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Description
DE ACERO INOXIDABLE AL CARBON CON PUNTA FINA Y PRECISA Y DESECHABLE DEL NO 23 EN EMPAQUE DE ALUMINIO INDIVIDUAL ESTERIL NÚMERO DE CATÁLOGO: BB523
Classification
Material médico. Consumibles de equipos medicos y de laboratorio
IMSS product code
379.440.0436.00
Presentation
CAJA CON 100 PIEZAS.
Each circle represents one of the 23 purchases of HOJA PARA BISTURI made by the IMSS between 2010 and 2018.
| INSTITUTION | STATE | QUANTITY | TOTAL AMOUNT | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | LAST PURCHASE |
|---|---|---|---|---|---|---|---|
| Delegación San Luis Potosí Almacen Delegacional En San Luis Potosi | San Luis Potosi | 9 | 44,265.1 MXN | 9,780.7 MXN | 16.2 % | 71.6 | 16 de abril de 2018 |
| Delegación Quintana Roo Almacen Delegacional En Quintana Roo | Quintana Roo | 6 | 25,344.6 MXN | 2,286.8 MXN | 16.8 % | 20.8 | 31 de julio de 2018 |
| Delegación Durango Almacen Delegacional En Durango | Durango | 2 | 26,863.9 MXN | 21.2 MXN | 9.12 % | 0.182 | 22 de mayo de 2018 |
| Delegación Quintana Roo | Quintana Roo | 2 | 1,332 MXN | 692.8 MXN | 108.4 % | 7.22 | 8 de marzo de 2013 |
| H. General, "Dr. Gaudencio González Garza" Centro Médico Nacional La Raza Umae Hospital General C.m.n. La Raza | Ciudad de México | 2 | 55,977 MXN | 8,322.1 MXN | 9.62 % | 53.5 | 12 de julio de 2017 |
| Delegación Norte del DF Almacen Delegacional En El D.f. Zona Norte | Ciudad de México | 1 | 26,615 MXN | 4,745.3 MXN | 21.7 % | 41.7 | 2 de julio de 2015 |
| Delegación Tabasco Almacen Delegacional En Tabasco | Tabasco | 1 | 2,198.2 MXN | 1,035.6 MXN | 89.1 % | 8.91 | 8 de abril de 2014 |
| SUPPLIER | PURCHASES | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | TIME PERIOD |
|---|---|---|---|---|---|
| ABASTECEDORA DE CONSUMIBLES MEDICOS, S.A. DE C.V. | 9 | 1,452.4 MXN | 10.7 % | 13.3 | 2010 - 2018 |
| LITO MEDICAL S.A. DE C.V. | 3 | 796.1 MXN | 7.57 % | 8.3 | 2013 - 2013 |
| MARTINEZ MONSIVAIS ABEL | 2 | 692.8 MXN | 108.4 % | 7.22 | 2013 - 2013 |
| DENTILAB, S. A. DE C. V. | 1 | - MXN | - | - | 2017 - 2017 |
| FERNANDEZ DIAZ CARLOS PABLO | 1 | 1,035.6 MXN | 89.1 % | 8.91 | 2014 - 2014 |
| GRUPO EMEQUR, S.A. DE C.V. | 1 | 8,985.2 MXN | 46.9 % | 62.4 | 2018 - 2018 |
| JTC PROVEEDOR MEDICO, S. A. DE C. V. | 1 | 4,745.3 MXN | 21.7 % | 41.7 | 2015 - 2015 |
| MARTINEZ SANTIAGO RAUL | 1 | 8,322.1 MXN | 19.2 % | 53.5 | 2017 - 2017 |
| RCA MEDICAL S.A. DE C.V.. | 1 | 21.2 MXN | 18.2 % | 0.182 | 2014 - 2014 |
| SEBS Y ASOCIADOS SA DE CV | 1 | 520.3 MXN | 44.8 % | 4.48 | 2014 - 2014 |
| SERVICIOS MEDICOS Y TECNICOS, S.A. DE C.V. | 1 | 55.3 MXN | 23 % | 0.691 | 2011 - 2011 |
| TECNO COMERCIAL PAKTLI, S. A. DE C. V. | 1 | 258.2 MXN | 12.9 % | 3.22 | 2011 - 2011 |
Entity type
Consumibles de equipos medicos y de laboratorio
Date updated in QQW
3 de octubre de 2022
Source(s)
Part of the project
Organization responsible for the import
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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=379.440.0436.00")
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=379.440.0436.00')
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=379.440.0436.00')