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Description
COMPLEMENTO DE CONEJO FRASCOCON 1 ML RTC.
IMSS product code
080.414.1208.10
Each circle represents one of the 6 purchases of SUSTANCIAS BIOLÓGICAS made by the IMSS between 2010 and 2018.
| INSTITUTION | STATE | QUANTITY | TOTAL AMOUNT | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | LAST PURCHASE |
|---|---|---|---|---|---|---|---|
| H. de Especialidades, "Dr. Antonio Fraga Mouret" Centro Médico Nacional La Raza Umae Especialidades C.m.n. La Raza | Ciudad de México | 5 | 14,960.3 MXN | 2,406.5 MXN | 17.4 % | 7.84 | 7 de noviembre de 2018 |
| H. de Especialidades, "Dr. Bernardo Sepulveda Gutiérrez" Centro Médico Nacional Siglo XXI Umae Especialidades C.m.n. Siglo Xxi | Ciudad de México | 1 | 4,222.4 MXN | 582.4 MXN | 16 % | 6.4 | 13 de mayo de 2010 |
| SUPPLIER | PURCHASES | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | TIME PERIOD |
|---|---|---|---|---|---|
| ABALAT, S.A. DE C.V. | 3 | 582.4 MXN | 5.33 % | 6.4 | 2010 - 2018 |
| INSTRUMENTOS Y EQUIPOS FALCON, S. A. DE C. V. | 3 | 2,406.5 MXN | 29 % | 7.84 | 2018 - 2018 |
Date updated in QQW
3 de octubre de 2022
Source(s)
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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=080.414.1208.10")
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=080.414.1208.10')
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=080.414.1208.10')