NumPy

• NumPy really defines the core of the Python scientific computing data stack.
• Short for Numerical Python, it is one of the most important foundational packages for numerical computing in Python.
• Useful for working with arrays, which are data structures that store values of the same data type in multiple dimensions.
  • Therefore NumPy Arrays are homogeneous, multidimensional and are similar in some ways to Python lists.
• Much of the knowledge about NumPy is transferable to pandas as well.
• For more information, visit the NumPy website.

The Structure of NumPy Arrays:

• Can’t have different types of data in a NumPy array.

• Array shape: describes how many elements in each dimension.
  • A 1D array shape like (8,) simply means eight elements in a single dimension. There is no distinction between rows and columns in 1D NumPy array.
  • A 2D NumPy array has shape (rows, columns).
• Array dtype: datatype of array elements.

• Array axes: order of indexing into the array.

  • Axis 0: moves down the rows (i.e., it moves between rows, operating vertically).
  • Axis 1: moves across the columns (i.e., it moves between columns, operating horizontally).

• The mental trick: the axis number tells you which axis shrinks/disappears in the result of a reduction operation (e.g., summing along an axis)

  • Axis = 0 → the row dimension collapses → you get one result per column
  • Axis = 1 → the column dimension collapses → you get one result per row

Creating a NumPy Array:

• You can create a NumPy array from the data in one or more Python lists.
• Suppose you have a list for each employee at a company containing that employee’s base salary payments over the past three months.

1import numpy as np
2jeff_salary = [2700, 3000, 3000]
3nick_salary = [2600, 2800, 2800]
4tom_salary = [2300, 2500, 2500]
5base_salary = np.array([jeff_salary, nick_salary, tom_salary])
6print(base_salary)

• Start by importing the NumPy library on line 1. Then define a set of lists, where each list contains the base salary data of an employee over the past three months. Finally, we combine these lists into a 2D NumPy array.

Output:

1[[2700 3000 3000]
2 [2600 2800 2800]
3 [2300 2500 2500]]

• The 2D array has two axes, which are indexed by integers, starting with 0.
• Axis 0 runs vertically downward across the array’s rows, while axis 1 runs horizontally across the columns.

Performing Element-Wise Operations:

• It’s easy to perform element-wise operations on multiple NumPy arrays of the same dimensions.
• For example, let’s follow the same process to create an array containing the employees’ monthly bonuses:

1jeff_bonus = [500, 400, 400]
2nick_bonus = [600, 300, 400]
3tom_bonus = [200, 500, 400]
4bonus = np.array([jeff_bonus, nick_bonus, tom_bonus])

• We can add the base salary and bonus arrays together to determine the total amount paid each month to each employee:

1salary_bonus = base_salary + bonus
2print(type(salary_bonus))
3print(salary_bonus)

Output:

1<class 'numpy.ndarray'>
2[[3200 3400 3400]
3 [3200 3100 3200]
4 [2500 3000 2900]]

Using NumPy Statistical Functions:

• NumPy’s statistical functions allow you to analyze the contents of an array.
• For example, you can find the maximum value of an entire array or the maximum value of an array along a given axis.
• Let’s imagine you want to find the maximum value in the salary_bonus array. We can do this with the NumPy array’s max() function:

1print(salary_bonus.max())

Output:

13400

• We can also find the maximum value of an array along a given axis.
• For example, if you want to determine the maximum amount paid to each employee in the past three months, you can use NumPy’s amax() function:

1print(np.amax(salary_bonus, axis=1))

• By specifying axis=1, we instruct amax() to search horizontally across the columns for a maximum in the salary bonus array, thus applying the function across each row.

Output:

1[3400 3200 3000]

• For the entire list of statistical functions supported by NumPy, visit the NumPy documentation.