A DataFrame represents labeled tabular data. Inspect column names, types and missing values before calculating summaries; row labels are not always consecutive positions.
Before you begin
Know Python functions and basic collections. Work from a copy of a small fictional dataset and record the column definitions.
Work through the idea
Keep the raw input unchanged and record the transformation steps. Inspect row counts, types and a few known records before and after the operation. Decide what missing or invalid values mean for this task rather than applying one universal cleaning rule. Save a short explanation with the resulting table.
- Load a small explicit dataset
- Inspect shape and dtypes
- Select columns and filter rows deliberately
Practice task
Create a fictional sales table and calculate totals by category.
Check your result
Reconcile one category manually and explain how missing amounts are handled.
A mistake to avoid
Do not confuse label-based selection with positional selection.
Keep a review note
Save the inputs or scenario, your reasoning and the result you checked. Explain one assumption and one case that needs more work. Revise the exercise after feedback and distinguish what you demonstrated from what remains untested.
Official reference for further study
Explore the related learning path · Choose another worksheet