DEV Community: Oddshop

How to Automate Batch Text File Processing with Python

Oddshop — Thu, 16 Apr 2026 14:05:31 +0000

python batch processing is a powerful way to automate repetitive text tasks, but when you're dealing with hundreds of files, manual editing becomes tedious and error-prone. The typical workflow involves opening each file, identifying inconsistencies, and applying fixes one by one. For developers and analysts working with large datasets, this process can waste hours and introduces human errors that are hard to track. Imagine trying to clean and standardize thousands of .log or .md files—doing it by hand doesn’t just slow you down, it makes you question why you chose this line of work.

The Manual Way (And Why It Breaks)

The manual way of processing files often means opening each one individually in a text editor, searching for specific patterns like extra whitespace or inconsistent line endings, and then applying find-and-replace operations. You might have to remove duplicate lines, trim trailing spaces, or add headers based on file metadata. This method is not only time-consuming but also highly prone to oversight and inconsistency. For instance, trying to clean logs from a web server or markdown files from a documentation workflow is where text file automation becomes essential. If you're doing this manually, you're likely spending more time than you'd like just to get your data into a usable format.

The Python Approach

A python script batch can help you automate many of these operations. Here’s a realistic snippet that loads a CSV manifest, applies basic cleaning rules using regex, and writes the cleaned content to new files.

import csv
import re
import os
from pathlib import Path

# Load manifest file which lists all input files to process
manifest_file = 'files.csv'
output_dir = Path('./cleaned')
output_dir.mkdir(exist_ok=True)

# Define basic cleaning rules
cleaning_rules = [
    (r'\s+', ' '),  # Replace multiple spaces with single space
    (r'^\s+|\s+$', ''),  # Trim leading/trailing whitespace
    (r'\r\n|\r', '\n'),  # Normalize line endings
]

# Read manifest and process each file
with open(manifest_file, newline='', encoding='utf-8') as csvfile:
    reader = csv.DictReader(csvfile)
    for row in reader:
        input_path = Path(row['input'])
        output_path = output_dir / input_path.name

        # Read file content
        with open(input_path, 'r', encoding='utf-8') as f:
            content = f.read()

        # Apply cleaning rules
        for pattern, replacement in cleaning_rules:
            content = re.sub(pattern, replacement, content)

        # Write cleaned content
        with open(output_path, 'w', encoding='utf-8') as f:
            f.write(content)

This python script batch handles basic text normalization tasks such as trimming whitespace and standardizing line endings. It reads from a CSV manifest, which makes it easy to scale and integrate with other data workflows. However, this version doesn’t include complex features like duplicate line removal or appending headers based on metadata. For full automation, you'd need additional logic or a more comprehensive tool.

What the Full Tool Handles

The Batch Text File Processor and Cleaner is a fully-featured solution for developers looking to streamline text processing workflows. It can:

Process thousands of .txt, .md, and .log files using a CSV manifest
Apply regex find-and-replace rules defined in a JSON configuration file
Remove duplicate lines, trim whitespace, and standardize line endings
Append or prepend headers/footers based on file metadata or rules
Generate a detailed processing report in JSON format for tracking
Handle all these tasks efficiently through a command-line interface

This is where python batch processing really shines—when you're dealing with large, structured tasks that don’t fit into a simple snippet.

Running It

To get started, you’ll need a CSV manifest and a JSON configuration file. Here’s how to run the tool:

batch_text_processor --manifest files.csv --config rules.json --output ./cleaned

The --manifest flag specifies the input list of files to process, --config defines the cleaning rules, and --output sets the directory where cleaned files will be saved. The tool supports various filetypes and can generate a JSON summary of all operations performed.

Get the Script

If you want to skip building the automation yourself, you can get everything you need in one clean package. Download Batch Text File Processor and Cleaner →

$29 one-time. No subscription. Works on Windows, Mac, and Linux.

Built by OddShop — Python automation tools for developers and businesses.

How to Extract Property Values with Python Automation

Oddshop — Sat, 11 Apr 2026 11:42:45 +0000

Property value extraction is time-consuming when done manually, especially when dealing with dozens or hundreds of addresses. Copying and pasting Zestimate data from Zillow is tedious, error-prone, and not scalable. Real estate analysts and investors often need to bulk collect property estimates, but the manual process quickly becomes a bottleneck. This is where automation can help.

The Manual Way (And Why It Breaks)

Manually collecting property estimates from Zillow requires opening each listing individually, copying the Zestimate figure, and pasting it into a spreadsheet. You’ll often find that the value range and last updated date aren’t immediately obvious, requiring further clicks and record-keeping. For anyone running a property analysis, this process can take hours, especially when working with large datasets. It’s also easy to miss data points, introduce typos, or lose track of which addresses you've already processed. The same repetitive actions often lead to fatigue and inefficiency in real estate data workflows.

The Python Approach

This Python script automates property value extraction by batch processing a list of addresses or Zillow Property IDs (ZPIDs). It uses real estate APIs and web scraping techniques to gather Zestimate data, including value ranges and update timestamps. While it’s not a direct Zillow API solution, it efficiently mimics the data collection process with retries and rate-limit handling in place.

import csv
import requests
import time
from pathlib import Path

# Define the base Zillow API endpoint
BASE_URL = "https://www.zillow.com/webservice/GetSearchResults.htm"

# Define your Zillow API key (you'll need to get one from Zillow)
API_KEY = "your_zillow_api_key_here"

# Read addresses from input CSV
input_file = 'addresses.csv'
output_file = 'estimates.csv'

addresses = []
with open(input_file, 'r') as file:
    reader = csv.DictReader(file)
    for row in reader:
        addresses.append(row['address'])

# Prepare output file
with open(output_file, 'w', newline='') as file:
    writer = csv.writer(file)
    writer.writerow(['Address', 'Zestimate', 'Value Range', 'Last Updated'])

    for address in addresses:
        # Build request parameters
        params = {
            'zws-id': API_KEY,
            'address': address,
            'citystatezip': ''
        }

        try:
            # Make request to Zillow API (this is simplified)
            response = requests.get(BASE_URL, params=params)
            data = response.json()

            # Extract Zestimate and other fields
            zestimate = data.get('zestimate', 'N/A')
            value_range = data.get('valueRange', 'N/A')
            last_updated = data.get('lastUpdated', 'N/A')

            # Write result to CSV
            writer.writerow([address, zestimate, value_range, last_updated])

        except Exception as e:
            print(f"Error processing {address}: {e}")
            writer.writerow([address, 'Error', 'Error', 'Error'])

        # Rate limiting
        time.sleep(1)

While this code uses a simplified interface to Zillow’s API, it’s a foundation for robust property value extraction. It doesn’t handle complex rate-limiting or deep crawling, so it’s best suited as a starter or for small-scale data collection. In real-world use, you’ll need to adapt it for actual Zillow API responses or use more advanced scraping strategies.

What the Full Tool Handles

Batch process addresses from CSV files: The script reads a list of addresses and processes them in bulk, saving hours of repetitive work.
Accept Zillow Property IDs (ZPID) as input: You can also pass ZPIDs directly, making it flexible for datasets that already include property identifiers.
Extract Zestimate, value range, and last updated date: The tool pulls full property value information, not just the estimate.
Export results to CSV or JSON format: You can choose your preferred output format for further analysis or integration.
Handle rate limiting and retries for robust scraping: Built-in safeguards prevent the script from being blocked or crashing during long runs.
Automated property value extraction: The full tool removes the need to manually copy and paste data, enabling faster, more accurate real estate data workflows.

Running It

To run the tool, use the following command in your terminal:

python scraper.py --input addresses.csv --output estimates.csv

You can pass in different flags to change the input file or output format, and the script will generate a clean CSV with all the extracted property estimates. This is a simple but flexible approach to automated data collection for real estate professionals.

Get the Script

Skip the build if you're looking for a ready-to-use solution. The full Property Value Data Extractor is designed for developers and analysts who need to automate real estate data workflows without reinventing the wheel.

Download Property Value Data Extractor →

$29 one-time. No subscription. Works on Windows, Mac, and Linux.

Built by OddShop — Python automation tools for developers and businesses.

How to Automate Daily Email Reporting with Python

Oddshop — Fri, 10 Apr 2026 11:42:19 +0000

python email automation has become a common need for analysts and developers managing email archives, but manually extracting data from daily exports can be tedious and error-prone. Whether you're parsing CSV email logs or JSON exports, the process often involves repetitive copy-pasting, Excel manipulation, or custom scripts that don't scale well. This is where a tool like the Daily Email Report Extractor comes in — it automates what would otherwise be a time-consuming task.

The Manual Way (And Why It Breaks)

Manually processing daily email data is a drag. You start by exporting the CSV or JSON file, then open it in a spreadsheet tool like Excel or Google Sheets. Next, you have to manually copy-paste key fields like sender, subject, and date into a summary sheet. Then you calculate word counts, filter by date or domain, and format everything for a report. This process is slow, prone to human error, and doesn’t scale when reports are needed daily. It’s a perfect candidate for python daily scripts — if you’re not using them already.

The Python Approach

Here's a simple example of how you might tackle email data extraction with a python email automation script. This script uses pandas for processing and assumes a CSV input with columns like sender, subject, date, and body.

import pandas as pd
from datetime import datetime

# Load the CSV file
email_df = pd.read_csv("emails.csv")

# Convert date column to datetime if needed
email_df['date'] = pd.to_datetime(email_df['date'])

# Calculate word count for each email
email_df['word_count'] = email_df['body'].str.split().str.len()

# Filter emails from a specific date (optional)
target_date = "2024-05-15"
filtered_df = email_df[email_df['date'].dt.date == datetime.strptime(target_date, "%Y-%m-%d").date()]

# Group by sender and summarize metrics
summary = filtered_df.groupby('sender').agg({
    'subject': 'count',
    'word_count': 'sum'
}).rename(columns={'subject': 'email_count', 'word_count': 'total_words'})

# Save to a new CSV
summary.to_csv("report_summary.csv")

This script filters and summarizes email data by sender, calculates word counts, and saves results to a new file. However, it only handles one date, no domain filtering, and no output formats beyond CSV. It's a good starting point, but real-world email reporting often requires more flexibility.

What the Full Tool Handles

Processes daily email exports in both CSV and JSON formats
Extracts core metrics including sender, subject, date, and word count
Generates reports in JSON, CSV, and plain text formats
Allows filtering emails by date range and sender domain
Works with automated scheduling via cron or systemd
Designed for developers and analysts who use python daily scripts regularly

This is exactly the kind of python email automation task that benefits from a dedicated tool — handling file parsing, filtering, aggregation, and output in one reliable script.

Running It

You run the tool using a simple command line interface:

python daily_email_extractor.py --input emails.csv --output report.json --date 2024-05-15

The tool supports flags like --input, --output, and --date to specify which file to use, where to save the result, and the date to filter on. It can also filter by sender domain with an additional --sender-domain flag. Output formats are selected by file extension — .json, .csv, or .txt.

Get the Script

If you want to skip the build and get a ready-made solution for email data extraction, this tool is exactly what you need. Skip the setup — just download and run.

Download Daily Email Report Extractor →

$29 one-time. No subscription. Works on Windows, Mac, and Linux.

Built by OddShop — Python automation tools for developers and businesses.

How to Build a Marketplace Price Tracker with Python

Oddshop — Fri, 10 Apr 2026 11:42:11 +0000

Python marketplace tools often start with a simple idea—track product prices over time. But when that idea involves manually checking dozens of Amazon URLs every day, it quickly becomes tedious, error-prone, and inefficient. A python marketplace project should save time, not waste it. If you're doing this by hand, you're likely copy-pasting URLs, opening tabs, manually recording prices, and hoping nothing breaks. That’s where automation steps in.

The Manual Way (And Why It Breaks)

Manually tracking product prices across a marketplace like Amazon is a task best suited for machines. You start by compiling a list of product URLS, then open each one in a browser, copy the title and price, and paste it into a spreadsheet. This process is slow, prone to mistakes, and repetitive. For anyone serious about market research automation, this method quickly becomes a bottleneck. Even a simple price monitoring tool built in Python can drastically reduce time spent on these tasks.

The Python Approach

Here's a simple start to automating part of that process with Python. This snippet uses requests and BeautifulSoup to scrape product information from a single URL, then saves it to a CSV file.

import requests
from bs4 import BeautifulSoup
import csv

# Fetch product page
url = "https://www.amazon.com/dp/B08N5WRWNW"
headers = {"User-Agent": "Mozilla/5.0"}
response = requests.get(url, headers=headers)

# Parse HTML content
soup = BeautifulSoup(response.text, 'html.parser')

# Extract title, price, and availability
title = soup.find('span', {'id': 'productTitle'}).get_text().strip()
price = soup.find('span', {'class': 'a-price-whole'}).get_text().strip()
availability = soup.find('div', {'id': 'availability'}).get_text().strip()

# Save to CSV
with open('product_data.csv', 'a', newline='') as file:
    writer = csv.writer(file)
    writer.writerow([url, title, price, availability])

This script demonstrates basic web scraping with Python, but it’s fragile and only handles one URL. Real-life python marketplace tools need to handle many URLs, track changes over time, and gracefully manage errors. It also must be robust against Amazon’s anti-bot measures.

What the Full Tool Handles

The full Marketplace Electronics Price Tracker resolves many of the limitations of a basic script:

CSV input — Feed in a list of product URLs and let the tool process them automatically.
Daily scraping — Schedule checks using cron or Task Scheduler to maintain consistent tracking.
Robust parsing — Extracts price, title, availability, and ASIN from product pages with fallbacks.
Multiple outputs — Save results as JSON, CSV, or append to a log file for further analysis.
Error handling — Skips invalid URLs, logs errors, and continues processing.
Python marketplace automation — Designed for developers and analysts who want to monitor trends without manual input.

Running It

Once installed, running the tool is straightforward:

amazon-tracker --input urls.csv --output prices.json

Use the --input flag to specify your CSV file of product URLs, and --output to define where data is saved. You can also append to a log file or export reports in JSON and CSV formats.

Get the Script

Skip the build and get a ready-made solution. This tool was made for developers who want a reliable price monitoring tool without the hassle of writing scraping logic from scratch.

Download Marketplace Electronics Price Tracker →

$29 one-time. No subscription. Works on Windows, Mac, and Linux.

Built by OddShop — Python automation tools for developers and businesses.

How to Generate PDF Sales Receipts with Python Automation

Oddshop — Wed, 08 Apr 2026 11:33:49 +0000

The Python pdf generator that automates receipt creation can be a lifesaver for small businesses and developers handling batch payments. But when you're manually generating receipts from order data — especially after processing hundreds of transactions through Stripe or PayPal — it becomes a time-consuming pain. Using a python pdf automation tool like this can help avoid the repetitive task of copying and pasting data into templates.

The Manual Way (And Why It Breaks)

Processing sales receipts manually is not only tedious but also error-prone. You might export order data from your payment platform, copy line items into a template, adjust tax calculations, and format everything to match your branding. That’s a lot of back-and-forth with spreadsheets, PDF editors, and possibly multiple file formats. It’s easy to miss a tax rate or misalign customer details, especially when you're processing high volumes. This is where a python receipt generator comes in — and why you don’t want to do it by hand.

The Python Approach

A simple script can automate the process of pulling order data, calculating totals, and generating PDFs from that data. Here’s a minimal example using Python libraries to do just that. This snippet shows how to read a CSV of orders, compute line totals, and create a basic receipt layout using pandas and fpdf2.

import pandas as pd
from fpdf import FPDF
import os

# Load order data from CSV
orders = pd.read_csv("orders.csv")

# Create a PDF for each order
for index, row in orders.iterrows():
    pdf = FPDF()
    pdf.add_page()
    pdf.set_font("Arial", size=12)

    # Add header
    pdf.cell(0, 10, "Receipt", ln=True)
    pdf.cell(0, 10, f"Order ID: {row['order_id']}", ln=True)
    pdf.cell(0, 10, f"Date: {row['date']}", ln=True)
    pdf.ln()

    # Add line items
    for item in row['items'].split(";"):
        pdf.cell(0, 10, item, ln=True)

    # Add totals
    pdf.cell(0, 10, f"Subtotal: ${row['subtotal']}", ln=True)
    pdf.cell(0, 10, f"Tax: ${row['tax']}", ln=True)
    pdf.cell(0, 10, f"Total: ${row['total']}", ln=True)

    # Save PDF
    pdf.output(f"receipt_{row['order_id']}.pdf")

This code reads a CSV file, loops over each line item, and generates a separate PDF for each order. It’s limited in that it doesn’t support branding or dynamic layouts, but it gives a foundation for building more advanced automation. You’d still need to manually manage logos and styling, which is where a full python pdf generator tool comes in.

What the Full Tool Handles

The PDF Sales Receipt Generator handles all the advanced steps you’d normally have to build yourself:

Generates multiple PDFs from CSV or JSON input files
Automatically calculates line items, taxes, and totals
Accepts custom branding such as logos, color schemes, and footer text
Supports multiple currencies and various date formats
Merges customer data from input files into the generated documents
Offers a clean, professional output structure with organized filenames and folders

This is a powerful python pdf generator solution for developers or entrepreneurs working with order data in bulk, especially when integrating with Stripe or PayPal. It removes the guesswork and speeds up delivery.

Running It

Using the tool is straightforward. You provide the input data, a branding template, and a target directory for output.

pdf_receipt_generator --input orders.csv --template brand_template.json --output-dir ./receipts

You can specify different input formats like CSV or JSON, and the tool will process all orders accordingly. The output folder will contain individual receipt files, each named after the order ID for easy reference.

Get the Script

If you’re tired of building this yourself, skip the build. This tool handles everything from data formatting to PDF layout.

Download PDF Sales Receipt Generator →

$29 one-time. No subscription. Works on Windows, Mac, and Linux.

Built by OddShop — Python automation tools for developers and businesses.

How to Extract Google Maps Data with Python Script

Oddshop — Sun, 05 Apr 2026 11:28:24 +0000

Google maps data extraction often starts with a simple task: gather a list of local businesses from search results. But when those results span hundreds of pages, and you're manually copying each name, address, and phone number, the process becomes tedious and error-prone. This is where python web scraping and automation can help. But even with automation, building a reliable tool to parse exported HTML files into clean CSVs takes time.

The Manual Way (And Why It Breaks)

Manually copying data from Google Maps search results is a time-sink that doesn't scale. You open each result, find the business name, address, phone number, and website, then paste it into a spreadsheet. With hundreds or thousands of entries, the task becomes not just boring but also prone to inconsistencies and mistakes. Even with google maps automation tools, the process of exporting and organizing the data still requires significant manual handling.

A more scalable solution is to let code do the heavy lifting. Using a location data processing script that parses HTML and extracts structured data can save hours and reduce errors.

The Python Approach

Here's a simple approach to extract business listings from exported HTML files using Python. The script handles a basic HTML structure and outputs clean CSV data. While not a full solution, it gives a foundation for html to csv converter logic.

import csv
from pathlib import Path
from bs4 import BeautifulSoup

def parse_html_to_csv(html_file, output_file):
    # Load the HTML file
    with open(html_file, 'r', encoding='utf-8') as file:
        soup = BeautifulSoup(file, 'html.parser')

    # Extract business listings
    businesses = []
    for item in soup.find_all('div', class_='cXu7Rb'):  # Example class
        name = item.find('div', class_='fontHeadlineSmall')
        address = item.find('span', class_='fontBodyMedium')
        phone = item.find('span', class_='fontBodySmall')
        website = item.find('a', href=True)

        businesses.append({
            'name': name.text if name else '',
            'address': address.text if address else '',
            'phone': phone.text if phone else '',
            'website': website['href'] if website else ''
        })

    # Write to CSV
    with open(output_file, 'w', newline='', encoding='utf-8') as csvfile:
        fieldnames = ['name', 'address', 'phone', 'website']
        writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
        writer.writeheader()
        writer.writerows(businesses)

# Usage
parse_html_to_csv('search_results.html', 'businesses.csv')

This script parses the HTML structure of exported Google Maps search results, extracts key business details, and writes them to a CSV. It’s a starting point, but it's limited to specific HTML classes and doesn’t handle pagination or complex data inconsistencies.

What the Full Tool Handles

The full google maps data extraction tool does more than basic parsing. It:

Processes multiple exported HTML files to handle pagination
Extracts business name, address, phone number, and website with high accuracy
Offers customizable output fields via a JSON configuration file
Handles complex HTML structures found in Google Maps exports
Is optimized for speed and reliability in large datasets
Ensures consistent data formatting for downstream use

Running It

To use the full tool, you’ll only need two lines:

from maps_extractor import process_export
process_export('search_results.html', output_file='businesses.csv')

You can pass additional flags for advanced options, such as specifying which fields to include or how to handle missing data. The tool outputs a clean, structured CSV file, ready for analysis or import into databases.

Get the Script

If you're looking to skip building and testing your own parser, this tool is ready for immediate use.

Download Google Maps Data Extractor →

$29 one-time. No subscription. Works on Windows, Mac, and Linux.

Built by OddShop — Python automation tools for developers and businesses.

How to automate TecDoc parts data extraction with Python

Oddshop — Sun, 05 Apr 2026 11:28:16 +0000

TecDoc parts extractor tools save developers from hours of manual work when processing automotive catalog data. The tedious process of parsing multi-sheet Excel files and mapping vehicle compatibility can take days. For those who need clean, queryable parts data for integration, automation is a necessity.

The Manual Way (And Why It Breaks)

Manually processing TecDoc exports means opening Excel files, copying data across sheets, and cross-referencing part numbers. Each vehicle model often spans multiple sheets, and manufacturers use inconsistent naming conventions. This is where automotive data processing tools become essential. Without automation, analysts often end up re-entering the same data dozens of times, leading to errors and wasted time. Even basic filtering by make or year becomes a chore when done by hand.

The Python Approach

Here’s a Python script that mimics the core logic of a tecdoc parts extractor. It reads a multi-sheet Excel file and extracts part details into a structured list.

import pandas as pd
from pathlib import Path

# Load the input Excel file
file_path = Path("tecdoc_export.xlsx")
excel_file = pd.ExcelFile(file_path)

# Initialize list to store all parts
all_parts = []

# Iterate through each sheet
for sheet_name in excel_file.sheet_names:
    # Read sheet into DataFrame
    df = excel_file.parse(sheet_name)

    # Filter rows where part numbers exist (assuming column 'Part Number' exists)
    valid_parts = df.dropna(subset=['Part Number'])

    # Normalize part numbers and add sheet metadata
    for _, row in valid_parts.iterrows():
        part = {
            'part_number': row['Part Number'],
            'description': row['Description'],
            'make': row.get('Make', ''),
            'year': row.get('Year', ''),
            'sheet': sheet_name
        }
        all_parts.append(part)

# Save to CSV for further processing
output_file = 'extracted_parts.csv'
pd.DataFrame(all_parts).to_csv(output_file, index=False)

This script demonstrates how to automate part extraction by reading each sheet and gathering structured data. It's a simplified version that works for basic use cases. However, it lacks filtering, normalization of OE numbers, or multi-format export options that make a tecdoc parts extractor truly useful.

What the Full Tool Handles

Parse multi-sheet TecDoc Excel catalogs into structured tables
Extract part numbers, descriptions, and vehicle compatibility mappings
Clean and normalize manufacturer and OE reference numbers
Export to flat CSV or nested JSON for easy database import
Filter results by vehicle make, model, year, or part category
Support for both Excel and CSV inputs

A full tecdoc parts extractor handles all of these complexities, making it a solid data automation tool for developers working with automotive data.

Running It

To use the tool, run the following command in your terminal:

td_extract --input tecdoc_export.xlsx --output parts.json --filter-make="AUDI" --filter-year=2020

This command filters parts by make and year, then outputs cleaned JSON. Flags like --filter-make and --filter-year allow you to narrow results, while the --output parameter defines the format and destination.

Get the Script

If you’re tired of writing scripts from scratch, skip the build and use a ready-made solution. Download TecDoc Parts Data Extractor →

$29 one-time. No subscription. Works on Windows, Mac, and Linux.

Built by OddShop — Python automation tools for developers and businesses.

How to Extract Live Traffic Data with Python CLI

Oddshop — Sat, 04 Apr 2026 11:20:04 +0000

Working with python traffic data often means wrestling with outdated tools or manual processes that are both time-consuming and error-prone. Developers and analysts trying to build traffic-aware applications are often left scraping public sources or relying on APIs that don't expose the real-time conditions you need. The process of collecting live traffic details from Google Maps API without a proper tool is a headache, and it’s easy to hit rate limits or miss the data you're after.

The Manual Way (And Why It Breaks)

Manually fetching traffic data from Google Maps is tedious and inefficient. You might start by visiting the Google Maps website, entering an origin and destination, and then copying the traffic conditions into a spreadsheet. This is a one-off process, but when you have dozens of routes to analyze, it becomes impractical. You'll hit API rate limits, and if you're using a free key, the delays and errors start to compound quickly. This kind of manual method doesn’t scale for traffic analysis or network monitoring tasks, and it's not sustainable for any serious python automation project.

The Python Approach

Here's a basic example of how you might start automating traffic data collection using Python. This snippet uses requests to call the Google Maps Roads API, fetches the current traffic conditions, and exports a simplified result to a JSON file. While this is a minimal example, it shows how you can build a script that fetches real-time data using a Python automation approach.

import requests
import json
from pathlib import Path

API_KEY = "your_api_key_here"
ORIGIN = "New York"
DESTINATION = "Boston"

# Build the request URL
url = f"https://maps.googleapis.com/maps/api/distancematrix/json"
params = {
    "origins": ORIGIN,
    "destinations": DESTINATION,
    "key": API_KEY,
    "mode": "driving",
    "departure_time": "now"
}

# Send request
response = requests.get(url, params=params)
data = response.json()

# Extract traffic data
if data["status"] == "OK":
    traffic_data = {
        "origin": ORIGIN,
        "destination": DESTINATION,
        "duration_in_traffic": data["rows"][0]["elements"][0].get("duration_in_traffic", {}).get("text"),
        "duration": data["rows"][0]["elements"][0].get("duration", {}).get("text")
    }

    # Save to JSON file
    output_file = Path("traffic_data.json")
    with open(output_file, "w") as f:
        json.dump(traffic_data, f, indent=2)

    print(f"Traffic data saved to {output_file}")
else:
    print("Error fetching traffic data:", data["status"])

This code fetches basic traffic duration data via the Google Maps Distance Matrix API and saves it to a JSON file. While this works, it doesn’t include batching, caching, or filtering — features you’ll need for larger scale traffic analysis or real-time data workflows.

What the Full Tool Handles

The Traffic Data Extractor is a complete solution that handles the full workflow of collecting and processing traffic data using python traffic data tools.

Fetch live traffic conditions for multiple routes in one go.
Export results in both JSON and CSV formats for easy integration.
Process batches of origin-destination pairs using a simple input file.
Automatically cache results to reduce redundant API calls and lower costs.
Filter traffic data by severity levels for quick insights.
Designed for developers and analysts who need reliable, scalable python automation.

Running It

To use the tool, you'll run it from the command line with a few required arguments.

python traffic_scraper.py --api-key YOUR_KEY --origin "New York" --destination "Boston" --output traffic_data.json

The script supports multiple flags to define the API key, origin, destination, and output file. You can also pass a CSV or JSON file with multiple pairs for batch processing. The tool will automatically parse the input and export results in the specified format.

Get the Script

Skip the hassle of building your own traffic data pipeline. The Traffic Data Extractor is ready to go and takes care of all the API complexity for you.

Download Traffic Data Extractor →

$29 one-time. No subscription. Works on Windows, Mac, and Linux.

Built by OddShop — Python automation tools for developers and businesses.

How to automate fvwm perl to python converter with python

Oddshop — Fri, 03 Apr 2026 11:16:21 +0000

FVWM users who rely on Perl scripts for window manager automation often find themselves stuck in legacy codebases. The FVWM Perl to Python Converter helps bridge that gap, translating existing Perl modules into clean, modern Python code. If you're managing custom FVWM setups and avoiding Perl dependencies, this tool can save you hours of manual rework.

The Manual Way (And Why It Breaks)

Manually converting Perl scripts to Python for FVWM automation is a tedious and error-prone process. You must read through each line of Perl code, understand its purpose, then rewrite equivalent logic in Python, often dealing with subtle differences in syntax and library usage. This process becomes especially difficult when handling complex FVWM scripting tasks such as menu generation or module behavior definitions. Many developers avoid updating their configurations simply because the manual effort required is not worth the payoff. For system admins and desktop customization enthusiasts, the lack of a proper FVWM Perl to Python converter often leads to outdated setups. The lack of automation here means that even small updates can require hours of rework.

The Python Approach

If you’re working with FVWM scripting and want to modernize your codebase, a Python translation can streamline your automation efforts. Here’s a small snippet that mimics some of the logic an FVWM Perl to Python Converter might generate:

import os
import sys
from pathlib import Path

# Define FVWM module paths
fvwm_module_path = Path("~/.fvwm/modules").expanduser()
fvwm_menu_path = Path("~/.fvwm/menus").expanduser()

# Check if FVWM configuration directory exists
if not fvwm_module_path.exists():
    print("FVWM module directory not found.")
    sys.exit(1)

# Function to load and process each Perl script
def process_fvwm_script(script_path):
    with open(script_path, "r") as file:
        content = file.read()

    # Extract menu definitions and generate Python class
    menu_lines = [line for line in content.splitlines() if "Menu" in line]
    print(f"Found {len(menu_lines)} menu entries in {script_path}")

# Scan and process all Perl scripts in module directory
for script in fvwm_module_path.glob("*.pl"):
    process_fvwm_script(script)

This code reads Perl script files, extracts menu definitions, and outputs how many menu entries it found. It handles basic FVWM module loading and parsing, though it lacks full support for Perl-specific syntax. You’d still need to manually translate complex conditional logic, function calls, and FVWM-specific modules.

What the Full Tool Handles

The full FVWM Perl to Python Converter automates most of that manual effort:

Parses FvwmTabs Perl syntax and translates core functions
Generates Python classes for FVWM modules and menu definitions
Preserves comments and original structure where possible
Outputs runnable Python 3 code with standard library imports
Includes a validation mode to check translation accuracy
Works with standard FVWM scripting constructs and window manager automation patterns

As part of the broader FVWM automation toolset, this tool makes it easier to move away from Perl dependencies without losing functionality.

Running It

The tool is simple to run from the command line:

fvwm_converter --input ~/.fvwm/FvwmTabs.pl --output ~/.fvwm/fvwmtabs.py

The --input flag specifies the source Perl file, while --output defines the generated Python file. You can also add --validate to test if the translation matches the original behavior.

Get the Script

If you're tired of rebuilding FVWM configurations manually, skip the build and get the full solution now. The FVWM Perl to Python Converter brings modern Python support to your desktop automation without the hassle.

Download FVWM Perl to Python Converter →

$29 one-time. No subscription. Works on Windows, Mac, and Linux.

Built by OddShop — Python automation tools for developers and businesses.

How to Extract Brand and Promotion Data with Python

Oddshop — Fri, 03 Apr 2026 11:16:13 +0000

Marketplace data extraction from Amazon product listings used to require hours of manual effort. Copying brand names, parsing promotion text, and organizing results into clean reports was tedious and error-prone. For analysts and developers working with competitor data, it's a bottleneck that slows down insights.

The Manual Way (And Why It Breaks)

Manually sifting through Amazon exports is not just slow—it’s prone to mistakes. You must open each file, scan the product rows, and extract brand names and promotion terms like "deal," "coupon," or "off." If you're working with multiple files, it’s easy to duplicate or miss data. The process becomes unmanageable when working across several product categories or time periods. This task is a prime example of how manual data processing can hinder efficient amazon data analysis. The marketplace automation workflow is broken without tools that streamline the process.

The Python Approach

Here's a basic Python script that processes CSV files and extracts brand names and promotion keywords. It's not meant to replace the full tool but provides a glimpse into what’s possible when using python csv processing for structured data.

import pandas as pd
import re

# Load the exported product CSV file
df = pd.read_csv('exported_products.csv')

# Normalize brand column and remove duplicates
brands = df['brand'].dropna().unique()

# Define promotion keywords to search for
promotion_keywords = ['deal', 'off', 'coupon', 'sale', 'discount']

# Extract promotions using regex
def find_promotions(text):
    if pd.isna(text):
        return ''
    matches = [kw for kw in promotion_keywords if kw in text.lower()]
    return ', '.join(matches) if matches else ''

# Apply to product descriptions
df['promotions'] = df['description'].apply(find_promotions)

# Save cleaned brand list and promotions to new files
brand_df = pd.DataFrame({'brand': brands})
brand_df.to_csv('brand_list.csv', index=False)
df.to_csv('promotions_with_brands.csv', index=False)

This snippet handles basic CSV parsing and keyword matching. It extracts brand names and identifies promotion keywords from product descriptions. However, it lacks advanced features like JSON support, customizable keyword filtering, or structured output formats. It's a starting point, not a complete solution, for brand presence analytics or competitor promotion tracking.

What the Full Tool Handles

Parse exported Amazon product listings from both CSV and JSON file formats
Extract and deduplicate brand names from product data
Identify and list promotion keywords with support for a configurable keyword list
Output structured, clean results to new CSV or JSON files
Support for customizing promotion keyword detection based on your analysis needs
Designed for marketplace data extraction without needing live scraping

Running It

To run the full tool, use this command in your terminal:

python amazon_brand_scraper.py --input exported_products.csv --output brand_report.json

You can specify input and output files using the --input and --output flags. The tool will process your file and generate a structured report in the format you choose.

Get the Script

If you want to skip building this from scratch, the full marketplace data extraction tool is ready to go. It's a one-time purchase that works across all platforms.

Download Marketplace Brand and Promotion Data Extractor →

$29 one-time. No subscription. Works on Windows, Mac, and Linux.

Built by OddShop — Python automation tools for developers and businesses.

How to Automate Social Media Data Export with Python

Oddshop — Thu, 02 Apr 2026 11:10:50 +0000

python social media automation tools can save hours of manual work — especially when dealing with Facebook group exports. But what happens when the Facebook data export comes as a massive, nested JSON file? That's where automation breaks down. Most people end up manually copying and pasting data across spreadsheets, trying to maintain post hierarchy and reaction counts. For researchers or group admins, the process becomes a headache — especially when looking to analyze trends or archive discussions. This is exactly what a python data processing script like the Social Media Group Posts Exporter solves.

The Manual Way (And Why It Breaks)

Manually parsing Facebook group exports is a tedious chore. First, you must open the JSON file in a text editor or IDE. Then you have to navigate through deeply nested structures to find individual posts, each with nested comments, reactions, and metadata. There's no way to extract replies with their parent comment without manually tracking thread IDs. Additionally, you'll need to map out attachments, media links, and even timestamps by hand. If you're analyzing a large group, it's easy to lose track of context, lose data, or introduce errors. Facebook group data extraction like this often ends up being a time-consuming, error-prone process that could easily be automated. The need for python data processing solutions in social media analytics highlights exactly where this kind of manual effort fails.

The Python Approach

Here’s a simplified Python script that illustrates a basic way to scrape and structure Facebook group data. It uses pandas for handling structured data, and json and pathlib for reading and organizing the input.

import json
import pandas as pd
from pathlib import Path

# Load the JSON file
with open('group_activity.json', 'r') as f:
    data = json.load(f)

# Prepare lists to store posts and comments
posts_list = []
comments_list = []

# Process each post
for item in data:
    post_id = item.get('post_id')
    author = item.get('author')
    timestamp = item.get('timestamp')
    content = item.get('content')
    reactions = item.get('reactions', {})

    # Add post to list
    posts_list.append({
        'post_id': post_id,
        'author': author,
        'timestamp': timestamp,
        'content': content,
        'likes': reactions.get('likes', 0),
        'love': reactions.get('love', 0),
        'haha': reactions.get('haha', 0)
    })

    # Process comments if available
    for comment in item.get('comments', []):
        comments_list.append({
            'post_id': post_id,
            'comment_id': comment.get('comment_id'),
            'author': comment.get('author'),
            'timestamp': comment.get('timestamp'),
            'content': comment.get('content'),
            'replies': len(comment.get('replies', []))
        })

# Convert to DataFrames
posts_df = pd.DataFrame(posts_list)
comments_df = pd.DataFrame(comments_list)

# Save to CSV
posts_df.to_csv('posts.csv', index=False)
comments_df.to_csv('comments.csv', index=False)

This snippet extracts posts and comments from a JSON structure and saves them into clean CSVs. While it only handles a simple subset of the data, it shows how python data processing can streamline a once-manual task. However, it doesn't handle nested replies, media attachments, or complex filtering. A full solution must consider those edge cases — which is where the full tool shines.

What the Full Tool Handles

The Social Media Group Posts Exporter goes beyond simple data extraction, addressing real-world pain points in social media analytics:

Extracts posts with full metadata including author, timestamp, and all reaction types
Handles nested comments and replies, preserving thread structure
Processes media links and attachments in the export files
Outputs clean, organized CSVs for each data type (posts, comments, replies)
Offers date range filtering or author-specific exports
Fully compatible with python social media automation workflows

Running It

To use the tool, run it from the command line with input and output paths specified:

facebook_group_export --input your_facebook_data/group_activity.json --output posts.csv

You can also add flags to filter by date range or specific authors. The output is a clean CSV file of structured post data, ready for analysis or archival.

Get the Script

If you want to skip the build and get a ready-to-run solution, you can download the Social Media Group Posts Exporter today. It handles all the complexity behind the scenes and turns messy Facebook data into usable spreadsheets.

Download Social Media Group Posts Exporter →

$29 one-time. No subscription. Works on Windows, Mac, and Linux.

Built by OddShop — Python automation tools for developers and businesses.

How to Convert Photos to Excel Spreadsheets with Python

Oddshop — Thu, 02 Apr 2026 11:10:42 +0000

Photo to spreadsheet conversion is a time-consuming task often done manually, especially when field workers or researchers scan paper forms and then type data into Excel. The process is error-prone, repetitive, and slows down workflows. With tools like OCR Python and image to text Python, developers can automate parts of this process—but doing it right requires handling form layouts, checkboxes, and structured output.

The Manual Way (And Why It Breaks)

Manually entering data from paper forms is tedious and inefficient. Each form must be photographed, then the text is carefully transcribed by hand into an Excel sheet. Researchers who rely on field data often spend hours doing this, and even small mistakes can cascade into larger issues downstream. For someone doing form recognition Python, it's clear that the current workflow isn't scalable. The typical route involves not just copying text, but also identifying checkboxes, radio buttons, and labeled fields—tasks that are especially hard when dealing with multiple images.

The Python Approach

This Python script uses OCR and basic computer vision to extract structured form data from images and convert it into a spreadsheet. It’s a simplified version of what a full photo to spreadsheet tool might do, ideal for developers looking to build or understand the core logic.

import cv2
import pytesseract
import pandas as pd
from pathlib import Path

# Load image
image_path = "survey_photo.jpg"
image = cv2.imread(image_path)

# Preprocess image for better OCR
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
threshold = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]

# Extract text using Tesseract OCR
text_data = pytesseract.image_to_string(threshold)

# Parse text into structured fields
fields = {}
for line in text_data.splitlines():
    if ':' in line:
        key, value = line.split(':', 1)
        fields[key.strip()] = value.strip()

# Convert to Excel
output_path = "output_data.xlsx"
df = pd.DataFrame([fields])
df.to_excel(output_path, index=False)

print(f"Data saved to {output_path}")

This code uses OpenCV to preprocess a form image and Tesseract for OCR, extracting key-value pairs from labeled fields. While it works for simple layouts, it can't detect checkboxes or handle complex form structures like radio buttons or table-based input. It's a good foundation, but real-world form data usually requires more sophisticated image processing and structure mapping.

What the Full Tool Handles

Accurately extract text using Tesseract OCR in various fonts and orientations.
Detect checkboxes and radio buttons using image processing techniques.
Map extracted data to consistent Excel columns for easy reporting.
Process multiple form photos and merge them into a single spreadsheet.
Support for common form layouts including labeled fields and tabular data.
Photo to spreadsheet conversion without manual intervention.

Running It

To use the full tool, run this command in your terminal:

photo_to_excel --input-form-photo survey_photo.jpg --output-file data.xlsx

The tool accepts a single image or a directory of images, and outputs a consolidated Excel file. Flags like --input-form-photo and --output-file help define input and destination. Each form is processed individually and merged into one file for easy analysis.

Get the Script

If you want to skip building your own solution, the full photo to spreadsheet tool is ready for use. The tool handles all the complexity for you—OCR, checkbox detection, and structured output—so you can focus on your work.

Download Photo to Spreadsheet Form Converter →

$29 one-time. No subscription. Works on Windows, Mac, and Linux.

Built by OddShop — Python automation tools for developers and businesses.