Machine Learning with NumPy, pandas, scikit-learn, and More/

...

/

Math

Math

Understand how arithmetic and linear algebra work in NumPy.

We'll cover the following...

Chapter Goals:

  • Learn how to perform math operations in NumPy
  • Write code using NumPy math functions

A. Arithmetic

One of the main purposes of NumPy is to perform multi-dimensional arithmetic. Using NumPy arrays, we can apply arithmetic to each element with a single operation.

The code below shows multi-dimensional arithmetic with NumPy.

Python 3.5
arr = np.array([[1, 2], [3, 4]])
# Add 1 to element values
print(repr(arr + 1))
# Subtract element values by 1.2
print(repr(arr - 1.2))
# Double element values
print(repr(arr * 2))
# Halve element values
print(repr(arr / 2))
# Integer division (half)
print(repr(arr // 2))
# Square element values
print(repr(arr**2))
# Square root element values
print(repr(arr**0.5))

Using NumPy arithmetic, we can easily modify large amounts of numeric data with only a few operations. For example, we could convert a dataset of Fahrenheit temperatures to their equivalent Celsius form.

The code below converts Fahrenheit to Celsius in NumPy.

Python 3.5
def f2c(temps):
  return (5/9)*(temps-32)
fahrenheits = np.array([32, -4, 14, -40])
celsius = f2c(fahrenheits)
print('Celsius: {}'.format(repr(celsius)))

It is important to note that performing arithmetic on NumPy arrays does not change the original array, and instead produces a new array that is the result of the arithmetic operation.

B. Non-linear functions

Apart from basic arithmetic operations, NumPy also allows you to use non-linear functions such as exponentials and logarithms.

The function ...