Delete data

We often focus on only the data that’s relevant to a specific question. Sometimes this means removing rows we don’t need — like transactions below a certain amount — or dropping entire columns that don’t help our analysis. Deleting unnecessary data makes your table easier to work with and interpret, just like trimming excess text in a report.

In this section, you’ll learn how to remove rows and columns from your dataset using simple Python commands.

Delete a column

Excel

In Excel, you right-click the column header and choose "Delete" or press the delete key after selecting the column.

t0 Prompt

Delete the "Currency" column

Remove the column called "Transaction"

Drop column X from the table

Code

The python code looks as follows:

transactions.drop(columns=["Currency"], inplace=True)
transactions

Delete multiple columns

Excel

In Excel, you select several columns and delete them together.

t0 Prompt

Drop the columns "Seller" and "Buyer"

Remove columns X and Z from my data

Code

The python code looks as follows:

transactions.drop(columns=["Seller", "Buyer"], inplace=True)
transactions

Delete rows by condition

Excel

In Excel, you might filter rows and then delete them manually, or use a formula to flag which rows to remove.

t0 Prompt

Delete all rows where amount is less than 5 million

Remove transactions before 2025.2.1

Drop rows that meet condition X > Y

Code

The python code looks as follows:

transactions = transactions[transactions["Amount"] >= 5_000_000]
transactions