Joshua nneji
Framer web design |excel data cleanup /va for us standard
import pandas as pd
# Load messy e-commerce data
df = pd.read_excel('ecommerce_products.xlsx')
# 1. Remove duplicates
df = df.drop_duplicates()
# 2. Clean price column (remove $ sign)
df['Price'] = df['Price'].replace('[\$,]', '', regex=True).astype(float)
# 3. Standardize category names
df['Category'] = df['Category'].str.title()
# 4. Fill missing stock with 0
df['Stock'] = df['Stock'].fillna(0)
# 5. Save clean file
df.to_excel('clean_products.xlsx', index=False)
print(f"Done! Cleaned {len(df)} products.")
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52
Entered 1000+ leads into CRM database with 99.8% accuracy. Verified all emails and phone numbers, removed 23 duplicate entries, and standardized company naming. Validated contact details and maintained clean data entry log for client. Completed 1000/1000 leads with full quality checks.
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53
Built product inventory tracker for 350 SKUs. Created organized sheet with stock levels, categories, pricing, and automated low-stock alerts. Client now tracks $38k+ inventory easily. Delivered clean, accurate Excel file in 24 hours.
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57
Built a verified lead list of 100 B2B contacts for a US startup. Researched LinkedIn and company websites, verified each email with zero bounce rate. Delivered organized Google Sheets with Name, Company, Email, LinkedIn URL. Completed in 24 hours with 100% accuracy.
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54