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Description
MONTADO EN BASE DE MADERA MEDIDAS 3 X 15 CMS
Classification
Artículos de consumo. Utiles de oficina
IMSS product code
312.820.0155.00
Each circle represents one of the 18 purchases of SELLO DE GOMA made by the IMSS between 2009 and 2015.
| SUPPLIER | PURCHASES | OVERCOST IN MXN | OVERCOST IN % | LOST QUANTITY | TIME PERIOD |
|---|---|---|---|---|---|
| MENA PENICHE CESAR AUGUSTO | 6 | 463.7 MXN | 22.2 % | 4.73 | 2009 - 2015 |
| DOMINGUEZ GONZALEZ SEBASTIANA | 4 | 1,882.4 MXN | 58.6 % | 14 | 2011 - 2012 |
| BAZAN SANTIAGO LORENZO | 1 | 2,454.1 MXN | 14.8 % | 20.2 | 2015 - 2015 |
| BERNAL ARELLANOS RAUL ALBERTO | 1 | - MXN | - | - | 2012 - 2012 |
| DELGADO FELIX JAZMIN | 1 | 89.6 MXN | 75.1 % | 0.751 | 2011 - 2011 |
| HERNANDEZ AGUILAR ANA ERIKA | 1 | 373.6 MXN | 67.4 % | 2.7 | 2012 - 2012 |
| LAZARO VILLEGAS CECILIO | 1 | 8.35 MXN | 7 % | 0.07 | 2011 - 2011 |
| OLVERES LANDEROS DAVID | 1 | - MXN | - | - | 2011 - 2011 |
| RAMA SERVICIOS, S.A. DE C.V. | 1 | - MXN | - | - | 2012 - 2012 |
| SOSA ZAVALA NAYELI GUADALUPE | 1 | 145.6 MXN | 16 % | 1.12 | 2013 - 2013 |
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=312.820.0155.00")
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=312.820.0155.00')
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=312.820.0155.00')