<jupyter_start><jupyter_text>Energy Performance of Buildings Data Cambridge  Data Sourcehttps://epc.opendatacommunities.org/domestic/search?address=&postcode=&local-authority=&constituency=&from-month=1&from-year=2008&to-month=12&to-year=2019Data contains 18,000,000 records, where each row is the energy performance of a building/property in England and Wales.<jupyter_code>import io

import pandas as pd
import requests
token = 'c2FiaWhoYXNhbjEyQGhvdG1haWwuY28udWs6MjEzYzNjM2NhYWZjZjkwOWVkZDFmN2FmMThjZWYyNGYyMWNhNDgzZQ=='
headers = {
    "Authorization": "Basic <KEY>
    "Accept": "text/csv"
}

         
url = "https://epc.opendatacommunities.org/api/v1/domestic/search?local-authority=E07000008"

local_authority = {'Cambridge': 'E07000008'}


request = requests.get(url, headers=headers)
pd.read_json(request.json())
# pd.read_csv(io.FileIO request.content)
_bytes = io.BytesIO(request.content)
pd.read_csv(_bytes)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.11777076761303891, 'fraction_numerical': 0.0452155625657203, 'mean_word_length': 7.0, 'pattern_counts': {'":': 2, '<': 6, '<?xml version=': 0, '>': 6, 'https://': 2, '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': '361094', 'license': 'MIT', 'n_tokens_mistral': 399, 'n_tokens_neox': 360, 'n_words': 54, 'path': 'Energy Performance.ipynb', 'repo_name': 'Sabihxh/jupyter-notebooks', 'size': '1040'}