Introduction
While working with your machine learning and data science projects, you will come across instances where you will need to join different numpy arrays for performing an operation on them. This can be done by using numpy append or numpy concatenate functions. In this article, we will learn about numpy.append() and numpy.concatenate() and understand indepth with some examples.
Importing Numpy Library
Let us commence this article by importing numpy library.
import numpy as np
We’ll begin this article with numpy append function.
Numpy Append : np.append()
The numpy.append() function is used to add items/elements or arrays to an already existing array.
Syntax
numpy.append(arr, values, axis=None)
arr : array_like – These are the values are appended to a copy of this array.
values : array_like – These values are appended to a copy of arr. It must be of the correct shape (the same shape as arr, excluding axis). If axis is not specified, values can be any shape and will be flattened before use.
axis : int (optional) – The axis along which values are appended. If the axis is not given, both arr and values are flattened before use.
The result obtained through numpy.append() is a copy of arr with values appended to the axis. This append is not inplace i.e. original array values are not changed, whereas a new array is allocated and filled. If axis is None, out is a flattened array.
Example 1: Appending multiple arrays to an array
Here array a is created and then two arrays are appended to a with the help of np.append(). The resulting array of append function is a copy of the original array with other arrays added to it. The original array is always at the beginning of the resulting array.
a = np.array([1,2,3])
np.append(a, [[4, 5, 6], [7, 8, 9]])
array([1, 2, 3, 4, 5, 6, 7, 8, 9])
Example 2: When axis is specified as ‘0’
This example shows that it is important to take care of the shape of values argument when axis is specified. The array[1,5,7] is appended to 2D array [[2,5,8],[3,4,7]].
np.array([[2,5,8],[3,4,7]])
array([[2, 5, 8], [3, 4, 7]])
np.array([[1,5,7]])
array([[1, 5, 7]])
np.append([[2, 5, 8], [3, 4, 7]], [[1, 5, 7]], axis=0)
array([[2, 5, 8], [3, 4, 7], [1, 5, 7]])
Example 3 : When axis is specified as ‘0’ but shape of appending array is incorrect
We may encounter an error if the shape of the arrays are not compatible. Here the array[7,8,9] is flattened array which has caused error in appending.
np.append([[1, 2, 3], [4, 5, 6]], [7, 8, 9], axis=0)
 ValueError Traceback (most recent call last) <ipythoninput29ab6e01c40c40> in <module> > 1 np.append([[1, 2, 3], [4, 5, 6]], [7, 8, 9], axis=0) H:\Anaconda\lib\sitepackages\numpy\lib\function_base.py in append(arr, values, axis) 4692 values = ravel(values) 4693 axis = arr.ndim1 > 4694 return concatenate((arr, values), axis=axis) 4695 4696 ValueError: all the input arrays must have same number of dimensions
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Moving onto the next function, we have concatenate function.
Numpy Concatenate : np.concatenate()
Whenever we wish to join two different arrays, then we use numpy concatenate function. Below we will learn about its syntax and arguments used in the function.
Syntax
numpy.concatenate((a1,a2,……), axis=0,out=None)
a1, a2,… : sequence of array_like – These are the arrays used for concatenating with each other. The arrays should have same shape.
axis : int (optional) – The axis along which the arrays will be joined. If axis is None, arrays are flattened before use. Default is 0.
out : ndarray (optional) – If provided, this is the destination to place the result. The shape must be correct, matching that of what concatenate would have returned if no out argument were specified.
After executing this function, we get a concatenated array.
Example 1: With axis as ‘None’
In this example, we will be using axis parameter value as ‘None’, here the arrays will be flattened and then concatenation will be performed.
a = np.array([[5, 2], [8, 4]])
a
array([[5, 2], [8, 4]])
b = np.array([[9, 7]])
b
array([[9, 7]])
np.concatenate((a, b), axis=None)
array([5, 2, 8, 4, 9, 7])
Example 2: With axis as ‘0’
When axis is ‘0’ then concatenation operation takes place on the columns. This is the reason array[5,6] is added columnwise to the 2D Array [[1,2],[3,4]]
np.concatenate((a, b), axis=0)
array([[5, 2], [8, 4], [9, 7]])
Here we have transposed the b array to match the shape of both arrays. When axis is ‘1’ then concatenation operation takes place on the rows. This is the reason array[5,6] is added rowwise to the 2D Array [[1,2],[3,4]]
# Transposing array 'b'
b.T
array([[9], [7]])
If the array ‘b’ is not transposed, then the shape of concatenating arrays will not match and error will be produced.
np.concatenate((a, b.T), axis=1)
array([[5, 2, 9], [8, 4, 7]])
Conclusion
We have reached the end of this article in which we learned about numpy append and numpy concatenate functions by studying the syntax and different practical usages. Both of these functions are helpful in joining elements/arrays to existing arrays.
Reference https://numpy.org/doc/
 Also Read – Python Numpy Array – A Gentle Introduction to beginners
 Also Read – Tutorial – numpy.arange() , numpy.linspace() , numpy.logspace() in Python
 Also Read – Complete Numpy Random Tutorial – Rand, Randn, Randint, Normal
 Also Read – Tutorial – Numpy Shape, Numpy Reshape and Numpy Transpose in Python

I am Palash Sharma, an undergraduate student who loves to explore and garner indepth knowledge in the fields like Artificial Intelligence and Machine Learning. I am captivated by the wonders these fields have produced with their novel implementations. With this, I have a desire to share my knowledge with others in all my capacity.