This SciPy Tutorial lesson is for a beginner. Read the short notes, run each example, then change one name or number and run it again.
SciPy adds scientific routines on top of NumPy: integration, optimisation, sparse matrices, and statistics.
The try-it runner cannot import SciPy. The standard math module covers the square root used in the example.
Example 1: Start here
Install scipy in a virtual environment before you call scipy.stats or scipy.optimize.
import math # load a tool that comes with Python
print(math.sqrt(81))
Example 2: Add two rows
SciPy Tutorial applies one operation to every number. zip pairs the rows, and the list keeps each sum.
left = [1, 2, 24] # 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, 24, 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, 24, 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
Install scipy in a virtual environment before you call scipy.stats or scipy.optimize.
