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Description
LASER JET 4L 4P 4ML 4MP HP 92274 A
Classification
Material médico. Materiales diversos
IMSS product code
370.206.8226.00
Each circle represents one of the 2 purchases of TONER made by the IMSS between 2009 and 2011.
| 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 | 1 | 9,890 MXN | 1,290 MXN | 15 % | 0.6 | 11 de agosto de 2009 |
| Delegación Sur del DF Almacen Delegacional En El D.f. Zona Sur | Ciudad de México | 1 | 18,819.8 MXN | 2,595.8 MXN | 16 % | 1.28 | 16 de noviembre de 2011 |
| SUPPLIER | PURCHASES | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | TIME PERIOD |
|---|---|---|---|---|---|
| DOMINGUEZ ARANGO NALLELY IVONNE | 1 | 1,290 MXN | 15 % | 0.6 | 2009 - 2009 |
| SURTIDORA ALEVI, S.A. DE C.V. | 1 | 2,595.8 MXN | 16 % | 1.28 | 2011 - 2011 |
Entity type
Materiales diversos
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=370.206.8226.00")
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=370.206.8226.00')
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=370.206.8226.00')