# ---
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#     text_representation:
#       extension: .py
#       format_name: light
#       format_version: '1.5'
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#     display_name: Python 3
#     language: python
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# + _uuid="8f2839f25d086af736a60e9eeb907d3b93b6e0e5" _cell_guid="b1076dfc-b9ad-4769-8c92-a6c4dae69d19"
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
# -

# # Ejercicio 01. Entrada - Salida
#
#
# <p style="color:#FF0000;border:2px solid Tomato;background-color:Yellow;"><b>Qué hay que hacer con las salidas (generado) y (seleccionado)?</b></p></body> 
#

# ![image.png](attachment:eb7a70fe-f1f2-486c-b161-4b2013c0f880.png)

# ![image.png](attachment:781393f7-5584-47d0-90bd-888d487d0b39.png)

# abrimos desde la dirección de github
namefile = "https://raw.githubusercontent.com/jovenluk/WFML/master/Datasets/bank_customer_data.txt"
df = pd.read_csv(namefile)
df.head()


# # Cargamos datos de empleados

# ![image.png](attachment:3b2ec634-ae54-48de-bf9c-29479b260ccd.png)

# abrimos desde la dirección de github
namefile = "https://raw.githubusercontent.com/jovenluk/WFML/master/Datasets/employee_data.csv"
df = pd.read_csv(namefile)
df.head()

# # Cargamos datos de clientes de Banca (EXCEL)
# Hace falta instalar una librería en Kaggle para que pandas entienda un XLSX

# ![image.png](attachment:2db08b26-6793-42e7-9eec-47516fc03baf.png)

# ! pip install openpyxl
# abrimos desde la dirección de github
namefile = "https://raw.githubusercontent.com/jovenluk/WFML/master/Datasets/bank_customer_data.xlsx"
df = pd.read_excel(namefile, engine='openpyxl')
df.head()

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