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Description
PARA ENCUADERNAR (WIRE) DE 5/8" 22 AROS POR CAJA COLOR NEGRO, TAMAÑO CARTA
Classification
Artículos de consumo. Utiles de oficina
IMSS product code
312.092.0180.02
Each circle represents one of the 2 purchases of ARILLO METALICO made by the IMSS between 2018 and 2018.
| INSTITUTION | STATE | QUANTITY | TOTAL AMOUNT | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | LAST PURCHASE |
|---|---|---|---|---|---|---|---|
| Delegación Tabasco Almacen Delegacional En Tabasco | Tabasco | 1 | 306,240 MXN | 42,677.6 MXN | 16.2 % | 388.6 | 18 de mayo de 2018 |
| Oficinas Centrales Ofnas Centrales -reforma- Coordinacion De Abastecimiento | 1 | 17,963.8 MXN | 2,040.2 MXN | 12.8 % | 18.6 | 13 de agosto de 2018 |
| SUPPLIER | PURCHASES | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | TIME PERIOD |
|---|---|---|---|---|---|
| CODESCO, S.A. DE C.V. | 1 | 2,040.2 MXN | 12.8 % | 18.6 | 2018 - 2018 |
| COMERCIALIZADORA TONYCOM,S.A DE C.V | 1 | 42,677.6 MXN | 16.2 % | 388.6 | 2018 - 2018 |
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=312.092.0180.02")
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=312.092.0180.02')
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=312.092.0180.02')