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Description
PARA CUBO DE HIELO, DE PLASTICO.
Classification
Artículos de consumo. Articulos de cocina y comedor
IMSS product code
120.198.0180.01
Each circle represents one of the 3 purchases of CHAROLA made by the IMSS between 2013 and 2016.
| INSTITUTION | STATE | QUANTITY | TOTAL AMOUNT | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | LAST PURCHASE |
|---|---|---|---|---|---|---|---|
| Delegación Estado de México Oriente Almacen Delegacional En Estado De Mexico Oriente | Estado de México | 1 | 451.2 MXN | 62.2 MXN | 16 % | 2.56 | 25 de abril de 2014 |
| Delegación Guanajuato Almacen Delegacional En Guanajuato | Guanajuato | 1 | 689 MXN | 95 MXN | 16 % | 3.52 | 9 de mayo de 2016 |
| Delegación Tamaulipas Almacen Delegacional En Tamaulipas | Tamaulipas | 1 | 269.8 MXN | 37.2 MXN | 16 % | 1.76 | 11 de octubre de 2013 |
| SUPPLIER | PURCHASES | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | TIME PERIOD |
|---|---|---|---|---|---|
| GRUPO ARAUJO SALCIDO, S.A. DE C.V. | 1 | 95 MXN | 16 % | 3.52 | 2016 - 2016 |
| MASI CIENTIFICA S.A. DE C.V. | 1 | 37.2 MXN | 16 % | 1.76 | 2013 - 2013 |
| SHOCK MKT, S.A. DE C.V. | 1 | 62.2 MXN | 16 % | 2.56 | 2014 - 2014 |
Entity type
Articulos de cocina y comedor
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=120.198.0180.01")
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=120.198.0180.01')
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=120.198.0180.01')