<jupyter_start><jupyter_text>English Wikipedia page views, 2008 - 2017For this assignment, your job is to analyze traffic on English Wikipedia over time, and then document your process and the resulting dataset and visualization according to best practices for open research that were outlined for you in class.  Example API requestYou can use this example API request as a starting point for building your API queries. Note that the [Legacy Pagecounts API](https://wikitech.wikimedia.org/wiki/Analytics/AQS/Legacy_Pagecounts) has slightly different schema than the [pageview API](https://wikitech.wikimedia.org/wiki/Analytics/AQS/Pageviews) shown here.This sample API request would get you all pageviews by web crawlers on the mobile website for English Wikipedia during the month of September, 2017.<jupyter_code>import requests

endpoint = 'https://wikimedia.org/api/rest_v1/metrics/pageviews/aggregate/{project}/{access}/{agent}/{granularity}/{start}/{end}'

headers={'User-Agent' : 'https://github.com/your_github_username', 'From' : '<EMAIL>'}

params = {'project' : 'en.wikipedia.org',
            'access' : 'mobile-web',
            'agent' : 'spider',
            'granularity' : 'monthly',
            'start' : '2017090100',
            'end' : '2017101000'#use the first day of the following month to ensure a full month of data is collected
            }

api_call = requests.get(endpoint.format(**params))
response = api_call.json()
print(response)<jupyter_output>{'items': [{'project': 'en.wikipedia', 'access': 'mobile-web', 'agent': 'spider', 'granularity': 'monthly', 'timestamp': '2017090100', 'views': 69790930}]}
{'fraction_non_alphanumeric': 0.11995104039167687, 'fraction_numerical': 0.031211750305997554, 'mean_word_length': 4.561224489795919, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 4, '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': '364593', 'license': 'MIT', 'n_tokens_mistral': 503, 'n_tokens_neox': 458, 'n_words': 155, 'path': 'hcds-a1-data-curation.ipynb', 'repo_name': 'jtmorgan/data-512-a1', 'size': '1647'}