Numpy is a great Python library for array manipulation. You can easily calculate mathematical calculation using the Numpy Library. As a data scientist, you should know how to create, index, add and delete Numpy arrays, As it is very helpful in data preparation and cleaning process. In this section of How to, you will know how to append and insert array or its elements using the numpy append and numpy insert function. So Let’s start.
If you have not installed numpy and wants to use numpy with Pycharm then read this tutorial of How to install numpy in pycharm?
Appending the Numpy Array
Here there are two function np.arange(24), for generating a range of the array from 0 to 24. The reshape(2,3,4) will create 3 -D array with 3 rows and 4 columns.
Lets we want to add the list [5,6,7,8] to end of the above-defined array a. To append one array you use numpy append() method. The syntax is given below.
append(array1, array2, axis = None or )
Where type is
array1: Numpy Array, original array
array2: Numpy Array, To Append the original array.
axis: It is optional default is 0. Axis along which values are appended.
Here axis is not passed as an argument so, elements will append with the original array a, at the end.
Appending the Numpy Array using Axis
Axis tells the python interpreter to append the elements along the axis. Let’s define the two 3 -Dimensional array.
Using axis = 0 ; Note that default value of axis is 0.
You can clearly see when you append a and b with axis=0. Then the matrix b appended with an at the end of the matrix. Now the shape of the matrix is 4x3x4. You can say when the axis is 0, then append operations are done along the first dimension of the matrix. In this case, it is 2×2 which is 4.
Appending the NumPy with axis =1
When you append the array using the axis =1, then append operations are done along the rows of the matrix a and b. It means all the first rows of b are appended with the first rows of a and the same for the other rows.
Appending the NumPy with axis =2.
When you use axis =2, then all the append operations are done along with the columns. Due to this, the column dimension changes to 2x3x8.
The np.append() method appends the entire matrix to the original matrix. But what about you only want to insert a certain element inside the matrix. In this case, you will use the numpy insert() method.
How to insert an element inside the Numpy 3 Dimensional Array?
You can also insert an element using the Numpy insert() method along the axis. The syntax for this is below.
insert(array,position, value, axis)
array: The array you want to insert.
position: Position where you want to insert the element.
value: The value you want to insert.
axis: For performing the row-wise or column-wise operation.
The axis has the default value that is 0.
Inserting the element using axis = 0
Let’s take a 3 Dimensional array of 2x3x4.
The insertion of the element along the axis =0, the dimension of the array changes to 3x3x4, with the same value 111 in the entire rows and columns.
Inserting the element using axis = 1
If the axis is equal to 1, then insertion operation will be done on the rows. The value 111 will insert along the position in the rows wise. It changes the dimension to 2,x4x4.
Inserting the element using axis = 2
When you use the axis =2, then the insertion operation has done along with the columns. The value 11 will be inserted along with the column position. Like, in this case, it changes the dimension to 2x3x5.
Appending and insertion in the Numpy are different. The Numpy append method is to append one array with another array and the Numpy insert method used to insert an element. The operation along the axis is very popular for doing row-wise or column-wise operations.
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Different between numpy.append() and list.append() in python
It is the query asked by many data science readers. The numpy.append and list.append in python is far different from each other. The numpy.append is like concatenate. It means you have an array and you take another array to append with the previous one. The result will be also a NumPy array. But in the case of the list.append(), if you want to append to the existing list then you have to use for loop. This makes it least efficient as compared to numpy.append.
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