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

id, title, challengeType, forumTopicId, dashedName

id	title	challengeType	forumTopicId	dashedName
5e46f7f8ac417301a38fb92a	Visualizador de datos médicos	10	462368	medical-data-visualizer

--description--

You will be working on this project with our Gitpod starter code.

Todavía estamos desarrollando la parte interactiva del currículo de Python. Por ahora, aquí hay algunos vídeos en nuestro canal de YouTube freeCodeCamp.org que te enseñará todo lo que necesitas saber para completer este proyecto:

• Curso en Vídeo de Python para Todos (Duración: 14 horas)

• Como analizar datos con Python Pandas (10 horas)

--instructions--

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

Las filas del conjunto de datos representan a los pacientes y las columnas representan información como medidas corporales, resultados de varios análisis de sangre y opciones de estilo de vida. Utilizarás el conjunto de datos para explorar la relación entre enfermedades cardiacas, medidas del cuerpo, indicadores sanguíneos y opciones de estilo de vida.

Nombre del archivo: medical_examination.csv

Característica	Tipo de variable	Variable	Tipo de unidad
Edad	Característica objetivo	age	int (días)
Altura	Característica objetivo	height	int (cm)
Peso	Característica objetivo	weight	float (kg)
Sexo	Característica objetivo	gender	código de categoría
Presión arterial sistólica	Características del examen	ap_hi	int
Presión arterial diastólica	Característica del examen	ap_lo	int
Colesterol	Característica del examen	cholesterol	1: normal, 2: por encima de lo normal, 3: muy por encima de lo normal
Glucosa	Característica del examen	gluc	1: normal, 2: por encima de lo normal, 3: muy por encima de lo normal
Fumador	Característica subjetiva	smoke	binario
Consumo de alcohol	Característica subjetiva	alco	binario
Actividad física	Característica subjetiva	active	binario
Presencia o ausencia de enfermedades cardiovasculares	Variable objetivo	cardio	binario

Tareas

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.
• 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.

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.

Pruebas

The unit tests for this project are in test_module.py. Hemos importado las pruebas de test_module.py a main.py para tu conveniencia.

Envío

Copia el enlace de tu proyecto y envíalo a freeCodeCamp.

--hints--

Debería pasar todas las pruebas de Python.

--solutions--

  # Python challenges don't need solutions,
  # because they would need to be tested against a full working project.
  # Please check our contributing guidelines to learn more.