Data collection
Collect relevant information from permitted websites, APIs or supplied files, subject to access conditions.
PYTHON · DATA · CSV · XLSX
Data collection & processing
Turn scattered business information into structured, usable datasets. Python workflows can collect permitted data, standardize fields, identify duplicates and prepare CSV or Excel files.
What can be automated
Data cleaning
Normalize text, email addresses and other agreed fields; identify incomplete or inconsistent records.
Deduplication
Detect repeated records using agreed matching rules and prepare a cleaner dataset.
Structured exports
Deliver processed data in CSV or XLSX format with agreed columns and validation results.
Common data collection and cleaning tasks
Business datasets often arrive from different sources with inconsistent columns, missing values and repeated records. Python can help prepare a consistent dataset when access permissions, required fields and processing rules are defined.
Company information collection
Collect agreed business fields from permitted websites, documented APIs or supplied files. Check source availability and access conditions before selecting a collection method.
Cleaning existing databases
Standardize company names, phone numbers, email addresses, cities and other agreed fields. Flag missing or inconsistent values instead of silently replacing uncertain information.
Finding duplicate records
Compare records using agreed identifiers and matching rules. Separate exact duplicates from possible matches that require manual review.
Preparing CSV and Excel exports
Combine validated records into a consistent column structure and prepare CSV or XLSX files with processing statistics and a list of unresolved issues.
Source data, validation and deliverables
Before development, we define the permitted data sources, required fields, expected record structure, update frequency and validation rules. Sample input and expected output help establish acceptance criteria.
Depending on the agreed scope, deliverables may include a reusable Python processing script, a cleaned CSV or Excel dataset, duplicate-review results and a validation summary. Data availability and completeness depend on the approved sources.
Example: cleaning company data
Illustrative example with fictional records, not a completed client project.
Before processing
| Company | City | |
|---|---|---|
| Alpha LLC | SALES@ALPHA.COM | Москва |
| Alpha LLC | sales@alpha.com | Москва |
| Beta Ltd | info@beta.com | Казань |
After processing
| Company | City | |
|---|---|---|
| Alpha LLC | sales@alpha.com | Москва |
| Beta Ltd | info@beta.com | Казань |
Python normalizes email and city values and removes a duplicate company record.
How the work is agreed
Data sources, permissions, required fields, processing rules, output format, acceptance criteria, timeline and price are confirmed before work begins.
Discuss a data automation project