<jupyter_start><jupyter_text>NodeXL  Graphistry ConverterNow you can explore the results of your NodeXL data workflows with Graphistry GPU visuals!* Takes a url to a NodeXL .xls file (you can upload to your Colab session as well) and creates a live Graphistry viz* Creates a function `graphistry.nodexl('http://my_url/file.xls').plot()`, see calls to it at the bottom* You can also specify the data source for more bindings, verbose to watch progress, and which Pandas-ready Excel engine: `graphistry.nodexl('http://my_url/file.xls', 'twitter', engine='xlrg', verbose=True)`* Can upload an XLS file here too, see file upload menu on left (file will be `"./my_file.xls"`)* Click graphistry logo to start a session or right-click to open in a new window and save its url* Rerun cells top-to-bottom by hitting shift-enter  Installs, imports, & creds  First run (non-Graphistry distributions)You may need to restart your notebook's Python runtime after<jupyter_code># ! pip install -q --user graphistry pandas
# Sometimes also need to install a new system Excel parser, and pass in as `graphistry.nodexl(..., engine='openpyxl')
# pip install -q --user openpyxl<jupyter_output><empty_output><jupyter_text>Imports & credentials<jupyter_code>import pandas as pd
import graphistry
graphistry.__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<jupyter_output><empty_output><jupyter_text>Sample use<jupyter_code>#g = NodeXLGraphistry().xls(xls, 'twitter')
g = graphistry.nodexl('https://nodexlgraphgallery.org/Pages/Workbook.ashx?graphID=220232')
print('%s nodes, %s edges' % (len(g._nodes), len(g._edges)))
g._nodes.sample(2)
g.plot()<jupyter_output><empty_output><jupyter_text>Twitter Demos  <NAME><jupyter_code>graphistry.nodexl('https://nodexlgraphgallery.org/Pages/Workbook.ashx?graphID=220055', 'twitter', verbose=True).plot()<jupyter_output><empty_output><jupyter_text>CES Samsung<jupyter_code>graphistry.nodexl('https://nodexlgraphgallery.org/Pages/Workbook.ashx?graphID=219924', 'twitter', verbose=True).plot()<jupyter_output><empty_output><jupyter_text>Larger Graph<jupyter_code>graphistry.nodexl('https://www.nodexlgraphgallery.org/Pages/Workbook.ashx?graphID=220124', 'twitter', verbose=True).plot()<jupyter_output><empty_output><jupyter_text>MediaWiki Demos  Demo 1<jupyter_code>graphistry.nodexl('https://nodexlgraphgallery.org/Pages/Workbook.ashx?graphID=203001', 'mediawiki').plot()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.12792033703561853, 'fraction_numerical': 0.012638835695135964, 'mean_word_length': 7.213836477987422, 'pattern_counts': {'":': 0, '<': 30, '<?xml version=': 0, '>': 30, 'https://': 6, 'lorem ipsum': 0, 'www.': 1, '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': '7', 'dir': 'jupyter-structured-clean-dedup', 'id': '337262', 'license': 'BSD-3-Clause', 'n_tokens_mistral': 886, 'n_tokens_neox': 841, 'n_words': 209, 'path': 'demos/demos_databases_apis/nodexl/official/nodexl_graphistry.ipynb', 'repo_name': 'vishalbelsare/pygraphistry', 'size': '2618'}