Hello Guys, How are you all? Hope You all Are Fine. Today We Are Going To learn about **How to zip two 1d numpy array to 2d numpy array** **in Python**. So Here I am Explain to you all the possible Methods here.

Without wasting your time, Let’s start This Article.

Table of Contents

## How to zip two 1d numpy array to 2d numpy array?

**How to zip two 1d numpy array to 2d numpy array?**Although my post gives the answer as requested by the OP, the conversion to list and back to NumPy array takes some overhead (noticeable for large arrays).

**zip two 1d numpy array to 2d numpy array**Although my post gives the answer as requested by the OP, the conversion to list and back to NumPy array takes some overhead (noticeable for large arrays).

## Method 1

If you have numpy arrays you can use `dstack()`

:

import numpy as np a = np.array([1,2,3,4,5]) b = np.array([6,7,8,9,10]) c = np.dstack((a,b)) #or d = np.column_stack((a,b)) >>> c array([[[ 1, 6], [ 2, 7], [ 3, 8], [ 4, 9], [ 5, 10]]]) >>> d array([[ 1, 6], [ 2, 7], [ 3, 8], [ 4, 9], [ 5, 10]]) >>> c.shape (1, 5, 2) >>> d.shape (5, 2)

## Method 2

The answer lies in your question:

np.array(list(zip(a,b)))

**Edit:**

Although my post gives the answer as requested by the OP, the conversion to list and back to NumPy array takes some overhead (noticeable for large arrays).

Hence, `dstack`

would be a computationally efficient alternative (ref. @zipa’s answer). I was unaware of `dstack`

at the time of posting this answer so credits to @zipa for introducing it to this post.

**Edit 2:**

As can be seen in the duplicate question, `np.c_`

is even shorter than `np.dstack`

.

>>> import numpy as np >>> a = np.arange(1, 6) >>> b = np.arange(6, 11) >>> >>> a array([1, 2, 3, 4, 5]) >>> b array([ 6, 7, 8, 9, 10]) >>> np.c_[a, b] array([[ 1, 6], [ 2, 7], [ 3, 8], [ 4, 9], [ 5, 10]])

**Summery**

It’s all About this issue. Hope all Methods helped you a lot. Comment below Your thoughts and your queries. Also, Comment below which Method worked for you? Thank You.

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