Pivot data

Pivoting helps you turn rows into summaries. It's how we answer practical questions, such as:
"How much was sold by country and type?" or "How many services did each seller provide?" In Excel, users create pivot tables by dragging fields into rows, columns, and values. In Python, we use the pivot_table() method to achieve the same — just more precisely and repeatably.

This section shows you how to create powerful summaries by combining groupings with calculations like sum, count, and average.

Create a basic pivot table

Excel

In Excel, you insert a pivot table and drag "Seller Country" into rows, and "Amount" into values (summed).

t0 Prompt

Create a pivot table with seller country as rows

Show total amount by seller country

Group amounts by country

Code

The python code looks as follows:

transactions["Amount"] = pd.to_numeric(transactions["Amount"])
pivot = transactions.pivot_table(
    values="Amount",
    index="Seller Country",
    aggfunc="sum"
)
pivot

Add multiple statistics

Excel

In Excel, you can show both sum and average for the same field by dragging "Amount" in twice.

t0 Prompt

Show both sum and average amount per transaction type

Give me multiple stats in a pivot

Total and average grouped by colum Type

Code

The python code looks as follows:

transactions["Amount"] = pd.to_numeric(transactions["Amount"])
pivot = transactions.pivot_table(
    values="Amount",
    index="Transaction",
    aggfunc=["sum", "mean"]
)
pivot

Pivot with rows and columns

Excel

In Excel, you drag "Seller Country" into rows and "Transaction" into columns, then summarize values by sum or count.

t0 Prompt

Compare transaction types across countries

Create a pivot with country rows and type columns

Show totals by country and seller

Code

The python code looks as follows:

transactions["Amount"] = pd.to_numeric(transactions["Amount"])
pivot = transactions.pivot_table(
    values="Amount",
    index="Seller Country",
    columns="Transaction",
    aggfunc="sum",
    fill_value=0
)
pivot