Course Content

NumPy in a Nutshell

1. Getting Started with NumPy

What is NumPy?Function array()Basic Array Creation Quiz

2. Dimensions in Arrays

1-D Arrays Mathematical Operations with 1-D Arrays 2-D Arrays 3-D Arrays Multidimensional Arrays Quiz

3. Indexing and Slicing

Access Array Elements Match Math Operations Access 2-D and 3-D Arrays Match the Slicing Negative Indexing Match the Indices Get One Dimensional Array Using Slice and Only Positive Indexes Get One Dimensional Array Using Only Negative Indexes

4. Important Functions

Reshaping Flattening Joining Sorting Arrays Copying Arrays

Joining

Another equally important operation in array manipulation is joining arrays. Joining arrays involves combining multiple arrays into a single array that includes all the elements from each of the original arrays.

We perform this concatenation along the specified axes:

if axis = 0 (which is the default value), this implies concatenating the arrays by rows;
if axis = 1, this means concatenating the arrays by columns.

Join two arrays:


              12345678
            
import numpy as np

array_1 = np.array([54, 6, 23, 1, 6])
array_2 = np.array([12, 67, 94, 2, 8 ])

array = np.concatenate((array_1, array_2))

print(array)

Concatenate two 2-D arrays along columns (axis=1):


              12345678
            
import numpy as np

array_1 = np.array([[0, 1, 2], [3, 4, 5]])
array_2 = np.array([[6, 7, 8], [9, 10, 11]])

array = np.concatenate((array_1, array_2), axis=1)

print(array)

Concatenate two 2-D arrays along rows (default axis=0):


              12345678
            
import numpy as np

array_1 = np.array([[0, 1, 2], [3, 4, 5]])
array_2 = np.array([[6, 7, 8], [9, 10, 11]])

array = np.concatenate((array_1, array_2))

print(array)

Task

You have two arrays:

[[12, 56, 78], [35, 1, 5]];
[[ 8, 65, 3], [ 1, 2, 3]].

You have to create the following combined array:

Task

You have two arrays:

[[12, 56, 78], [35, 1, 5]];
[[ 8, 65, 3], [ 1, 2, 3]].

You have to create the following combined array:

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Section 4. Chapter 3

Joining

We perform this concatenation along the specified axes:

if axis = 0 (which is the default value), this implies concatenating the arrays by rows;
if axis = 1, this means concatenating the arrays by columns.

Join two arrays:


              12345678
            
import numpy as np

array_1 = np.array([54, 6, 23, 1, 6])
array_2 = np.array([12, 67, 94, 2, 8 ])

array = np.concatenate((array_1, array_2))

print(array)

Concatenate two 2-D arrays along columns (axis=1):


              12345678
            
import numpy as np

array_1 = np.array([[0, 1, 2], [3, 4, 5]])
array_2 = np.array([[6, 7, 8], [9, 10, 11]])

array = np.concatenate((array_1, array_2), axis=1)

print(array)

Concatenate two 2-D arrays along rows (default axis=0):


              12345678
            
import numpy as np

array_1 = np.array([[0, 1, 2], [3, 4, 5]])
array_2 = np.array([[6, 7, 8], [9, 10, 11]])

array = np.concatenate((array_1, array_2))

print(array)

Task

You have two arrays:

[[12, 56, 78], [35, 1, 5]];
[[ 8, 65, 3], [ 1, 2, 3]].

You have to create the following combined array:

Task

You have two arrays:

[[12, 56, 78], [35, 1, 5]];
[[ 8, 65, 3], [ 1, 2, 3]].

You have to create the following combined array:

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Section 4. Chapter 3

Joining

We perform this concatenation along the specified axes:

if axis = 0 (which is the default value), this implies concatenating the arrays by rows;
if axis = 1, this means concatenating the arrays by columns.

Join two arrays:


              12345678
            
import numpy as np

array_1 = np.array([54, 6, 23, 1, 6])
array_2 = np.array([12, 67, 94, 2, 8 ])

array = np.concatenate((array_1, array_2))

print(array)

Concatenate two 2-D arrays along columns (axis=1):


              12345678
            
import numpy as np

array_1 = np.array([[0, 1, 2], [3, 4, 5]])
array_2 = np.array([[6, 7, 8], [9, 10, 11]])

array = np.concatenate((array_1, array_2), axis=1)

print(array)

Concatenate two 2-D arrays along rows (default axis=0):


              12345678
            
import numpy as np

array_1 = np.array([[0, 1, 2], [3, 4, 5]])
array_2 = np.array([[6, 7, 8], [9, 10, 11]])

array = np.concatenate((array_1, array_2))

print(array)

Task

You have two arrays:

[[12, 56, 78], [35, 1, 5]];
[[ 8, 65, 3], [ 1, 2, 3]].

You have to create the following combined array:

Task

You have two arrays:

[[12, 56, 78], [35, 1, 5]];
[[ 8, 65, 3], [ 1, 2, 3]].

You have to create the following combined array:

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

We perform this concatenation along the specified axes:

if axis = 0 (which is the default value), this implies concatenating the arrays by rows;
if axis = 1, this means concatenating the arrays by columns.

Join two arrays:


              12345678
            
import numpy as np

array_1 = np.array([54, 6, 23, 1, 6])
array_2 = np.array([12, 67, 94, 2, 8 ])

array = np.concatenate((array_1, array_2))

print(array)

Concatenate two 2-D arrays along columns (axis=1):


              12345678
            
import numpy as np

array_1 = np.array([[0, 1, 2], [3, 4, 5]])
array_2 = np.array([[6, 7, 8], [9, 10, 11]])

array = np.concatenate((array_1, array_2), axis=1)

print(array)

Concatenate two 2-D arrays along rows (default axis=0):


              12345678
            
import numpy as np

array_1 = np.array([[0, 1, 2], [3, 4, 5]])
array_2 = np.array([[6, 7, 8], [9, 10, 11]])

array = np.concatenate((array_1, array_2))

print(array)

Task

You have two arrays:

[[12, 56, 78], [35, 1, 5]];
[[ 8, 65, 3], [ 1, 2, 3]].

You have to create the following combined array:

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Section 4. Chapter 3

Switch to desktop for real-world practiceContinue from where you are using one of the options below