This step-by-step guide will help you learn how to program a Python script that can be launched from the console. We will gradually build the script, adding new features and functionality at each step. The final code will include a graphical user interface (GUI) with various features such as BMI calculation, data logging, data storage in CSV format, data visualization, and more.
Let’s start with a simple “Hello, World!” program to ensure that everything is set up correctly.
script.py.print("Hello, World!")
Save the file.
To run the script:
script.py file is located.Enter the following command:
python3 script.py
You should see the output Hello, World! in the console.
Congratulations! You’ve successfully created and executed a simple Python script. Let’s move on to the next step.
Next, let’s modify the script to accept a name as an argument and customize the greeting message.
script.py file.import sys
if len(sys.argv) > 1:
name = sys.argv[1]
else:
name = "World"
print(f"Hello, {name}!")
To run the script with a name:
script.py file is located.python3 script.py Alice
Hello, Alice! in the console.If you don’t provide a name as an argument, the script will use “World” as the default name.
Great job! You’ve enhanced the script to accept an argument and customized the greeting message. Let’s continue to the next step.
In this step, we’ll add functionality to calculate the body mass index (BMI) based on user-provided height and weight values.
script.py file.def calculate_bmi(height=0, weight=0):
if height == 0:
height = float(input("Enter height in meters: "))
if weight == 0:
weight = float(input("Enter weight in kilograms: "))
bmi = weight / (height ** 2)
return bmi
bmi = calculate_bmi()
print(f"Your BMI is: {bmi:.2f}")
To run the script and calculate BMI:
script.py file is located.python script.py Alice
Hello, Alice! followed by the calculated BMI.The script will ask for the height and weight values if they are not provided as command-line arguments. The BMI will be calculated and displayed with two decimal places.
Fantastic progress! You’ve implemented a BMI calculator in your script. Let’s move on to the next step.
In this step, we’ll enhance the script by adding a graphical user interface (GUI) to input the height and weight values.
pip3 install tkinter
script.py file.import tkinter as tk
def calculate_bmi(height=0, weight=0):
if height == 0:
# If height and weight are not provided, retrieve them from the GUI entry fields
height = float(height_entry.get())
weight = float(weight_entry.get())
# Calculate BMI
bmi = weight / (height ** 2)
result_label.config(text=f"Patient's BMI is: {bmi:.2f}")
return bmi
window = tk.Tk()
window.title("BMI Calculator")
height_label = tk.Label(window, text="Enter height in meters:")
height_label.pack()
height_entry = tk.Entry(window)
height_entry.pack()
weight_label = tk.Label(window, text="Enter weight in kilograms:")
weight_label.pack()
weight_entry = tk.Entry(window)
weight_entry.pack()
result_label = tk.Label(window)
result_label.pack()
calculate_button = tk.Button(window, text="Calculate BMI", command=calculate_bmi)
calculate_button.pack()
window.mainloop()
To run the script with the GUI interface:
script.py file is located.python3 script.py
You now have a graphical interface for entering the height and weight values. The BMI calculation and result display are handled through the GUI. Well done!
Let’s proceed to the next step.
In this step, we’ll introduce logging to the script to track and log various events and messages.
script.py file.import statements:import logging
# Configure logger
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
logger = logging.getLogger("EDC")
calculate_bmi function with the following code:def calculate_bmi(height=0, weight=0):
if height == 0:
# If height and weight are not provided, retrieve them from the GUI entry fields
height = float(height_entry.get())
weight = float(weight_entry.get())
# Calculate BMI
bmi = weight / (height ** 2)
result_label.config(text=f"Patient's BMI is: {bmi:.2f}")
logger.info(f"Height: {height} m, Weight: {weight} kg")
logger.info(f"BMI: {bmi:.2f}")
return bmi
# ...
To run the script with logging:
script.py file is located.python3 script.py
You’ve successfully added a logger component to the script, and now you can track and log important events and messages during execution.
Let’s proceed to the next step.
In this step, we’ll add a the variable cardiac event and add functionality to save the height, weight, and cardiac event data entered through the GUI to a CSV file.
script.py file.import statement at the beginning of the file, after the other import statements:import csv
weight_entry.pack() line:# ...
weight_entry.pack()
cardiac_event_var = tk.StringVar()
cardiac_event_var.set("No") # Set the default value to "No"
cardiac_event_checkbox = tk.Checkbutton(window, text="Cardiac Event (Yes/No)", variable=cardiac_event_var, onvalue="Yes", offvalue="No")
cardiac_event_checkbox.pack()
# ...
calculate_bmi function with the following code:def calculate_bmi(height=0, weight=0):
if height == 0:
# If height and weight are not provided, retrieve them from the GUI entry fields
height = float(height_entry.get())
weight = float(weight_entry.get())
# Calculate BMI
bmi = weight / (height ** 2)
result_label.config(text=f"Patient's BMI is: {bmi:.2f}")
cardiac_event = cardiac_event_var.get()
logger.info(f"Height: {height} m, Weight: {weight} kg")
logger.info(f"BMI: {bmi:.2f}, Cardiac Event: {cardiac_event}")
# Save patient data
with open("data.csv", "a", newline="") as file:
writer = csv.writer(file)
writer.writerow([height, weight, bmi, cardiac_event])
return bmi
# ...
To run the script and save data to a CSV file:
script.py file is located.python3 script.py
data.csv in the same directory.Congratulations! You’ve added the functionality to save patient data to a CSV file. The data will include height, weight, BMI, and cardiac event information.
Let’s move on to the next step.
In this step, we’ll add functionality to display all the rows from the CSV file in the GUI.
script.py file.calculate_bmi function:def calculate_bmi():
# ...
def display_data():
# Read data from the CSV file
with open("data.csv", "r") as file:
reader = csv.reader(file)
data = list(reader)
# Create a new window to display the data
display_window = tk.Toplevel(window)
display_window.title("Records")
# Create a label for each row of data and pack them in the window
for row in data:
row_label = tk.Label(display_window, text=row)
row_label.pack()
window = tk.Tk()
# ...
window definition code as follows:window = tk.Tk()
window.title("Electronic Data Capture")
# ...
calculate_button.pack() line:# ...
calculate_button.pack()
display_button = tk.Button(window, text="Records", command=display_data)
display_button.pack()
# ...
To run the script and display the data:
script.py file is located.python3 script.py
data.csv file.You’ve successfully implemented the feature to display all the rows from the CSV file in a separate window of the GUI.
Great work! Let’s proceed to the next step.
In this step, we’ll add functionality to delete rows from the CSV file using the GUI.
script.py file.display_data function with the following code:def display_data():
# Read data from the CSV file
with open("data.csv", "r") as file:
reader = csv.reader(file)
data = list(reader)
# Create a new window to display the data
display_window = tk.Toplevel(window)
display_window.title("Data Display")
# Create a label for each row of data, a delete button, and pack them in the window
for i, row in enumerate(data):
row_label = tk.Label(display_window, text=row)
row_label.pack()
delete_button = tk.Button(display_window, text="Delete", command=lambda i=i: delete_row(i, display_window))
delete_button.pack()
def delete_row(index, display_window):
# Read data from the CSV file
with open("data.csv", "r") as file:
reader = csv.reader(file)
data = list(reader)
# Delete the selected row
del data[index]
# Write the updated data back to the CSV file
with open("data.csv", "w", newline="") as file:
writer = csv.writer(file)
writer.writerows(data)
display_window.destroy()
display_data()
window = tk.Tk()
# ...
To run the script and delete rows from the CSV file:
script.py file is located.python3 script.py
data.csv file, with a “Delete” button next to each row.data.csv file, and the window will update to reflect the changes.Well done! You’ve added the functionality to delete rows from the CSV file using the GUI.
Let’s proceed to the next step.
In this step, we’ll enhance the code by adding error handling and raise error tests to make it more stable.
script.py file.delete_row function with the following code:def calculate_bmi(height=0, weight=0):
if height == 0:
# If height and weight are not provided, retrieve them from the GUI entry fields
height = float(height_entry.get())
weight = float(weight_entry.get())
# Calculate BMI
if height <= 0 or weight <= 0:
raise ValueError("Height and weight must be positive values.")
bmi = weight / (height ** 2)
result_label.config(text=f"Patient's BMI is: {bmi:.2f}")
cardiac_event = cardiac_event_var.get()
logger.info(f"Height: {height} m, Weight: {weight} kg")
logger.info(f"BMI: {bmi:.2f}, Cardiac Event: {cardiac_event}")
# Save patient data
with open("data.csv", "a", newline="") as file:
writer = csv.writer(file)
writer.writerow([height, weight, bmi, cardiac_event])
return bmi
# ...
delete_row function with the following code:def delete_row(index, display_window):
# Read data from the CSV file
with open("data.csv", "r") as file:
reader = csv.reader(file)
data = list(reader)
# Delete the selected row
if index < 0 or index >= len(data):
raise IndexError("Invalid row index.")
del data[index]
# Write the updated data back to the CSV file
with open("data.csv", "w", newline="") as file:
writer = csv.writer(file)
writer.writerows(data)
display_window.destroy()
display_data()
# ...
To run the script and test error handling:
script.py file is located.python3 script.py
Great job! You’ve added error handling and raise error tests to improve the stability of the code.
Let’s move on to the next step.
In this step, we’ll add features that will allow us to visualize the CSV file’s data points and color them in accordance with cardiac events.
pip3 install matplotlib
script.py file.import statement at the beginning of the file, after the other import statements:import matplotlib.pyplot as plt
display_button.pack() line:# ...
display_button.pack()
visualize_button = tk.Button(window, text="Visualize Data", command=visualize_data)
visualize_button.pack()
# ...
visualize_data function:def visualize_data():
# Read data from the CSV file
with open("data.csv", "r") as file:
reader = csv.reader(file)
data = list(reader)
# Extract the necessary data for visualization
heights = []
weights = []
cardiac_events = []
bmis = []
for row in data:
if len(row) != 4:
raise ValueError("Invalid data format in CSV file.")
height = float(row[0])
weight = float(row[1])
bmi = float(row[2])
cardiac_event = row[3]
heights.append(height)
weights.append(weight)
cardiac_events.append(cardiac_event)
bmis.append(bmi)
# Group the BMI values by CardiacEvent
grouped_data = {}
for event, bmi_value, height_value, weight_value in zip(cardiac_events, bmis, heights, weights):
if event not in grouped_data:
grouped_data[event] = {'BMI': [], 'Height': [], 'Weight': []}
grouped_data[event]['BMI'].append(bmi_value)
grouped_data[event]['Height'].append(height_value)
grouped_data[event]['Weight'].append(weight_value)
# Create a figure with two subplots
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6))
# Boxplot for BMI
ax1.boxplot([grouped_data['No']['BMI'], grouped_data['Yes']['BMI']], labels=['No', 'Yes'])
ax1.set_xlabel('Cardiac Event')
ax1.set_ylabel('BMI [kg/m²]')
ax1.set_title('Body mass index (BMI) Distribution by Cardiac Event')
# Scatterplot for height and weight
ax2.scatter(grouped_data['No']['Height'], grouped_data['No']['Weight'], c='blue', label='No')
ax2.scatter(grouped_data['Yes']['Height'], grouped_data['Yes']['Weight'], c='red', label='Yes')
ax2.set_xlabel('Height [m]')
ax2.set_ylabel('Weight [kg]')
ax2.set_title('Relationship between Height and Weight')
ax2.legend()
# Adjust spacing between subplots
plt.tight_layout()
# Show the plot
plt.show()
window = tk.Tk()
# ...
To run the script and visualize the data:
script.py file is located.python3 script.py
Fantastic work! You’ve implemented data visualization functionality in your script.
Let’s move on to the final step.
In this final step, we’ll add functionality to generate a random patient with random height and weight values.
pip3 install requests
script.py file.import statement at the beginning of the file, after the other import statements:import requests
import random
Add the following code after the display_button.pack() line:
# ...
visualize_button.pack()
randompatient_button = tk.Button(window, text="Generate one Random Patient", command=create_random_patient)
randompatient_button.pack()
# ...
Add following create_random_patient function:
def create_random_patient():
# Generate random height and weight values
height = float(get_random_height())
weight = float(get_random_weight())
height_entry.delete(0, tk.END)
height_entry.insert(0, height)
weight_entry.delete(0, tk.END)
weight_entry.insert(0, weight)
logger.info(f"Generated random patient: Height: {height} m, Weight: {weight} kg")
# Calculate BMI for the generated patient
bmi = weight / (height ** 2)
result_label.config(text=f"Patient's BMI is: {bmi:.2f}")
# Determine the cardiac event for the generated BMI
event = get_cardiac_event_distribution(bmi)
# Set the cardiac event value in the checkbox
cardiac_event_var.set(event)
logger.info(f"Height: {height} m, Weight: {weight} kg")
logger.info(f"BMI: {bmi:.2f}, Cardiac Event: {event}")
# Save the generated patient data
with open("data.csv", "a", newline="") as file:
writer = csv.writer(file)
writer.writerow([height, weight, bmi, event])
def get_random_height():
# Retrieve a random height value from an API
response = requests.get("http://www.randomnumberapi.com/api/v1.0/random", params={"min": 130, "max": 210, "count": 1})
if response.status_code != 200:
raise requests.RequestException("Failed to retrieve random height.")
data = response.json()
if not data:
raise ValueError("Empty response received for random height.")
height = float(data[0])
if height < 130:
raise ValueError("Invalid random height value received.")
logger.info(f"Random height generated: {height} cm")
return height / 100
def get_random_weight():
# Retrieve a random weight value from an API
response = requests.get("http://www.randomnumberapi.com/api/v1.0/random", params={"min": 40, "max": 140, "count": 1})
if response.status_code != 200:
raise requests.RequestException("Failed to retrieve random weight.")
data = response.json()
if not data:
raise ValueError("Empty response received for random weight.")
weight = data[0]
if weight < 40:
raise ValueError("Invalid random weight value received.")
logger.info(f"Random weight generated: {weight} kg")
return weight
def get_cardiac_event_distribution(bmi):
if bmi < 9 or bmi > 83:
raise ValueError("BMI value should be between 9 and 83.")
if bmi <= 22:
return "No"
if bmi <= 38:
risk = (bmi - 22) / (38 - 22)
return 'Yes' if random.random() < risk else 'No'
return "Yes"
window = tk.Tk()
# ...
Save the file.
To run the script and generate a random patient:
script.py file is located.Enter the following command:
python3 script.py
Amazing work! You’ve added the functionality to generate a random patient with random height and weight values.
Congratulations! You have successfully created a step-by-step guide to learning programming a Python script that can be launched from the console. The final code includes features such as a GUI interface, BMI calculation, data logging, data storage in CSV format, data visualization, error handling, and random patient generation.
Feel free to explore and enhance the script further to suit your needs and expand your Python programming skills. Happy coding!