Gittata Explorer: Step-by-step course uses Beaupsoup, YFinance, Matplotlib, Pywidget, and FPDF Release Financial Data, Active PDF recognition

In this lesson, we will guide you to create an advanced reporting tool in Google Colab by integrating multiple Python libraries. You will learn how to organize live financial data from Web pages, receive historical stock data using the YFINANCE, and in mind the matplotlib styles. Also, the lesson shows how you can unite the contact UI using Ipywidget, up to a PDF Moving PDF reported by FPDF.
!pip install fpdf beautifulsoup4 yfinance ipywidgets
First, we include the required libraries of our project: FPDF for PDF Reports, Beaups4 Returning Web Beauty, Ipywidget to restore the active UI items in the writing book.
import requests
from bs4 import BeautifulSoup
from fpdf import FPDF
import yfinance as yf
import matplotlib.pyplot as plt
import ipywidgets as widgets
from IPython.display import display, FileLink
Here, we import libraries to create a complete financial data.
def generate_report(b):
symbol = symbol_text.value.upper().strip()
start_date = start_date_picker.value
end_date = end_date_picker.value
output_area.clear_output() # Clear previous outputs
if not (symbol and start_date and end_date):
with output_area:
print("Please provide valid inputs for stock symbol and both dates.")
return
with output_area:
print(f"Generating report for {symbol} from {start_date} to {end_date}...")
# 1. Retrieve current price using yfinance
try:
stock = yf.Ticker(symbol)
current_price = stock.info.get('regularMarketPrice', 'N/A')
except Exception as e:
current_price = "Error retrieving price"
with output_area:
print("Error retrieving current price:", e)
# 2. Fetch historical data using yfinance
try:
hist = stock.history(start=start_date, end=end_date)
except Exception as e:
hist = None
with output_area:
print("Error fetching historical data:", e)
# 3. Plot historical closing prices
if hist is not None and not hist.empty:
plt.figure(figsize=(10, 5))
plt.plot(hist.index, hist['Close'], marker="o", linestyle="-", label="Close Price")
plt.title(f"{symbol} Historical Closing Prices")
plt.xlabel("Date")
plt.ylabel("Close Price (USD)")
plt.grid(True)
plt.xticks(rotation=45)
plt.tight_layout()
graph_filename = "graph.png"
plt.savefig(graph_filename)
plt.show()
else:
graph_filename = None
with output_area:
print("No historical data available for the selected date range.")
# 4. Create a PDF report using FPDF
pdf = FPDF()
pdf.add_page()
pdf.set_font("Arial", "B", 16)
pdf.cell(0, 10, f"Financial Report for {symbol}", ln=True, align="C")
pdf.ln(10)
pdf.set_font("Arial", size=12)
pdf.cell(0, 10, f"Current Price: {current_price}", ln=True)
pdf.cell(0, 10, f"Date Range: {start_date} to {end_date}", ln=True)
pdf.ln(10)
if graph_filename:
pdf.cell(0, 10, "Historical Closing Prices:", ln=True)
# Adjust the image width to fit the page layout
pdf.image(graph_filename, w=180)
pdf_filename = "financial_report.pdf"
pdf.output(pdf_filename)
# 5. Display the download link for the PDF report
with output_area:
print(f"PDF Report generated: {pdf_filename}")
display(FileLink(pdf_filename))
For the above work, we return the user's inputs to the stock sign and the list of days, and delete current financial data from Yahoo Financial Data while downloading History Data with the YFINANCE. It puts historical closing prices using the matplotlib, producing a PDF report to embed the burned data and graph using FPDF, and eventually shows the download link for the PDF Report.
# Create UI widgets
symbol_text = widgets.Text(
value="AAPL",
description="Stock Symbol:",
placeholder="e.g., AAPL"
)
start_date_picker = widgets.DatePicker(
description='Start Date'
)
end_date_picker = widgets.DatePicker(
description='End Date'
)
generate_button = widgets.Button(
description="Generate Report",
button_style="success"
)
output_area = widgets.Output()
generate_button.on_click(generate_report)
display(widgets.VBox([symbol_text, start_date_picker, end_date_picker, generate_button, output_area]))
Finally, this Code Block puts the interface connector using the Ipywidget. It forms the installation fields of the stock mark, the first and ended Dete, and the revivee button for a report. The UI components are directly organized using VBOX composition, and the output area is given to display reply and link to download PDF.
Release and PDF sample
In conclusion, in terms of this lesson, you have successfully integrated the scraping web, data analysis, effective UI formation, and PDF Report Generation one brochure One of the Google Colab. This process of step-by-step process shows how you can tie the power of various Python libraries to create a strong, financial friendly.
Here is the Colab Notebook of the above project. Also, don't forget to follow Sane and join ours Telegraph station including LinkedIn Grtopic. Don't forget to join ours 80k + ml subreddit.
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