<jupyter_start><jupyter_text>Add Edges<jupyter_code>g = Graph()
idx = g.new_vertex_property("int")
kind = g.new_vertex_property("string")
v2
kind = "M"
%%timeit
kind == 'M'
model=True
%%timeit
model 
g = Graph()
nb_iterations = 2
nb_attributes = 10**1
nb_models = nb_iterations * nb_attributes

nb_vertices = nb_attributes + nb_models

g = Graph()
v = g.add_vertex(nb_vertices)

idx = g.new_vertex_property("int")
kind = g.new_vertex_property("string")


    
for m_idx, m in m_list
import numpy as np
for attribute in range(nb_attributes):
    
    pass

for model in range(nb_models):
    pass<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1184, 'fraction_numerical': 0.008, 'mean_word_length': 5.198019801980198, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '73150', 'license': 'MIT', 'n_tokens_mistral': 249, 'n_tokens_neox': 243, 'n_words': 55, 'path': 'note/integrations/graph-tool.ipynb', 'repo_name': 'MattiasDC/mercs', 'size': '625'}