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Commit9623eaa

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Merge pull request4GeeksAcademy#7 from Lorenagubaira/Pandas00-05
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2 parentsfe9b1f5 +90d320c commit9623eaa

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‎.learn/exercises/00-welcome/README.es.md

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#`00` Welcometo Pandas!
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#`00` Welcome
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¡Estamos muy entusiasmados por tenerte aquí! 🎉 😂
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##💬 Fundamentos:
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Durante este curso te vamos a enseñar las funcionalidades más populares de Pandas, y a ayudarte a escribir tu propio código de Python con ello. Vas a estara estaraprendiendo:
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Durante este curso te vamos a enseñar las funcionalidades más populares de Pandas, y a ayudarte a escribir tu propio código de Python con ello. Vas a estar aprendiendo:
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+ Cómo instalar Pandas.
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‎.learn/exercises/00-welcome/README.md

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#`00` Welcometo Pandas!
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#`00` Welcome
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We are very excited to have you here! 🎉 😂
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‎.learn/exercises/01-How_to_open_a_new_terminal/README.es.mdrenamed to‎.learn/exercises/01-new-terminal/README.es.md

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#`01`How to open a new terminal
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#`01`New Terminal
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Este motor de ejercicios se hace cargo de tu terminal, eso significa que no puedes escribir en el terminal actual en la parte inferior de tu pantalla.
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‎.learn/exercises/01-How_to_open_a_new_terminal/README.mdrenamed to‎.learn/exercises/01-new-terminal/README.md

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#`01`How to open a new terminal
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#`01`New Terminal
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This exercise engine takes over your terminal, meaning that you cannot type on the current terminal at the bottom of your screen.
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‎.learn/exercises/01.2-Pipenv_ Package_Manager/README.es.mdrenamed to‎.learn/exercises/01.2-pipenv/README.es.md

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#`01.2` Pipenv: Package Manager
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#`01.2` Pipenv
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Antes de empezar cuaquier proyecto de Python, siempre es buena idea crear un nuevo enviroment (entorno). Los entornos aislados de Python previenen errores y hacen que su aplicación sea independiente.
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‎.learn/exercises/01.2-Pipenv_ Package_Manager/README.mdrenamed to‎.learn/exercises/01.2-pipenv/README.md

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#`01.2` Pipenv: Package Manager
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#`01.2` Pipenv
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Before starting any Python project is always a good idea to create a new environment. An isolated Python environment prevents bugs and makes your application independent.
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‎.learn/exercises/02-Installing_Pandas/README.es.mdrenamed to‎.learn/exercises/02-install/README.es.md

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#`02`Installing Pandas
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#`02`Install
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Iniciemos nuestro entorno instalando Pandas.
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‎.learn/exercises/02-Installing_Pandas/README.mdrenamed to‎.learn/exercises/02-install/README.md

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#`02`Installing Pandas
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#`02`Install
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Let's kickoff our environment by installing Pandas.
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‎.learn/exercises/02.2-Create_your_script/README.es.mdrenamed to‎.learn/exercises/02.1-create-a-script/README.es.md

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#`02.2` Createyour script
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#`02.1` Createa Script
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Python es un lenguaje de secuencias de comandos, lo que significa que para ejecutar el código de Python no es necesario copilar, todo lo que tienes que hacer es crear un archivo con la extensión`.py` y correrlo utilizando`python name_of_file.py`.
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‎.learn/exercises/02.2-Create_your_script/README.mdrenamed to‎.learn/exercises/02.1-create-a-script/README.md

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#`02.2` Createyour script
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#`02.1` Createa Script
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Python is a scripting language, meaning that to run a Python code you don't need to compile, all you have to do is create a file with the`.py` extension and run it by using`python name_of_file.py`.
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‎.learn/exercises/02.3-Import_Pandas/README.es.mdrenamed to‎.learn/exercises/02.2-import/README.es.md

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#`02.3` ImportPandas
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#`02.2` Import
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Ahora, vamos a añadir Pandas en nuestro script utilizando el comando`import`.
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‎.learn/exercises/02.3-Import_Pandas/README.mdrenamed to‎.learn/exercises/02.2-import/README.md

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#`02.3` ImportPandas
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#`02.2` Import
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Now let's add Pandas into our script by using the`import` command.
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‎.learn/exercises/03-Download_the_Dataset/README.es.mdrenamed to‎.learn/exercises/03-datasets/README.es.md

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#`03`Download the Dataset
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#`03`Datasets
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En Machine Learning los "Datasets" son los datos que utilizamos para nuestros experimentos. Por lo general, alimentamos estos Datasets a "modelos" y experimentamos de diferentes maneras, el objetivo siempre es predecir algo.
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‎.learn/exercises/03-Download_the_Dataset/README.mdrenamed to‎.learn/exercises/03-datasets/README.md

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#`03`Download the Dataset
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#`03`Datasets
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In Machine Learning "Datasets" are the data we use for our experiments. We usually feed these Datasets to "models" and experiment in different ways, the goal is always to predict something.
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‎.learn/exercises/04.1-Series_Date_Range/README.es.mdrenamed to‎.learn/exercises/04.1-date-range/README.es.md

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#`04.1`SeriesDate Range
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#`04.1` Date Range
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##📝 Instrucciones:
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‎.learn/exercises/04.1-Series_Date_Range/README.mdrenamed to‎.learn/exercises/04.1-date-range/README.md

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#`04.1`SeriesDate Range
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#`04.1` Date Range
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##📝 Instructions:
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‎.learn/exercises/04.2-Series_Apply/README.es.mdrenamed to‎.learn/exercises/04.2-series-apply/README.es.md

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#Series Apply
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#`04.2`Series Apply
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Dada una serie variable como:
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‎.learn/exercises/04.2-Series_Apply/README.mdrenamed to‎.learn/exercises/04.2-series-apply/README.md

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#Series Apply
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#`04.2`Series Apply
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Given a series variable like:
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‎.learn/exercises/05-Pandas_Data_Frame/README.es.mdrenamed to‎.learn/exercises/05-dataframes/README.es.md

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#Pandas Data Frame
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#`05` DataFrames
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Los DataFrames (marco de datos) son un concepto similar a las "tablas" o "datos tabulados". Puedes crear un DataFrame a partir cualquier Dataset.
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‎.learn/exercises/05-Pandas_Data_Frame/README.mdrenamed to‎.learn/exercises/05-dataframes/README.md

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#Pandas Data Frame
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#`05` DataFrames
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DataFrames are a similar concept to "tables" or "tabulated data". You can create a DataFrame from any Dataset.
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