How to add element to numpy array

Add single element to array in numpy

I have a numpy array containing:

I want to create an array containing:

That is, I want to add the first element on to the end of the array.

I have tried the obvious:

But I get an error saying ValueError: arrays must have same number of dimensions

9 Answers 9

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append() creates a new array which can be the old array with the appended element.

I think it’s more normal to use the proper method for adding an element:

When appending only once or once every now and again, using np.append on your array should be fine. The drawback of this approach is that memory is allocated for a completely new array every time it is called. When growing an array for a significant amount of samples it would be better to either pre-allocate the array (if the total size is known) or to append to a list and convert to an array afterward.

Using python list converting to array afterward:

Pre-allocating numpy array:

When the final size is unkown pre-allocating is difficult, I tried pre-allocating in chunks of 50 but it did not come close to using a list.

Append/ Add an element to Numpy Array in Python (3 Ways)

In this article, we will discuss different ways to add / append single element in a numpy array by using append() or concatenate() or insert() function.

Table of Contents

Add element to Numpy Array using append()

Numpy module in python, provides a function to numpy.append() to add an element in a numpy array. We can pass the numpy array and a single value as arguments to the append() function. It doesn’t modifies the existing array, but returns a copy of the passed array with given value added to it. For example,

Output:

The append() function created a copy of the array, then added the value 10 at the end of it and final returned it.

Add element to Numpy Array using concatenate()

Numpy module in python, provides a function numpy.concatenate() to join two or more arrays. We can use that to add single element in numpy array. But for that we need to encapsulate the single value in a sequence data structure like list and pass a tuple of array & list to the concatenate() function. For example,

It returned a new array containing values from both sequences i.e. array and list. It didn’t modified the original array, but returned a new array containing all values from original numpy array and a single value added along with them in the end.

Add element to Numpy Array using insert()

Using numpy.insert() function in the NumPy module, we can also insert an element at the end of a numpy array. For example,
C
Output:
O

We passed three arguments to the insert() function i.e. a numpy array, index position and value to be added. It returned a copy of array arr with value added at the given index position. As in this case we wanted to add the element at the end of array, so as the index position, we passed the size of array. Therefore it added the value at the end of array.

Important point is that it did not modifies the original array, it returned a copy of the original array arr with given value added at the specified index i.e. as the end of array.

Summary:

We learned about three different ways to append single element at the end of a numpy array in python.

Python NumPy array tutorial

NumPy is a Python Library/ module which is used for scientific calculations in Python programming. In this tutorial, you will learn how to perform many operations on NumPy arrays such as adding, removing, sorting, and manipulating elements in many ways.

NumPy provides a multidimensional array object and other derived arrays such as masked arrays or masked multidimensional arrays.

Table of Contents

Why using NumPy

The NumPy module provides a ndarray object using which we can use to perform operations on an array of any dimension. The ndarray stands for N-dimensional array where N is any number. That means NumPy array can be any dimension.

NumPy has a number of advantages over the Python lists. We can perform high performance operations on the NumPy arrays such as:

How to install NumPy?

To install NumPy, you need Python and Pip on your system.

Run the following command on your Windows OS:

Now you can import NumPy in your script like this:

Add array element

You can add a NumPy array element by using the append() method of the NumPy module.

The syntax of append is as follows:

The values will be appended at the end of the array and a new ndarray will be returned with new and old values as shown above.

The axis is an optional integer along which define how the array is going to be displayed. If the axis is not specified, the array structure will be flattened as you will see later.

Consider the following example where an array is declared first and then we used the append method to add more values to the array:

The output will be like the following:

Add a column

We can use the append() method of NumPy to insert a column.

Consider the example below where we created a 2-dimensional array and inserted two columns:

The output will be like the following:

If the axis attribute is not used, the output will be like the following:

This is how the structure of the array is flattened.

In NumPy, we can also use the insert() method to insert an element or column. The difference between the insert() and the append() method is that we can specify at which index we want to add an element when using the insert() method but the append() method adds a value to the end of the array.

Consider the example below:

The output will be as follows:

Here the insert() method adds the element at index 1. Remember the array index starts from 0.

Append a row

In this section, we will be using the append() method to add a row to the array. It’s as simple as appending an element to the array. Consider the following example:

The output will be as follows:

Delete an element

You can delete a NumPy array element using the delete() method of the NumPy module:

This is demonstrated in the example below:

The output is as follows:

In the above example, we have a single dimensional array. The delete() method deletes the element at index 1 from the array.

Delete a row

Similarly, you can delete a row using the delete() method.

Consider the following example, where we have deleted a row from a 2-dimensional array:

The output will be as follows:

In the delete() method, you give the array first and then the index for the element you want to delete. In the above example, we deleted the second element which has the index of 1.

Check if NumPy array is empty

We can use the size method which returns the total number of elements in the array.

In the following example, we have an if statement that checks if there are elements in the array by using ndarray.size where ndarray is any given NumPy array:

The output is as follows:

In the above code, there are three elements, so it’s not empty and the condition will return false.

If there are no elements, the if condition will become true and it will print the empty message.

If our array is equal to:

The output of the above code will be as below:

Find the index of a value

To find the index of value, we can use the where() method of the NumPy module as demonstrated in the example below:

The output will be as follows:

The where() method will also return the datatype. If you want to just get the index, use the following code:

Then the output will be:

NumPy array slicing

Array slicing is the process of extracting a subset from a given array. You can slice an array using the colon (:) operator and specify the starting and ending of the array index, for example:

This is highlighted in the example below:

Here we extracted the elements starting from index 2 to index 5. The output will be:

If we want to extract the last three elements. We can do this by using negative slicing as follows:

The output will be:

Apply a function to all array element

In the following example, we are going to create a lambda function on which we will pass our array to apply it to all elements:

The output is as follows:

In this example, a lambda function is created which increments each element by two.

NumPy array length

To get the length of a NumPy array, you can use the size attribute of the NumPy module as demonstrated in the following example:

This code will generate the following result:

Create NumPy array from List

Lists in Python are a number of elements enclosed between square brackets.

Suppose you have a list as:

Now to create an array from this list, we will use the array() method of the NumPy module:

The output will be as follows:

Similarly, using the array() method, we can create a NumPy array from a tuple. A tuple contains a number of elements enclosed in round brackets as follows:

The output will be:

Convert NumPy array to list

To convert an array to a list, we can use the tolist() method of the NumPy module.

Consider the code below:

The output will be as follows:

In this code, we simply called the tolist() method which converts the array to a list. Then we print the newly created list to the output screen.

NumPy array to CSV

To export the array to a CSV file, we can use the savetxt() method of the NumPy module as illustrated in the example below:

This code will generate a CSV file in the location where our Python code file is stored. You can also specify the path. When you run the script, the file will be generated like this:

The content of this file will be like the following:

You can remove the extra zero padding like this:

Sort NumPy array

You can sort NumPy array using the sort() method of the NumPy module:

Consider the example below:

In this example, we called the sort() method in the print statement. The array “a” is passed to the sort function. The output of this will be as follows:

Normalize array

The formula for normalization is as follows:

Now we will just apply this formula to our array to normalize it. To find the maximum and minimum items in the array, we will use the max() and min() methods of NumPy respectively.

The output will be as follows:

Array Indexing

Indexing means refer to an element of the array. In the following examples, we used indexing in single dimensional and 2-dimensional arrays as well:

The output will be as below:

Now indexing with a 2-dimensional array:

The output will be:

The index [1][2] means the second row and the third column (as indexing starts from 0). Therefore, we have 9 on the output screen.

Append NumPy array to another

You can append a NumPy array to another NumPy array by using the append() method.

Consider the following example:

The output will be as follows:

In this example, a NumPy array “a” is created and then another array called “b” is created. Then we used the append() method and passed the two arrays. As the array “b” is passed as the second argument, it is added at the end of the array “a”.

As we saw, working with NumPy arrays is very simple. NumPy arrays are very essential when working with most machine learning libraries. So, we can say that NumPy is the gate to artificial intelligence.

Ayesha Tariq is a full stack software engineer, web developer, and blockchain developer enthusiast. She has extensive knowledge of C/C++, Java, Kotlin, Python, and various others.

numpy.append(): How to Add Elements to a NumPy Array

Artturi Jalli

A NumPy array does not have a built-in append method. Instead, to append elements to a NumPy array, use a separate numpy.append() function.

Notice how numpy.append() creates a new copy of the original array. It does not directly append values to it.

In this guide, you learn:

How to Append to a NumPy Array

Appending to an array means adding a value or values to the end (right side) of the array.

Intuitively, appending means modifying the original array by adding the element to it.

However, when it comes to NumPy arrays, appending works slightly differently than you would expect.

There is a function called numpy.append() you can use to append elements to an array.

The full syntax of this function is:

Notice how the numpy.append() function does not directly append the values into the array.

Instead, the result is a completely new array that is a copy of the original array with the appended element(s).

Here is an illustration:

Here we assign the resulting array of the append() function call back to the original array arr.

If we did not do this, there would be no appended value at the end of the array.

Now you understand how to append elements to NumPy arrays.

Next, let’s take a look at how you can append an array to the end of another.

How to Append a NumPy Array to Another

Appending a NumPy array at the end of another NumPy array works using the numpy.append() method.

Where the elements in arr2 are appended to arr1.

Now you understand how to append both singular values and entire arrays to the end of NumPy arrays.

Lastly, let’s have a look at another approach, concatenation, which you are going to see a lot.

Append Alternative: Concatenation

You can also use numpy.concatenate() function to add elements to the end of an array.

To do this, pass the element/array arguments as a sequence into the concatenate() function call.

Notice that the dimensions of the arguments must match.

In other words, you cannot for example concatenate a single value to the end of the array. Instead, you would have to put that value into an array or list to make the dimensions match first.

Let’s see some examples of concatenation.

For instance, let’s add an array of numbers, arr2, to the end of another array of numbers, arr1:

Similar to appending a NumPy array, concatenation does not modify the original array!

Instead, the numpy.concatenate() function creates a new copied array with the concatenated elements.

As another example, let’s add a single number to the array of numbers.

Because a single value and an array has different dimensions, this is not directly possible. Instead, put the single value into the list (of the same dimensions as the array) to make the dimensions match.

Here is how it looks in code:

Now you know two ways to add elements/arrays to the end of another NumPy array.

Finally, let’s make a quick comparison between appending and concatenating.

numpy.append() vs numpy.concatenate()

The difference between numpy.append() and numpy.concatenate() is that numpy.append() uses numpy.concatenate() behind the scenes.

You can view the implementation of numpy.append() in the official implementation.

Anyway, the implementation of numpy.append() looks like this:

As you can see, the last line produces a result by calling the numpy.concatenate() function.

Conclusion

Today you learned how to append to a NumPy array.

Python NumPy Add Tutorial

In this Python tutorial, we will learn how do we add to NumPy arrays in Python. With the Python NumPy add function, we will cover these topics.

If you are new to NumPy, check Python Numpy to know how to use Python NumPy.

Python numpy add

Syntax:

Let’s have a look at the syntax and understand the working of python numpy.add() function

Example:

Let’ take an example and understand how to add elements in a numpy array by using numpy.add() function in Python

Source Code:

In the above code the numpy.add() function is adding the elements of ‘array 1’ to another numpy array ‘array2’. Once you will print ‘result’ then the output will display the adding elements in an array.

Here is the Screenshot of the following given code

Python numpy add element to array

Syntax:

Here is the Syntax of numpy.insert() function

Example:

Let’s take an example and understand the working of numpy.insert() function in Python

Source Code:

In the above code, we have imported the numpy library and then we have defined the numpy array by using the np.array() function. While using the numpy.insert() function we have inserted the array name ‘new_arr’ and index number ‘2’ that indicates where the value needs to be inserted and ’78’ represents the value to be inserted.

Here is the execution of the following given code

Python numpy add column to array

Syntax:

Here is the Syntax of Python numpy.insert() function

Example:

Let’s take an example and check how to add the new column in a Numpy array Python

Source Code:

Here is the implementation of the following given code

We can also add a new column in an array by using the numpy.append() function. But we have already covered this topic in the Python numpy append() post. You can easily check the solution on Python numpy append column topic.

Python numpy add dimension

Syntax:

Let’s have a look at the Syntax and understand the working of numpy.expand_dims() function

Example:

Let’s take an example and check how to add a dimension in the NumPy array Python

Source Code:

In the above code, we have imported the numpy library and then create an array by using the np.array() function. After that, we have applied the np.expand_dims() function and within this function, we have assigned the axis as an argument.

You can refer to the below Screenshot

As you can see in the Screenshot the output displays the new dimension.

Python numpy add two arrays

Syntax:

Here is the Syntax of Python numpy.add() function

Example:

In the above code, we have used the numpy.add() function and assign the arrays as an argument. Once you will print ‘result’ then the output will display the newly added elements in an array.

Here is the Screenshot of the following given code.

Python numpy add row to array

Syntax:

Let’s have a look at the syntax and understand the working of the numpy.vstack() function

Note: It consists of only one parameter ‘tup’ which represents the input arrays.

Example:

Let’s take an example and check how to add a row in the Python NumPy array by using the np.vstack() function

Source Code:

In the above program, we added one array vertically. Firstly we imported the numpy library and then initialize an array by using the np.array() function. After that, with the np.vstack() function we added one-dimensional array ‘add_row’ in it. Once you will print ‘result’ then the output will display new array elements.

Here is the implementation of the following given code

Python numpy add multiple arrays

Example:

Let’s take an example and check how to add multiple arrays in Python

Source Code:

You can refer to the below Screenshot

Python numpy add element to list

Syntax:

Here is the Syntax of the list.append() function

Let’s have a look at the example and understand the working of the list.append() function

Source Code:

Here is the execution of the following given code

Python numpy array add element at beginning

Example:

You can refer to the below Screenshot

As you can see in the Screenshot the output displays the element has located at beginning of the array.

Python np.add.reduce

Syntax:

Here is the Syntax of Python numpy.ufunc.reduce() function

Example:

Let’s take an example and understand the working of numpy.ufunc.reduce() function. In this example, we are going to use the add universal function

Source Code:

In the above program, we created an array by using the np.array() function. After that, we have declared a variable ‘result1’ and result2′. Now we have assigned the np.add.reduce() universal function and within this function, we assigned the array along with axis=0,1. Once you will print ‘result1’ and ‘result2’ the output displays the reduced array as per the condition.

Here is the implementation of the following given code

Python numpy sum of squares

Source Code:

In the above program, we used the np.sum() function and within the function, we have assigned the array along with the axis as an argument. Now we have used the new_arr**2 method that represents the square of all elements

You can refer to the below Screenshot

As you can see in the Screenshot the output displays the square of sum [6965 2461].

Python np.add.at

Syntax:

Let’s have a look at the Syntax and understand the working of np.ufunc.at() function

Example:

In the above code we have imported the numpy library and then create an array by using the np.array() function. Now our task is to add the last 2 elements with the first 2 elements. To do this task we have used the np.add.at() function.

And within this function, we have set the array along with the index number that represents which elements we want to increment in an array.

Here is the implementation of the following given code

Python np.savetxt append

Syntax:

Here is the Syntax of numpy.savetxt() function

Example:

Let’s take an example and understand the working of numpy.savetxt() function

Source Code:

Here is the execution of the following given code

CSV File Screenshot

Python add numpy array to dictionary

Syntax:

Here is the Syntax of Python dictionary zip() function

Example:

In the above example, we have defined two numpy arrays by using the np.array() function and we need to add these arrays to the dictionary. The first array will be considered a key to the dictionary and the second array items will be considered as values. After that, we have declared a variable ‘result’ and assigned the zip() function for returning the iterator.

Here is the Screenshot of the following given code

Python add numpy array to dataframe

Syntax:

Let’s have a look at the Syntax and understand the working of Pandas.dataframe() method

Example:

In the above code we have imported the numpy and pandas library and then initialize an array. Now by using the pd.dataframe() function, we can easily add numpy arrays in dataframe.

Here is the output of the following given code

Python numpy add gaussian noise

Syntax:

Here is the Syntax of numpy.random.normal() function

Example:

Here is the implementation of the following given code

You may also like to read the following tutorials on Python Numpy.

In this Python tutorial, we have learned how do we add to NumPy arrays in Python. With the Python NumPy add function, we will cover these topics.

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