In data science and numerical computing, combining datasets is a common task. Joining (concatenating) arrays in NumPy is a powerful and essential feature for assembling data. Whether you're stacking arrays vertically or horizontally, NumPy provides a suite of functions for seamless array joining.

In this article, you'll learn:

• ✅ What joining arrays means in NumPy

• How to join arrays using concatenate(), stack(), hstack(), vstack(), and dstack()

• Full working examples

• ✅ Best practices and tips

• ⚠️ Common pitfalls and how to avoid them

What Is Joining in NumPy?

Joining in NumPy refers to combining two or more arrays along an existing or new axis. This is different from merging (used in DataFrames) and is strictly about array structures.

1. Using np.concatenate()

This is the most flexible method and works for arrays of matching dimensions.

➕ Example: Join 1D Arrays

import numpy as np

a = np.array([1, 2, 3])
b = np.array([4, 5, 6])

joined = np.concatenate((a, b))
print(joined)  # Output: [1 2 3 4 5 6]

Example: Join 2D Arrays Along Rows (axis=0)

a = np.array([[1, 2], [3, 4]])
b = np.array([[5, 6]])

result = np.concatenate((a, b), axis=0)
print(result)

Output:

[[1 2]
 [3 4]
 [5 6]]

⚠️ Note: Shapes must be compatible (same number of columns in this case).

2. Using np.stack()

Use stack() when you want to add a new dimension while joining arrays.

a = np.array([1, 2])
b = np.array([3, 4])

stacked = np.stack((a, b), axis=0)
print(stacked)

Output:

[[1 2]
 [3 4]]

• axis=0 → vertical stack

• axis=1 → horizontal stack

3. Using np.hstack() – Horizontal Stack

Stacks arrays side-by-side (along columns).

a = np.array([1, 2, 3])
b = np.array([4, 5, 6])

result = np.hstack((a, b))
print(result)  # [1 2 3 4 5 6]

For 2D:

a = np.array([[1], [2]])
b = np.array([[3], [4]])

result = np.hstack((a, b))
print(result)

Output:

[[1 3]
 [2 4]]

4. Using np.vstack() – Vertical Stack

Stacks arrays on top of each other (along rows).

a = np.array([1, 2, 3])
b = np.array([4, 5, 6])

result = np.vstack((a, b))
print(result)

Output:

[[1 2 3]
 [4 5 6]]

5. Using np.dstack() – Depth Stack

Stacks arrays along the third dimension (useful for image processing or 3D structures).

a = np.array([1, 2])
b = np.array([3, 4])

result = np.dstack((a, b))
print(result)

Output:

[[[1 3]
  [2 4]]]

Full Working Code Example

import numpy as np

a = np.array([[1, 2], [3, 4]])
b = np.array([[5, 6], [7, 8]])

# Concatenate along rows
concat_rows = np.concatenate((a, b), axis=0)

# Concatenate along columns
concat_cols = np.concatenate((a, b), axis=1)

# Stack along new axis (axis=0)
stacked_axis0 = np.stack((a, b), axis=0)

# Horizontal and Vertical stacking
h_stacked = np.hstack((a, b))
v_stacked = np.vstack((a, b))

# Print results
print("Concatenated Rows:\n", concat_rows)
print("Concatenated Columns:\n", concat_cols)
print("Stacked (axis=0):\n", stacked_axis0)
print("Horizontally Stacked:\n", h_stacked)
print("Vertically Stacked:\n", v_stacked)

✅ Tips and Best Practices

⚠️ Common Pitfalls

Conclusion

Joining arrays is a crucial part of working with numerical data in Python. NumPy provides multiple tools—each with its strengths:

• Use concatenate() for general-purpose joining

• Use stack() when you need a new axis

• Use hstack(), vstack(), and dstack() for quick stacking in specific directions

Understanding these tools will help you manipulate and prepare data more effectively, especially when working with larger datasets or machine learning models.

More to Explore

• NumPy Splitting Arrays

• Reshape and Resize in NumPy

• Broadcasting Explained