This Normal 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 normal distribution is the bell shape: most values sit near the mean, and fewer sit far away.
random.gauss draws samples from that shape. Seed the generator when you want the same sample again.
Example 1: Start here
The first argument of gauss is the mean. The second is the standard deviation.
import random # load a tool that comes with Python
random.seed(1)
samples = [round(random.gauss(75, 8), 1) for _ in range(5)] # a list; the values stay in this order
print(samples)
Example 2: Add two rows
Normal Data Distribution applies one operation to every number. zip pairs the rows, and the list keeps each sum.
left = [1, 2, 16] # 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, 16, 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, 16, 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
The first argument of gauss is the mean. The second is the standard deviation.
