Filter

Filtering helps you focus on relevant data. Whether you're reviewing transactions above a certain amount, or isolating rows by country or product type, filtering lets you view only the rows that matter — without deleting anything. It's like using sunglasses to block glare: the full dataset is still there, but you're seeing only what you need.

In this section, we’ll learn how to filter a dataset in Python the same way you would in Excel — based on values, text, or logic.

Filter rows by numeric condition

Excel

In Excel, you use filters or formulas like >10000000 to show only large values.

t0 Prompt

Show only transactions above 10 million

Filter rows where amount is more than 8 million

Display only high-value rows

Code

The python code looks as follows:

transactions["Amount"] = pd.to_numeric(transactions["Amount"])
transactions[transactions["Amount"] > 10_000_000]

Filter rows by text condition

Excel

In Excel, you click the dropdown arrow and select a country, name, or keyword to filter.

t0 Prompt

Filter rows where the seller is in Italy

Show only transactions with Sweden as buyer

Display services only

Code

The python code looks as follows:

transactions[transactions["Buyer Country"] == "Sweden"]

Filter using multiple conditions

Excel

In Excel, you can apply filters to multiple columns, or use advanced filter formulas.

t0 Prompt

Show only rows where amount > 10 million and country is Brazil

Filter by column X and Y at the same time

Apply multiple conditions: X, Y, and Z

Code

The python code looks as follows:

transactions["Amount"] = pd.to_numeric(transactions["Amount"])
filtered = transactions[
    (transactions["Amount"] > 10_000_000) &
    (transactions["Buyer Country"] == "Brazil")
]
filtered