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Description
GASA SECA CORTADA DE 7.5 X 5CM.
IMSS product code
060.436.0057.10
Each circle represents one of the 3 purchases of GASAS made by the IMSS between 2009 and 2014.
| INSTITUTION | STATE | QUANTITY | TOTAL AMOUNT | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | LAST PURCHASE |
|---|---|---|---|---|---|---|---|
| Delegación Sonora Almacen Delegacional En Sonora | Sonora | 2 | 4,628.4 MXN | 638.4 MXN | 16 % | 6.72 | 24 de junio de 2014 |
| Delegación Jalisco | Jalisco | 1 | 1,503.2 MXN | 0.00501 MXN | 0.000333 % | 0.000115 | 9 de noviembre de 2009 |
| SUPPLIER | PURCHASES | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | TIME PERIOD |
|---|---|---|---|---|---|
| DIAGNOSTICA COMERCIAL, S.A. DE C.V. | 1 | 11.2 MXN | 16 % | 0.32 | 2011 - 2011 |
| MEDICA AVANZADA, S. A. DE C. V. | 1 | 627.2 MXN | 16 % | 6.4 | 2014 - 2014 |
| Proveedor local no identificado (Compra Emergente) | 1 | 0.00501 MXN | 0.000333 % | 0.000115 | 2009 - 2009 |
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=060.436.0057.10")
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=060.436.0057.10')
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=060.436.0057.10')