## In This Article, You Will Learn About Python NumPy Joining

Python Numpy Joining – Before moving ahead, let’s know a little bit about **Python NumPy Iterating**

**Joining NumPy Arrays**

A join is when you combine the contents of multiple arrays into one.

SQL joins tables based upon a key; NumPy joins arrays by axes.

A sequence of arrays to be joined to the concatenate() function is passed along with the axis. If axis isn’t explicitly passed, it will be taken as 0.

Example – Joining two arrays.

import numpy as np Array_1 = np.array([11, 12, 13]) Array_2 = np.array([14, 15, 16]) Array = np.concatenate((Array_1, Array_2)) print(Array)

Output - [11 12 13 14 15 16]

As a result, it returned a joint array.

Example – Joining two 2-D arrays along rows (axis=1).

import numpy as np Array_1 = np.array([[11, 12], [13, 14]]) Array_2 = np.array([[15, 16], [17, 18]]) Array = np.concatenate(( Array_1 , Array_2), axis=1) print(Array)

Output - [[11 12 15 16] [13 14 17 18]]

As can be seen, it returned concatenated arrays along axis 1.

**Joining Arrays Using Stack Functions**

Stacking is same as concatenation, the only difference is that stacking is done along a new axis.

We can concatenate two 1-D arrays along the second axis which would result in putting them one over the other, ie. stacking.

We pass a sequence of arrays that we want to join to the stack() method along with the axis. If axis is not explicitly passed it is taken as 0.

**Stacking Along Rows**

NumPy provides a helper function: hstack() to stack along rows.

Example – Joining two arrays.

import numpy as np Array_1 = np.array([11, 12, 13]) Array_2 = np.array([14, 15, 16]) Array = np.hstack((Array_1, Array_2)) print(Array)

Output - [11 12 13 14 15 16]

All things considered, it returned concatenated arrays along axis 1.

**Stacking Along Columns**

NumPy provides a helper function: vstack() to stack along columns.

Example – Joining two arrays along columns.

import numpy as np Array_1 = np.array([11, 12, 13]) Array_2 = np.array([14, 15, 16]) Array = np.vstack((Array_1, Array_2)) print(Array)

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

As has been noted, it returned concatenated arrays along columns.

**Stacking along Height (depth)**.

Example – Joining two arrays along depth.

NumPy provides a helper function: dstack() to stack along height, which is the same as depth.

import numpy as np Array_1 = np.array([11, 12, 13]) Array_2 = np.array([14, 15, 16]) Array = np.dstack((Array_1, Array_2)) print(Array)

Output - [[[11 14] [12 15] [13 16]]]

Henceforth, it returned concatenated arrays along columns.

**Joining NumPy Arrays**

A join is when you combine the contents of multiple arrays into one.

SQL joins tables based upon a key; NumPy joins arrays by axes.

A sequence of arrays to be joined to the concatenate() function is passed along with the axis. If axis isn’t explicitly passed, it will be taken as 0.

Example – Joining two arrays.

import numpy as np Array_1 = np.array([1, 2, 3]) Array_2 = np.array([4, 5, 6]) Array = np.concatenate((Array_1, Array_2)) print(Array)

Output - [1 2 3 4 5 6]

To summarize, it returned concatenate arrays.

Example – Joining two 2-D arrays along rows (axis=1).

import numpy as np Array_1 = np.array([[11, 12], [13, 14]]) Array_2 = np.array([[15, 16], [17, 18]]) Array = np.concatenate((Array_1, Array_2), axis=1) print(Array)

Output - [[11 12 15 16] [13 14 17 18]]

As a result, it returned concatenation of two 2-D arrays along rows (axis=1).

**Joining Arrays Using Stack Functions**

Stacking is same as concatenation, the only difference is that stacking is done along a new axis.

We can concatenate two 1-D arrays along the second axis which would result in putting them one over the other, ie. stacking.

We pass a sequence of arrays that we want to join to the stack() method along with the axis. If axis is not explicitly passed it is taken as 0.

**Stacking Along Rows**

Example – Joining two arrays along rows.

NumPy provides a helper function: hstack() to stack along rows.

import numpy as np Array_1 = np.array([11, 12, 13]) Array_2 = np.array([14, 15, 16]) Array = np.hstack((Array_1, Array_2)) print(Array)

Output - [11 12 13 14 15 16]

Overall, it returned concatenation of two 1-D arrays along rows (axis=1).

**Stacking Along Columns**

NumPy provides a helper function: vstack() to stack along columns.

Example – Adding two arrays along columns.

import numpy as np Array_1 = np.array([11, 12, 13]) Array_2 = np.array([14, 15, 16]) Array = np.vstack((Array_1, Array_2)) print(Array)

As a result, it returned addition of two arrays along columns.

**Stacking along Height (depth)**.

NumPy provides a helper function: dstack() to stack along height, which is the same as depth.

Example – Adding two arrays along height.

Example – Adding two arrays along height.

import numpy as np Array_1 = np.array([11, 12, 13]) Array_2 = np.array([14, 15, 16]) Array = np.dstack((Array_1, Array_2)) print(Array)

Output - [11 12 13 14 15 16]

As shown above , it returned Addition of two arrays along columns.

If you find anything incorrect in the above-discussed topic and have any further questions, please comment below.

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