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Description
CELULAS TIPADAS A1, A2, B Y O. FRASCO CON 5 ML. POR GRUPO SANGUINEO. RTC.
IMSS product code
080.783.4668.11
Each circle represents one of the 2 purchases of SUSTANCIAS BIOLÓGICAS made by the IMSS between 2011 and 2019.
| 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 | 1 | 33,533.3 MXN | 4,625.3 MXN | 16 % | 1.92 | 1 de enero de 2019 |
| H. de Pediatría, Centro Médico Nacional Siglo XXI Umae Pediatria C.m.n. Siglo Xxi | Ciudad de México | 1 | 50,738.4 MXN | 6,998.4 MXN | 16 % | 5.76 | 25 de febrero de 2011 |
| SUPPLIER | PURCHASES | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | TIME PERIOD |
|---|---|---|---|---|---|
| BIO LEBEN, S.A. DE C.V. | 1 | 6,998.4 MXN | 16 % | 5.76 | 2011 - 2011 |
| INSTRUMENTOS Y EQUIPOS FALCON, S. A. DE C. V. | 1 | 4,625.3 MXN | 16 % | 1.92 | 2019 - 2019 |
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=080.783.4668.11")
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=080.783.4668.11')
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=080.783.4668.11')