You will get CSV Cleaning & Data Prep — Cleaned CSV + Report (low-code)
Project details
Problem
Small businesses and analysts often receive messy CSV exports (Shopify, exports, manual logs) that contain inconsistent formatting, special null markers, duplicate rows and wrong datatypes — making analysis and reporting slow and error-prone.
Solution
I built a small, production-ready **CSV cleaning pipeline** using Python (pandas & numpy) that automates:
• replacement of special null markers (e.g. `\N`, `NA`), trimming whitespace
• conversion of columns to proper datatypes (dates, numeric)
• sensible filling of missing values (mode/date defaults or "Unknown")
• duplicate detection & removal
• summary report generation (rows before/after, duplicates removed)
Deliverables
• `output_cleaned.csv` — cleaned CSV ready for analysis
• `report_summary.csv` — quick one-page table: rows_before, rows_after, duplicates_removed, top issues
• `README.pdf` — run instructions + how to integrate into your workflow
• Optional: 30-min walkthrough session
Why this helps
• Saves analyst time (ready-to-use data)
• Reduces reporting errors and missing-value surprises
• Repeatable for future exports (can be scheduled via GitHub Actions or Apps Script)
Small businesses and analysts often receive messy CSV exports (Shopify, exports, manual logs) that contain inconsistent formatting, special null markers, duplicate rows and wrong datatypes — making analysis and reporting slow and error-prone.
Solution
I built a small, production-ready **CSV cleaning pipeline** using Python (pandas & numpy) that automates:
• replacement of special null markers (e.g. `\N`, `NA`), trimming whitespace
• conversion of columns to proper datatypes (dates, numeric)
• sensible filling of missing values (mode/date defaults or "Unknown")
• duplicate detection & removal
• summary report generation (rows before/after, duplicates removed)
Deliverables
• `output_cleaned.csv` — cleaned CSV ready for analysis
• `report_summary.csv` — quick one-page table: rows_before, rows_after, duplicates_removed, top issues
• `README.pdf` — run instructions + how to integrate into your workflow
• Optional: 30-min walkthrough session
Why this helps
• Saves analyst time (ready-to-use data)
• Reduces reporting errors and missing-value surprises
• Repeatable for future exports (can be scheduled via GitHub Actions or Apps Script)
Data Tool
PythonWhat's included $50
These options are included with the project scope.
$50
- Delivery Time 2 days
- Number of Revisions 2
About Daniel
Data Cleaning&Automation | Low-Code ETL, Dashboards, Shopify/SQL/Excel
Sofia, Bulgaria - 7:33 am local time
I help small businesses, e-commerce shops, and busy teams automate their data workflows — without expensive software or long timelines.
What I can do for you:
🧹 Data Cleaning & Formatting — Remove duplicates, fix dates, unify formats, fill missing values
🔄 ETL Pipelines — Extract from CSV/Excel/Shopify, Transform in Python (pandas) or SQL, Load to Google Sheets or dashboards
📊 Dashboards & Reports — Build interactive Looker Studio or Excel dashboards for KPIs, revenue tracking, top products
⚙️ Process Automation — Schedule daily/weekly reports, integrate with Google Sheets & APIs
Tools & Tech:
Python (pandas, numpy), SQL (SQLite, MySQL basics), Google Sheets, Excel, Looker Studio, Shopify exports, GitHub, Zapier/Make (Integromat), GitHub Actions
Why work with me?
Low-code approach: quick delivery, minimal setup costs
Clear communication & documented steps — so you can maintain the process later
Flexible: 2–6 hours/day work available, ideal for ongoing support
📩 Let’s talk about your data challenge — I can review your file or process for free and give you an improvement plan.
Steps for completing your project
After purchasing the project, send requirements so Daniel can start the project.
Delivery time starts when Daniel receives requirements from you.
Daniel works on your project following the steps below.
Revisions may occur after the delivery date.
Receive sample & confirm
What I do: Ask for a sample (≤ 500 rows preferred) and any business rules (columns to keep, date format, required fills). Deliverable: short confirmation message with agreed scope.
Quick audit
Run automated checks: missing values, duplicate rows, inconsistent types, common null markers (\N, NA, empty strings). Deliverable: one-page audit (key issues + proposed fixes).