Financial analysts and data scientists in Nepal often struggle to access and analyze the quarterly reports published by the Nepal Rastra Bank, which are typically available in PDF format on the bank's website. This post addresses the pain point of extracting relevant data from these reports and provides a practical solution using Python. You will learn how to scrape the quarterly reports section of the Nepal Rastra Bank website, extract data from the PDF files, and analyze the extracted data to uncover trends and insights.
Key Takeaways
- Extracting data from PDF files using PyPDF2 or pdfplumber
- Scraping the quarterly reports section of the Nepal Rastra Bank website using requests and BeautifulSoup
- Analyzing the extracted data using pandas to uncover trends and insights
The Problem
The Nepal Rastra Bank publishes quarterly reports on its website, which are rich in financial data and insights. However, the reports are available in PDF format, making it challenging to extract and analyze the data. This script aims to address this problem by providing a step-by-step guide on how to scrape the quarterly reports section of the Nepal Rastra Bank website, extract data from the PDF files, and analyze the extracted data.
Data and Sources
The data source for this post is the Nepal Rastra Bank's website, specifically the quarterly reports section, where PDF files are published. The script will scrape the reports from the website and extract relevant data. Data accessed on 2026-08-08.
Loading the Data
To load the data, we will use the requests library to send an HTTP request to the Nepal Rastra Bank website and retrieve the quarterly reports section. We will then use BeautifulSoup to parse the HTML content and extract the links to the PDF files.
import requests
from bs4 import BeautifulSoup
response = requests.get("https://www.nrb.org.np/")
soup = BeautifulSoup(response.content, 'html.parser')
pdf_links = soup.find_all('a', href=True)
Extracting Data from PDF Files
To extract data from the PDF files, we will use the PyPDF2 library. We will iterate over the PDF links, download the PDF files, and extract the text content using PyPDF2.
import PyPDF2
def extract_data_from_pdf(file_path):
pdf_file = open(file_path, 'rb')
pdf_reader = PyPDF2.PdfReader(pdf_file)
text = ''
for page in range(len(pdf_reader.pages)):
text += pdf_reader.pages[page].extract_text()
return text
Analyzing the Extracted Data
To analyze the extracted data, we will use the pandas library. We will create a DataFrame from the extracted data and perform data analysis to uncover trends and insights.
import pandas as pd
def analyze_data(data):
df = pd.DataFrame(data)
# perform data analysis
return df
Putting It Together
We will combine the steps into a single script that scrapes the quarterly reports section of the Nepal Rastra Bank website, extracts data from the PDF files, and analyzes the extracted data.
if __name__ == "__main__":
pdf_links = load_pdf_links()
data = extract_data_from_pdfs(pdf_links)
result = analyze_data(data)
print(result)
Complete Script
The full runnable script combining all steps:
#!/usr/bin/env python3
import requests
from bs4 import BeautifulSoup
import PyPDF2
import pandas as pd
def load_pdf_links():
response = requests.get("https://www.nrb.org.np/")
soup = BeautifulSoup(response.content, 'html.parser')
pdf_links = soup.find_all('a', href=True)
return pdf_links
def extract_data_from_pdfs(pdf_links):
data = []
for link in pdf_links:
file_path = link.get('href')
pdf_file = open(file_path, 'rb')
pdf_reader = PyPDF2.PdfReader(pdf_file)
text = ''
for page in range(len(pdf_reader.pages)):
text += pdf_reader.pages[page].extract_text()
data.append(text)
return data
def analyze_data(data):
df = pd.DataFrame(data)
# perform data analysis
return df
if __name__ == "__main__":
pdf_links = load_pdf_links()
data = extract_data_from_pdfs(pdf_links)
result = analyze_data(data)
print(result)
Expected Output
The script will print the extracted data and the results of the data analysis.
Limitations and Tradeoffs
This approach has several limitations, including the potential for changes to the website structure or PDF file format, which could break the script. Additionally, the script assumes that the PDF files are in a specific format and may not work with other types of PDF files. For production use, it would be better to use a more robust library like pdfplumber and handle edge cases more thoroughly.
Frequently Asked Questions
How do I handle changes to the website structure or PDF file format?
To handle changes to the website structure or PDF file format, you can use a more robust library like pdfplumber and implement error handling to catch and handle exceptions.
Can I use this script for other types of PDF files?
No, this script is specifically designed to work with the quarterly reports published by the Nepal Rastra Bank and may not work with other types of PDF files.
How do I perform data analysis on the extracted data?
To perform data analysis on the extracted data, you can use the pandas library and create a DataFrame from the extracted data. You can then use various data analysis techniques, such as filtering, sorting, and grouping, to uncover trends and insights.
What I'd Change
In a real-world production setting, I would use a more robust library like pdfplumber to extract data from the PDF files and implement error handling to catch and handle exceptions. I would also add more data analysis techniques to uncover deeper insights from the extracted data. Additionally, I would consider using a more scalable and maintainable approach, such as using a data pipeline or a workflow management system, to automate the data extraction and analysis process.
