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Description
TALLA 32.0 COLORES/COD. PANTONE: CAFE 476 C, LOGOSIMBOLO: COLOR/COD. PANTONE VERDE 561 C O 561 U. TIPO: CHOCLO, ACABADO LISO BRILLANTE. CORTE: PIEL DE GANADO VACUNO, CURTIDA AL CROMO FORRO: COMPLETO DE PIEL DE CABRA
Classification
Ropa y Telas. Ropa contractual
IMSS product code
210.005.3822.03
Each circle represents one of the 3 purchases of CALZADO MASCULINO made by the IMSS between 2009 and 2009.
| 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 | 482.2 MXN | 62.9 MXN | 15 % | 0.3 | 1 de enero de 2009 |
| Delegación Jalisco Almacen Delegacional En Jalisco | Jalisco | 1 | 241.1 MXN | 31.5 MXN | 15 % | 0.15 | 2 de enero de 2009 |
| Delegación San Luis Potosí Almacen Delegacional En San Luis Potosi | San Luis Potosi | 1 | 482.2 MXN | 62.9 MXN | 15 % | 0.3 | 1 de enero de 2009 |
| SUPPLIER | PURCHASES | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | TIME PERIOD |
|---|---|---|---|---|---|
| EXIMEX, S.A. DE C.V. | 3 | 157.2 MXN | 15 % | 0.75 | 2009 - 2009 |
Entity type
Ropa contractual
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=210.005.3822.03")
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=210.005.3822.03')
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=210.005.3822.03')