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Description
TAPA DE POLIESTIRENO DE TRES DIVISIONES.
Classification
Artículos de consumo. Articulos de cocina y comedor
IMSS product code
120.198.1394.01
Each circle represents one of the 22 purchases of CHAROLA made by the IMSS between 2014 and 2018.
| INSTITUTION | STATE | QUANTITY | TOTAL AMOUNT | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | LAST PURCHASE |
|---|---|---|---|---|---|---|---|
| H. de Especialidades, "Dr. Bernardo Sepulveda Gutiérrez" Centro Médico Nacional Siglo XXI Umae Especialidades C.m.n. Siglo Xxi | Ciudad de México | 10 | 43,101 MXN | 16,640.9 MXN | 4,674.2 % | 11,993.1 | 30 de noviembre de 2018 |
| Delegación Sonora Almacen Delegacional En Sonora | Sonora | 3 | 275,935.9 MXN | 79,962.3 MXN | 3,134.1 % | 29,660.3 | 5 de abril de 2018 |
| H. de Gineco Obstetricia No. 3 "Dr. Víctor Manuel Espinosa de los Reyes Sánchez" La Raza Umae Gineco - Obstetricia La Raza | Ciudad de México | 3 | 80,156 MXN | 2,551.5 MXN | 3.05 % | 2,286.4 | 1 de enero de 2017 |
| Delegación Estado de México Poniente Almacen Delegacional En El Estado De Mexico Poniente | Estado de México | 1 | 19,140 MXN | 11,384.9 MXN | 146.8 % | 8,074.3 | 3 de marzo de 2015 |
| Delegación Guanajuato Almacen Delegacional En Guanajuato | Guanajuato | 1 | 1,496.4 MXN | 1,453 MXN | 3,349.1 % | 1,004.7 | 14 de agosto de 2018 |
| Delegación Hidalgo Almacen Delegacional En Hidalgo | Hidalgo | 1 | 7.89 MXN | 6.48 MXN | 459.6 % | 4.6 | 6 de agosto de 2015 |
| Delegación Oaxaca Almacen Delegacional En Oaxaca | Oaxaca | 1 | 12,934 MXN | 5,703.1 MXN | 78.9 % | 3,943.6 | 25 de enero de 2018 |
| H. de Oncología, Centro Médico Nacional Siglo XXI Umae Oncologia C.m.n. Siglo Xxi | Ciudad de México | 1 | 12,862.1 MXN | - MXN | - | - | 4 de octubre de 2017 |
| H. de Pediatría, Centro Médico Nacional Siglo XXI Umae Pediatria C.m.n. Siglo Xxi | Ciudad de México | 1 | 22,736 MXN | 3,136 MXN | 16 % | 3,200 | 13 de octubre de 2014 |
| SUPPLIER | PURCHASES | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | TIME PERIOD |
|---|---|---|---|---|---|
| ALVAREZ VARGAS ERIC | 9 | 6,224.3 MXN | 24.4 % | 4,790.3 | 2016 - 2018 |
| JARAMILLO ORTIZ VERONICA | 4 | 84,006.6 MXN | 2,405.3 % | 32,658.7 | 2015 - 2018 |
| DAYEALI, S.A. DE C.V. | 2 | 2,551.5 MXN | 4.57 % | 2,286.4 | 2016 - 2017 |
| JOSE MARIA MARQUEZ GONZALEZ | 2 | 10,416.6 MXN | 23,261.6 % | 7,202.9 | 2018 - 2018 |
| BA?OS REYES PEDRO | 1 | 6.48 MXN | 459.6 % | 4.6 | 2015 - 2015 |
| COCINA Y ASEO INSTITUCIONAL, S.A. DE C.V. | 1 | 13,043.8 MXN | 6.68 % | 9,019.5 | 2018 - 2018 |
| COMERCIALIZADORA LUIV, S. A. DE C. V. | 1 | 3,136 MXN | 16 % | 3,200 | 2014 - 2014 |
| HECTOR CORREA LEE | 1 | - MXN | - | - | 2015 - 2015 |
| RUIZ DUARTE ARELI MIREYA | 1 | 1,453 MXN | 3,349.1 % | 1,004.7 | 2018 - 2018 |
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=120.198.1394.01")
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=120.198.1394.01')
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=120.198.1394.01')