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Description
Compra Emergente
IMSS product code
060.088.0017.01
Each circle represents one of the 3 purchases of APÓSITOS made by the IMSS between 2009 and 2009.
| INSTITUTION | STATE | QUANTITY | TOTAL AMOUNT | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | LAST PURCHASE |
|---|---|---|---|---|---|---|---|
| Delegación Guanajuato | Guanajuato | 1 | 660 MXN | 613.2 MXN | 1,311.3 % | 15.1 | 7 de enero de 2009 |
| Delegación San Luis Potosí Almacen Delegacional En San Luis Potosi | San Luis Potosi | 1 | 2,087.3 MXN | 1,477.3 MXN | 242.2 % | 36.3 | 11 de agosto de 2009 |
| Delegación Tamaulipas | Tamaulipas | 1 | 339.3 MXN | - MXN | - | - | 24 de noviembre de 2009 |
| SUPPLIER | PURCHASES | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | TIME PERIOD |
|---|---|---|---|---|---|
| COMERCIAL ALPRO, S.A. DE C.V. | 1 | - MXN | - | - | 2009 - 2009 |
| GUERRA CORDERO ESTEBAN | 1 | 613.2 MXN | 1,311.3 % | 15.1 | 2009 - 2009 |
| SAY QUIMICA MEDICA, S.A. DE C.V. | 1 | 1,477.3 MXN | 242.2 % | 36.3 | 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=060.088.0017.01")
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=060.088.0017.01')
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=060.088.0017.01')