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#data-cleaning

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WrenTools1d ago · 8 min read

Deduplicate a lead list in Python: five ways drop_duplicates misses

Disclosure: we built and sell the Apify Actor this post compares against, Merge & Dedup Datasets, CSV, JSON & Excel Files (publisher WrenTools). The post was written with AI assistance and reviewed by

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Richard C2d ago · 5 min read

How to catch wrong-parent Contacts after a Salesforce migration

By Richard C · AIL Studio · 7x certified Salesforce professional A Contact can keep its name and email after a Salesforce migration and still sit under the wrong Account. Free CSV compare tools miss t

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Locate Next6d ago · 24 min read

How to Merge Skip Trace Results Back into Your Property List with SQLite and Python

You pulled an absentee owner list or a tax-delinquent list and sent it out for skip tracing. The return file that comes back rarely lines up one-to-one with your list. One owner has four phones, anoth

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Locate NextOct 22 · 12 min read

Flagging absentee owners with Python and pandas by comparing situs and mailing addresses

County parcel files often hold a useful signal: the property address and the owner's mailing address don't always match. If an owner gets their tax bill somewhere other than the property, they may not

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Locate NextOct 22 · 13 min read

How to Deduplicate Property Owner Records Across Multiple Lead Lists with Python and pandas

All names, addresses and IDs in the examples are fictional. Why the same owner shows up on more than one lead list Real estate teams often pull several lead lists for the same area: an absentee owner

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Dalya AkramOct 19 · 15 min read

What I learned from analyzing more than 1M online retail transactions

When I started looking for a new data analysis project, I didn’t want another small and already-clean dataset where I could immediately tell what charts I was supposed to make. I wanted something clos

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amailukOct 20 · 13 min read

Data Cleaning Standards

1. Why standards matter Picture opening a spreadsheet you've just been handed. It's full. Hundreds of rows, all neat in their little cells. You feel good. The hard part's done, right? Not so fast. I'v

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Muhammad Ariel ShakaramiroAug 31 · 7 min read

Predicting Baldness Probability: From Messy Data to a Working Machine Learning Model

If you think baldness is purely a matter of genetics, here's a plot twist: a few surprisingly sensible patterns show up once you dig into age, smoking habits, and stress levels together. This post wal

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NumberCheckerAug 21 · 6 min read

How to resolve identity fragmentation by mapping Telegram and WhatsApp account-identifier signals to B2B CRM records

In B2B environments, the CRM is often treated as the "source of truth," yet it frequently suffers from a silent degradation known as identity fragmentation. This occurs when a single lead interacts wi

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Shreyash KadamAug 3 · 6 min read

Outlier Detection and Treatment

Every dataset has a story to tell — but sometimes a few data points shout louder than they should. These are outliers: values that stray far from the rest of your data. Handled well, they can reveal f

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Deduplicate a lead list in Python: five ways drop_duplicates misses

How to catch wrong-parent Contacts after a Salesforce migration

How to Merge Skip Trace Results Back into Your Property List with SQLite and Python

Flagging absentee owners with Python and pandas by comparing situs and mailing addresses

How to Deduplicate Property Owner Records Across Multiple Lead Lists with Python and pandas

What I learned from analyzing more than 1M online retail transactions

Data Cleaning Standards

Predicting Baldness Probability: From Messy Data to a Working Machine Learning Model

How to resolve identity fragmentation by mapping Telegram and WhatsApp account-identifier signals to B2B CRM records

Outlier Detection and Treatment