms
+
+The below methods let you quickly manipulate graphs directly and with dataframe methods: Search, pattern mine, transform, and more:
+
+```python
+from graphistry import n, e_forward, e_reverse, e_undirected
+g = (graphistry
+  .edges(pd.DataFrame({
+    's': ['a', 'b'],
+    'd': ['b', 'c'],
+    'k1': ['x', 'y']
+  }))
+  .nodes(pd.DataFrame({
+    '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
+
+(g
+  .materialize_nodes()
+  .get_degrees()
+  .get_indegrees()
+  .get_outdegrees()
+  .pipe(lambda g2: g2.nodes(g2._nodes.assign(t=x))) # transform
+  .filter_edges_by_dict({"k1": "x"})
+  .filter_nodes_by_dict({"k2": 4})
+  .prune_self_edges()
+  .hop( # filter to subgraph
+    #almost all optional
+    direction='forward', # 'reverse', 'undirected'
+    hops=1, # number or None if to_fixed_point
+    to_fixed_point=False, 
+    source_node_match={"k2": 0},
+    edge_match={"k1": "x"},
+    destination_node_match={"k2": 2})
+  .chain([ # filter to subgraph
+    n(),
+    n({'k2': 0}),
+    n(name="start"), # add column 'start':bool
+    e_forward({'k1': 'x'}, hops=1), # same API as hop()
+    e_undirected(name='second_edge'),
+  ])
+  .collapse(node='some_id', column='some_col', attribute='some val')
+```
+
+#### Table to graph
+
+```python
+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, ... |
+g.plot()
+```
+
+#### Generate node table
+
+```python
+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
+
+```python
+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:
+
+```python
+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()`:
+
+```python
+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:
+
+```python
+
+# (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')
+
+g5.plot()
+```
+
+Rich compound patterns are enabled via `.chain()`:
+
+```python
+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(hops=3),
+  e_forward(name="final_edge"), # tag edge matches
+  n(name="end")
+])
+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 ]))
+```
+
+#### Pipelining
+
+```python
+def capitalize(df, col):
+  df2 = df.copy()
+  df2[col] df[col].str.capitalize()
+  return df2
+
+g
+  .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
+
+```python
+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
+
+```python
+# 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:
+
+```python
+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:
+
+```python
+g3 = g
+for v in g._nodes['some_col'].unique():
+  g3 = g3.collapse(node='root_node_id', column='some_col', attribute=v)
+```
+
+### Control layouts
+
+#### Tree
+
+```python
+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](https://igraph.org/python/doc/api/igraph.Graph.html#layout):
+
+```python
+g.layout_igraph('sugiyama').plot()
+g.layout_igraph('sugiyama', directed=True, params={}).plot()
+```
+
+See list [`layout_algs`](https://github.com/graphistry/pygraphistry/blob/master/graphistry/plugins/igraph.py#L365)
+
+### Plugin: cugraph
+
+With [Nvidia RAPIDS cuGraph](https://www.rapids.ai) install:
+
+```python
+g.layout_cugraph('force_atlas2').plot()
+help(g.layout_cugraph)
+```
+
+See list [`layout_algs`](https://github.com/graphistry/pygraphistry/blob/master/graphistry/plugins/cugraph.py#L315)
+
+#### Group-in-a-box layout
+
+[Group-in-a-box layout](https://ieeexplore.ieee.org/document/6113135) with igraph/pandas and cugraph/cudf implementations:
+
+```python
+g.group_in_a_box_layout().plot()
+g.group_in_a_box_layout(
+  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
+  #encode_colors=False,
+  #colors=['#FFF', '#FF0', ...]
+  engine='cudf'
+).plot()
+```
+
+### Control render settings
+
+```python
+g = graphistry.edges(pd.DataFrame({'s': ['a', 'b', 'b'], 'd': ['b', 'c', 'd']}))
+g2 = g.scene_settings(
+  #hide menus
+  menu=False,
+  info=False,
+  #tweak graph
+  show_arrows=False,
+  point_size=1.0,
+  edge_curvature=0.0,
+  edge_opacity=0.5,
+  point_opacity=0.9
+).plot()
+
+```
+
+With `pip install graphistry[igraph]`, you can also use [`igraph` layouts](https://igraph.org/python/doc/api/igraph.Graph.html#layout):
+
+```python
+g.layout_igraph('sugiyama').plot()
+g.layout_igraph('sugiyama', directed=True, params={}).plot()
+```
+
+## Next Steps
+
+1. Create a free public data [Graphistry Hub](https://www.graphistry.com/get-started) account or [one-click launch a private Graphistry instance in AWS](https://www.graphistry.com/get-started)
+2. Check out the [analyst](demos/for_analysis.ipynb) and [developer](demos/for_developers.ipynb) introductions, or [try your own CSV](demos/upload_csv_miniapp.ipynb)
+3. Explore the [demos folder](demos) for your favorite [file format, database, API](demos/demos_databases_apis), use case domain, kind of analysis, and [visual analytics feature](demos/more_examples/graphistry_features)
+
+## Resources
+
+* Graphistry [In-Tool UI Guide](https://hub.graphistry.com/docs/ui/index/)
+* [General and REST API docs](https://hub.graphistry.com/docs/api/):
+  * [URL settings](https://hub.graphistry.com/docs/api/1/rest/url/#urloptions)
+  * [Authentication](https://hub.graphistry.com/docs/api/1/rest/auth/)
+  * [Uploading](https://hub.graphistry.com/docs/api/2/rest/upload/#createdataset2), including multiple file formats and settings
+  * [Color bindings](https://hub.graphistry.com/docs/api/2/rest/upload/colors/#extendedpalette2) and [color palettes](https://hub.graphistry.com/docs/api/api-color-palettes/) (ColorBrewer)
+  * Bindings and colors, REST API, embedding URLs and URL parameters, dynamic JS API, and more
+  * JavaScript and more!
+* Python-specific
+  * [Python API ReadTheDocs](http://pygraphistry.readthedocs.org/en/latest/)
+  * Within a notebook, you can always run `help(graphistry)`, `help(graphistry.hypergraph)`, etc.
+* [Administration docs](https://github.com/graphistry/graphistry-cli) for sizing, installing, configuring, managing, and updating Graphistry servers
+* [Graph-App-Kit Dashboarding](https://github.com/graphistry/graph-app-kit) dashboarding
+
+
+%prep
+%autosetup -n graphistry-0.29.1
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-graphistry -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.29.1-1
+- Package Spec generated