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If you cannot comply with these requirements, please contact us and tell us about your project.
Corrections to the information
The information gathered on QuiénEsQuién.Wiki comes from public and official sources, such as Compranet and the BMV. If you find that it is inaccurate or contains errors, those are errors in the original sources.
If the information presented about {title} is not correct, please contact us reporting the incorrect information and where we can obtain the correct information. We offer to accompany you in filing a claim with the corresponding entities.
I am the victim of a scam or extortion
Unfortunately it is very common for companies and people not to fulfill their commitments when providing goods or services. In case of non-compliance in Mexico you can go to Profeco and file a complaint or report as appropriate. If you are the victim of cyber fraud, we invite you to read this guide from the Campaña Nacional Antifraude Cibernético.
If you are the victim of extortion, take screenshots of the messages you received and go to the public prosecutor's office to request protection, or call 088 for guidance on filing the corresponding report. Recommendations for reporting phone extortion. If the extortionists have explicitly mentioned QuienEsQuien.wiki in their messages, please send us a copy of the messages.
Contact is not possible
If you want to request quotes, offer your services, file claims against companies or contact people, we recommend looking for their official website or using another means of contact. QuienEsQuien.wiki is not an advertising site and has no contact with the companies and people mentioned on the site.
Error report
We really appreciate you taking the time to report errors and suggest improvements. Please contact us with your ideas or open an issue in our GitHub repository.
Import and use data
Note: CSV download is temporarily disabled due to the server migration. Please use the JSON format to import the data.
import json, urllib
#Read JSON data into python
response = urllib2.urlopen('http://localhost:10010/v3/companies?limit=1&sort=&embed=true&db_prefix=&id=evidens-sc')
data = json.loads(response.read())
import pandas as pd
#Read JSON data into pandas
df = pd.read_json('http://localhost:10010/v3/companies?limit=1&sort=&embed=true&db_prefix=&id=evidens-sc')