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Description
PINZA SPURLING, RECTA, MORDIDA DE 4 X 10 MM., LONGITUD DE 189 MM.
IMSS product code
537.703.9556.01
Each circle represents one of the 3 purchases of PINZAS made by the IMSS between 2009 and 2017.
| INSTITUTION | STATE | QUANTITY | TOTAL AMOUNT | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | LAST PURCHASE |
|---|---|---|---|---|---|---|---|
| Delegación Baja California Almacen Delegacional En Baja California Norte | Baja California | 1 | 9,020.2 MXN | 1,244.2 MXN | 16 % | 0.64 | 20 de agosto de 2010 |
| Delegación Michoacán Almacen Delegacional En Michoacan | Michoacán | 1 | 2,610 MXN | 360 MXN | 16 % | 0.16 | 27 de junio de 2017 |
| Delegación San Luis Potosí Almacen Delegacional En San Luis Potosi | San Luis Potosi | 1 | 1,676.7 MXN | 218.7 MXN | 15 % | 0.15 | 1 de octubre de 2009 |
| SUPPLIER | PURCHASES | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | TIME PERIOD |
|---|---|---|---|---|---|
| QUIRURGICA ORTOPEDICA, S. A. DE C. V. | 3 | 1,822.9 MXN | 15.7 % | 0.95 | 2009 - 2017 |
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=537.703.9556.01")
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=537.703.9556.01')
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=537.703.9556.01')