NumPy Arrays

Learn about NumPy arrays and how they're used.

We'll cover the following

Chapter Goals:

  • Learn about NumPy arrays and how to initialize them
  • Write code to create several NumPy arrays

A. Arrays

NumPy arrays are basically just Python lists with added features. In fact, you can easily convert a Python list to a Numpy array using the np.array function, which takes in a Python list as its required argument. The function also has quite a few keyword arguments, but the main one to know is dtype. The dtype keyword argument takes in a NumPy type and manually casts the array to the specified type.

The code below is an example usage of np.array to create a 2-D matrix. Note that the array is manually cast to np.float32.

import numpy as np
arr = np.array([[0, 1, 2], [3, 4, 5]],
               dtype=np.float32)
print(repr(arr))

When the elements of a NumPy array are mixed types, then the array's type will be upcast to the highest level type. This means that if an array input has mixed int and float elements, all the integers will be cast to their floating-point equivalents. If an array is mixed with int, float, and string elements, everything is cast to strings.

The code below is an example of np.array upcasting. Both integers are cast to their floating-point equivalents.

arr = np.array([0, 0.1, 2])
print(repr(arr))

B. Copying

Similar to Python lists, when we make a reference to a NumPy array it doesn't create a different array. Therefore, if we change a value using the reference variable, it changes the original array as well. We get around this by using an array's inherent copy function. The function has no required arguments, and it returns the copied array.

In the code example below, c is a reference to a while d is a copy. Therefore, changing c leads to the same change in a, while changing d does not change the value of b.

a = np.array([0, 1])
b = np.array([9, 8])
c = a
print('Array a: {}'.format(repr(a)))
c[0] = 5
print('Array a: {}'.format(repr(a)))
d = b.copy()
d[0] = 6
print('Array b: {}'.format(repr(b)))

C. Casting

We cast NumPy arrays through their inherent astype function. The function's required argument is the new type for the array. It returns the array cast to the new type.

The code below shows an example of casting using the astype function. The dtype property returns the type of an array.

arr = np.array([0, 1, 2])
print(arr.dtype)
arr = arr.astype(np.float32)
print(arr.dtype)

D. NaN

When we don't want a NumPy array to contain a value at a particular index, we can use np.nan to act as a placeholder. A common usage for np.nan is as a filler value for incomplete data.

The code below shows an example usage of np.nan. Note that np.nan cannot take on an integer type.

arr = np.array([np.nan, 1, 2])
print(repr(arr))
arr = np.array([np.nan, 'abc'])
print(repr(arr))
# Will result in a ValueError: If we uncomment line 8 and run again.
#np.array([np.nan, 1, 2], dtype=np.int32)
np.array([np.nan, 1, 2], dtype=np.float32)

E. Infinity

To represent infinity in NumPy, we use the np.inf special value. We can also represent negative infinity with -np.inf.

The code below shows an example usage of np.inf. Note that np.inf cannot take on an integer type.

print(np.inf > 1000000)
arr = np.array([np.inf, 5])
print(repr(arr))
arr = np.array([-np.inf, 1])
print(repr(arr))
# Will result in a OverflowError: If we uncomment line 10 and run again.
#np.array([np.inf, 3], dtype=np.int32)
np.array([np.inf, 3], dtype=np.float32)

Time to Code!

The first array we'll create comes straight from a list of integers and np.nan. The list contains np.nan as the first element, and the integers from 2 to 5, inclusive, as the next four elements.

Set arr equal to np.array applied to the specified list.

# CODE HERE

We now want to copy the array so we can change the first element to 10. This way we don't modify the original array.

Set arr2 equal to arr.copy(), then set the first element of arr2 equal to 10.

# CODE HERE

The next two arrays will use floating point numbers. The first array will be upcast to floating point numbers, while we manually cast the second array using np.float32.

For manual casting, we use an array's inherent astype function, which takes in the new type as an argument and returns the casted array.

Set float_arr equal to np.array applied to a list with elements 1, 5.4, and 3, in that order.

Set float_arr2 equal to arr2.astype, with argument np.float32.

# CODE HERE

The final array will be a multi-dimensional array, specifically a 2-D matrix. The 2-D matrix will have the integers 1, 2, 3 in its first row, and the integers 4, 5, 6 in its second row. We'll also manually set its type to np.float32.

Set matrix equal to np.array with a list of lists (representing the specified 2-D matrix) as the first argument, and np.float32 as the dtype keyword argument.

# CODE HERE