chore(i18n,learn): processed translations (#54077)

Co-authored-by: Naomi Carrigan <nhcarrigan@gmail.com>

curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-projects/medical-data-visualizer.md

@@ -18,7 +18,7 @@ Todavía estamos desarrollando la parte interactiva del currículo de Python. Po
 
 # --instructions--
 
-En este proyecto, visualizarás y harás algunos cálculos a partir de datos de exámenes médicos utilizando matplotlib, seabron y pandas. Los valores del conjunto de datos (dataset) se recogieron durante los exámenes médicos.
+In this project, you will visualize and make calculations from medical examination data using `matplotlib`, `seaborn`, and `pandas`. Los valores del conjunto de datos (dataset) se recogieron durante los exámenes médicos.
 
 ## Descripción de datos
 
@@ -43,23 +43,49 @@ Nombre del archivo: medical_examination.csv
 
 ## Tareas
 
-Crear un gráfico similar a `ejemplos/Figure_1. ng`, donde mostramos las cifras de resultados buenos y malos para las variables `colesterol`, `gluc`, `alco`, `activo` y `humo` en los pacientes con cardio=1 y cardio=0 en diferentes paneles.
+Create a chart similar to `examples/Figure_1.png`, where we show the counts of good and bad outcomes for the `cholesterol`, `gluc`, `alco`, `active`, and `smoke` variables for patients with `cardio=1` and `cardio=0` in different panels.
 
 Utiliza los datos para completar las siguientes tareas en `medical_data_visualizer.py`:
 
-- Add an `overweight` column to the data. To determine if a person is overweight, first calculate their BMI by dividing their weight in kilograms by the square of their height in meters. If that value is > 25 then the person is overweight. Use the value 0 for NOT overweight and the value 1 for overweight.
-- Normalize the data by making 0 always good and 1 always bad. If the value of `cholesterol` or `gluc` is 1, make the value 0. If the value is more than 1, make the value 1.
-- Convert the data into long format and create a chart that shows the value counts of the categorical features using seaborn's `catplot()`. The dataset should be split by 'Cardio' so there is one chart for each `cardio` value. The chart should look like `examples/Figure_1.png`.
+- Add an `overweight` column to the data. To determine if a person is overweight, first calculate their BMI by dividing their weight in kilograms by the square of their height in meters. If that value is > 25 then the person is overweight. Use the value `0` for NOT overweight and the value `1` for overweight.
+- Normalize the data by making `0` always good and `1` always bad. If the value of `cholesterol` or `gluc` is `1`, make the value `0`. If the value is more than `1`, make the value `1`.
+- Convert the data into long format and create a chart that shows the value counts of the categorical features using `seaborn`'s `catplot()`. The dataset should be split by `Cardio` so there is one chart for each `cardio` value. The chart should look like `examples/Figure_1.png`.
 - Clean the data. Filter out the following patient segments that represent incorrect data:
   - diastolic pressure is higher than systolic (Keep the correct data with `(df['ap_lo'] <= df['ap_hi'])`)
   - height is less than the 2.5th percentile (Keep the correct data with `(df['height'] >= df['height'].quantile(0.025))`)
   - height is more than the 97.5th percentile
   - weight is less than the 2.5th percentile
   - weight is more than the 97.5th percentile
-- Create a correlation matrix using the dataset. Plot the correlation matrix using seaborn's `heatmap()`. Mask the upper triangle. The chart should look like `examples/Figure_2.png`.
+- Create a correlation matrix using the dataset. Plot the correlation matrix using `seaborn`'s `heatmap()`. Mask the upper triangle. The chart should look like `examples/Figure_2.png`.
 
 Cada vez que una variable está establecida en `Ninguno`, asegúrese de establecerla en el código correcto.
 
+Unit tests are written for you under `test_module.py`.
+
+## Instructions
+By each number in the `medical_data_visualizer.py` file, add the code from the associated instruction number below.
+
+1. Import the data from `medical_examination.csv` and assign it to the `df` variable
+2. Create the `overweight` column in the `df` variable
+3. Normalize data by making `0` always good and `1` always bad. If the value of `cholesterol` or `gluc` is 1, set the value to `0`. If the value is more than `1`, set the value to `1`.
+4. Draw the Categorical Plot in the `draw_cat_plot` function
+5. Create a DataFrame for the cat plot using `pd.melt` with values from `cholesterol`, `gluc`, `smoke`, `alco`, `active`, and `overweight` in the `df_cat` variable.
+6. Group and reformat the data in `df_cat` to split it by `cardio`. Show the counts of each feature. You will have to rename one of the columns for the `catplot` to work correctly.
+7. Convert the data into `long` format and create a chart that shows the value counts of the categorical features using the following method provided by the seaborn library import : `sns.catplot()`
+8. Get the figure for the output and store it in the `fig` variable
+9. Do not modify the next two lines
+10. Draw the Heat Map in the `draw_heat_map` function
+11. Clean the data in the `df_heat` variable by filtering out the following patient segments that represent incorrect data:
+    - height is less than the 2.5th percentile (Keep the correct data with `(df['height'] >= df['height'].quantile(0.025))`)
+    - height is more than the 97.5th percentile
+    - weight is less than the 2.5th percentile
+    - weight is more than the 97.5th percentile
+12. Calculate the correlation matrix and store it in the `corr` variable
+13. Generate a mask for the upper triangle and store it in the `mask` variable
+14. Set up the `matplotlib` figure
+15. Plot the correlation matrix using the method provided by the `seaborn` library import: `sns.heatmap()`
+16. Do not modify the next two lines
+
 ## Desarrollo
 
 Write your code in `medical_data_visualizer.py`. For development, you can use `main.py` to test your code.