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cleaned up notebooks added to gfql demo folder, readme update #559
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instead of making a new file & url, replace the old file
(and revert any changed links)
README.md
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@@ -147,7 +147,7 @@ It is easy to turn arbitrary data into insightful graphs. PyGraphistry comes wit | |||
g2.plot() | |||
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* GFQL: Cypher-style graph pattern mining queries on dataframes with optional GPU acceleration ([ipynb demo](demos/more_examples/graphistry_features/hop_and_chain_graph_pattern_mining.ipynb), [benchmark](demos/gfql/benchmark_hops_cpu_gpu.ipynb)) | |||
* GFQL: Cypher-style graph pattern mining queries on dataframes with optional GPU acceleration ([ipynb demo](demos/more_examples/graphistry_features/hop_and_chain_graph_pattern_mining.ipynb), [chain and hop demo](demos/gfql/simple_gfql_notebook.ipynb), [benchmark](demos/gfql/gfql_cpv_gpu_enchmark.ipynb) |
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lingering stale edit?
README.md
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@@ -1248,7 +1248,7 @@ assert 'pagerank' in g2._nodes.columns | |||
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PyGraphistry supports GFQL, its PyData-native variant of the popular Cypher graph query language, meaning you can do graph pattern matching directly from Pandas dataframes without installing a database or Java | |||
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See also [graph pattern matching tutorial](demos/more_examples/graphistry_features/hop_and_chain_graph_pattern_mining.ipynb) and the CPU/GPU [benchmark](demos/gfql/benchmark_hops_cpu_gpu.ipynb) | |||
See also [graph pattern matching tutorial](demos/more_examples/graphistry_features/hop_and_chain_graph_pattern_mining.ipynb), [starting with chain and hop](demos/gfql/simple_gfql_notebook.ipynb) and the CPU/GPU [benchmark](demos/gfql/gfql_cpv_gpu_enchmark.ipynb) |
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lingering stale edit?
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fix dangling links from stale edit and that's it!
README.md
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@@ -147,7 +147,7 @@ It is easy to turn arbitrary data into insightful graphs. PyGraphistry comes wit | |||
g2.plot() | |||
``` | |||
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* GFQL: Cypher-style graph pattern mining queries on dataframes with optional GPU acceleration ([ipynb demo](demos/more_examples/graphistry_features/hop_and_chain_graph_pattern_mining.ipynb), [benchmark](demos/gfql/benchmark_hops_cpu_gpu.ipynb)) | |||
* GFQL: Cypher-style graph pattern mining queries on dataframes with optional GPU acceleration ([ipynb demo](demos/more_examples/graphistry_features/hop_and_chain_graph_pattern_mining.ipynb),[benchmark](demos/gfql/gfql_hops_cpu_gpu.ipynb) |
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this shouldn't be changed, should still be old benchmark_hops_cpu_gpu
, not gfql_...
@@ -1248,7 +1248,7 @@ assert 'pagerank' in g2._nodes.columns | |||
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PyGraphistry supports GFQL, its PyData-native variant of the popular Cypher graph query language, meaning you can do graph pattern matching directly from Pandas dataframes without installing a database or Java | |||
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See also [graph pattern matching tutorial](demos/more_examples/graphistry_features/hop_and_chain_graph_pattern_mining.ipynb) and the CPU/GPU [benchmark](demos/gfql/benchmark_hops_cpu_gpu.ipynb) | |||
See also [graph pattern matching tutorial](demos/more_examples/graphistry_features/hop_and_chain_graph_pattern_mining.ipynb), and the CPU/GPU [benchmark](demos/gfql/gfql_hops_cpu_gpu.ipynb) | |||
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same problem here
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- still need to back out readme.md url changes
- readme.md has conflicts
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