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Description
MONTADO EN BASE DE MADERA MEDIDAS 5 X 3.5 CMS
Classification
Artículos de consumo. Utiles de oficina
IMSS product code
312.820.0361.00
Each circle represents one of the 30 purchases of SELLO DE GOMA made by the IMSS between 2009 and 2017.
| INSTITUTION | STATE | QUANTITY | TOTAL AMOUNT | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | LAST PURCHASE |
|---|---|---|---|---|---|---|---|
| Delegación Sonora Almacen Delegacional En Sonora | Sonora | 9 | 9,916.6 MXN | 2,071.9 MXN | 60.7 % | 12.7 | 29 de diciembre de 2017 |
| H. de Cardiología, Centro Médico Nacional Siglo XXI Umae Cardiologia C.m.n. Siglo Xxi | Ciudad de México | 9 | 8,092.2 MXN | 3,555.7 MXN | 95.9 % | 21.2 | 30 de septiembre de 2014 |
| Delegación Nayarit Almacen Delegacional En Nayarit | Nayarit | 4 | 473.3 MXN | 40 MXN | 5.93 % | 0.712 | 7 de octubre de 2014 |
| Delegación Michoacán Almacen Delegacional En Michoacan | Michoacán | 2 | 2,557.8 MXN | - MXN | - | - | 4 de abril de 2017 |
| H. de Especialidades No. 1, "Lic. Ignacio García Tellez" Mérida, Yucatán. Umae Especialidades Yucatan | Yucatán | 2 | 344.5 MXN | 14.5 MXN | 4.31 % | 0.0862 | 1 de diciembre de 2014 |
| Delegación Baja California Sur Almacen Delegacional En Baja California Sur | Baja California Sur | 1 | 352 MXN | 270.5 MXN | 331.8 % | 3.32 | 27 de agosto de 2009 |
| Delegación Jalisco Almacen Delegacional En Jalisco | Jalisco | 1 | 1,897.5 MXN | - MXN | - | - | 2 de enero de 2009 |
| Delegación Tamaulipas Almacen Delegacional En Tamaulipas | Tamaulipas | 1 | 390 MXN | 212.3 MXN | 119.5 % | 2.39 | 22 de octubre de 2012 |
| H. de Gineco Obstetricia Núm. 4 "Dr. Luis Castelazo Ayala" Umae Gineco - Obstetricia No. 4 (d.f. Sur) | Ciudad de México | 1 | 52.2 MXN | - MXN | - | - | 18 de febrero de 2010 |
| SUPPLIER | PURCHASES | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | TIME PERIOD |
|---|---|---|---|---|---|
| BARBOSA PEREZ JOSE ANTONIO MANUEL | 9 | 3,555.7 MXN | 95.9 % | 21.2 | 2014 - 2014 |
| CRUZ MUNGUIA MARTHA OLIVIA | 7 | 1,024.9 MXN | 17.1 % | 3.4 | 2013 - 2017 |
| COMERCIALIZADORA YIREH, S.A. DE C.V. | 2 | - MXN | - | - | 2012 - 2012 |
| GUZMAN PEREZ MARCELO CLAUDIO | 2 | - MXN | - | - | 2014 - 2017 |
| MENA PENICHE CESAR AUGUSTO | 2 | 14.5 MXN | 4.31 % | 0.0862 | 2014 - 2014 |
| CARLOS ALBERTO CRUZ REYES | 1 | - MXN | - | - | 2014 - 2014 |
| DOMINGUEZ GONZALEZ SEBASTIANA | 1 | 270.5 MXN | 331.8 % | 3.32 | 2009 - 2009 |
| LEON ORTEGA JONATHAN | 1 | - MXN | - | - | 2010 - 2010 |
| MARIA INES STERLING CRUZ | 1 | 382 MXN | 18.6 % | 1.12 | 2017 - 2017 |
| OLVERES LANDEROS DAVID | 1 | - MXN | - | - | 2009 - 2009 |
| SOCORRO PATRICIA GERARDO SANCHEZ | 1 | 665 MXN | 407.9 % | 8.16 | 2009 - 2009 |
| SOTO GIL AMANDO | 1 | 212.3 MXN | 119.5 % | 2.39 | 2012 - 2012 |
| TREVINO CONCHAS MARIA ASUNCION | 1 | 40 MXN | 23.7 % | 0.712 | 2010 - 2010 |
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=312.820.0361.00")
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=312.820.0361.00')
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=312.820.0361.00')