Thank you for your answer.
Description
CLORURO DE MERCURIO (BICLORURO DE MERCURIO) RA (ACS) GRANULADO TA.
IMSS product code
080.830.0610.10
Each circle represents one of the 9 purchases of REACTIVOS QUÍMICOS made by the IMSS between 2009 and 2017.
| INSTITUTION | STATE | QUANTITY | TOTAL AMOUNT | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | LAST PURCHASE |
|---|---|---|---|---|---|---|---|
| Delegación Sonora | Sonora | 3 | 23,055 MXN | 0.00000000000273 MXN | 0.0000000000000118 % | 0.000000000000000412 | 16 de noviembre de 2016 |
| Delegación Quintana Roo Almacen Delegacional En Quintana Roo | Quintana Roo | 2 | 2,643.6 MXN | 382.6 MXN | 16.9 % | 0.338 | 9 de marzo de 2010 |
| Delegación Sonora Almacen Delegacional En Sonora | Sonora | 2 | 21,359.1 MXN | 2,946.1 MXN | 16 % | 0.96 | 3 de marzo de 2017 |
| Delegación Puebla Almacen Delegacional En Puebla | Puebla | 1 | 15,715.7 MXN | 2,149.7 MXN | 15.8 % | 1.9 | 1 de enero de 2010 |
| H. de Especialidades, "Dr. Antonio Fraga Mouret" Centro Médico Nacional La Raza Umae Especialidades C.m.n. La Raza | Ciudad de México | 1 | 1,300 MXN | 169.6 MXN | 15 % | 0.15 | 1 de enero de 2009 |
| SUPPLIER | PURCHASES | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | TIME PERIOD |
|---|---|---|---|---|---|
| INSTRUMENTOS Y ACCESORIOS AUTOMATIZADOS, S.A. DE C | 4 | 1,060 MXN | 4 % | 0.16 | 2016 - 2016 |
| CONTROL TECNICO Y REPRESENTACIONES, S. A. DE C. V. | 3 | 2,532.3 MXN | 16.6 % | 2.24 | 2010 - 2010 |
| CASLAB DE MEXICO, S. A. DE C. V. | 1 | 1,886.1 MXN | 16 % | 0.8 | 2017 - 2017 |
| EIBL LABORATORIES, S.A. DE C.V. | 1 | 169.6 MXN | 15 % | 0.15 | 2009 - 2009 |
Date updated in QQW
3 de octubre de 2022
Source(s)
Part of the project
Organization responsible for the import
Information from QuienEsQuien.wiki can be reused without asking for permission, as long as you cite us as the source and use the CC-BY-SA license for your publications.
If you cannot comply with these requirements, please contact us and tell us about your project.
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.
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.
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.
Choose the system where you want to use the data to see the instructions. For research methodology, see the Manual for investigating open contracting.
Add this to your code:
library(rjson)
#Read JSON data into R
data <- fromJSON(file="http://localhost:10010/v3/products?limit=1&sort=&embed=true&db_prefix=&id=080.830.0610.10")
Add this to your code:
import json, urllib
#Read JSON data into python
response = urllib2.urlopen('http://localhost:10010/v3/products?limit=1&sort=&embed=true&db_prefix=&id=080.830.0610.10')
data = json.loads(response.read())
Add this to your code:
import pandas as pd
#Read JSON data into pandas
df = pd.read_json('http://localhost:10010/v3/products?limit=1&sort=&embed=true&db_prefix=&id=080.830.0610.10')