Numpy

numpy

• NumPy is one of the fundamental packages for scientific computing in Python.
• It contains functionality for multi-dimensional arrays, and also mathematical functions such as linear algebra operations, the Fourier transform, and pseudo-random number generators.

• Usually, numerical calculations in your code will be faster if you use numpy.

• loading library

  import numpy as np
  

• as np is not mandatory but often used.

• Using numpy array

  import numpy as np
  a = np.zeros(2)
  print(a)
  

convert list to numpy array

• numpy array can be made by defining the list first, as

  import numpy as np
  a = [1, 2, 3, 4, 5]
  b = np.array(a)
  print(a)
  print(b)
  

Array Creation

• numpy.zeros(n): array with n zeros.
• numpy.ones(n): array with n ones.
• np.arange(n): sequence of numbers with n elements.

Linear spacing

• You can have an uniformly-ditributed numbers by linspace function.

  import numpy as np
  x = np.linspace(-10, 10, 100)  # start, end, number of points
  

Random Number Generation

• numpy.random.rand(): generate random numbers from a uniform distribution.
• numpy.random.randn(): generate random numbers from a normal distribution.
• numpy.random.randint(): generate random integers.

Mathematical functions

• Several functions are available in numpy.
  • numpy.sin(): sine function
  • numpy.cos(): cosine function
  • numpy.exp(): exponential
  • numpy.log(): natural logarithm function
  • numpy.log10(): base 10 logarithm function
  • numpy.pi: $\pi = 3.141592...$

maximum and minimum

• Maximum and minimum values in an array can be easily found by numpy.max and numpy.min functions.
• The max/min argument i.e. the index corresponding to the max/min value is obtained by numpy.argmax and numpy.argmin functions.

  import numpy as np
  a = [1, 2, 4, 2, 1]
  b = np.array(a)
  print(np.max(b))
  print(np.argmax(b))
  

Exercise (numpy)

• Let's say you have sales data for a week represented as a NumPy array. Calculate the total sales for the week. answer