NumPy: Combining Two 1D Arrays into a 2D Array Step-by-Step Examples

 

In this blog post, we'll walk through the process step by step, unraveling the magic of NumPy.

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Step 1: Importing NumPy

Before we dive into the examples, let's ensure we have NumPy installed and imported.

pip install numpy

Now, let's import NumPy:

import numpy as np

Step 2: Creating 1D Arrays

Let's start by creating two simple 1D arrays. For the sake of this example, we'll use the arrays arr1 and arr2:

arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])

Step 3: Combining into a 2D Array - np.vstack

NumPy provides the np.vstack function to vertically stack arrays. This means combining them along the rows to create a 2D array. Let's see how it's done:

result_2d = np.vstack((arr1, arr2))
print("Resulting 2D Array:\n", result_2d)

import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
result_2d = np.vstack((arr1, arr2))
print("Resulting 2D Array:\n", result_2d)  
  

This will output:

Resulting 2D Array:
 [[1 2 3]
 [4 5 6]]

 

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Step 4: Combining into a 2D Array - np.concatenate

Alternatively, you can use the np.concatenate function with the axis parameter set to 0 for vertical stacking:

result_concat = np.concatenate((arr1.reshape(1, -1), arr2.reshape(1, -1)), axis=0)
print("Resulting 2D Array (using concatenate):\n", result_concat)

Here, we use reshape(1, -1) to convert the 1D arrays into 2D arrays with a single row.

import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
result_concat = np.concatenate((arr1.reshape(1, -1), arr2.reshape(1, -1)), axis=0)
print("Resulting 2D Array (using concatenate):\n", result_concat)  
  

Resulting 2D Array (using concatenate):
 [[1 2 3]
 [4 5 6]]  
  

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Step 5: Conclusion

combined two 1D arrays into a 2D array using NumPy. This skill is particularly useful in various data manipulation and analysis tasks, making NumPy a versatile tool for scientific computing in Python.

In this blog post, we covered the essential steps, from importing NumPy to creating 1D arrays and finally combining them into a 2D array using both np.vstack and np.concatenate. Armed with this knowledge, you're ready to tackle more complex data manipulation challenges with NumPy.