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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Sparse Datasets
This notebook is used for benchmarking and debugging sparse datasets
Import the necessary libaries
Step1:  Check the version of the Graphistry module
Step2:  1000 sparse columns with 8K edges, 800K elements
In many datasets, there may ~1000 possible attributes, though each node/edge likely only has 5-100.
Attributes are selected from 100 different float values
The time-to-render for an uploaded dataset should be 20s.
Opening the edges panel, and seeing the results, should also be within 20s.
Step3:  1000 sparse (random float) columns with 8K edges, 800K elements
There are ~1000 possible attributes, though each node/edge likely only has 5-100.
Attributes are selected from 100 a random float value
Step4:  1000 sparse (string) columns with 8K edges, 800K elements
There are ~1000 possible attributes, though each node/edge likely only has 5-100.
Attributes are selected from a set of 100 different string values.
Step5:  1000 sparse (random string) columns with 8K edges, 800K elements
There are ~1000 possible attributes, though each node/edge likely only has 5-100.
Attributes are randomly generated strings.
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Python Code:
import random
import graphistry as g
import pandas as pd
Explanation: Sparse Datasets
This notebook is used for benchmarking and debugging sparse datasets
Import the necessary libaries
End of explanation
g.__version__
# To specify Graphistry account & server, use:
# graphistry.register(api=3, username='...', password='...', protocol='https', server='hub.graphistry.com')
# For more options, see https://github.com/graphistry/pygraphistry#configure
Explanation: Check the version of the Graphistry module
End of explanation
pd.DataFrame([{'a': 1, 'b': 2}, {'b': 2, 'c': 3}, {'c': 4, 'd': 5}])
edges = [{'src': x, 'dst': (x + 1) % 8000} for x in range(0, 8000)]
for i, edge in enumerate(edges):
    for fld in range(0, 100):
        edge['fld' + str((i + fld) % 1000)] = fld
edges = pd.DataFrame(edges)
edges[:3]
g.edges(edges).bind(source='src', destination='dst').plot()
Explanation: 1000 sparse columns with 8K edges, 800K elements
In many datasets, there may ~1000 possible attributes, though each node/edge likely only has 5-100.
Attributes are selected from 100 different float values
The time-to-render for an uploaded dataset should be 20s.
Opening the edges panel, and seeing the results, should also be within 20s.
End of explanation
edges = [{'src': x, 'dst': (x + 1) % 8000} for x in range(0, 8000)]
for i, edge in enumerate(edges):
    for fld in range(0, 100):
        edge['fld' + str((i + fld) % 1000)] = random.random()
edges = pd.DataFrame(edges)
edges[:3]
g.edges(edges).bind(source='src', destination='dst').plot()
Explanation: 1000 sparse (random float) columns with 8K edges, 800K elements
There are ~1000 possible attributes, though each node/edge likely only has 5-100.
Attributes are selected from 100 a random float value
End of explanation
edges = [{'src': x, 'dst': (x + 1) % 8000} for x in range(0, 8000)]
for i, edge in enumerate(edges):
    for fld in range(0, 100):
        edge['fld' + str((i + fld) % 1000)] = 'String: ' + str(fld)
edges = pd.DataFrame(edges)
edges[:3]
g.edges(edges).bind(source='src', destination='dst').plot()
Explanation: 1000 sparse (string) columns with 8K edges, 800K elements
There are ~1000 possible attributes, though each node/edge likely only has 5-100.
Attributes are selected from a set of 100 different string values.
End of explanation
edges = [{'src': x, 'dst': (x + 1) % 8000} for x in range(0, 8000)]
for i, edge in enumerate(edges):
    for fld in range(0, 100):
        edge['fld' + str((i + fld) % 1000)] = 'String: ' + str(random.random())
edges = pd.DataFrame(edges)
edges[:3]
g.edges(edges).bind(source='src', destination='dst').plot()
Explanation: 1000 sparse (random string) columns with 8K edges, 800K elements
There are ~1000 possible attributes, though each node/edge likely only has 5-100.
Attributes are randomly generated strings.
End of explanation
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