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Joining | Important Functions
NumPy in a Nutshell
course content

Course Content

NumPy in a Nutshell

NumPy in a Nutshell

1. Getting Started with NumPy
2. Dimensions in Arrays
3. Indexing and Slicing
4. Important Functions

bookJoining

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)
copy

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)
copy

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)
copy

Task

You have two arrays:

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

You have to create the following combined array:

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 4. Chapter 3
toggle bottom row

bookJoining

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)
copy

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)
copy

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)
copy

Task

You have two arrays:

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

You have to create the following combined array:

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 4. Chapter 3
toggle bottom row

bookJoining

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)
copy

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)
copy

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)
copy

Task

You have two arrays:

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

You have to create the following combined array:

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!

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)
copy

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)
copy

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)
copy

Task

You have two arrays:

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

You have to create the following combined array:

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 4. Chapter 3
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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