DEV Community: Oddshop

How to Convert Excel to PDF with Python Automation

Oddshop — Sat, 06 Jun 2026 02:44:13 +0000

Excel to pdf converter tools are a common necessity when you need to share structured data from spreadsheets in a portable format. But manually exporting each sheet, adjusting layout, and handling formatting across multiple files can quickly become a time-consuming chore. If you’re doing this often, especially in a professional or automation setting, it’s worth exploring a more efficient, programmatic option.

The Manual Way (And Why It Breaks)

Manually converting Excel files to PDF involves several tedious steps. First, you open the Excel file, select the sheet you want, and go to File > Save As > PDF. Then, you must adjust the print area, set page orientation, and tweak margins to match your needs. If you're working with multiple sheets, you have to repeat this process for each one. And when dealing with dozens of files, it’s easy to overlook small formatting inconsistencies. This kind of manual effort is especially painful for teams relying on excel spreadsheet conversion workflows or those doing regular command line automation tasks. Even worse, Excel's PDF export doesn’t always preserve font styles and borders, especially when you're trying to generate clean reports.

The Python Approach

Using Python for this task gives you more control and allows you to automate repetitive processes. While you can’t do full formatting preservation in pure Python, libraries like openpyxl and reportlab let you build a basic pipeline to extract data and render it into a PDF. Here’s a realistic code snippet that shows how to process a single Excel sheet into PDF format using Python, with clear comments and variable names relevant to the domain.

import pandas as pd
from reportlab.lib.pagesizes import letter
from reportlab.platypus import SimpleDocTemplate, Table, TableStyle, Paragraph
from reportlab.lib.styles import getSampleStyleSheet
from reportlab.lib import colors

# Load the Excel file and select the first sheet
excel_file = "data.xlsx"
sheet_name = "Sheet1"
df = pd.read_excel(excel_file, sheet_name=sheet_name)

# Convert the DataFrame to a list of lists for the PDF table
data = [df.columns.tolist()] + df.values.tolist()

# Create a PDF document
pdf_path = "output.pdf"
doc = SimpleDocTemplate(pdf_path, pagesize=letter)
styles = getSampleStyleSheet()

# Add a title
title = Paragraph("Excel Report", styles['Title'])
story = [title]

# Create the table and style it
table = Table(data)
table.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'),
    ('FONTSIZE', (0, 0), (-1, 0), 14),
    ('BOTTOMPADDING', (0, 0), (-1, 0), 12),
    ('BACKGROUND', (0, 1), (-1, -1), colors.beige),
    ('GRID', (0, 0), (-1, -1), 1, colors.black)
]))

# Add the table to the document
story.append(table)

# Build the PDF
doc.build(story)

This approach works for basic data export but lacks support for complex formatting, borders, or header/footer customization. It’s a good starting point for developers looking to explore pdf generation python or excel file processing, but it can’t fully replace a dedicated excel to pdf converter tool.

What the Full Tool Handles

The Spreadsheet to PDF Converter addresses most of these pain points and more. It:

Converts both single and multiple Excel sheets into PDFs with full formatting preserved
Maintains borders, fonts, and text styles from the original spreadsheet
Allows users to set page orientation, margins, and scaling options via CLI
Supports custom text for headers and footers in generated PDFs
Enables batch processing of entire folders of Excel files
Integrates smoothly into automated workflows for python spreadsheet automation

Running It

To use the tool, you simply run a command from the terminal:

python excel2pdf.py input.xlsx output.pdf --orientation landscape

You can pass additional flags to control output settings like page size, scaling, and whether to include headers or footers. The script handles the entire process from Excel parsing to PDF creation, making it a great fit for developers who want to excel spreadsheet conversion without writing code. Output files are generated in the same directory by default, but you can modify paths or process multiple files in batch mode.

Get the Script

If you don't want to build the tool yourself, skip the setup and get the ready-to-use script. It’s already configured for excel to pdf converter workflows and works on Windows, Mac, and Linux.

Download Spreadsheet to PDF Converter →

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

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

How to automate realtor.ca data scraper with Python

Oddshop — Fri, 05 Jun 2026 02:38:41 +0000

The realtor.ca scraper tool solves a common issue in real estate data analysis: manually converting exported CSVs into structured formats that analysts can process. After downloading listings from Realtor.ca, many professionals spend hours reformatting data by hand or using clunky spreadsheets. That’s where a reliable realtor.ca scraper like this Python utility comes in — it automates the entire workflow.

The Manual Way (And Why It Breaks)

Manually parsing Realtor.ca CSV exports is tedious and error-prone. You have to open each file in Excel or Google Sheets, copy-paste into a new sheet, and manually clean up the data. With dozens or hundreds of listings, this process becomes unmanageable. Analysts often end up wasting hours on data entry instead of focusing on insights. This kind of manual real estate data extraction is not only inefficient but also prone to inconsistencies, especially when dealing with special characters, missing fields, or malformed rows.

The Python Approach

Here's a simplified version of how a realtor.ca scraper might begin to tackle this task. It uses pandas to load CSVs, processes each row, and outputs JSON. While manual steps are still required, automating data transformation can speed things up significantly. However, this basic script does not handle validation or batch operations — which is where a dedicated tool shines.

import pandas as pd
import json
from pathlib import Path

# Load the CSV file
csv_path = "listings.csv"
df = pd.read_csv(csv_path)

# Initialize list to hold processed data
properties = []

# Loop through rows
for _, row in df.iterrows():
    # Extract key fields
    listing = {
        "price": row.get("Price", None),
        "address": row.get("Address", None),
        "bedrooms": row.get("Bedrooms", None),
        "bathrooms": row.get("Bathrooms", None),
        "sqft": row.get("Square Footage", None),
        "url": row.get("Listing URL", None)
    }
    properties.append(listing)

# Output as JSON
print(json.dumps(properties, indent=2))

This code takes a CSV and turns it into basic JSON output. It handles some fields but lacks features like error logging, batch support, or validation. For production use, a full tool is necessary to manage large datasets reliably.

What the Full Tool Handles

Parses Realtor.ca CSV exports into clean, nested JSON objects
Extracts all fields including price, address, bedrooms, bathrooms, square footage, and listing URL
Supports batch processing of multiple CSV files in one command
Outputs to stdout, file, or pretty-printed JSON with optional indentation
Validates CSV structure and reports malformed rows with line numbers
Designed as a realtor.ca scraper that integrates into larger data pipelines

Running It

You can run the tool directly from the command line with a basic usage pattern:

python realtorscraper.py listings.csv --output properties.json

Use --output to specify where to save the JSON file. Optional flags control indentation and output destination. The script will automatically detect malformed rows and report them with line numbers.

Get the Script

If you'd prefer not to build your own realtor.ca scraper, skip the coding and get a ready-to-use solution. The full tool supports one-time purchase and works across platforms.

Download Realtor.ca Data Scraper →

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

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

How to Automate Ecommerce CSV Migration Script with Python

Oddshop — Thu, 04 Jun 2026 02:36:00 +0000

ecommerce csv migration is a common bottleneck for store owners switching platforms. Manual CSV transformation can take hours, especially when importing product data from Shopify to Magento. When you're dealing with hundreds or thousands of products, it’s easy to make mistakes that break the import process. Even small inconsistencies in SKU formatting or image URLs can cause errors. That’s where automation comes in.

The Manual Way (And Why It Breaks)

Manually mapping fields between Shopify and Magento is time-consuming and error-prone. You often have to open each row, copy and paste data into new columns, and ensure that image URLs are formatted correctly. If your categories are structured in Shopify using forward slashes (like /electronics/mobiles/iphones), you’ll need to convert that into Magento's category_ids format — a process that’s tedious at best. The same goes for price fields and description formatting. This kind of shopify to magento import task is ripe for automation, especially when you're handling csv data transformation manually.

The Python Approach

This snippet shows how a simple Python script can automate part of the ecommerce csv migration process by renaming columns and standardizing image URLs. It uses pandas for data processing and pathlib for file handling.

import pandas as pd
from pathlib import Path

# Load the Shopify CSV
shopify_csv = Path("shopify_products.csv")
df = pd.read_csv(shopify_csv)

# Rename columns to match Magento requirements
df.rename(columns={
    'Handle': 'sku',
    'Title': 'name',
    'Body HTML': 'description'
}, inplace=True)

# Format image URLs for Magento import
def format_image_urls(urls):
    # Example: https://cdn.shopify.com/image.jpg -> http://magento.com/media/catalog/product/image.jpg
    return urls.str.replace("https://cdn.shopify.com", "http://magento.com/media/catalog/product")

df['image'] = format_image_urls(df['Image Src'])

# Save as Magento-compatible CSV
magento_csv = Path("magento_products.csv")
df.to_csv(magento_csv, index=False)

This script handles basic field mapping and image URL reformats. It's a good starting point, but doesn’t include validation or advanced category handling. It also assumes your data structure is consistent — which isn’t always the case in real-world scenarios.

What the Full Tool Handles

This ecommerce csv migration tool goes beyond a basic script. It’s designed to reduce manual effort, even for complex imports.

Maps Shopify product fields like Handle, Title, and Body HTML to Magento’s sku, name, and description.
Transforms Shopify category paths into Magento’s category_ids format, simplifying category assignment.
Reformats image URLs from Shopify’s CDN to a format Magento accepts during import.
Supports custom field mapping via a JSON configuration file for more complex setups.
Validates the output CSV for errors like missing SKUs or invalid prices, preventing failed imports.
Handles large datasets efficiently, making it ideal for automated product import of entire catalogs.

Running It

You run the script via the command line with input and output file paths specified.

python migrate.py --input shopify_products.csv --output magento_products.csv

The --input and --output flags are required. The script will process the CSV and produce a new file with Magento-ready data.

Get the Script

If you're tired of spending hours on manual CSV manipulation, this ecommerce csv migration tool cuts that time down to minutes. It automatically handles the field mapping, category formatting, and image URL changes needed for a successful Magento import.

Download Ecommerce CSV Migration Script →

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

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

How to automate ecommerce product scraper & migrator with Python

Oddshop — Thu, 04 Jun 2026 02:35:50 +0000

The process of migrating product data from WooCommerce to another platform is often a tedious task that can quickly become a bottleneck for developers managing complex product catalogs. An ecommerce product scraper that can automate this workflow can save hours of manual effort and reduce the risk of errors. But before you start building your own tool, it's worth understanding what makes a good solution.

The Manual Way (And Why It Breaks)

Manually migrating product data across platforms is a time-consuming and error-prone process. You’re often copying data between spreadsheets, pasting product titles, descriptions, and SKUs one by one. For variable products with multiple variants and images, this becomes even more unwieldy. You'll find yourself spending hours aligning field formats, renaming columns, and ensuring no data is lost in translation — a classic case where manual handling leads to inefficiency.

In larger setups, this task becomes a major drag on productivity. Tools like Excel or Google Sheets can only take you so far when dealing with thousands of products. This is where a python csv parser becomes essential — it helps you automate repetitive operations without losing control over the output. A basic python scraper tool is a good start, but when it comes to full ecommerce data extraction and transformation, you need something more robust.

The Python Approach

Here’s a practical snippet that demonstrates how you might begin building such a tool in Python using pandas and csv. It reads a WooCommerce export, filters for products with variants, and begins mapping fields to a standard schema.

import pandas as pd
import csv
import os

# Load the input CSV file exported from WooCommerce
input_file = 'products.csv'
df = pd.read_csv(input_file)

# Filter only products with variations (e.g., variable products)
variable_products = df[df['Type'] == 'variable']

# Define a mapping of WooCommerce fields to Shopify fields
field_mapping = {
    'Name': 'title',
    'Description': 'body_html',
    'SKU': 'sku',
    'Price': 'price',
    'Image Src': 'image'
}

# Create a new DataFrame with mapped fields
mapped_df = variable_products.rename(columns=field_mapping)

# Save transformed data to a new file
output_file = 'shopify_products.csv'
mapped_df.to_csv(output_file, index=False)

print(f"Transformed data saved to {output_file}")

This code demonstrates the core concept: reading product data, selecting relevant rows, mapping fields, and saving the result. However, it lacks support for advanced features like image handling, variant management, or bulk logging — which are essential in real-world migrations. A full solution like the ecommerce product scraper needs to be more structured and resilient than a basic prototype.

What the Full Tool Handles

Parses WooCommerce exports in both CSV and JSON formats, including variable products with attributes and associated images.
Allows custom field mapping through a JSON configuration file to align with target platforms like Shopify or BigCommerce.
Generates clean, ready-to-import output in either CSV or JSON, depending on platform requirements.
Processes large datasets with progress indicators and error logging to ensure nothing is lost.
Validates product data integrity and reports missing or malformed fields before migration starts.
Handles bulk operations efficiently without requiring manual input or interruption.

Using this tool simplifies the entire process of ecommerce data extraction and ensures accurate transformations, even when dealing with complex product catalogs. It's particularly useful for developers managing WooCommerce data migration projects, especially those involving product catalog automation.

Running It

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

python wc_migrate.py --input products.csv --mapping map.json --output shopify_products.csv

The tool accepts three main flags: --input for the source file, --mapping for your field mapping config, and --output for the destination file. The output format is automatically inferred from the extension you provide.

Get the Script

If you're not building it from scratch, you can skip the development phase entirely and get a ready-to-use solution. Download Ecommerce Product Scraper & Migrator →

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

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

How to Automate Data Extraction with Python Scripts

Oddshop — Tue, 02 Jun 2026 02:25:40 +0000

python script automation doesn’t have to be tedious. When you’re dealing with data extraction from files and sending it to external systems, the manual approach becomes slow, error-prone, and repetitive. It’s a common pain point in data workflows—especially when you’re doing ETL automation or API integration for batch processing. A few clicks to manually import a CSV, map fields, and post to an endpoint can take hours when done at scale. That’s where the right python script automation tool can save the day.

The Manual Way (And Why It Breaks)

Doing data extraction and input manually is a time sink. You often start with CSV or Excel files, then open them in tools like Excel or Google Sheets to inspect data. Then you must manually map fields and manually upload or post information to an external API. The process is error-prone, especially when you're repeating the same steps for hundreds of records. Each manual cycle increases the chance of data misalignment or human error. This kind of work also doesn’t scale, especially in ETL automation or when integrating with REST APIs.

The Python Approach

A simple python script can automate this, but writing it from scratch takes time. Here's a snippet that handles basic data extraction and API posting using pandas and requests.

import pandas as pd
import requests
from pathlib import Path

# Load data from CSV
file_path = Path("data.csv")
df = pd.read_csv(file_path)

# Define API endpoint
api_url = "https://api.example.com/items"

# Loop through rows and send data
for _, row in df.iterrows():
    payload = {
        "name": row["name"],
        "email": row["email"],
        "value": row["value"]
    }
    response = requests.post(api_url, json=payload)
    print(f"Sent {row['name']}, status: {response.status_code}")

This snippet shows how to load a CSV, iterate over rows, and send data to an API using Python. It works, but it’s fragile—no error handling, no support for different input formats, and no configuration options. It’s a basic automation, not a full python script automation solution. For production use, you'd need to add batch logic, retries, and schema validation.

What the Full Tool Handles

This tool is built for real-world data workflows. It handles:

Multi-format input — Read CSV, JSON, or Excel with automatic schema detection.
Configurable output — POST to REST APIs or insert into SQL databases using connection strings.
Template-based mapping — Define field mappings and transformations in a YAML config file.
Dry-run mode — Preview all operations without executing, with detailed logs.
Batching & rate limiting — Control concurrency and delays to avoid API throttling.
Python script automation — Reduce boilerplate and manage complex mappings with a single command.

Running It

You can run the tool with a simple CLI command:

python extract_input.py --input data.csv --config mapping.yaml --output api --endpoint https://api.example.com/items

The --input flag specifies the source file, --config defines the mapping logic, --output indicates the destination (either api or db), and --endpoint sets the API URL. The tool logs each action in real-time and provides clear feedback on the operations it performs.

Get the Script

Skip the build and get a ready-to-use solution. Download Automated Data Extraction & Input Scripts →

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

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

How to Automate Indeed Job Applications with Python

Oddshop — Tue, 02 Jun 2026 02:25:31 +0000

indeed automation python tools help reduce the tedium of applying to dozens of jobs manually, but the process still breaks down when you're clicking through forms one by one. Every time you copy-paste your email, phone number, and resume, you’re risking errors and wasting time that could be spent tailoring your approach. A better way exists — one that uses Python to automate the submission process using simple inputs like CSV data.

The Manual Way (And Why It Breaks)

Applying to jobs on Indeed manually is tedious and error-prone. You must open each job link, fill in your name, email, phone, and upload your resume — often multiple times for the same information. If you're applying to 50 jobs, you're looking at hours of repetitive work. Each form might vary slightly, and some require typing your resume text into a field instead of uploading it. This kind of job search automation through manual input can quickly become a bottleneck in your job hunt. Even experienced developers can fall into the trap of indeed scraper patterns that are just too slow for large volume.

The Python Approach

This Python snippet automates the form-filling part of job submission using pandas and selenium. It reads job URLs and personal details from a CSV, then navigates the pages to auto-fill and submit applications. While it doesn’t handle CAPTCHAs or complex form validation, it greatly speeds up the process for simple public forms. The tool is designed for developers or job seekers who want to batch-apply using a simple data structure.

import pandas as pd
from selenium import webdriver
from selenium.webdriver.common.by import By
import time

# Load job data from CSV
df = pd.read_csv("jobs.csv")

# Initialize the browser in headless mode
driver = webdriver.Chrome(options=webdriver.ChromeOptions().add_argument("--headless"))

# Loop through each job
for index, row in df.iterrows():
    job_url = row['job_url']
    email = row['email']
    phone = row['phone']
    name = row['name']

    driver.get(job_url)
    time.sleep(2)  # Wait for page to load

    # Fill in form fields
    driver.find_element(By.NAME, "email").send_keys(email)
    driver.find_element(By.NAME, "phone").send_keys(phone)
    driver.find_element(By.NAME, "name").send_keys(name)

    # Submit the form
    submit_button = driver.find_element(By.XPATH, '//input[@type="submit"]')
    submit_button.click()

    time.sleep(1)  # Wait for submission

driver.quit()

This code does not handle uploaded files or complex form layouts, but it works well for straightforward job applications. It demonstrates how python job automation can simplify repetitive tasks like form filling.

What the Full Tool Handles

CSV input — read job URLs and personal details from a CSV file
Auto-fill forms — populate name, email, phone, and resume fields
Submit applications — click submit button after filling
Headless mode — run without browser UI for background automation
Error logging — save failed applications to a separate CSV for review
indeed automation python — designed specifically for public job application forms

Running It

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

python indeed_apply.py --input jobs.csv --email myemail@example.com --resume resume.pdf

The script accepts three flags: --input for the job list, --email for your email address, and --resume for the path to your resume file. It saves a log of failed applications to failed_applications.csv for review.

Get the Script

Skip building from scratch and get a ready-to-run script designed for indeed automation python.

Download Indeed Application Automation Script →

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

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

How to Automate Outreach Workflows with Python

Oddshop — Mon, 01 Jun 2026 02:21:58 +0000

A python automation tool like this can save hours of repetitive work when building outreach workflows, especially for sales teams and marketers who rely on Make.com for automation. Manually configuring each contact, setting up email sequences, and scheduling follow-ups in Make.com is time-consuming and error-prone. The process becomes even more tedious when handling hundreds of contacts, making a python automation tool an essential productivity helper.

The Manual Way (And Why It Breaks)

Building outreach workflows manually in Make.com involves creating individual scenarios for each contact, setting up email steps with personalized merge tags, and scheduling delays between messages. For every lead, you'd need to add a new scenario, define a webhook trigger, and configure a follow-up sequence. With a typical CSV of 200 contacts, this process can take several hours — if not days. This repetitive task is a common pain point for marketers and sales automation specialists. The manual method also raises the risk of inconsistencies, especially when handling custom fields or varying follow-up intervals. A python automation tool helps avoid this by automating the entire scenario structure, reducing setup time from hours to minutes.

The Python Approach

Here’s a simplified version of what a python automation tool could do to process a CSV and generate JSON for Make.com:

import pandas as pd
from pathlib import Path
import json

# Load CSV with contact info
contacts_df = pd.read_csv("contacts.csv")

# Define template email steps with merge tags
email_template = {
    "step_1": "Hi {name}, I'm reaching out about {company}.",
    "step_2": "I noticed {company} recently launched {product}.",
    "step_3": "Would you be open to a quick 15-minute call?"
}

# Build JSON scenario for each contact
scenarios = []
for _, row in contacts_df.iterrows():
    scenario = {
        "contact": {
            "name": row["name"],
            "email": row["email"],
            "company": row["company"]
        },
        "sequence": [
            {"delay": 2, "email": email_template["step_1"].format(**row)},
            {"delay": 5, "email": email_template["step_2"].format(**row)},
            {"delay": 3, "email": email_template["step_3"].format(**row)}
        ]
    }
    scenarios.append(scenario)

# Save each scenario to a JSON file
output_dir = Path("scenarios")
output_dir.mkdir(exist_ok=True)
for i, scenario in enumerate(scenarios):
    with open(output_dir / f"contact_{i}.json", "w") as f:
        json.dump(scenario, f, indent=2)

This python script reads a CSV, builds a structured JSON email sequence for each contact, and exports it to a folder. It’s a basic version of how a python automation tool might work — handling personalization, scheduling, and file generation. Its limitations include hardcoded delays and a fixed email template structure.

What the Full Tool Handles

CSV input parsing with support for custom fields and contact details
Personalized email sequences using merge tags and dynamic content
Exportable JSON configurations designed for Make.com webhook triggers
Configurable time delays and follow-up intervals between steps
Validation features and dry-run mode to preview sequences before export
A python automation tool that simplifies complex automation setup for marketers and sales teams

Running It

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

python outreach_builder.py contacts.csv --template outreach_template.json --output scenarios/

This command uses contacts.csv as input, applies a base email template, and exports JSON files into the scenarios/ directory. Flags like --template and --output define where the tool looks for the email layout and where it saves the results.

Get the Script

If you'd rather not build this yourself, skip the development and get the full python automation tool. It handles everything from CSV parsing to JSON export — all in one easy-to-use package.

Download Automated Outreach Workflow Builder →

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

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

How to Automate SolidWorks Drawings with Python Scripts

Oddshop — Mon, 01 Jun 2026 02:21:49 +0000

solidworks python automation saves engineers from the tedium of updating title blocks and parameters across dozens of drawing files—especially when those updates involve hundreds of lines. Manual processes are time-consuming, error-prone, and often require switching between tools. For CAD admins managing large libraries of drawings, the repetitive workflow of opening each file, modifying fields, and exporting formats is a bottleneck that eats productivity.

The Manual Way (And Why It Breaks)

Editing SolidWorks drawings manually is a slow and inconsistent process. Engineers must open each .SLDDRW file, navigate to the title block, locate fields like part number or revision, and manually input or replace data. This is especially tedious when working with hundreds of files. The process is also prone to human error—typos or missed updates can lead to inconsistencies in documentation. Even when using solidworks api calls or solidworks macro programming, manually stepping through each file limits scalability. Without proper logging, tracking changes becomes nearly impossible, especially in collaborative or audit-driven environments.

The Python Approach

The script below demonstrates a simplified version of how solidworks python automation can read drawing parameters from CSV and batch update them in SolidWorks. It uses libraries like pandas and pathlib to load and process data, and csv for writing logs. While it doesn’t cover full SolidWorks integration (since that requires Windows COM), it shows how this can be layered into a larger automation pipeline.

import pandas as pd
from pathlib import Path
import csv
import sys

# Load drawing parameter updates from CSV
input_file = sys.argv[1]
df = pd.read_csv(input_file)

# Define output log file
log_file = "update_log.csv"

# Iterate over each drawing file and apply updates
for index, row in df.iterrows():
    drawing_path = row['drawing_path']
    part_number = row['part_number']
    revision = row['revision']

    # Simulate applying fields to a drawing
    print(f"Updating {drawing_path} with part number: {part_number}, revision: {revision}")

# Log changes to CSV
with open(log_file, 'w', newline='') as csvfile:
    fieldnames = ['drawing_path', 'part_number', 'revision', 'status']
    writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
    writer.writeheader()
    for index, row in df.iterrows():
        writer.writerow({
            'drawing_path': row['drawing_path'],
            'part_number': row['part_number'],
            'revision': row['revision'],
            'status': 'updated'
        })

This code block loads drawing updates from a CSV, simulates updating fields, and logs the process. It’s a basic example showing how data-driven automation can begin to streamline workflows. However, real-world use cases require SolidWorks API integration to actually modify files, which is why full tools like this one exist.

What the Full Tool Handles

Batch update title block fields like part number, revision, and date from CSV or JSON input
Export drawings to PDF, DXF, or image formats with configurable resolution
Replace or insert custom properties like material or weight into drawing metadata
Log all changes and errors to a timestamped CSV report for traceability
Support dry-run mode to preview changes without modifying files
Works with solidworks api over COM, enabling full automation on Windows while supporting cross-platform use via remote execution

Running It

The full tool supports a command-line interface with flexible options:

python solidworks_auto.py --input updates.csv --drawings *.SLDDRW --export pdf --dry-run

This command reads drawing parameters from updates.csv, applies them to all .SLDDRW files in the directory, exports them to PDF, and runs in dry-run mode to prevent actual file changes. The --export flag supports multiple formats, and the --dry-run option is critical for validation before pushing changes.

Get the Script

Skip the build and get a ready-to-use solution for solidworks python automation. This tool handles repetitive tasks while integrating cleanly with your existing CAD workflow.

Download SolidWorks Drawing Automation Script →

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

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

How to Create Interactive Python Exercises with CLI Tools

Oddshop — Sun, 31 May 2026 02:13:37 +0000

The Python exercise tool I’m about to describe solves a real pain point for educators and learners: manually crafting coding drills is tedious and error-prone. Creating consistent, varied questions for Python practice often means copying and pasting code, writing explanations, and trying to generate unique answer choices. If you're into python automation or building learning materials, you know how easy it is to get lost in this process. The solution? Automate it.

The Manual Way (And Why It Breaks)

Creating Python exercises by hand is a slow, repetitive process. First, you write a code snippet for a specific concept. Then, you come up with a question — often a fill-in-the-blank or output prediction — and carefully construct answer choices. For multiple-choice questions, you must ensure only one is correct and the others are plausible but wrong. It's easy to make mistakes or forget to randomize order for fairness. If you're using a python learning tool or a cli python script for drills, you might find yourself manually editing the same files over and over. The lack of structure and automation makes it frustrating to scale, especially when you're trying to create varied exercises for a class.

The Python Approach

Here’s a minimal Python script that demonstrates how you can begin automating part of this process. It reads a CSV file with Python topics, explanations, and code snippets, then generates a basic fill-in-the-blank question. This snippet lays the groundwork for a more complete python exercise tool, but it lacks features like randomization, answer validation, and multiple question types.

import csv
import random

# Read input CSV
with open('exercises.csv', 'r') as file:
    reader = csv.DictReader(file)
    exercises = list(reader)

# Pick a random exercise
exercise = random.choice(exercises)

# Display question and explanation
print(f"Topic: {exercise['topic']}")
print(f"Explanation: {exercise['explanation']}")
print(f"Code Snippet:\n{exercise['code']}")

# Ask user to fill in the blank (simplified)
print("\nWhat should be the output of this code?")
answer = input("Your answer: ")

# Compare with expected output
expected = "42"  # This would be dynamically generated in a real tool
if answer.strip() == expected:
    print("Correct!")
else:
    print(f"Wrong. Expected: {expected}")

This simple code block pulls one random exercise from a CSV file and asks the user for an answer. It’s far from complete, but it shows how a basic python exercise tool might start to work. Limitations include hardcoded output, no randomized answer choices, and no tracking of progress or multiple question types.

What the Full Tool Handles

The full python exercise tool handles the following:

CSV input support — You enter topics, explanations, and code snippets in a CSV, and the tool parses it directly.
Multiple question types — It generates fill-in-the-blank, multiple choice, and code output prediction questions.
Randomized order — Questions and answers are shuffled each time to avoid memorization.
Score tracking — Displays correct/incorrect counts and overall percentage at the end.
Difficulty filtering — You can add a difficulty column to your CSV and filter exercises by level.
No scripting required — No need to write any code to generate new exercises.

This type of python learning tool is ideal for educators looking to create a consistent set of drills, or self-learners who want to practice specific Python concepts using a cli python script.

Running It

To run the tool, you simply use the command line with the input CSV file and exercise type you want:

python edu_cli.py --input exercises.csv --type fill_blank

You can change --type to multiple_choice or output_prediction to generate different kinds of exercises. The tool will display the questions, prompt you for answers, and finally show your score.

Get the Script

If you’re tired of building this yourself, skip the build and grab the full python exercise tool. It’s designed to save you time and effort, especially when you’re working with a python automation workflow.

Download Python Educational CLI Tool →

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

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

How to Automate Bulk Product Imports with Python

Oddshop — Wed, 27 May 2026 02:07:18 +0000

Bulk product import is a critical step in scaling an online store, but doing it manually for hundreds or thousands of items is error-prone and time-consuming. Whether you're migrating from another platform or launching a new catalog, the process of entering product data one by one can derail your workflow and introduce inconsistencies.

The Manual Way (And Why It Breaks)

Manually entering product data into Shopify or any e-commerce platform is a tedious and fragile process. You'd typically copy and paste from a spreadsheet, filling out fields like title, price, SKU, and description for each item. With large catalogs, this quickly becomes a nightmare—especially when you need to handle variants or batch update inventory. The lack of automation increases the chance of typos, duplicate SKUs, or missing required fields. Even small errors can cause failed uploads or inconsistent product listings. For developers and store owners looking to manage bulk product import efficiently, the manual workflow doesn’t scale. The same challenges apply when using tools like "csv to shopify import" or "automated product catalog" solutions that lack flexibility or proper validation.

The Python Approach

A Python script can simplify bulk product import by automating the mapping, validation, and output generation. Here’s a sample snippet that handles CSV input, maps columns, and prepares a Shopify-ready file:

import pandas as pd
import requests
from pathlib import Path

# Load CSV file into a DataFrame
csv_file = "products.csv"
df = pd.read_csv(csv_file)

# Define mapping from CSV headers to Shopify fields
column_mapping = {
    "title": "Title",
    "price": "Price",
    "sku": "SKU"
}

# Map and rename columns
df.rename(columns=column_mapping, inplace=True)

# Validate required fields
required_fields = ["title", "price", "sku"]
missing_fields = [field for field in required_fields if field not in df.columns]
if missing_fields:
    raise ValueError(f"Missing required fields: {missing_fields}")

# Convert price to numeric and validate
df["price"] = pd.to_numeric(df["price"], errors='coerce')
df.dropna(subset=["price"], inplace=True)

# Save output as CSV
output_file = "shopify_import.csv"
df.to_csv(output_file, index=False)
print(f"Product import file created: {output_file}")

This code reads a CSV, renames columns to match Shopify’s expected format, validates that essential fields like price and SKU are present, and exports a clean file. It supports basic validation but lacks advanced features like variant creation or image handling. For full functionality, tools that integrate with "shopify bulk upload" and "batch product management" are needed.

What the Full Tool Handles

The full bulk product import tool handles a range of complex tasks that go beyond simple CSV processing:

Maps your custom CSV headers to Shopify fields, WooCommerce, or BigCommerce using a flexible flag-based system.
Supports up to 100 product variants per item by treating each row as a distinct variant in the import.
Downloads and rehosts images from URLs or embeds image links directly for Shopify import.
Validates data for missing fields, duplicate SKUs, and invalid prices to prevent failed uploads.
Generates output in either Shopify-compatible CSV or JSONL format, ready for direct upload via API or admin panel.
Handles bulk product import in a way that's both scalable and error-resistant.

Running It

The tool works from the command line and requires minimal setup. Here's how to run it:

python bulk_import.py products.csv --output shopify_import.csv --map title:Title,price:Price,sku:SKU

The --map flag lets you define which CSV columns correspond to Shopify fields. You can also specify output format and other flags to control behavior. The tool will generate a clean, validated import file that’s ready to upload.

Get the Script

If you're tired of building this yourself, skip the development and get a working solution. This Python CLI tool streamlines "csv to shopify import" workflows and saves hours of manual labor.

Download Bulk Product Import Tool →

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

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

How to Fix Date Import Issues with Python

Oddshop — Sat, 23 May 2026 02:00:41 +0000

python spreadsheet automation often begins with a simple task: importing dates from Excel into a database or pipeline. But what starts as a quick data import quickly turns into a headache when date formats vary wildly across rows — some are MM/DD/YYYY, others DD/MM/YYYY, and some even include timezones or mixed formats. These inconsistencies break data pipelines, cause downstream errors, and waste hours of manual data cleaning. When you're working on python spreadsheet automation projects, this is one of those moments where you wish there was a faster, safer way.

The Manual Way (And Why It Breaks)

Manually fixing date issues in spreadsheets means going row by row, selecting each cell, and carefully checking its format. You might use Excel’s "Text to Columns" feature, or apply a custom date format to all cells, then re-save the file. If you're handling multiple sheets or large datasets, this becomes tedious and error-prone. You risk missing ambiguous dates or misinterpreting timezone-aware timestamps — the kind of issue that breaks your data pipeline automation when it’s already too late. For data engineers or analysts doing python spreadsheet automation, this method is not just slow, it’s fragile and inconsistent.

The Python Approach

Here’s a simple script that cleans up a basic Excel date column using pandas. It handles a few common formats and provides a fallback if parsing fails.

import pandas as pd
from dateutil import parser
from datetime import datetime
import sys

# Load the Excel file
input_file = sys.argv[1]
sheet_name = sys.argv[2] if len(sys.argv) > 2 else 0
column_names = sys.argv[3].split() if len(sys.argv) > 3 else ['Date']

# Read Excel sheet
df = pd.read_excel(input_file, sheet_name=sheet_name)

# Try to parse dates
for col in column_names:
    if col in df.columns:
        df[col] = df[col].apply(lambda x: parser.parse(str(x)) if pd.notna(x) else x)

# Save cleaned data
output_file = f"cleaned_{input_file}"
df.to_excel(output_file, index=False)

This code reads an Excel file and attempts to parse any date columns into a consistent datetime format. It uses dateutil.parser to detect and convert various formats, which is better than a fixed format. However, it lacks support for timezone handling, fallback logic for ambiguous formats, and doesn’t offer output flexibility like CSV or dry-run mode — all of which are crucial for real-world data pipeline automation.

What the Full Tool Handles

The Spreadsheet Date Import Fixer goes beyond basic parsing to handle real-world edge cases:

Auto-detects and parses multiple date formats including 22/05/2026, 05/22/2026, and 2026-05-22
Parses timezone-aware timestamps like 13:26 EDT or 13:26 UTC-4
Exports to either CSV or a new Excel file with consistent datetime columns
Offers a configurable fallback for ambiguous dates (e.g., DD/MM vs MM/DD)
Includes a dry-run mode to preview changes before committing output
Designed for python spreadsheet automation workflows, not just simple scripts

Running It

Here’s how to use the tool from the command line:

python fix_dates.py input.xlsx --sheet Sheet1 --columns 'Date' 'Timestamp' --output clean.xlsx

This runs the script on input.xlsx, focusing on the Sheet1 sheet, and processes the Date and Timestamp columns. The --output flag specifies the name of the output file, which can be either .xlsx or .csv. Other optional flags include --dry-run for previewing changes and --fallback for specifying how to handle ambiguous dates.

Get the Script

Skip the build and get a working solution that handles all the edge cases for you. The Spreadsheet Date Import Fixer is designed to save time and reduce errors in python spreadsheet automation tasks.

Download Spreadsheet Date Import Fixer →

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

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

How to Extract Group Contacts from CSV with Python

Oddshop — Fri, 22 May 2026 01:57:00 +0000

The python group contact extractor solves a real pain point in data automation: manually sifting through CSV exports of LinkedIn group members or Facebook group data to extract contact information. When you're working with hundreds or thousands of group members, the repetitive task of copying and pasting becomes not just time-consuming, but error-prone. This is where a python group contact extractor tool like this one shines — it automates what would otherwise be a frustrating, manual effort.

The Manual Way (And Why It Breaks)

Manually extracting contact information from LinkedIn or Facebook group exports is tedious and inefficient. You start by downloading a CSV file of group members, then spend hours opening spreadsheets, copying data row by row, and organizing it into a clean list. There's no deduplication, no filtering by location or headline, and no way to ensure the data stays consistent across multiple exports. You may be using the same tool for a few weeks, but as your group grows, so does the burden of manual entry.

The Python Approach

This script demonstrates how a simple python group contact extractor can automate the basic parsing of group CSVs. It uses pandas to read the data and extracts key fields like name and profile URL, then exports the result as a CSV.

import pandas as pd
import sys

# Load the input CSV file
input_file = sys.argv[1] if len(sys.argv) > 1 else 'group_members.csv'
df = pd.read_csv(input_file)

# Select relevant columns for contact info
contacts = df[['Name', 'Profile URL', 'Headline', 'Location']]

# Remove rows with missing names or URLs
contacts = contacts.dropna(subset=['Name', 'Profile URL'])

# Export to CSV
output_file = 'contacts.csv'
contacts.to_csv(output_file, index=False)

print(f"Exported {len(contacts)} contacts to {output_file}")

This snippet reads a group CSV and extracts a small subset of fields, but it’s limited in scope. It doesn’t handle deduplication, filtering, or multiple input formats — that’s where a full python group contact extractor tool becomes useful.

What the Full Tool Handles

Parses both LinkedIn and Facebook group member exports with consistent field mapping
Automatically deduplicates contacts using fuzzy matching across name fields
Supports filtering by keywords in headline or location fields
Exports to CSV, JSON, or Excel with customizable column selections
Handles multiple input files in one run
Works offline and fully customizable for marketing or recruiting needs

This is exactly the kind of automation that makes a python group contact extractor a valuable addition to any marketer’s toolkit, especially when working with LinkedIn group members or Facebook group data in bulk.

Running It

You can run the tool from the command line with the following syntax:

python group_contacts.py --input linkedin_group.csv --output contacts.xlsx --filter 'location:San Francisco'

This command takes a CSV input file, applies a filter to only include contacts from San Francisco, and exports the results to Excel. You can use various flags to specify the input file, output format, and filtering options.

Get the Script

Skip the build — this tool is ready to go. Download the full python group contact extractor package and start automating your CSV processing today.

Download Group Contact CSV Extractor →

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

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