Python NumPy – Concatenate() Function

You can use the Python NumPy concatenate() function to join a sequence of arrays along an axis (row/column). This function is used to join two or more arrays of the same shape along a specified axis. In this article, I will explain NumPy concatenate() function syntax and usage with examples.

1. Quick Examples of Python NumPy Concatenate()

If you are in a hurry, below are some quick examples of how to use Python NumPy concatenate() function.

# Below are the quick examples

# Example 1: Use np.concatenate() function
con = np.concatenate((arr, arr1))
print(con)

# Example 2: Use Joining the two arrays along axis 0
con = np.concatenate((arr, arr1), axis = 0)
print(con)

# Example 3: Use Joining the two arrays along axis 1
con = np.concatenate((arr, arr1), axis = 1)
print(con)

# Example 4: Use Joining the two arrays along axis=None
con = np.concatenate((arr, arr1), axis = None)
print(con)

2. NumPy concatenate() Syntax

Following is the syntax of the concatenate() function.

# numpy.concatenate() syntax
numpy.concatenate((arr, arr1, ...), axis=0, out=None)

• arr,arr1 : a sequence of array_like. The arrays must have the same shape, except in the dimension corresponding to the axis (the first, by default).
• axis : The axis along which the arrays will be joined. If the axis is None, arrays are flattened before use. Default is 0.
• out : If provided, the destination to place the result.
• Returns : The concatenated array.

3. Use numpy.concatenate() Method

Use numpy.concatenate() to put the content of two or more arrays into a single array. This function takes several arguments along with the NumPy arrays to concatenate and returns a Numpy array ndarray. If the axis is not passed; it is taken as 0.

import numpy as np
arr = np.array([[4, 6], [9, 13]])
arr1 = np.array([[8, 3], [12, 19]])
# Use np.concatenate() function
con = np.concatenate((arr, arr1))
print(con)

Yields below output.

# Output:
[[ 4  6]
 [ 9 13]
 [ 8  3]
 [12 19]]

4. Use Joining the Two Arrays along Axis = 0

You can use join two NumPy arrays row-wise by specifying axis=0. Now the resulting array is a wide matrix with more columns than rows; follow the below example, 4 rows, and 2 columns.

import numpy as np

# Create NumPy arrays 
arr = np.array([[4, 6], [9, 13]])
arr1 = np.array([[8, 3], [12, 19]])

# Use Joining the two arrays along axis 0
con = np.concatenate((arr, arr1), axis = 0)
print(con)

Yields the same output as above.

5. Use Joining the Two Arrays along Axis = 1

You can also join two NumPy arrays column-wise by specifying axis=1. Now the resulting array is a wide matrix with more columns than rows.

import numpy as np

# Create NumPy arrays   
arr = np.array([[4, 6], [9, 13]])
arr1 = np.array([[8, 3], [12, 19]])

# Use Joining the two arrays along axis 1
con = np.concatenate((arr, arr1), axis = 1)
print(con)

Yields below output.

# Output:
[[ 4  6  8  3]
 [ 9 13 12 19]]

6. Use numpy.concatenate() with Axis=None

import numpy as np

# Create NumPy arrays 
arr = np.array([[4, 6], [9, 13]])
arr1 = np.array([[8, 3], [12, 19]])

# Use Joining the two arrays along axis=None
con = np.concatenate((arr, arr1), axis = None)
print(con)

Yields below output.

# Output:
[ 4  6  9 13  8  3 12 19]

7. Conclusion

In this article, I have explained how to use NumPy concatenate() function to join two or more arrays into a single Numpy array with examples.

Happy Learning!!

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References

• https://numpy.org/doc/stable/reference/generated/numpy.concatenate.html