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‎.learn/exercises/02.2-import/README.es.md

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Ahora, vamos a añadir Pandas en nuestro script utilizando el comando`import`.
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El comando`import` está destinadoa cargar librerías deterceras partes (como Pandas) u otros archivos de Python que hayas creado (que haremos en el futuro).
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El comando`import` está destinadopara cargar librerías deterceros (como Pandas) u otros archivos de Python que hayas creado (que haremos en el futuro).
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##📝 Instrucciones:
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1. Escribe`import pandas as pd` dentro del archivo`app.py` para importar la librería de Pandas.
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2.Utiliza la función de Pandas`read_csv` para importar el contenido del archivo CSV en una variable llamada`data_frame`.
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2.Crea una variable llamada`data_frame`.
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3.Imprime lavariable enelterminal usandolafunción`print`.
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3.Utiliza lafunción de Pandas`read_csv` para importarelcontenido del archivo CSV en esta ruta`.learn/assets/pokemon_data.csv` y asígnalo alavariable`data_frame`
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##Resultado Esperado:
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4. Imprime la variable en el terminal usando la función`print`.
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##💻 Resultado Esperado:
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Corre tu script y deberías ver la siguiente salida:
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![print file](../../assets/print-file.png)
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![correr archivo app.py](../../assets/print-file.png)
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##💡 Pista:
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+ Tu código debería ser algo así
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```python
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import pandasas pd
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data_frame= pd.read_csv('.learn/assets/pokemon_data.csv')
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print(data_frame)
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```
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+ Echa un vistazo a la documentación de`read_csv`:https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html
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>Nota: La función`read_csv` devuelve algo llamado`DataFrame`; nos estaremos refiriendo a eso como una variable de ahora en adelante.

‎.learn/exercises/02.2-import/README.md

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Now let's add Pandas into our script by using the`import` command.
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The`import` command is meant for loading 3rd part libraries (like Pandas) or other Python files that you have created (which we will do in the future).
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The`import` command is meant for loading 3rd-party libraries (like Pandas) or other Python files that you have created (which we will do in the future).
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##📝 Instructions:
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1. Type`import pandas as pd` inside the file`app.py` to import the Pandas library.
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2.Use the Pandas`read_csv` function to import the content of the CSV file into a variable called`data_frame`.
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2.Create a variable called`data_frame`.
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3.Print thevariable on the terminal using the`print` function.
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3.Use thePandas`read_csv` function to import thecontents of a CSV file in this path`.learn/assets/pokemon_data.csv`; assign it to the variable`data_frame`.
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##Expected Result:
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4. Print the variable on the terminal using the`print` function.
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Run your script and you should see the following output:
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##💻 Expected Result:
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![print file](../../assets/print-file.png)
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Run your script, and you should see the following output:
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##💡 Hint:
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+ Your code should be something like this
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![Run app.py file](../../assets/print-file.png)
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```python
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import pandasas pd
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##💡 Hint:
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data_frame= pd.read_csv('.learn/assets/pokemon_data.csv')
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print(data_frame)
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```
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+ Check the`read_csv` documentation:https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html
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>Note: The`read_csv` function returns something called a`DataFrame`; we will berefering to it as a variable from now on.
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>Note: The`read_csv` function returns something called a`DataFrame`; we will bereferring to it as a variable from now on.
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importpandasaspd
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data_frame=pd.read_csv('.learn/assets/pokemon_data.csv')
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print(data_frame)

‎.learn/exercises/03-datasets/README.es.md

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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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Posibles Datasets basados en tu predicción objetiva:
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Posibles Datasets basados enel objetivo detu predicción:
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- Si vendemos zapatos, ¿Cuántos zapatos necesito en mi almacén para el próximo mes? Un buen Datasetserán las ventas de los últimos 2 años.
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- Si vendemos zapatos, ¿Cuántos zapatos necesito en mi almacén para el próximo mes? Un buen Datasetserían las ventas de los últimos 2 años.
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- Si estamos construyendo un programa para diagnosticar neumonía, un buen Datasetserán 100 radiografías con neumonía y 100 radiografías sin neumonía.
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- Si estamos construyendo un programa para diagnosticar neumonía, un buen Datasetserían 100 radiografías con neumonía y 100 radiografías sin neumonía.
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Tienes que ser creativo al crear tus Datasets, piensa en todas las variables que afectan la predicción y trata de recolectar y organizar todo en uno o varios Datasets.
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Este proyecto ya viene con un Dataset de Pokémon, ubicado en la ruta:`./.learn/assets/pokemon_data.csv`.
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Este proyecto ya viene con un Dataset de Pokémon, ubicado en la ruta:`.learn/assets/pokemon_data.csv`.
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##📝 Instrucciones:
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1. Puedes abrir el archivo manualmente o corriendo el siguiente comando en tu terminal:
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```bash
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$ code ./.learn/assets/pokemon_data.csv
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$ code .learn/assets/pokemon_data.csv
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```
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##💡 Pista:
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Después de abrir el archivo, verás algo como esto:
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![Pokemon CSVPreview](../../assets/csv-preview.png)
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![Pokemon CSVprevisualización](../../assets/csv-preview.png)
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Como puedes ver, los datos están representados en un archivo CSV donde cada fila es unpokemón diferente con su propio ID, Nombre, Tipo, HP, Ataque, etc.
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Como puedes ver, los datos están representados en un archivo CSV donde cada fila es unPokemon diferente con su propio ID, Nombre, Tipo, HP, Ataque, etc.
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Por favorhas clic en`next ->` y ve al siguiente paso del ejercicio.
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>Por favorhaz clic en`Next ->` y ve al siguiente paso del ejercicio.
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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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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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Possible datasets based on your objective prediction:
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- If we sell shoes, howmuch shoes do I need in mywharehouse for the next month? A good Datasetwill be sales from the past 2 years.
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- If we sell shoes, howmany shoes do I need in mywarehouse for the next month? A good Datasetwould be the sales from the past 2 years.
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- If we are building a program todiagnost pneumonia, a good Datasetwill be 100 x-rays with pneumonia and 100 x-rays without pneumonia.
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- If we are building a program todiagnose pneumonia, a good Datasetwould be 100 x-rays with pneumonia and 100 x-rays without pneumonia.
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You have to be creative when creating your datasets, think about all the variables that affect a prediction and try collecting and organizingeverthing in one or many Datasets.
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You have to be creative when creating your datasets. Think about all the variables that affect a prediction, and try collecting and organizingeverything in one or many Datasets.
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This project already comes with a Pokemon dataset located in the path:`./.learn/assets/pokemon_data.csv`.
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This project already comes with a Pokemon dataset located in the path:`.learn/assets/pokemon_data.csv`.
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##📝 Instructions:
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1. You can open the file manually or by running the followingcommnad on your terminal:
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1. You can open the file manually or by running the followingcommand on your terminal:
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```bash
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$ code ./.learn/assets/pokemon_data.csv
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$ code .learn/assets/pokemon_data.csv
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```
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##💡 Hint:
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![Pokemon CSV Preview](../../assets/csv-preview.png)
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As you can see, the data is represented in a CSV file where every row is a differentpokemon with its own ID, Name, Type, HP, Attack, etc.
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As you can see, the data is represented in a CSV file where every row is a differentPokemon with its own ID, Name, Type, HP, Attack, etc.
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Please click`next ->` and move to the next step of the exercise.
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>Please click`Next ->` and move to the next step of the exercise.

‎.learn/exercises/04-Series/README.es.md

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El resultado de imprimir`data` en el terminal debe ser algo así:
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```shell
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```bash
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1 94
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##📝 Instrucciones:
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1. Crea una serie de esta lista:`ages = [23,45,7,34,6,63,36,78,54,34]`
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1. Copia esta lista de Python que contiene edades a tu código:`[23,45,7,34,6,63,36,78,54,34]`
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2. Crea una Serie a partir de esa lista.
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##Resultado Esperado:
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3. Imprime tu nueva Serie en la consola.
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##💻 Resultado Esperado:
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```bash
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dtype: int64
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```
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```

‎.learn/exercises/04-Series/README.md

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#`04` Series
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A series is similar to an array or list, it's a one dimentional data structure.
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A series is similar to an array or list; it's a one-dimensional data structure.
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You can create a series like this:
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The result of printing`data` on the terminal will be something like this:
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```shell
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```bash
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1 94
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##📝 Instructions:
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Create a series from this list:`ages = [23,45,7,34,6,63,36,78,54,34]`
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1. Copy this Python list of ages to your code:`[23,45,7,34,6,63,36,78,54,34]`
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2. Create a Series from the given list.
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##Expected Result:
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3. Print your new Series on the console.
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##💻 Expected Result:
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```bash
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dtype: int64
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```
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```
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importpandasaspd
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# two dimensional array of name,age values.
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data=pd.Series([23,45,7,34,6,63,36,78,54,34])
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print(data)
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ages=pd.Series([23,45,7,34,6,63,36,78,54,34])
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print(ages)

‎.learn/exercises/04-Series/test.py

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"""incaptured.out
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@pytest.mark.it('The variable ages must exist')
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deftest_vatiable_existence():
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fromappimportages

‎.learn/exercises/04.1-date-range/README.es.md

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##📝 Instrucciones:
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1. Usa la función`pd.date_range` para crear una serie del`05-01-2021` al`05-12-2021` y imprime el resultado en la terminal.
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1. Usa la función`pd.date_range` para crear una serie del`2021-05-01` al`2021-05-12` e imprime el resultado en la terminal.
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##Resultado Esperado:
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##💻Resultado Esperado:
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```bash
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DatetimeIndex(['2021-05-01','2021-05-02','2021-05-03','2021-05-04',
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'2021-05-05','2021-05-06','2021-05-07','2021-05-08',
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'2021-05-09','2021-05-10','2021-05-11','2021-05-12'],
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dtype='datetime64[ns]', freq='D')
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```
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```
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##💡 Pista:
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+ En las fechas puedes usar los formatos`DD-MM-AAAA` o`AAAA-MM-DD`.

‎.learn/exercises/04.1-date-range/README.md

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##📝 Instructions:
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1. Use the`pd.date_range` function to create a series from`05-01-2021` to`05-12-2021` and print it to the terminal.
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1. Use the`pd.date_range` function to create a series from`2021-05-01` to`2021-05-12` and print it to the terminal.
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##Expected Result:
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##💻Expected Result:
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```bash
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DatetimeIndex(['2021-05-01','2021-05-02','2021-05-03','2021-05-04',
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'2021-05-05','2021-05-06','2021-05-07','2021-05-08',
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'2021-05-09','2021-05-10','2021-05-11','2021-05-12'],
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dtype='datetime64[ns]', freq='D')
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```
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```
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##💡 Hint:
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+ In the date format you can use`DD-MM-YYYY` or`YYYY-MM-DD`.
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importpandasaspd
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date_series=pd.date_range(start='05-01-2021',end='05-12-2021')
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date_series=pd.date_range(start='2021-05-01',end='2021-05-12')
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print(date_series)
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print(date_series)

‎.learn/exercises/04.2-series-apply/README.es.md

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#`04.2` Series Apply
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Dada unaserievariablecomo:
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Dada una variableque contiene una serie:
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```py
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1. Usa la función`my_series.apply` para dividir todos los números de la siguiente serie por 2 e imprime el resultado a la terminal.
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##Resultado Esperado:
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##💻Resultado Esperado:
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```bash
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‎.learn/exercises/04.2-series-apply/README.md

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##📝 Instructions:
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1. Use the function`my_series.apply` to divide all numberson the following series by 2 and print the result to the terminal.
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1. Use the function`my_series.apply` to divide all numbersin the following series by 2 and print the result to the terminal.
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##Expected Result:
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##💻Expected Result:
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```bash
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‎.learn/exercises/04.2-series-apply/solution.hide.py

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my_series=pd.Series([2,4,6,8,10])
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modified_series=my_series.apply(lambdax:x/2)
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print(modified_series)
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print(modified_series)

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