Unit 6 · Heavy
NumPy and pandas, and the shift from writing loops to describing operations on whole columns. Most of the difficulty is not syntax but knowing what a dataset actually contains before analysing it.
What a strong answer looks like
A strong Unit 6 answer inspects the data before computing on it, and states what was done about missing values rather than ignoring them.
Know
A NumPy array applies an operation across every element at once, which is both faster and clearer than an explicit loop.
Apply
Express element-wise work as an operation on the whole array.
Watch out
Writing a Python loop over an array, which discards the main reason for using one.
Study move
Convert a loop that doubles every element into a single vectorised expression.
Know
A DataFrame is a table with labelled rows and columns. Selecting by label and selecting by position are different operations with different methods.
Apply
Select columns by name and rows by condition, keeping the two selection styles distinct.
Watch out
Chained selection that returns a copy rather than a view, so an assignment appears to do nothing.
Study move
Filter rows by a condition and then modify a column, confirming the change actually persisted.
Know
Real data has gaps, duplicates, and wrong types. Every choice about them changes the result, so each must be deliberate and recorded.
Apply
Inspect the shape, the types, and the count of missing values before any analysis.
Watch out
Dropping rows with missing values silently, which can remove exactly the group you were studying.
Study move
Take a dataset with gaps, report the missing count per column, and justify one handling choice.
Know
Grouping splits rows by a key, applies an aggregation to each group, and combines the results into a new table.
Apply
Use grouping to answer per-category questions rather than looping over categories manually.
Watch out
Reporting an average over groups of very different sizes without saying so, which hides the imbalance.
Study move
Group a dataset by one column, compute two aggregations, and report the group sizes alongside.
Connections and techniques that receive extra attention in this unit.
Related topics some schools attach to this unit and others leave out. Covered on request rather than assumed.
Modeling drill
Take a dataset with missing values and uneven categories, produce a per-category summary, and state explicitly what you did about the gaps.
