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Description
Compra Emergente
IMSS product code
060.040.8801.10
Each circle represents one of the 4 purchases of AGUJAS made by the IMSS between 2009 and 2013.
| INSTITUTION | STATE | QUANTITY | TOTAL AMOUNT | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | LAST PURCHASE |
|---|---|---|---|---|---|---|---|
| Delegación Sinaloa Almacen Delegacional En Sinaloa | Sinaloa | 3 | 5,642.3 MXN | 1,581.8 MXN | 45.3 % | 6.71 | 12 de marzo de 2013 |
| Delegación Tabasco | Tabasco | 1 | 563.5 MXN | 217.5 MXN | 62.9 % | 0.723 | 21 de octubre de 2009 |
| SUPPLIER | PURCHASES | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | TIME PERIOD |
|---|---|---|---|---|---|
| COMETA MERCANTIL, S.A. DE C.V. | 2 | 1,441.6 MXN | 59.9 % | 4.79 | 2009 - 2009 |
| MANUEL MARTIN VILLEGAS ORTIZ | 1 | 217.5 MXN | 62.9 % | 0.723 | 2009 - 2009 |
| SAINZ ROSAS LUIS CARLOS | 1 | 140.2 MXN | 16 % | 1.92 | 2013 - 2013 |
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=060.040.8801.10")
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=060.040.8801.10')
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=060.040.8801.10')