<jupyter_start><jupyter_text>Getting Data Browse [data.gov](https://www.data.gov/):- [climate](https://www.data.gov/climate/)- [Data](https://catalog.data.gov/dataset?groups=climate5434&topic=climate_navigation)- Third [HTML](https://www.ncdc.noaa.gov/stormevents/)  (Some) More Sources- [Open Data Census](http://us-cities.survey.okfn.org/)- [Humanitarian Data Exchange](https://data.humdata.org/)- [curated data set lists](https://github.com/awesomedata/awesome-public-datasets)- [curated API lists](https://github.com/abhishekbanthia/Public-APIs)- [Ohio Checkbook](http://ohiotreasurer.gov/Transparency/Ohios-Online-Checkbook)  From PythonInstall and import [requests]()<jupyter_code>import requests
tb_data_dict_url = 'https://extranet.who.int/tme/generateCSV.asp?ds=dictionary'
resp = requests.get(tb_data_dict_url)
print(resp.text[:500])
import pandas as pd
import io
pd.read_csv?
buffer = io.StringIO(resp.text)
tb = pd.read_csv(buffer)
tb.head()<jupyter_output><empty_output><jupyter_text>Options available for authentication [In case of zipfiles...](https://techoverflow.net/2018/01/16/downloading-reading-a-zip-file-in-memory-using-python/)<jupyter_code>import zipfile
def download_extract_zip(url):
    """
    Download a ZIP file and extract its contents in memory
    yields (filename, file-like object) pairs
    """
    response = requests.get(url)
    with zipfile.ZipFile(io.BytesIO(response.content)) as thezip:
        for zipinfo in thezip.infolist():
            with thezip.open(zipinfo) as thefile:
                yield zipinfo.filename, thefile
url='http://datasets.wri.org/dataset/540dcf46-f287-47ac-985d-269b04bea4c6/resource/27c271ef-63c3-49c5-a06a-f21bb7b96371/download/globalpowerplantdatabasev110'
(filename, file_buffer) = next(download_extract_zip(url))
url='http://datasets.wri.org/dataset/540dcf46-f287-47ac-985d-269b04bea4c6/resource/27c271ef-63c3-49c5-a06a-f21bb7b96371/download/globalpowerplantdatabasev110'
for (filename, file_buffer) in download_extract_zip(url):
    print(filename)
url='http://datasets.wri.org/dataset/540dcf46-f287-47ac-985d-269b04bea4c6/resource/27c271ef-63c3-49c5-a06a-f21bb7b96371/download/globalpowerplantdatabasev110'
for (filename, file_buffer) in download_extract_zip(url):
    if filename.endswith('.csv'):
        powerplants = pd.read_csv(file_buffer)
powerplants.head()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.14177215189873418, 'fraction_numerical': 0.0620253164556962, 'mean_word_length': 5.53168044077135, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 9, 'lorem ipsum': 0, 'www.': 3, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 9, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '597582', 'license': 'MIT', 'n_tokens_mistral': 941, 'n_tokens_neox': 850, 'n_words': 113, 'path': 'notebooks/getting-data.ipynb', 'repo_name': 'catherinedevlin/harvest-wild-data-olf-2018', 'size': '2370'}