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Description
PARA ENCUADERNAR (GUSANO LOMO), DE9/16", VARIOS COLORES
Classification
Artículos de consumo. Utiles de oficina
IMSS product code
312.092.0321.00
Each circle represents one of the 2 purchases of ARILLO DE PLASTICO made by the IMSS between 2010 and 2014.
| INSTITUTION | STATE | QUANTITY | TOTAL AMOUNT | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | LAST PURCHASE |
|---|---|---|---|---|---|---|---|
| Delegación Tlaxcala Almacen Delegacional En Tlaxcala | Tlaxcala | 1 | 214 MXN | 29.5 MXN | 16 % | 13.6 | 25 de julio de 2014 |
| H. de Especialidades, "Gral. Div. Manuel Avila Camacho", Centro Médico Nacional, Puebla Umae Especialidades Puebla | Puebla | 1 | 556.8 MXN | 76.8 MXN | 16 % | 48 | 1 de enero de 2010 |
| SUPPLIER | PURCHASES | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | TIME PERIOD |
|---|---|---|---|---|---|
| COMERCIALIZADORA GEDA, S.A. DE C.V. | 1 | 29.5 MXN | 16 % | 13.6 | 2014 - 2014 |
| DISTRIBUIDORA PAPELERA JARDINES, S.A. DE C.V. | 1 | 76.8 MXN | 16 % | 48 | 2010 - 2010 |
Entity type
Utiles de oficina
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=312.092.0321.00")
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=312.092.0321.00')
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=312.092.0321.00')