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Description
Compra Emergente
IMSS product code
060.182.0087.11
Each circle represents one of the 4 purchases of CEMENTOS made by the IMSS between 2009 and 2014.
| INSTITUTION | STATE | QUANTITY | TOTAL AMOUNT | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | LAST PURCHASE |
|---|---|---|---|---|---|---|---|
| Delegación Veracruz Sur Almacen Delegacional En Veracruz Sur | Veracruz | 2 | 13,920 MXN | 1,920 MXN | 16 % | 1.6 | 4 de diciembre de 2013 |
| Delegación Coahuila | Coahuila | 1 | 782 MXN | 0.000000000000114 MXN | 0.0000000000000145 % | 0.000000000000000167 | 16 de abril de 2009 |
| Delegación Oaxaca Almacen Delegacional En Oaxaca | Oaxaca | 1 | 1,740 MXN | 240 MXN | 16 % | 0.16 | 13 de octubre de 2014 |
| SUPPLIER | PURCHASES | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | TIME PERIOD |
|---|---|---|---|---|---|
| DIAGNOSTICO Y CIRUGIA, S.A. DE C.V. | 1 | 0.000000000000114 MXN | 0.0000000000000145 % | 0.000000000000000167 | 2009 - 2009 |
| DISTRIBUIDORA MORA ESPECIALISTA EN EQUIPO MEDICO, | 1 | 960 MXN | 16 % | 0.8 | 2013 - 2013 |
| MORA HERMILO DAVID | 1 | 960 MXN | 16 % | 0.8 | 2013 - 2013 |
| SUMINISTROS MEDICOS Y SERVICIOS ORTOPEDICOS S.A. D | 1 | 240 MXN | 16 % | 0.16 | 2014 - 2014 |
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=060.182.0087.11")
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=060.182.0087.11')
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=060.182.0087.11')