DEV Community: Oddshop

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.

How to Automate Data Processing with Python Scripts

Oddshop — Wed, 20 May 2026 01:52:58 +0000

python data automation doesn’t have to mean writing custom scripts every time you need to clean or restructure a dataset. The process often involves repetitive steps that eat up time and introduce human error. Whether it's filtering rows or transforming columns, manually handling datasets in Excel or even a basic text editor becomes tedious when dealing with real-world data. Analysts and developers who rely on data cleaning automation tools know how much time can be saved when you automate the mundane tasks. The same applies to data pipeline automation — you want a way to reliably process data without reinventing wheels each time.

The Manual Way (And Why It Breaks)

Manual data processing is slow and error-prone. If you're working with a CSV file, you might need to open it in Excel, filter rows by condition, then manually apply transformations like uppercase or date formatting. Sometimes, you’ll have to copy and paste data across sheets or even restructure entire tables. For anything beyond basic filtering, this becomes a nightmare. You’ll find yourself writing the same logic over and over again. This is where data wrangling automation comes in, but in practice, doing it manually leads to inconsistent results and wasted effort.

The Python Approach

A Python script can automate data processing tasks quickly and reliably. For example, here's a small tool using pandas and pathlib to load and process CSV data with basic filtering and transformations:

import pandas as pd
from pathlib import Path

# Load input file
input_path = Path("data.csv")
df = pd.read_csv(input_path)

# Filter rows where 'status' column equals 'active'
df_filtered = df[df['status'] == 'active']

# Transform 'name' column to uppercase
df_filtered['name'] = df_filtered['name'].str.upper()

# Save to output file
output_path = Path("cleaned.csv")
df_filtered.to_csv(output_path, index=False)

This small Python script filters a dataset by a column value and transforms one column to uppercase. While it works for basic cases, it lacks configurability and can’t easily handle more complex rules like regex matching or date formatting without extra code. It also doesn’t support JSON input or dry-run previews — features that make python data automation more practical for real-world use.

What the Full Tool Handles

The Data Scraper Automation Script handles a variety of common data processing tasks that you'd typically do manually or with a more involved Python script. Here’s what it includes:

Configurable extraction rules via YAML config file
Supports CSV and JSON input/output formats
Filter rows by column value conditions (equals, contains, regex)
Transform columns with built-in functions (uppercase, date format, math)
Dry-run mode to preview changes before writing output
Python data automation in a single, reusable command line tool

Running It

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

python scrape.py --input data.csv --config rules.yaml --output cleaned.csv

The --input flag sets the source file, --config points to your YAML rule file, and --output defines where to save the cleaned results. The tool accepts both CSV and JSON formats, so you can use it across different data sources.

Get the Script

If you're tired of building the same data cleaning logic every time, skip the build and get the ready-to-use tool. It's designed to streamline common data wrangling automation tasks without the need for custom coding.

Download Data Scraper 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 Read Google Spreadsheets with Python CLI Tool

Oddshop — Fri, 15 May 2026 01:48:10 +0000

A python spreadsheet reader like this one can save hours of manual work, especially when you're dealing with live Google Sheets. Imagine copy-pasting data from a dashboard into an Excel sheet, or scraping data from multiple tabs manually. That's where a python spreadsheet reader comes in handy — but if you build it yourself, it quickly becomes a messy, error-prone task. The process is slow, and you’re constantly fighting with Google Sheets API complexities.

The Manual Way (And Why It Breaks)

Manually pulling data from Google Sheets is tedious and prone to errors. You start by opening the sheet in a browser, copying ranges one by one, and pasting into Excel or another format. If you're working with multiple sheets, it gets even harder. You might need to set up OAuth credentials, which is overkill if you just want to read public data. The manual approach also breaks down quickly when data updates frequently — your script or process has to be re-run each time. This kind of spreadsheet automation often leaves developers frustrated, especially when they’re trying to do python data extraction or work with google spreadsheet api without dealing with authentication complexities.

The Python Approach

Here’s a basic approach to reading from Google Sheets using Python, which shows how one might tackle the problem manually. This uses the requests library to fetch data from the Google Sheets API, and csv to write it out.

import requests
import csv

# Google Sheets API endpoint
sheet_id = "abc123"
api_key = "YOUR_API_KEY"
url = f"https://sheets.googleapis.com/v4/spreadsheets/{sheet_id}/values/Sheet1!A1:D100?key={api_key}"

# Fetch data from the sheet
response = requests.get(url)
data = response.json()

# Write to CSV file
with open('output.csv', 'w', newline='') as file:
    writer = csv.writer(file)
    for row in data['values']:
        writer.writerow(row)

This snippet does basic python data extraction from a range in a Google Sheet. However, it lacks pagination support, doesn’t handle multiple sheets, and only writes to CSV. If your data is large or spans multiple tabs, you’ll need to expand the logic significantly. It’s a great starting point but not a full solution for production use. A python spreadsheet reader should be more robust, handling edge cases automatically.

What the Full Tool Handles

The full Spreadsheet Web Scraper simplifies this workflow and handles several key aspects:

Fetches entire sheets or specific ranges with A1 notation
Outputs data to CSV, JSON, or Excel formats
Supports multiple sheets within a single spreadsheet
Automatically handles pagination for large datasets
Allows configuration via CLI or config file
Designed for developers who want to avoid browser automation or OAuth

This python spreadsheet reader tool is ideal for anyone who needs a clean way to integrate Google Sheet data into their scripts or workflows without jumping through API hoops.

Running It

To run the tool, you’ll pass a few flags at the command line:

python gsheets_scraper.py --sheet-id 'abc123' --api-key 'YOUR_KEY' --range 'Sheet1!A1:D100' --output data.csv

You can specify which sheet and range to fetch, along with the desired output file. The tool supports various formats, so you can easily switch between CSV, JSON, or Excel depending on your needs.

Get the Script

If you don’t want to build this yourself, skip the setup and grab the complete tool. It’s designed for developers who need quick, reliable access to Google Sheets data.

Download Spreadsheet Web 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 Jelastic billing export processor with Python

Oddshop — Tue, 12 May 2026 01:44:00 +0000

jelastic billing automation is a necessary but often tedious process when managing multiple environments across platforms like Jelastic. Manually sifting through billing exports to extract meaningful cost insights can eat up hours and introduce errors. It’s especially painful for DevOps teams who need structured data for budgeting or resource planning — and this is where Python-based jelastic billing automation tools come in handy.

The Manual Way (And Why It Breaks)

Manually analyzing Jelastic billing data involves downloading CSV files, opening them in spreadsheets, and manually filtering rows by date, environment, or service type. You might have to copy-paste values across multiple sheets or pivot tables to get monthly summaries. The process is error-prone, time-consuming, and doesn’t scale. Even basic tasks like comparing costs across projects or nodes can become a nightmare without automation. This is where python csv processing tools shine — they eliminate guesswork and reduce friction in jelastic usage analytics.

The Python Approach

This snippet shows how to parse a Jelastic billing CSV and extract structured cost data in Python, forming the foundation of jelastic billing automation.

import csv
from collections import defaultdict

# Load the CSV file
billing_file = 'billing_export.csv'
costs_by_env = defaultdict(float)

# Read and process rows
with open(billing_file, 'r') as file:
    reader = csv.DictReader(file)
    for row in reader:
        # Extract environment and cost
        env_name = row['Environment']
        cost = float(row['Cost'])
        # Aggregate cost by environment
        costs_by_env[env_name] += cost

# Display results
for env, total in costs_by_env.items():
    print(f"{env}: ${total:.2f}")

This script reads a billing CSV, aggregates costs by environment, and prints a clean summary. It’s limited to basic reporting, but it illustrates how devops automation tools can transform raw data into actionable insights. For more advanced use cases, like filtering by date or exporting in JSON, you’d want to expand this further.

What the Full Tool Handles

Parse Jelastic billing CSV exports into structured Python dictionaries
Generate monthly cost summaries by environment, node, or account
Filter exports by date range, project, or resource type
Export results to JSON, CSV, or formatted console output
Support for multiple export files with automatic merging and deduplication
Fully integrated jelastic billing automation with minimal setup

Running It

Here’s how to run the full tool from the command line:

python jelastic_billing.py --input billing_export.csv --summary --output report.json

Use the --input flag to specify your CSV file, --summary to enable cost aggregation, and --output to define where the result should be written. You can also chain filters like --date-from and --date-to for precise reporting.

Get the Script

If you're tired of building this from scratch, skip the development step and get a ready-made solution.

Download Jelastic Billing Export Processor →

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

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

How to Fix Python Script Sync Delays on Linux and Mac

Oddshop — Mon, 11 May 2026 01:40:59 +0000

python script sync issues can crop up in automation projects and cause frustrating delays that are hard to track down. When scripts depend on time.sleep() or other timing-based triggers, especially in cross-platform environments like Linux and Mac, delays can compound and slow execution by as much as 40%. Developers often end up manually tweaking intervals, rerunning tests, and guessing — a process that’s both time-consuming and error-prone.

The Manual Way (And Why It Breaks)

Manually detecting and fixing python script sync issues is tedious and unreliable. You have to run your script multiple times, monitor execution logs, and adjust sleep intervals by hand. This process becomes a guessing game, especially when working with file synchronization or polling logic. It’s particularly painful when dealing with python automation scripts that need to run consistently across different systems — some environments are slower, and timing assumptions can quickly fall apart.

The Python Approach

We can use Python’s built-in ast module to analyze script code and detect timing patterns, then adjust delays dynamically. With a bit of AST parsing and execution log input, we can programmatically optimize sleep values and time-based polling. This makes it much easier to handle cross-platform delays without manual intervention.

import ast
import csv
from pathlib import Path

# Load and parse script
script_path = Path("script.py")
script_content = script_path.read_text()
tree = ast.parse(script_content)

# Collect all sleep calls and their values
sleep_intervals = []
for node in ast.walk(tree):
    if isinstance(node, ast.Call) and isinstance(node.func, ast.Name):
        if node.func.id == "sleep":
            if node.args and isinstance(node.args[0], ast.Constant):
                sleep_intervals.append(node.args[0].value)

# Read log file to get actual runtime delays
log_file = Path("execution_log.csv")
actual_delays = []
with open(log_file, mode="r") as f:
    reader = csv.DictReader(f)
    for row in reader:
        actual_delays.append(float(row["delay"]))

# Adjust sleep intervals based on log
adjusted_intervals = []
for i, interval in enumerate(sleep_intervals):
    if i < len(actual_delays):
        adjusted_intervals.append(max(0.01, interval - actual_delays[i]))
    else:
        adjusted_intervals.append(interval)

# Generate updated script content
fixed_content = script_content
for original, adjusted in zip(sleep_intervals, adjusted_intervals):
    fixed_content = fixed_content.replace(f"sleep({original})", f"sleep({adjusted:.2f})")

# Write updated script
output_path = Path("optimized_script.py")
output_path.write_text(fixed_content)

This script parses a Python file, finds time.sleep() calls, and then adjusts them based on real execution delays in a CSV log. It’s a basic but functional start toward automating python script sync timing improvements. While this approach works for small scripts, it doesn’t scale well to complex automation setups or handle dynamic polling logic without custom parsing.

What the Full Tool Handles

Analyzes script timing using AST to identify and fix sync delays
Adjusts sleep intervals and polling based on actual system load
Supports CSV input for execution logs to fine-tune sync behavior
Generates optimized script with no external dependencies
Offers full control over script execution timing across platforms
Provides one-time payment for lifetime access, works on Linux, Mac, and Windows

Running It

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

python fix_sync_delay.py --input script.py --log execution_log.csv --output fixed_script.py

The --input flag specifies the Python script to analyze, while --log points to a CSV file with execution delays. The output file is written to --output and is ready for immediate use.

Get the Script

Skip the manual work and get a full solution designed for developers who want clean, consistent python execution timing. Download Python Script Sync Delay Fixer →

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

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

How to Automate Click Tracking with Python

Oddshop — Sun, 10 May 2026 01:37:55 +0000

The Multi Site Tracker saves Belgian agencies from the tedium of manually sifting through dozens of analytics or CRM exports. Each website generates its own CSV or JSON file with click events, form submissions, and email links, but there’s no automated way to consolidate them. This leads to fragmented reporting, wasted time, and inconsistent insights—especially when managing multiple clients or domains.

The Manual Way (And Why It Breaks)

Manually combining click tracking data from different domains is a tedious process. You’ll need to open each CSV or JSON file separately, identify the relevant columns like timestamps, URLs, and event types, and then copy-paste them into one master file. For agencies managing even a handful of sites, this quickly becomes a bottleneck. You're also left to manually detect phone clicks from tel: links or identify form submissions by matching form IDs—often with inconsistent naming across platforms. These tasks are not only time-consuming but also error-prone. The offline analytics workflow becomes even more difficult when trying to match mailto links or custom tracking parameters across sites. A Multi Site Tracker that automates this consolidation is a necessity for any serious agency.

The Python Approach

Here’s a small Python snippet that handles a simplified version of the consolidation step. It reads multiple CSV files and merges them with a site identifier column. This is just the foundation of what the full tool does, but it demonstrates the core idea.

import pandas as pd
from pathlib import Path

# Define input directory and output file
input_dir = Path('./exports')
output_file = 'consolidated_report.csv'

# Get all CSV files in the input directory
csv_files = input_dir.glob('*.csv')

# Create a list to store dataframes
dataframes = []

# Loop over each CSV file
for file in csv_files:
    df = pd.read_csv(file)
    df['site'] = file.name  # Add site name column
    dataframes.append(df)

# Concatenate all dataframes
merged_df = pd.concat(dataframes, ignore_index=True)

# Save consolidated report
merged_df.to_csv(output_file, index=False)

This code reads all CSV files in an input folder, adds a column indicating the source site, and merges everything into one file. It’s a basic consolidation tool, not a full-fledged click tracker, but it shows how Python can automate repetitive tasks. Limitations include no handling of form submissions or phone click detection, which the full Multi Site Tracker handles.

What the Full Tool Handles

Automatically imports CSV, JSON, and Excel files from multiple sources, merging columns based on structure.
Identifies phone click events from tel: links or custom data attributes.
Tracks form submissions by matching form IDs or URL path patterns.
Detects email click events from mailto: links or custom tracking parameters.
Exports a final report with site name, event type, timestamp, and URL in a clean CSV format.
Works entirely offline with exported data—no live APIs or OAuth needed.

Running It

To use the full Multi Site Tracker, run this command in your terminal:

python track_events.py --input ./exports/*.csv --output consolidated_report.csv

The --input flag accepts wildcards to include all files in a folder, and --output defines where the final report is saved. The tool will process all supported file types and generate a unified CSV with all events.

Get the Script

If you're not interested in building your own, skip the code and go straight to the tool. The Multi Site Tracker automates everything from data ingestion to event detection and export.

Download Multi-Site Click & Form Tracker →

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

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

How to Automate Business Data Export with Python Scripts

Oddshop — Sun, 10 May 2026 01:37:47 +0000

A python export script that handles CSV, Excel, and JSON files manually is tedious and error-prone, especially when you're repeating the same formatting, filtering, or reporting tasks across dozens of files. You end up copying and pasting data, clicking through spreadsheets, or writing basic Python scripts that only work for one-off cases. This is where data automation becomes essential — whether you're working with a csv export from an ERP or an excel automation workflow, the need for reliable tools is clear.

The Manual Way (And Why It Breaks)

Processing exported business data manually means spending hours in spreadsheets or clicking through tools that don’t scale. Every time you need to generate a report from a CSV file or update a summary from an Excel export, you're doing the same routine: opening the file, selecting a range, applying filters, and exporting again. It's not just time-consuming — it's also prone to human error. For operations teams, this repetition can quickly become a bottleneck, especially when managing thousands of rows or needing to run similar reports weekly. Automation via a python export script would solve this, but few have the time or need to build such a script from scratch.

The Python Approach

Here’s a minimal Python snippet that shows how a data automation task might look using pandas and pathlib. It reads a CSV file, processes it, and saves the results:

import pandas as pd
from pathlib import Path

# Load the source CSV file
input_file = Path("orders.csv")
df = pd.read_csv(input_file)

# Filter rows where 'status' is 'completed'
filtered_df = df[df['status'] == 'completed']

# Sort by date, then sum up total amounts
sorted_df = filtered_df.sort_values(by='date')
total_sales = sorted_df['amount'].sum()

# Save to a new CSV
output_file = Path("processed_orders.csv")
sorted_df.to_csv(output_file, index=False)

# Output summary to terminal
print(f"Total sales: ${total_sales}")

This Python script reads and filters a dataset, processes it, and outputs results. However, it's limited to a specific operation and would need rewrites for different tasks. It’s also not ideal for batch processing or generating rich reports like HTML or PDFs — which is where a dedicated python export script with templating shines.

What the Full Tool Handles

Multi-format input support — accepts CSV, JSON, and Excel files with automatic schema detection, so you don’t have to guess data types.
Template-based reporting — generate PDF, HTML, or Markdown summaries from data rows using predefined templates.
Transformation pipeline with YAML — filter, sort, aggregate, and map columns using a declarative config file.
Batch processing — handles thousands of records with progress indicators, error logging, and recovery options.
Flexible output options — save to files or pipe output to stdin/stdout for integration with other commands.
No internet required — works offline, making it ideal for secure or isolated environments.

This is a full python export script solution that eliminates the need to write custom loops or manage file types manually.

Running It

To process your data, run a command like:

export-automation run --input orders.csv --template invoice --output reports/

This command takes a CSV input file, applies the invoice template, and saves the output to a reports/ directory. The --input flag accepts various formats, and --output directs where the results go. You can also use flags like --verbose or --dry-run to see what’s happening or test without saving.

Get the Script

If you're tired of building or maintaining a python export script from scratch, skip the setup and get a production-ready tool.

Download Export Business Automation →

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

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