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Description
DESECHABLE COLOR SEPIA
Classification
Artículos de consumo. Utiles de oficina
IMSS product code
312.711.0249.00
Each circle represents one of the 3 purchases of PLUMIN MARCADOR made by the IMSS between 2009 and 2016.
| INSTITUTION | STATE | QUANTITY | TOTAL AMOUNT | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | LAST PURCHASE |
|---|---|---|---|---|---|---|---|
| Delegación Morelos Almacen Delegacional En Morelos | Morelos | 2 | 979.3 MXN | 182.9 MXN | 11.6 % | 3.47 | 7 de octubre de 2016 |
| Delegación Querétaro Almacen Delegacional En Queretaro | Querétaro | 1 | 115 MXN | 15 MXN | 15 % | 1.5 | 27 de febrero de 2009 |
| SUPPLIER | PURCHASES | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | TIME PERIOD |
|---|---|---|---|---|---|
| MANTENIMIENTO PREVENTIVO Y CORRECTIVO SA DE CV | 1 | 182.9 MXN | 23.1 % | 3.47 | 2016 - 2016 |
| OLVERA PEDROZA CONCEPCION | 1 | 15 MXN | 15 % | 1.5 | 2009 - 2009 |
| TERGUS, S. A. DE C. V. | 1 | - MXN | - | - | 2016 - 2016 |
Entity type
Utiles de oficina
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=312.711.0249.00")
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=312.711.0249.00')
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=312.711.0249.00')