This Data Distribution lesson is for a beginner. Read the short notes, run each example, then change one name or number and run it again.
A distribution is the pattern of how often each value appears. Counting bins is the simplest picture of that pattern.
A flat count means the values are spread out. A tall bin means many rows landed in one range.
Example 1: Start here
Look at the bins before you pick a model. A skewed pile of scores is not a straight line.
scores = [61, 64, 72, 75, 81, 90, 92] # a list; the values stay in this order
bins = {} # a dictionary; each key has one value
for score in scores: # repeat the indented lines once per value
bucket = (score // 10) * 10 # a tuple; a fixed group of values
bins[bucket] = bins.get(bucket, 0) + 1 # change this one field
print(dict(sorted(bins.items()))) # a new list, sorted
Example 2: Add two rows
Data Distribution applies one operation to every number. zip pairs the rows, and the list keeps each sum.
left = [1, 2, 15] # 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, 15, 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, 15, 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
Look at the bins before you pick a model. A skewed pile of scores is not a straight line.
