Python Lab

Analyze data with Python and pandas right in your browser, using JupyterLite. Nothing to install.

The console below creates a sample sales dataset in the variable df. Copy each step, paste it into the console and press Shift + Enter.

The first load can take 20 to 60 seconds. The data is made up and nothing is saved when you close the page.

Guide: your first sales analysis

See the first five sales and the available columns.

df.head()

Check the data types and basic statistics of the numeric columns.

df.info()
df.describe()

Select only Website sales with a total above $1,000.

df[(df["channel"] == "Website") & (df["total_amount"] > 1000)]

Count sales and add up revenue by channel, highest first.

(df.groupby("channel")["total_amount"]
   .agg(sales="count", revenue="sum")
   .sort_values("revenue", ascending=False))

Add up revenue by month to see the trend over the year.

df.set_index("date")["total_amount"].resample("MS").sum().round(2)

Which category has the highest average ticket (average amount per sale)? Write the code yourself and compare with a classmate.

# your answer here