<jupyter_start><jupyter_text>Module 3 - Accessing Data on the Web_My suggested use for this file is as an extra tab you keep open on the right side of your JupyterLab screen, and treat it like a notepad you'd use in a classroom. The left half of the screen will be good for files that have involved coding that should be stand alone files._ Skills AND CONCEPTS to learn in this Module You can find the tasks for this module [on the dashboard page]. Below are the key skills and concepts I want you to learn in this module in _roughly_ the order we will cover them. _**Note: This list is the key stuff, but not everything. We are scraping the surface **_- [ ] Scraping data    - [ ] download webpages with Pandas    - [ ] know how to use APIs to scrape data- [ ] Opening and parsing webpages    - [ ] retrieve URLs and send manual API queries    - [ ] parse single pages for data/text/links    - [ ] build spiders to scrape and save many webpages- [ ] Working with textual data    - [ ] turn unstructured data into structured data    - [ ] learn to work with Python strings    - [ ] use regex to search for patterns in strings    - [ ] use `near_regex` to search for patterns close to each other  Web Scraping Notes<jupyter_code>put your notes here<jupyter_output><empty_output><jupyter_text>Opening/Parsing Webpages Notes<jupyter_code>put your notes here<jupyter_output><empty_output><jupyter_text>Strings/Regex/Near_Regex Notes<jupyter_code>put your notes here (including common functions/methods)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.05759162303664921, 'fraction_numerical': 0.0006544502617801048, 'mean_word_length': 4.183050847457627, 'pattern_counts': {'":': 0, '<': 13, '<?xml version=': 0, '>': 13, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '490060', 'license': 'MIT', 'n_tokens_mistral': 426, 'n_tokens_neox': 407, 'n_words': 208, 'path': 'handouts/Module 3 Notes.ipynb', 'repo_name': 'Theo-Faucher/ledatascifi-2022', 'size': '1528'}