Why aggregation matters in NumPy - Quick Recap

Recall & Review

beginner

What is aggregation in data science?

Aggregation means combining many data points into a single summary value, like a total or average. It helps us understand big data by simplifying it.

beginner

Why do we use aggregation functions like sum or mean?

We use them to get quick insights, like total sales or average temperature, which help us make decisions without looking at every detail.

beginner

How does aggregation help in real life? Give an example.

Aggregation helps by turning many numbers into one useful number. For example, a store owner looks at total daily sales instead of every single purchase to see how business is doing.

intermediate

What is the difference between aggregation and filtering?

Filtering picks certain data points based on conditions, while aggregation combines data points into summary values.

beginner

Name three common aggregation functions in numpy.

Three common aggregation functions are numpy.sum() for total, numpy.mean() for average, and numpy.max() for the highest value.

What does aggregation do in data science?

A Sorts data points alphabetically

B Deletes data points

C Combines data points into summary values

D Duplicates data points

Aggregation combines many data points into a single summary value like a sum or average.

Which numpy function gives the average of numbers?

A numpy.mean()

B numpy.sum()

C numpy.min()

D numpy.count()

numpy.mean() calculates the average value of an array.

Why is aggregation useful when working with large data?

A It makes data harder to understand

B It increases data size

C It removes important information

D It simplifies data to see overall trends

Aggregation simplifies large data sets to help us see overall trends and patterns.

Which of these is NOT an aggregation function?

A sum

B filter

C mean

D max

Filter is used to select data, not to aggregate it.

If you want to find the highest value in a numpy array, which function do you use?

A numpy.max()

B numpy.sum()

C numpy.mean()

D numpy.min()

numpy.max() returns the maximum value in an array.

Explain in your own words why aggregation matters when analyzing data.

Think about how you summarize your expenses or scores.

Describe three common aggregation functions in numpy and what they do.

Focus on total, average, and maximum.