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Description
CANULAS PARA DRENAR VENAS CAVAS. ANGULADA CON PUNTA METALICA CALIBRE 20 FR.
IMSS product code
060.166.0905.01
Each circle represents one of the 16 purchases of CÁNULAS made by the IMSS between 2011 and 2018.
| INSTITUTION | STATE | QUANTITY | TOTAL AMOUNT | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | LAST PURCHASE |
|---|---|---|---|---|---|---|---|
| H. de Especialidades Cardiológicas No. 34, Centro Médico Nacional del Noreste. Umae Cardiologia En Nuevo Leon | Nuevo León | 16 | 551,092.5 MXN | 133,109.5 MXN | 26.4 % | 141.3 | 1 de enero de 2018 |
| SUPPLIER | PURCHASES | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | TIME PERIOD |
|---|---|---|---|---|---|
| SELECCIONES MEDICAS DEL CENTRO. S. A. DE C. V. | 5 | 10,853 MXN | 12.9 % | 14.4 | 2011 - 2017 |
| SELECCIONES MEDICAS, S. A. DE C. V. | 5 | - MXN | - | - | 2011 - 2012 |
| COMERCIALIZADORA ARGAVI, S.A. DE C.V. | 3 | 110,272.6 MXN | 103.3 % | 113.1 | 2011 - 2012 |
| DIAGNOSTICO Y CIRUGIA, S.A. DE C.V. | 2 | 7,760.3 MXN | 15.9 % | 9.88 | 2013 - 2014 |
| TERUMO MEDICAL DE MEXICO, S.A. DE C.V. | 1 | 4,223.6 MXN | 16 % | 3.84 | 2018 - 2018 |
Date updated in QQW
3 de octubre de 2022
Source(s)
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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=060.166.0905.01")
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=060.166.0905.01')
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=060.166.0905.01')