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Description
ANTI HLA - B27 RTC.
IMSS product code
080.081.4162.10
Each circle represents one of the 6 purchases of SUSTANCIAS BIOLÓGICAS ANTÍGENOS SUEROS made by the IMSS between 2009 and 2012.
| INSTITUTION | STATE | QUANTITY | TOTAL AMOUNT | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | LAST PURCHASE |
|---|---|---|---|---|---|---|---|
| H. de Especialidades Núm 25, Monterrey, N.L. Umae Especialidades Nuevo Leon | Nuevo León | 5 | 244,948.6 MXN | 32,991 MXN | 15.2 % | 91.7 | 22 de marzo de 2012 |
| Delegación Chiapas Almacen Subdelegacional En Tapachula, Chiapas | Chiapas | 1 | 1,104 MXN | 461.6 MXN | 71.9 % | 2.16 | 20 de enero de 2009 |
| SUPPLIER | PURCHASES | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | TIME PERIOD |
|---|---|---|---|---|---|
| ABALAT, S.A. DE C.V. | 5 | 32,991 MXN | 15.2 % | 91.7 | 2009 - 2012 |
| DISTRIBUIDORA MEDICA GRIJALVA SA DE CV | 1 | 461.6 MXN | 71.9 % | 2.16 | 2009 - 2009 |
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=080.081.4162.10")
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=080.081.4162.10')
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=080.081.4162.10')