This Scatter Plot lesson is for a beginner. Read the short notes, run each example, then change one name or number and run it again.
A scatter plot is how you look for a relationship between two columns before you fit a line.
If the dots climb together, a simple regression may be enough. If they form a curve or a cloud, it will not.
Example 1: Start here
Plot the raw pairs before you trust a coefficient.
hours = [1, 2, 4, 5] # a list; the values stay in this order
scores = [60, 70, 85, 88] # a list; the values stay in this order
print(list(zip(hours, scores))) # zip keeps each pair together
Example 2: Add two rows
Scatter Plot applies one operation to every number. zip pairs the rows, and the list keeps each sum.
left = [1, 2, 12] # a list; the values stay in this order
right = [10, 20, 30] # a list; the values stay in this order
print([a + b for a, b in zip(left, right)]) # pair the lists, then combine each pair
Example 3: Average a column
The average is the total divided by how many numbers you have. That is the same summary a statistics page starts with.
scores = [70, 12, 90] # a list; the values stay in this order
print(sum(scores) / len(scores)) # len counts the items
Example 4: Keep values above a line
A cutoff drops the low values. Change 80 and run the example again to see who remains.
scores = [70, 12, 90] # a list; the values stay in this order
print([score for score in scores if score >= 80]) # keep the items that pass the test
Plot the raw pairs before you trust a coefficient.
