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Description
TAMAÑO CARTA DE 7 POSICIONES
Classification
Artículos de consumo. Papeleria
IMSS product code
311.814.0916.00
Each circle represents one of the 5 purchases of SEPARADORES made by the IMSS between 2010 and 2016.
| INSTITUTION | STATE | QUANTITY | TOTAL AMOUNT | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | LAST PURCHASE |
|---|---|---|---|---|---|---|---|
| Delegación Estado de México Oriente Almacen Delegacional En Estado De Mexico Oriente | Estado de México | 5 | 11,327.5 MXN | 1,562.4 MXN | 16 % | 302.7 | 7 de abril de 2016 |
| SUPPLIER | PURCHASES | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | TIME PERIOD |
|---|---|---|---|---|---|
| DEPORTES Y REGALOS SAMAR, S. A. DE C. V. | 2 | 501.3 MXN | 16 % | 92.5 | 2010 - 2011 |
| DAVLU DISTRIBUIDORA S.A. DE C.V. | 1 | 691.8 MXN | 16 % | 122.9 | 2016 - 2016 |
| DISTRIBUIDORA GARDI, S.A. DE C.V. | 1 | 270.9 MXN | 16 % | 69.1 | 2015 - 2015 |
| R&M ROSCO MEXICO, S. A. DE C. V. | 1 | 98.3 MXN | 16 % | 18.2 | 2013 - 2013 |
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=311.814.0916.00")
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=311.814.0916.00')
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=311.814.0916.00')