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Description
TAMAÑO CARTA. AZUL PK-1. DE 72 G/M2.
Classification
Artículos de consumo. Papeleria
IMSS product code
311.685.3528.00
Each circle represents one of the 19 purchases of PAPEL BOND made by the IMSS between 2009 and 2018.
| INSTITUTION | STATE | QUANTITY | TOTAL AMOUNT | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | LAST PURCHASE |
|---|---|---|---|---|---|---|---|
| H. de Especialidades Núm 71, Torreón, Coahuila Umae Especialidades Coahuila | Coahuila | 6 | 4,354.4 MXN | 1,685.6 MXN | 747.8 % | 80.5 | 1 de abril de 2017 |
| Delegación Michoacán Almacen Delegacional En Michoacan | Michoacán | 2 | 6,264 MXN | 4,980.6 MXN | 1,115.1 % | 446 | 25 de julio de 2018 |
| H. de Gineco Pediatría Núm 48, León Gto. Umae Gineco - Pediatria Guanajuato | Guanajuato | 2 | 9,010.8 MXN | 554.3 MXN | 8.3 % | 6.97 | 1 de enero de 2015 |
| Oficinas Centrales Almacen De Programas Especiales Y Red Fria | 2 | 924.4 MXN | 126.9 MXN | 15.5 % | 1.75 | 24 de septiembre de 2010 | |
| Delegación Guanajuato Almacen Delegacional En Guanajuato | Guanajuato | 1 | 4,872 MXN | - MXN | - | - | 26 de julio de 2018 |
| Delegación Morelos Almacen Delegacional En Morelos | Morelos | 1 | 533.6 MXN | 458.6 MXN | 611 % | 61.1 | 1 de abril de 2016 |
| Delegación Nayarit Almacen Delegacional En Nayarit | Nayarit | 1 | 175 MXN | 24.1 MXN | 16 % | 0.16 | 1 de agosto de 2011 |
| Delegación Norte del DF Almacen Delegacional En El D.f. Zona Norte | Ciudad de México | 1 | 355 MXN | 124.3 MXN | 53.9 % | 1.08 | 24 de junio de 2015 |
| H. de Especialidades No. 14 "Adolfo Ruíz Cortines", Veracruz. | Veracruz | 1 | 64.4 MXN | - MXN | - | - | 27 de abril de 2012 |
| H. de Especialidades No. 14 "Adolfo Ruíz Cortines", Veracruz. Umae Especialidades Veracruz (nte.) | Veracruz | 1 | 160.9 MXN | 22.2 MXN | 16 % | 0.8 | 26 de marzo de 2012 |
| H. de Especialidades Núm 71, Torreón, Coahuila | Coahuila | 1 | 333.6 MXN | - MXN | - | - | 7 de marzo de 2013 |
| SUPPLIER | PURCHASES | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | TIME PERIOD |
|---|---|---|---|---|---|
| SERVICIOS Y SISTEMAS IMPRESOS, S.A. DE C.V. | 4 | 681.3 MXN | 11.9 % | 8.72 | 2009 - 2015 |
| CASI DE LA LAGUNA, S.A. DE C.V. | 3 | 425 MXN | 1,388.9 % | 42.5 | 2016 - 2017 |
| DISTRIBUIDORA DE INSUMOS PARA OFICINA SA DE CV | 2 | 222.9 MXN | 122.4 % | 29 | 2014 - 2016 |
| OFIX, S. A. DE C. V. | 2 | 22.2 MXN | 8 % | 0.8 | 2012 - 2012 |
| PAPELERIA MADERO VALLADOLID SA DE CV | 2 | 4,980.6 MXN | 1,115.1 % | 446 | 2016 - 2018 |
| SOCORRO GONZALEZ MEDINA | 2 | 1,037.6 MXN | 37.5 % | 9 | 2013 - 2015 |
| COMERCIALIZADORA DAMAG, S.A. DE C.V. | 1 | 124.3 MXN | 53.9 % | 1.08 | 2015 - 2015 |
| ESPINOZA BARRAGAN DAVID | 1 | 24.1 MXN | 16 % | 0.16 | 2011 - 2011 |
| MANTENIMIENTO PREVENTIVO Y CORRECTIVO SA DE CV | 1 | 458.6 MXN | 611 % | 61.1 | 2016 - 2016 |
| OROZCO ESPARZA LORENA | 1 | - MXN | - | - | 2018 - 2018 |
Entity type
Papeleria
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=311.685.3528.00")
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=311.685.3528.00')
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=311.685.3528.00')