# Python NumPy – Concatenate() Function

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• Post category:NumPy / Python

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 a quick example

# 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.

``````
[[ 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.

``````
[[ 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.

``````
[ 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!!

## References 