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Description
DE PLASTICO PARA CUBOS DE HIELO.
Classification
Artículos de consumo. Articulos de cocina y comedor
IMSS product code
120.198.0198.01
Each circle represents one of the 4 purchases of CHAROLA made by the IMSS between 2012 and 2017.
| 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 | 2 | 625.5 MXN | 78 MXN | 14.2 % | 1.14 | 8 de agosto de 2017 |
| Delegación Morelos Almacen Delegacional En Morelos | Morelos | 2 | 203.5 MXN | 36.3 MXN | 19.6 % | 2.06 | 19 de abril de 2016 |
| SUPPLIER | PURCHASES | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | TIME PERIOD |
|---|---|---|---|---|---|
| COCINA Y ASEO INSTITUCIONAL, S.A. DE C.V. | 1 | 44.8 MXN | 16 % | 0.64 | 2017 - 2017 |
| DISTRIBUIDORA KALA S.A. DE C.V. | 1 | 33.2 MXN | 12.4 % | 0.497 | 2016 - 2016 |
| ESTRATEGIAS COMERCIALES BUXOM, S.A. DE C.V. | 1 | 5.34 MXN | 16 % | 1.6 | 2012 - 2012 |
| MANTENIMIENTO PREVENTIVO Y CORRECTIVO SA DE CV | 1 | 31 MXN | 23.2 % | 0.463 | 2016 - 2016 |
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=120.198.0198.01")
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=120.198.0198.01')
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=120.198.0198.01')