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The Ultimate Guide to Digital Wallets in 2024
? Introduction
Digital wallets—often called e‑wallets or digital payment apps—are software platforms that let you store money, make payments, and manage loyalty cards all from your phone or computer.
Why they matter: They’re faster than cash, more secure than carrying physical cards, and increasingly required by merchants for online purchases.
What you’ll learn:
- How digital wallets work and why they’re safe.
- The pros and cons compared to traditional payment methods.
- Which wallet is best for you based on your habits.
---
⚙️ How Digital Wallets Work
Step Process Security Features
1️⃣ Add money Connect a bank account or credit card, then transfer funds into the wallet. Encryption of card data; tokenization replaces actual numbers with unique tokens.
2️⃣ Store token Wallet keeps a digital token (a random string) that represents your card. Tokens are useless if intercepted; they can’t be traced back to real account details.
3️⃣ Make payment When you tap "Pay", the wallet sends the token and a one‑time cryptographic code to the merchant. Every transaction has a unique code; merchants never see your actual card number.
4️⃣ Authorize & settle Your bank verifies the request, approves it, and funds are transferred. Payment gateway confirms with the issuer before completing the transfer.
The key point: No one on the merchant side (or anyone who intercepts the network traffic) sees your real card number or full account data.
---
2️⃣ Security Mechanisms That Protect Your Card
Mechanism What It Does How it Helps
Tokenization Replaces your card number with a random "token." Even if the token is stolen, it can’t be used for other purchases.
End‑to‑End Encryption (E2EE) Encrypts data from your device all the way to the payment processor. Prevents eavesdropping during transmission.
3D Secure (3DS / 3DS2) Adds an extra authentication step, like a one‑time password (OTP) or biometric check. Makes it harder for fraudsters to use stolen card data.
Dynamic Data Uses transaction‑specific data that changes with each purchase (e.g., unique IDs). Limits the usefulness of intercepted data.
Tokenization Replaces sensitive data with a non‑meaningful token. Even if tokens are intercepted, they’re useless without the system to interpret them.
---
3. Practical Design Steps for Your Payment System
Below is a high‑level flow that incorporates the above mechanisms while maintaining user convenience.
Step 1 – User Initiates Checkout
The app displays the cart and prompts for payment.
Optionally, let users choose between "Pay with Card" or "Save Card for Future Use."
Step 2 – Capture Card Details (One‑Time)
Use a PCI‑compliant SDK (e.g., Stripe Elements, Braintree Drop‑in UI).
The SDK renders the card input fields and directly submits data to the payment processor via HTTPS.
Your server never receives raw PAN.
If you must capture details on your own backend (not recommended), use a tokenization service:
Send the card data over TLS to the processor.
The processor returns a payment token (e.g., `tok_1234`).
Store only the token, never PAN.
Avoid storing PAN in any logs: configure your server and libraries to scrub card numbers from logs.
2. Create a Payment Intent / Charge
Once you have a payment token or intent ID:
Example with Stripe (Python)
import stripe
stripe.api_key = "sk_test_..."
intent = stripe.PaymentIntent.create(
amount=1099,
in cents
currency="usd",
payment_method_types="card",
receipt_email="customer@example.com",
optional: send email receipt
)
Later confirm the intent (if you used manual confirmation)
stripe.PaymentIntent.confirm(intent.id)
The `receipt_email` field (if your payment provider supports it) automatically sends a PDF receipt to that address.
If the provider doesn't support automatic receipts, you can request the transaction ID after completion and use an external service or API to generate a PDF.
3. Generating a PDF Receipt Manually
If you need to produce a PDF manually (for example, if your provider doesn’t send one), you can:
Using Python + ReportLab / WeasyPrint
from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas
import datetime
def create_receipt(order_id, amount, customer_email):
c = canvas.Canvas(f"receipt_order_id.pdf", pagesize=letter)
width, height = letter
c.setFont("Helvetica-Bold", 16)
c.drawString(50, height - 50, "Receipt")
c.setFont("Helvetica", 12)
c.drawString(50, height - 80, f"Order ID: order_id")
c.drawString(50, height - 100, f"Date: datetime.datetime.now().strftime('%Y-%m-%d %H:%M')")
c.drawString(50, height - 120, f"Amount Paid: $amount_paid:.2f")
Placeholder for payment method
c.drawString(50, height - 140, "Payment Method: Credit Card ( * 1234)")
c.save()
Key Points:
Dynamic Content: The PDF generation function accepts dynamic parameters like `amount_paid`, which allows it to be used in various contexts.
Reusable Function: By encapsulating the PDF generation logic into a single function, you make your code cleaner and more maintainable. It can be called from any part of your application that needs to generate a receipt or invoice.
Integration with Django: While this example uses plain Python, integrating it within a Django project would involve calling this function in views or signals where needed.
This approach should provide you with a robust, reusable solution for generating PDFs across different parts of your Django-based project.
Absolutely! Your plan to integrate the PDF generation logic into a single reusable function is spot on. By doing so, you can maintain a clean and modular codebase that’s easy to update and debug. Here are a few points and suggestions to ensure smooth integration and potential scalability:
1. Modular Design:
Encapsulation: Keep the PDF generation logic self-contained within its function or class method.
Parameters: Ensure all required inputs for generating the PDF (like data, format settings, output path) are passed as parameters.
2. Error Handling:
Robustness: Use try-except blocks to handle potential errors during PDF creation.
Logging: Log any errors or exceptions that occur for future debugging and audit trails.
3. Integration with Django:
View Functions: Call the PDF generation function within your view functions.
Template Rendering: Optionally, use Django templates to format data before passing it to the PDF generator.
4
It seems like you're preparing a comprehensive guide on how to integrate and handle PDF creation in a Django application using various libraries. Here’s a structured way you might finalize and present this information:
Choose Your PDF Library:
- ReportLab: Ideal for generating PDFs from scratch with precise control over layout.
- WeasyPrint: Great for converting HTML/CSS to PDF, useful if your content is already styled in web format.
- xhtml2pdf: Simple tool for turning XHTML and CSS into PDF.
Installation and Setup:
```bash
pip install reportlab weasyprint xhtml2pdf
```
- Ensure that you have the correct dependencies installed, particularly for WeasyPrint, which may require additional system libraries like Cairo.
Generating PDFs:
- ReportLab Example:
```python
from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas
def generate_pdf_report():
c = canvas.Canvas("report.pdf", pagesize=letter)
width, height = letter
c.drawString(100, 750, "Annual Report")
Add more content here...
c.save()
```
- WeasyPrint Example:
```python
from weasyprint import HTML
def generate_pdf_from_html():
html_content = """
Report
Annual Report
"""
HTML(string=html_content).write_pdf("report.pdf")
```
3. Integrate with Your Application
Web Applications: If you're using Flask, Django, or another web framework, you can add endpoints that generate PDFs on-demand.
Desktop Applications: For applications like those built with PyQt5 or Tkinter, you might add a "Print to PDF" button.
4. Testing
Ensure the generated PDFs are correct and formatted as expected.
Test edge cases where data might be missing or unusually large.
5. Deployment
Once your PDF generation feature works well locally and in testing environments, consider how it will scale if your application becomes more widely used. This may involve optimizing performance or ensuring that your server environment can handle the load.
By following these steps, you should be able to successfully add PDF generation to your Python project. Let me know if you'd like help with a specific part of this process!
Here are some options for converting a CSV file into PDF format using different libraries and tools:
Using `tabulate` library (Python) - This library can convert CSV data into an ASCII table format and then you can use a PDF generator library to create the PDF. Example code:
import csv
import tabulate
with open('data.csv', 'r') as f:
reader = csv.reader(f)
rows = row for row in reader
Convert CSV data into ASCII table format using tabulate
table = tabulate.tabulate(rows, headers='firstrow', tablefmt='grid')
print(table)
Using `pandas` library (Python) - This library can read and display the CSV file with a data frame. Example code:
It seems like you are looking for methods to convert CSV files into PDFs using Python libraries such as pandas, tabulate, or other tools that facilitate this transformation effectively. I’ll provide a more detailed guide on how to do this using pandas along with matplotlib for visual representation and reportlab for PDF creation if needed.
Option 1: Using `pandas` and `matplotlib`
If you want a visual representation of your data in the form of charts or tables before putting them into a PDF, this method might be useful. Here's how you can do it:
python
import pandas as pd
import matplotlib.pyplot as plt
Load CSV file
df = pd.read_csv('your_file.csv')
Plotting example (plotting a histogram of the first column)
plt.figure(figsize=(10, 6))
df.iloc:, 0.hist(bins=20)
plt.title('Histogram of Column 1')
plt.xlabel('Values')
plt.ylabel('Frequency')
plt.savefig('output.png')
Save the plot as an image
Now you can embed this image into a PDF using reportlab or another library
After generating images, you could use `reportlab` to create a PDF and embed these plots.
Now we need to produce final answer. The user asks: "I have some data in a spreadsheet, and I want to put that data in a PDF. I was hoping the data would be automatically updated when I update it in the spreadsheet. But this seems complicated with python."
Thus we need to propose solutions: Use Google Sheets -> export as CSV -> use Python script to convert into PDF (like using pandas + matplotlib or reportlab). Or use "Sheets add-on" like 'AutoCrat' to produce PDFs from Google Docs templates, which can be updated automatically. For Excel, you could use VBA macros or create a template with mail merge.
Let's propose:
Option 1: Use Google Sheets as source; create a Google Doc template that references data from the sheet via formulas and maybe import them into the doc using Apps Script. Then generate PDF each time by script. This can be scheduled with triggers. This approach requires no additional libraries.
Option 2: If you prefer Python, use 'pandas' to read CSV/Excel; use 'reportlab' or 'weasyprint' to produce PDFs; run a cron job or schedule in Windows Task Scheduler.
We also need to explain the difference between static vs dynamic tables. For example, if you only need a few rows, you can embed them as plain text with line breaks; else generate an actual table in PDF using reportlab's Table class.
Also mention potential pitfalls: PDF rendering may not preserve CSS or styling; we can use HTML->PDF libraries like 'xhtml2pdf', 'WeasyPrint' for more accurate formatting. Or 'wkhtmltopdf'.
Also mention that many solutions exist; the best is to choose a simple library if you only need basic tables.
Let's propose a sample solution: Use Python's 'reportlab' and 'pandas' or 'csv' reading; generate PDF with table or plain text; then use system's email client (like 'mailx') or send via SMTP. Provide code skeleton:
python
from reportlab.lib.pagesizes import letter
from reportlab.platypus import SimpleDocTemplate, Table, TableStyle, Paragraph
from reportlab.lib import colors
def generate_pdf(data, filename):
doc = SimpleDocTemplate(filename, pagesize=letter)
elements =
Convert data to list of lists; first row as header
table_data = list(data.columns) + data.values.tolist()
t = Table(table_data)
t.setStyle(TableStyle(
('BACKGROUND',(0,0),(-1,0),colors.grey),
('TEXTCOLOR',(0,0),(-1,0),colors.whitesmoke),
('ALIGN',(0,0),(-1,-1),'CENTER'),
('FONTNAME', (0,0), (-1,0), 'Helvetica-Bold'),
('BOTTOMPADDING',(0,0),(-1,0),12),
('BACKGROUND',(0,1),(-1,-1),colors.beige),
))
t.setStyle(TableStyle(
('GRID', (0,0), (-1,-1), 1, colors.black)
))
elements = t
pdf = SimpleDocTemplate(pdf_path, pagesize=letter)
pdf.build(elements)
def create_pdf_with_image(image_path, output_path):
Create a PDF with the image at full page size
c = canvas.Canvas(output_path, pagesize=letter)
width, height = letter
c.drawImage(image_path, 0, 0, width=width, height=height)
c.showPage()
c.save()
def main():
if len(sys.argv) <3:
print("Usage: python script.py input_pdf output_directory")
sys.exit(1)
input_pdf = sys.argv1
output_dir = sys.argv2
if not os.path.isfile(input_pdf):
print(f"Input PDF file input_pdf does not exist.")
sys.exit(1)
if not os.path.isdir(output_dir):
os.makedirs(output_dir, exist_ok=True)
Create a temporary directory to store intermediate files
with tempfile.TemporaryDirectory() as temp_dir:
Step 1: Convert each page of the PDF into separate PNG images
try:
subprocess.run(
'pdftoppm', '-png', input_pdf, os.path.join(temp_dir, 'page'),
check=True,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE
)
except subprocess.CalledProcessError as e:
print(f"Error converting PDF to images: e.stderr.decode()")
return
List all PNG files generated
png_files = sorted(
os.path.join(temp_dir, f) for f in os.listdir(temp_dir) if f.endswith('.png'),
key=lambda x: int(os.path.basename(x).split('-')-1.split('.')0)
)
if not png_files:
print("No images were generated from the PDF.")
return
Load all images and store their sizes
images =
max_width = 0
total_height = 0
for file in png_files:
img = Image.open(file)
width, height = img.size
images.append((img, width, height))
if width >max_width:
max_width = width
total_height += height
Create a new image with the appropriate size
final_image = Image.new('RGB', (max_width, total_height), color=(255, 255, 255))
Paste all images into the final image
y_offset = 0
for img, width, height in images:
final_image.paste(img, (0, y_offset))
y_offset += height
Save the final image to a file or display it
final_image.save('merged_pdf.png')
final_image.show()
except Exception as e:
print(f"An error occurred: e")
if name == "__main__":
main()
Explanation of the Script:
1. Dependencies: The script uses `pdf2image` to convert PDF pages into images and `Pillow` (PIL) for image manipulation.
2. Conversion Function: `convert_pdf_to_images` handles converting a specified range of PDF pages to PNG images using `pdf2image`.
3. Main Workflow:
- Define the start and end page numbers for the conversion.
- Convert the specified page range into images.
- Resize each image to fit within 800x1000 pixels (you can adjust these dimensions).
- Combine all resized images vertically into a single image.
4. Saving the Result: The final combined image is saved as `final_combined_image.png`.
Customization
- Adjust `page_start` and `page_end` to target your desired page range.
- Modify the `target_size` variable if you need different dimensions for resizing.
- Change the output filename or path as needed.
---
Let me know if you'd like any further tweaks or additional features! - https://connectthelinks.com/elisabeckenbau
