Как добавить элементы в массив NumPy (3 примера)
Вы можете использовать следующие методы для добавления одного или нескольких элементов в массив NumPy:
Способ 1: добавить одно значение в конец массива
Способ 2: добавить несколько значений в конец массива
Способ 3: вставить одно значение в определенную позицию в массиве
Способ 4: вставить несколько значений в определенную позицию в массиве
В этом руководстве объясняется, как использовать каждый метод на практике со следующим массивом NumPy:
Пример 1: добавление одного значения в конец массива
В следующем коде показано, как использовать np.append() для добавления одного значения в конец массива NumPy:
В конец массива NumPy добавлено значение 15 .
Пример 2. Добавление нескольких значений в конец массива
В следующем коде показано, как использовать np.append() для добавления нескольких значений в конец массива NumPy:
Значения 15 , 17 и 18 были добавлены в конец массива NumPy.
Пример 3. Вставка одного значения в определенную позицию в массиве
В следующем коде показано, как вставить одно значение в определенную позицию в массиве NumPy:
Значение 95 было вставлено в позицию индекса 2 массива NumPy.
Пример 4. Вставка нескольких значений в определенную позицию в массиве
В следующем коде показано, как вставить несколько значений, начиная с определенной позиции в массиве NumPy:
Значения 95 и 99 были вставлены, начиная с позиции индекса 2 массива NumPy.
Дополнительные ресурсы
В следующих руководствах объясняется, как выполнять другие распространенные задачи в NumPy:
numpy.insert python prepend element to numpy array with axis parameter
numpy has an insert function that's accesible via numpy.insert referto its document.
Let’s have a look at this code in Jupyter python:
You can try this code
The output will be:
What is axis in numpy.insert actually?
Here the original array is a matrix with two dimensions:
In this case the axis=0 means we want to insert a row and if axis=1 it means we want to insert a column.
The argument axis= specifies that the insertion should happen as a column or row.
Attention:
- row index 0 = [1 11]
- row index 1 = [2 22]
- row index 2 = [3 33]
- column index 0 = 1 2 3 sorry for this presentation, you understand that it is a column
- column index 1 = 11 22 33
Now lets look at the numpy.insert parameters command again
Explaining parameters of python numpy.insert:
- As you see the first argument a specifies the object (original array) to be inserted into.
- The second argument specifies where we want to insert. ( before which index of original array, regarding we want to insert as column or row )
- The third argument specifies what is to be inserted.
In General form numpy.insert has this form:
arr : Input array
obj : The index before which insertion is to be made
values : The array of values to be inserted. It can be also one number or array of numbers
axis : The axis along which to insert. If not given, the input array is flattened
np.append() — How To Use NumPy Append in Python
The NumPy programming library is considered to be a best-of-breed solution for numerical computing in Python.
NumPy stands out for its array data structure. NumPy arrays are excellent for handling ordered data. Moreover, they allow you to easily perform operations on every element of th array — which would require a loop if you were using a normal Python list.
One of the core capabilities available to NumPy arrays is the append method. In this tutorial, I will explain how to use the NumPy append method to add data to a NumPy array.
Table of Contents
You can skip to a specific section of this tutorial using the table of contents below:
How to Import NumPy
This tutorial makes extensive use of the NumPy package for Python. Accordingly, let’s start by importing NumPy into our development environment
You can import NumPy under the alias np (which is standard convention) with the following command:
If you’ve never used NumPy before, you might be wondering why we import the package under the np alias.
It’s because it makes it much easier to reference the package later in our program.
Instead of calling objects and methods from numpy with the dot operator, we can simply call them from np instead.
If this is not clear, do not worry. We will see plenty of examples of this later in this tutorial.
What is a NumPy Array?
To understand how to use the np.append method, you first need to understand what a NumPy array is.
NumPy arrays are the main data structure available in the NumPy package. They are similar to normal Python lists, but come with additional functionality.
There are a few different ways that programmers can create NumPy arrays, but the most common is to pass a Python list into the np.array method.
Here is an example:
You could also pass the list into the np.array method in a single command, like this:
Here’s what the my_array object looks like if you print it to the Python console:
The array() notation indicates that this is indeed a NumPy array.
How to Use the NumPy Append Method
Now that you have an understanding of how to create a NumPy array, let’s learn about the np.append method.
The append method is used to add a new element to the end of a NumPy array. It accepts two parameters:
- arr : the array that you’d like to append the new value to.
- values : the value (or values) that you’d like to append to arr .
Let’s consider a few examples to see how the np.append method works in practice.
First, consider the following NumPy array:
This NumPy array contains the integers from 1 to 3 , inclusive. Let’s add 4 to the end of this array using the np.append method:
The np.append method actually returns the value of the new array. In this case, here is the output:
In most cases, you will want to store the new array in another variable. This is done like any other variable assignment: using the = assignment operator.
Here is an example:
You can then reference second_array later in your program, perhaps by using the various NumPy methods and operations that come included in the numerical computing package.
How to Append Two NumPy Arrays Together Using np.append
One of the more common use cases of the np.append method is to join two (or more) NumPy arrays together. This section of this tutorial will demonstrate this capability.
For illustration’s sake, we will be using the following NumPy arrays;
Here’s how you would append array2 to the end of array1 using the np.append method:
Here is what the output of this code looks like:
Similarly, if you wanted to append array1 to the end of the array1 , here’s how you would do it:
In this case, here’s the output:
It is even possible to append more than three arrays together using np.append . To demonstrate this, I will be using the following 3 arrays:
You might think that the following code will properly append the three NumPy arrays together:
However, this results in the following error:
What is the solution?
To append more than two NumPy arrays together using np.append , you must wrap all but the first array in a Python list.
Here is how we would properly append array2 and array3 to array1 using np.append :
Here is the output of this code:
For a more extreme example, here’s how you would append array2 and array3 twice to the end of array1 :
And here is the output of this code:
In this tutorial, you learned how to use the np.append method available in the NumPy numerical computing library. You also learned how to append multiple NumPy arrays using np.append .
If you have any other tutorials that you’d like me to write, please email me. I look forward to hearing from you!
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.
Frequently Asked:
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
Latest Python — Video Tutorial
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.