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Ultimate NumPy
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Ultimate NumPy

Ultimate NumPy

1. NumPy Basics
Introduction to NumPyBasic Array CreationCreating Higher Dimensional ArraysCreation Functions for 1D ArraysCreation Functions for 2D ArraysGeneral Array Creation FunctionsRandom Arrays
2. Indexing and Slicing
Basic IndexingMultidimensional IndexingSlicingSlicing in 2D ArraysInteger Array IndexingInteger Array Indexing in 2D ArraysBoolean IndexingMore about Comparisons Boolean Indexing in 2D ArraysAssigning Values to Indexed ElementsAssigning Values to Indexed Subarrays
3. Commonly used NumPy Functions
Sorting ArraysSorting 2D ArraysCopying ArraysReshaping ArraysFlattening ArraysArray Concatenation
4. Math with NumPy
BroadcastingBasic Mathematical OperationsStatistical OperationsBasic Linear Algebra with NumPy

bookGeneral Array Creation Functions

NumPy also has array creation functions that can automatically create an array of a given shape (dimensions). Here are the most common ones:

  • zeros();
  • ones();
  • full().

There are also functions that create random arrays of given shapes; however, we'll discuss them separately in the next chapter.

zeros()

The name of this function speaks for itself: it creates an array of zeros of a given shape. The shape of the array is specified via the shape parameter and can either be an integer (size of a 1D array) or a tuple of integers for higher-dimensional arrays.

Let’s take a look at an example:


              
123456789101112

            
import numpy as np
# Сreating a 1D array of zeros with 5 elements
zeros_1d = np.zeros(5)
print(zeros_1d)
print('-' * 16)
# Сreating a 1D array of zeros with specifying dtype
zeros_1d_int = np.zeros(5, dtype=np.int8)
print(zeros_1d_int)
print('-' * 16)
# Сreating a 2D array of zeros of shape 5x3
zeros_2d = np.zeros((5, 3))
print(zeros_2d)
copy

As you can see, we can also specify the dtype parameter in the same way we did for other types of arrays.

Note

numpy.zeros() is often used as a placeholder to initialize arrays of a given shape, which will be later filled with other values. Therefore, be careful when explicitly specifying the dtype.

ones()

This function is similar to the zeros() function; however, instead of an array of zeros, it creates an array of ones. Without further ado, let’s see it in action:


              
123456789101112

            
import numpy as np
# Сreating a 1D array of ones with 5 elements
ones_1d = np.ones(5)
print(ones_1d)
print('-' * 16)
# Сreating a 1D array of ones with specifying dtype
ones_1d_int = np.ones(5, dtype=np.int8)
print(ones_1d_int)
print('-' * 16)
# Сreating a 2D array of ones of shape 5x3
ones_2d = np.ones((5, 3))
print(ones_2d)
copy

In terms of syntax, everything here is the same as with the zeros() function.

Note

numpy.ones() is also often used as a placeholder to initialize arrays of a given shape, so be cautious with the dtype as well.

full()

The numpy.full() function is similar to the functions mentioned above; however, it has a second parameter, fill_value, to specify the value to fill the array with. Its first parameter, shape, can be either an integer or a tuple of integers:


              
1234567

            
import numpy as np
# Сreate an array of fours of size 5
array_fours_1d = np.full(5, 4)
# Сreate an array of fives of shape 4x2
array_fives_2d = np.full((4, 2), 5)
print(f'1D fours array: {array_fours_1d}')
print(f'2D fives array:\n{array_fives_2d}')
copy

More Applications

All of these functions have more use cases than simply being placeholders. They are quite often used directly in mathematical operations in linear algebra. They can be applied in various fields of machine and deep learning, such as image processing.

If you want to explore more about these functions, feel free to refer to their documentation: zeros, ones, and full.

Task

  1. Create a one-dimensional array of zeros with a size of 5 and assign it to zeros_array_1d.
  2. Create a two-dimensional array of zeros with a shape of 2x4 and assign it to zeros_array_2d.
  3. Create a one-dimensional array of ones with a size of 3 and assign it to ones_array_1d.
  4. Create a two-dimensional array of ones with a shape of 2x3 and assign it to ones_array_2d.
  5. Create a two-dimensional array of sevens with a shape of 2x2 and assign it to sevens_array_2d.

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Section 1. Chapter 6
toggle bottom row

bookGeneral Array Creation Functions

NumPy also has array creation functions that can automatically create an array of a given shape (dimensions). Here are the most common ones:

  • zeros();
  • ones();
  • full().

There are also functions that create random arrays of given shapes; however, we'll discuss them separately in the next chapter.

zeros()

The name of this function speaks for itself: it creates an array of zeros of a given shape. The shape of the array is specified via the shape parameter and can either be an integer (size of a 1D array) or a tuple of integers for higher-dimensional arrays.

Let’s take a look at an example:


              
123456789101112

            
import numpy as np
# Сreating a 1D array of zeros with 5 elements
zeros_1d = np.zeros(5)
print(zeros_1d)
print('-' * 16)
# Сreating a 1D array of zeros with specifying dtype
zeros_1d_int = np.zeros(5, dtype=np.int8)
print(zeros_1d_int)
print('-' * 16)
# Сreating a 2D array of zeros of shape 5x3
zeros_2d = np.zeros((5, 3))
print(zeros_2d)
copy

As you can see, we can also specify the dtype parameter in the same way we did for other types of arrays.

Note

numpy.zeros() is often used as a placeholder to initialize arrays of a given shape, which will be later filled with other values. Therefore, be careful when explicitly specifying the dtype.

ones()

This function is similar to the zeros() function; however, instead of an array of zeros, it creates an array of ones. Without further ado, let’s see it in action:


              
123456789101112

            
import numpy as np
# Сreating a 1D array of ones with 5 elements
ones_1d = np.ones(5)
print(ones_1d)
print('-' * 16)
# Сreating a 1D array of ones with specifying dtype
ones_1d_int = np.ones(5, dtype=np.int8)
print(ones_1d_int)
print('-' * 16)
# Сreating a 2D array of ones of shape 5x3
ones_2d = np.ones((5, 3))
print(ones_2d)
copy

In terms of syntax, everything here is the same as with the zeros() function.

Note

numpy.ones() is also often used as a placeholder to initialize arrays of a given shape, so be cautious with the dtype as well.

full()

The numpy.full() function is similar to the functions mentioned above; however, it has a second parameter, fill_value, to specify the value to fill the array with. Its first parameter, shape, can be either an integer or a tuple of integers:


              
1234567

            
import numpy as np
# Сreate an array of fours of size 5
array_fours_1d = np.full(5, 4)
# Сreate an array of fives of shape 4x2
array_fives_2d = np.full((4, 2), 5)
print(f'1D fours array: {array_fours_1d}')
print(f'2D fives array:\n{array_fives_2d}')
copy

More Applications

All of these functions have more use cases than simply being placeholders. They are quite often used directly in mathematical operations in linear algebra. They can be applied in various fields of machine and deep learning, such as image processing.

If you want to explore more about these functions, feel free to refer to their documentation: zeros, ones, and full.

Task

  1. Create a one-dimensional array of zeros with a size of 5 and assign it to zeros_array_1d.
  2. Create a two-dimensional array of zeros with a shape of 2x4 and assign it to zeros_array_2d.
  3. Create a one-dimensional array of ones with a size of 3 and assign it to ones_array_1d.
  4. Create a two-dimensional array of ones with a shape of 2x3 and assign it to ones_array_2d.
  5. Create a two-dimensional array of sevens with a shape of 2x2 and assign it to sevens_array_2d.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 1. Chapter 6
toggle bottom row

bookGeneral Array Creation Functions

NumPy also has array creation functions that can automatically create an array of a given shape (dimensions). Here are the most common ones:

  • zeros();
  • ones();
  • full().

There are also functions that create random arrays of given shapes; however, we'll discuss them separately in the next chapter.

zeros()

The name of this function speaks for itself: it creates an array of zeros of a given shape. The shape of the array is specified via the shape parameter and can either be an integer (size of a 1D array) or a tuple of integers for higher-dimensional arrays.

Let’s take a look at an example:


              
123456789101112

            
import numpy as np
# Сreating a 1D array of zeros with 5 elements
zeros_1d = np.zeros(5)
print(zeros_1d)
print('-' * 16)
# Сreating a 1D array of zeros with specifying dtype
zeros_1d_int = np.zeros(5, dtype=np.int8)
print(zeros_1d_int)
print('-' * 16)
# Сreating a 2D array of zeros of shape 5x3
zeros_2d = np.zeros((5, 3))
print(zeros_2d)
copy

As you can see, we can also specify the dtype parameter in the same way we did for other types of arrays.

Note

numpy.zeros() is often used as a placeholder to initialize arrays of a given shape, which will be later filled with other values. Therefore, be careful when explicitly specifying the dtype.

ones()

This function is similar to the zeros() function; however, instead of an array of zeros, it creates an array of ones. Without further ado, let’s see it in action:


              
123456789101112

            
import numpy as np
# Сreating a 1D array of ones with 5 elements
ones_1d = np.ones(5)
print(ones_1d)
print('-' * 16)
# Сreating a 1D array of ones with specifying dtype
ones_1d_int = np.ones(5, dtype=np.int8)
print(ones_1d_int)
print('-' * 16)
# Сreating a 2D array of ones of shape 5x3
ones_2d = np.ones((5, 3))
print(ones_2d)
copy

In terms of syntax, everything here is the same as with the zeros() function.

Note

numpy.ones() is also often used as a placeholder to initialize arrays of a given shape, so be cautious with the dtype as well.

full()

The numpy.full() function is similar to the functions mentioned above; however, it has a second parameter, fill_value, to specify the value to fill the array with. Its first parameter, shape, can be either an integer or a tuple of integers:


              
1234567

            
import numpy as np
# Сreate an array of fours of size 5
array_fours_1d = np.full(5, 4)
# Сreate an array of fives of shape 4x2
array_fives_2d = np.full((4, 2), 5)
print(f'1D fours array: {array_fours_1d}')
print(f'2D fives array:\n{array_fives_2d}')
copy

More Applications

All of these functions have more use cases than simply being placeholders. They are quite often used directly in mathematical operations in linear algebra. They can be applied in various fields of machine and deep learning, such as image processing.

If you want to explore more about these functions, feel free to refer to their documentation: zeros, ones, and full.

Task

  1. Create a one-dimensional array of zeros with a size of 5 and assign it to zeros_array_1d.
  2. Create a two-dimensional array of zeros with a shape of 2x4 and assign it to zeros_array_2d.
  3. Create a one-dimensional array of ones with a size of 3 and assign it to ones_array_1d.
  4. Create a two-dimensional array of ones with a shape of 2x3 and assign it to ones_array_2d.
  5. Create a two-dimensional array of sevens with a shape of 2x2 and assign it to sevens_array_2d.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

NumPy also has array creation functions that can automatically create an array of a given shape (dimensions). Here are the most common ones:

  • zeros();
  • ones();
  • full().

There are also functions that create random arrays of given shapes; however, we'll discuss them separately in the next chapter.

zeros()

The name of this function speaks for itself: it creates an array of zeros of a given shape. The shape of the array is specified via the shape parameter and can either be an integer (size of a 1D array) or a tuple of integers for higher-dimensional arrays.

Let’s take a look at an example:


              
123456789101112

            
import numpy as np
# Сreating a 1D array of zeros with 5 elements
zeros_1d = np.zeros(5)
print(zeros_1d)
print('-' * 16)
# Сreating a 1D array of zeros with specifying dtype
zeros_1d_int = np.zeros(5, dtype=np.int8)
print(zeros_1d_int)
print('-' * 16)
# Сreating a 2D array of zeros of shape 5x3
zeros_2d = np.zeros((5, 3))
print(zeros_2d)
copy

As you can see, we can also specify the dtype parameter in the same way we did for other types of arrays.

Note

numpy.zeros() is often used as a placeholder to initialize arrays of a given shape, which will be later filled with other values. Therefore, be careful when explicitly specifying the dtype.

ones()

This function is similar to the zeros() function; however, instead of an array of zeros, it creates an array of ones. Without further ado, let’s see it in action:


              
123456789101112

            
import numpy as np
# Сreating a 1D array of ones with 5 elements
ones_1d = np.ones(5)
print(ones_1d)
print('-' * 16)
# Сreating a 1D array of ones with specifying dtype
ones_1d_int = np.ones(5, dtype=np.int8)
print(ones_1d_int)
print('-' * 16)
# Сreating a 2D array of ones of shape 5x3
ones_2d = np.ones((5, 3))
print(ones_2d)
copy

In terms of syntax, everything here is the same as with the zeros() function.

Note

numpy.ones() is also often used as a placeholder to initialize arrays of a given shape, so be cautious with the dtype as well.

full()

The numpy.full() function is similar to the functions mentioned above; however, it has a second parameter, fill_value, to specify the value to fill the array with. Its first parameter, shape, can be either an integer or a tuple of integers:


              
1234567

            
import numpy as np
# Сreate an array of fours of size 5
array_fours_1d = np.full(5, 4)
# Сreate an array of fives of shape 4x2
array_fives_2d = np.full((4, 2), 5)
print(f'1D fours array: {array_fours_1d}')
print(f'2D fives array:\n{array_fives_2d}')
copy

More Applications

All of these functions have more use cases than simply being placeholders. They are quite often used directly in mathematical operations in linear algebra. They can be applied in various fields of machine and deep learning, such as image processing.

If you want to explore more about these functions, feel free to refer to their documentation: zeros, ones, and full.

Task

  1. Create a one-dimensional array of zeros with a size of 5 and assign it to zeros_array_1d.
  2. Create a two-dimensional array of zeros with a shape of 2x4 and assign it to zeros_array_2d.
  3. Create a one-dimensional array of ones with a size of 3 and assign it to ones_array_1d.
  4. Create a two-dimensional array of ones with a shape of 2x3 and assign it to ones_array_2d.
  5. Create a two-dimensional array of sevens with a shape of 2x2 and assign it to sevens_array_2d.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 1. Chapter 6
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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