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Reference guide: Arrays

A NumPy array is a central data structure in the NumPy library, which is a fundamental package for scientific computing in Python. It is a grid of values, all of the same type, and is indexed by a tuple of non-negative integers. NumPy arrays are highly efficient for numerical operations and are optimized for performance, especially when dealing with large datasets.

np.array()

This creates an ndarray (n-dimensional array). There is no limit to how many dimensions a NumPy array can have, but arrays with many dimensions can be more difficult to work with.

1-D array:

import numpy as np
array_1d = np.array([1, 2, 3])
array_1d

Notice that a one-dimensional array is similar to a list.

2-D array:

array_2d = np.array([(1, 2, 3), (4, 5, 6)])
array_2d

Notice that a two-dimensional array is similar to a table.

Array methods

NumPy arrays have many methods that allow you to manipulate and operate on them. For a full list, refer to the NumPy array documentation. Some of the most commonly used methods follow:

array_2d = np.array([(1, 2, 3), (4, 5, 6)])
print(array_2d)
print()
array_2d.flatten()

ndarray.reshape()

  • This gives a new shape to an array without changing its data.
array_2d = np.array([(1, 2, 3), (4, 5, 6)])
print(array_2d)
print()
array_2d.reshape(3, 2)

Adding a value of -1 in the designated new shape makes the process more efficient, as it indicates for NumPy to automatically infer the value based on other given values.

array_2d = np.array([(1, 2, 3), (4, 5, 6)])
print(array_2d)
print()
array_2d.reshape(3, -1)

ndarray.tolist()

  • This converts an array to a list object. Multidimensional arrays are converted to nested lists. 
array_2d = np.array([(1, 2, 3), (4, 5, 6)])
print(array_2d)
print()
array_2d.tolist()
Array attributes

NumPy arrays have several attributes that enable you to access information about the array. Some of the most commonly used attributes include the following:

  • ndarray.shape: returns a tuple of the array’s dimensions.
  • ndarray.dtype: returns the data type of the array’s contents.
  • ndarray.size: returns the total number of elements in the array.
  • ndarray.T: returns the array transposed (rows become columns, columns become rows).
Indexing and slicing

Access individual elements of a NumPy array using indexing and slicing. Indexing in NumPy is similar to indexing in Python lists, except multiple indices can be used to access elements in multidimensional arrays.

a = np.array([(1, 2, 3), (4, 5, 6)])
print(a)
print()

print(a[1])
print(a[0, 1])
print(a[1, 2])

NumPy arrays work by allocating a contiguous block of memory at the time of instantiation. Most other structures in Python don’t do this; their data is scattered across the system’s memory. This is what makes NumPy arrays so fast; all the data is stored together at a particular address in the system’s memory

import numpy as np
import time

# Python list
start_time = time.time()
my_list = [i for i in range(1000000)]
sum_list = sum(my_list)
end_time = time.time()
print(f"Python list sum time: {end_time - start_time}")

# NumPy array
start_time = time.time()
my_array = np.arange(1000000)
sum_array = np.sum(my_array)
end_time = time.time()
print(f"NumPy array sum time: {end_time - start_time}")

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