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Description
GRADUACION B TURQUEZA
Classification
Artículos de consumo. Utiles de oficina
IMSS product code
312.570.0165.00
Each circle represents one of the 2 purchases of LAPIZ made by the IMSS between 2014 and 2016.
| INSTITUTION | STATE | QUANTITY | TOTAL AMOUNT | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | LAST PURCHASE |
|---|---|---|---|---|---|---|---|
| Oficinas Centrales Almacen De Programas Especiales Y Red Fria | 1 | 59.7 MXN | 8.24 MXN | 16 % | 1.6 | 6 de octubre de 2014 | |
| Oficinas Centrales Ofnas Centrales -reforma- Coordinacion De Abastecimiento | 1 | 12,127.8 MXN | 1,672.8 MXN | 16 % | 328 | 28 de julio de 2016 |
| SUPPLIER | PURCHASES | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | TIME PERIOD |
|---|---|---|---|---|---|
| MEVDI, S.A. DE C.V. | 1 | 8.24 MXN | 16 % | 1.6 | 2014 - 2014 |
| PRODUCCIONES CONTI, S.A. DE C.V. | 1 | 1,672.8 MXN | 16 % | 328 | 2016 - 2016 |
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=312.570.0165.00")
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=312.570.0165.00')
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=312.570.0165.00')