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Description
Compra Emergente
IMSS product code
060.168.6660.03
Each circle represents one of the 3 purchases of CATÉTERES made by the IMSS between 2009 and 2012.
| INSTITUTION | STATE | QUANTITY | TOTAL AMOUNT | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | LAST PURCHASE |
|---|---|---|---|---|---|---|---|
| Delegación Nuevo León | Nuevo León | 1 | 5,730.4 MXN | - MXN | - | - | 5 de febrero de 2010 |
| Delegación Sonora Almacen Delegacional En Sonora | Sonora | 1 | 2,101.1 MXN | 274 MXN | 15 % | 1.05 | 22 de mayo de 2009 |
| H. de Especialidades Núm 25, Monterrey, N.L. Umae Especialidades Nuevo Leon | Nuevo León | 1 | 406 MXN | 56 MXN | 16 % | 0.16 | 9 de febrero de 2012 |
| SUPPLIER | PURCHASES | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | TIME PERIOD |
|---|---|---|---|---|---|
| COMERCIALIZADORA Y DISTRIBUIDORA ECAS | 1 | 274 MXN | 15 % | 1.05 | 2009 - 2009 |
| DURFA, S. A. DE C. V. | 1 | 56 MXN | 16 % | 0.16 | 2012 - 2012 |
| SOPORTE MEDICO INTEGRAL, S.A. DE C.V. | 1 | - MXN | - | - | 2010 - 2010 |
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=060.168.6660.03")
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=060.168.6660.03')
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=060.168.6660.03')