You will get clean and automated Excel or CSV data using Python
Project details
I will clean, organize, merge, and automate your Excel or CSV data using Python and pandas.
If you have messy spreadsheets, duplicate records, missing values, inconsistent column names, or multiple files that need to be combined, I can turn them into a clean and structured dataset.
Depending on the selected package, I can also create a reusable Python script, calculate summary metrics, and generate a structured Excel report.
You can receive:
• Clean and standardized data
• Duplicate removal
• Missing-value handling
• Multiple-file merging
• Column and format normalization
• Calculated metrics and summaries
• Excel or CSV output
• Reusable Python automation script
I focus on accurate, reproducible data processing and clear final deliverables.
If you have messy spreadsheets, duplicate records, missing values, inconsistent column names, or multiple files that need to be combined, I can turn them into a clean and structured dataset.
Depending on the selected package, I can also create a reusable Python script, calculate summary metrics, and generate a structured Excel report.
You can receive:
• Clean and standardized data
• Duplicate removal
• Missing-value handling
• Multiple-file merging
• Column and format normalization
• Calculated metrics and summaries
• Excel or CSV output
• Reusable Python automation script
I focus on accurate, reproducible data processing and clear final deliverables.
Data Tool
PythonWhat's included
| Service Tiers |
Starter
$25
|
Standard
$60
|
Advanced
$120
|
|---|---|---|---|
| Delivery Time | 2 days | 3 days | 5 days |
Number of Revisions | 1 | 2 | 2 |
About Xeniya
Python Data Analyst | Data Processing, Automation & Machine Learning
Brno, Czech Republic - 1:11 am local time
I can help with:
• Data cleaning, transformation, and preprocessing
• Python scripts and workflow automation
• CSV, Excel, JSON, and API data processing
• Exploratory data analysis and visualization
• SQL queries, joins, filtering, and aggregation
• Statistical analysis and hypothesis testing
• Machine learning with scikit-learn and XGBoost
• Feature engineering, cross-validation, and model evaluation
• Time-series and signal data analysis
• Debugging and improving existing Python code
I have hands-on experience building end-to-end Python data and ML pipelines, including data preprocessing, feature extraction, statistical analysis, classification, and reproducible automated workflows.
My main stack includes Python, pandas, NumPy, SciPy, scikit-learn, SQL, Excel, Power BI, Git, and pytest.
I focus on practical solutions, clear communication, clean code, and delivering work that is easy to understand and reuse.
Steps for completing your project
After purchasing the project, send requirements so Xeniya can start the project.
Delivery time starts when Xeniya receives requirements from you.
Xeniya works on your project following the steps below.
Revisions may occur after the delivery date.
Review the input data
I review the provided files, data structure, quality issues, and requested output.
Clean and process the data
I clean and standardize the data, handle missing values and duplicates, merge files, and apply the requested transformations.