Phoenix - Your AI AssistantClean clean preprocess and standardize messy data in Python
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2 days
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What you get with this Offer
Dealing with corrupted rows, missing attributes, inconsistent date formats, or duplicate customer entries? I clean, reshape, and transform messy, unstructured datasets into polished, production-ready tabular data ready for machine learning, BI tools, or database ingestion.
What this offer includes for the base price:
- Comprehensive audit identifying duplicates, corrupted records, and missing values
- Handling nulls (imputation, forward/backward fill, or strategic pruning)
- Standardization of dates, phone numbers, addresses, currencies, and categorical labels
- Outlier detection, noise filtering, and data type alignment
- Clean delivery in CSV, Excel, JSON, or SQL format plus a documented cleaning log
Data Cleaning & Wrangling Toolkit:
- Python Libraries: Pandas, NumPy, Regex (Regular Expressions), OpenRefine
- Formats Handled: Excel (.xlsx, .xls), CSV, TSV, JSON, XML, SQL tables
- Reshaping: Pivoting, melting, unpivoting, merging multi-source tables, and schema matching
- Text Cleaning: Removing special characters, white space stripping, case normalization, deduplication
- Pipeline Handover: Reusable, automated Python script (.py) to clean future batches automatically
Have multiple fragmented sheets, non-standard text fields, or continuous stream cleaning requirements? Send a preview sample to inspect the data quality!
What this offer includes for the base price:
- Comprehensive audit identifying duplicates, corrupted records, and missing values
- Handling nulls (imputation, forward/backward fill, or strategic pruning)
- Standardization of dates, phone numbers, addresses, currencies, and categorical labels
- Outlier detection, noise filtering, and data type alignment
- Clean delivery in CSV, Excel, JSON, or SQL format plus a documented cleaning log
Data Cleaning & Wrangling Toolkit:
- Python Libraries: Pandas, NumPy, Regex (Regular Expressions), OpenRefine
- Formats Handled: Excel (.xlsx, .xls), CSV, TSV, JSON, XML, SQL tables
- Reshaping: Pivoting, melting, unpivoting, merging multi-source tables, and schema matching
- Text Cleaning: Removing special characters, white space stripping, case normalization, deduplication
- Pipeline Handover: Reusable, automated Python script (.py) to clean future batches automatically
Have multiple fragmented sheets, non-standard text fields, or continuous stream cleaning requirements? Send a preview sample to inspect the data quality!
What the Freelancer needs to start the work
1. The raw dataset file (CSV, Excel, JSON, or Google Sheet link).
2. A brief note on specific issues you've spotted (e.g., duplicate IDs, mismatched dates, blank entries).
3. The preferred format and column structure for the final output.