Data Cleaning Techniques for Analyzing Ethereum Addresses and Transactions

Ethereum transactions pile up fast. Addresses send and receive tokens constantly, creating messy datasets filled with duplicates, errors, and misleading entries. If you don’t clean this data properly, analysis can go sideways. Wallet movements won’t make sense, transaction patterns will look off, and your insights will be unreliable.

The problem? Ethereum data is full of noise. Randomized addresses, token swaps, failed transactions, and smart contract interactions make it harder to see what’s really happening. Cleaning up this data isn’t just about removing errors—it’s about structuring it in a way that reveals real patterns.

Let’s go beyond the usual “remove duplicates” advice. Here’s how to clean Ethereum transaction data properly.

Understanding Ethereum Data Issues Before Cleaning

Before jumping into cleaning methods, it’s key to understand why Ethereum data is so messy in the first place. Unlike traditional financial transactions, Ethereum operates on a decentralized, pseudonymous system. This creates unique challenges.

Duplicate and Redundant Entries

Some block explorers record the same transaction multiple times due to internal indexing methods.
Failed transactions can clutter datasets if not properly filtered out.
Miners sometimes include multiple versions of a transaction with different gas prices, leading to confusion.

Smart Contract Interactions

A single transaction can trigger multiple internal transactions, making it look like one wallet is sending funds when it’s actually a contract execution.
Token transfers don’t always appear in standard Ether transactions, making ERC-20 transactions tricky to track.

Address Spoofing and Dusting Attacks

Some wallets receive tiny amounts of ETH or tokens (dusting attacks) to track them, cluttering analysis.
Addresses can be generated programmatically, making it hard to tell real users from bots.

Understanding these quirks helps in designing better cleaning methods. Now, let’s fix them.

Removing Duplicate and Failed Transactions

Ethereum networks can register multiple versions of the same transaction. Keeping duplicates distorts the data and makes trends hard to follow.

Identifying True Duplicates

Not all duplicate-looking transactions are the same. Some might be legitimate retries with adjusted gas fees. To catch real duplicates:

Look at hash values. If the hash is identical, it’s the same transaction.
Compare timestamps and sender addresses to find retries.
Use block numbers—if a transaction didn’t make it into a block, it likely failed.

Filtering Out Failed Transactions

Failed transactions eat up gas but don’t move funds. To remove them:

Check the status field. A 0 means failure, while 1 means success.
Remove transactions with high gas usage but no actual transfers.
Watch for contract reverts—transactions might appear valid but didn’t execute as expected.

Standardizing Ethereum Addresses

Ethereum addresses are case-insensitive but sometimes show up in mixed case. This matters for consistency.

Converting to a Uniform Format

Lowercase all addresses to avoid mismatches.
Use checksum addresses only when displaying data to humans.

Handling Address Clustering

Some entities use multiple addresses for the same purpose. For example:

Exchanges use many deposit addresses but pool funds into a few wallets.
Smart contracts interact with the same addresses through proxies.

Grouping related addresses helps with transaction flow analysis.

Cleaning ERC-20 and NFT Transactions

Ethereum transactions aren’t just about ETH. Tokens move in different ways, making cleaning harder.

Handling ERC-20 Transactions Separately

ERC-20 transfers don’t show up in normal transaction lists. Instead, they’re in contract logs. Steps to clean them:

Extract transfer events from logs.
Use to and from fields instead of value in main transaction records.
Watch out for token wrapping—some ETH transfers are actually WETH conversions.

Correcting Timestamps and Block Data

Ethereum timestamps can be unreliable depending on data sources. Some tools pull UTC time, others convert it differently.

Fixing Time Discrepancies

Always use block numbers instead of timestamps when tracking transaction order.
If using timestamps, standardize to UTC.
Check for daylight savings shifts in datasets that mix time zones.

Handling Pending and Stuck Transactions

Some transactions sit in mempools for a long time, never getting confirmed. Best way to handle them:

Remove transactions that stayed pending for over an hour.
If analyzing user behavior, separate pending transactions from confirmed ones.

Detecting Wash Trading and Fake Activity

Ethereum’s open nature allows wash trading—fake buying and selling to inflate numbers.

Spotting Suspicious Transaction Loops

Look for wallets repeatedly trading the same NFT or token back and forth.
Check for transactions where sender and receiver are linked to the same smart contract.
Track large amounts of activity with zero profit.

Filtering Out Bots and Scripted Trades

Some wallets operate programmatically, making thousands of trades a day. To filter them:

Remove transactions from wallets that interact with too many addresses too fast.
Look at gas fees—bots often optimize for the lowest possible fees.
Use free delimiter tool options to split large datasets and analyze patterns more effectively.

Final Thoughts

Cleaning Ethereum transaction data is about more than just deleting bad entries. It’s about structuring it in a way that makes sense.

Messy data hides insights. Once you clean up duplicates, remove fake trades, standardize addresses, and separate failed transactions, the patterns start to show. Suddenly, you can track whale movements, analyze real token usage, and spot trading trends without being misled by noise.

It’s a bit of work upfront, but clean data makes analysis way more accurate. And when it comes to Ethereum, accurate insights are everything.