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.

• Python numpy add element to array
• Python numpy add column to array
• Python numpy add dimension
• Python numpy add two arrays
• Python numpy add row to array
• Python numpy add multiple arrays
• Python numpy add element to list
• Python numpy append not working
• Python numpy array add element at beginning
• Python np.add.reduce
• Python numpy sum of squares
• Python np.add.at
• Python np.savetxt append
• Python add numpy array to dictionary
• Python add numpy array to dataframe
• Python numpy add gaussian noise

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

Python numpy add

• In this section, we will discuss how to add an element in a numpy array by using numpy.add() function in Python.
• In Python the numpy.add() function is used to add the values or elements in numpy arrays. It will check the condition if the shape of two numpy arrays is not the same then the shapes must be broadcastable to a common shape.
• In this function, we have to take the same size of arrays with the same number of rows and columns. If we are going to use the same size arrays in numpy.add() function than the second array elements add with the first array elements easily.

Syntax:

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

numpy.add
         (
          x1,
          x2,
           /,
          out=None,
          *,
          where=True,
          casting='same_kind',
          order='K',
          dtype=None,
          subok=True
          )

• It consists of a few parameters
  • x1,x2: This parameter indicates the first and second input array and these inputs are numpy arrays which we are using in numpy.add() function and if the shape array is not the same then by default they must be broadcastable.
  • out: This parameter specifies the output of np.add() function the contains items sum of the values of numpy array.
  • dtype: This is an optional parameter and by default, it takes none value.
  • Return: The add of x1 and x2 element-wise.

Example:

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

Source Code:

import numpy as np

array1=np.array([13,23,45,67])
array2=np.array([43,12,15,17])
result= np.add(array1,array2)
print("add elements:",result)

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

Also, check: Python NumPy Divide

Python numpy add element to array

• Let us see how to add an element to the numpy array in Python.
• To perform this particular task we are going to use the np.insert() function. In Python the numpy.insert() function is used to insert elements in an array along with the axis. If the axis is not defined then by default the array is flattened.
• This method is available in the NumPy package module and we will insert the element before the given indices. There are several arguments for executing this operation.

Syntax:

Here is the Syntax of numpy.insert() function

numpy.insert
            (
             arr,
             obj,
             values,
             axis=None
            )

• It consists of a few parameters
  • arr: This parameter indicates the numpy array on which the operation has to be performed and values will be inserted.
  • obj: This specifies the index and it can be an integer value.
  • values: Values to insert in the array.
  • axis: It is an optional parameter and by default, it takes none value and it helps us to add the value in a particular given axis.

Example:

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

Source Code:

import numpy as np

new_arr=np.array([[15,17,13],
                  [23,26,28]])
result=np.insert(new_arr,2,78)
print("Add new element to array:",result)

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

Read: Python NumPy diff

Python numpy add column to array

• In this Program, we will learn how to add a new column in a NumPy array by using Python numpy.insert() function.
• By using the numpy.insert() function we can easily perform this particular task and add the new column in an array. First, we will import a numpy library and then initialize an array by using the np.array() function.
• After that, we have declared a variable ‘add_col’ that represents which elements we want to add to an array.
• Now use the numpy.insert() function and assign the axis, array, and index number as an argument. Once you will print ‘new_output’ then the output will display the newly added column elements in a given array.

Syntax:

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

numpy.insert
            (
             arr,
             obj,
             values,
             axis=None
            )

Example:

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

Source Code:

import numpy as np

new_values=np.array([[47,89,897],
                  [156,267,345]])
add_col=[12,67]
new_output=np.insert(new_values,2,add_col,axis=1)
print("Add new column to array:",new_output)

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

• In this section, we will discuss how to add dimension in Python Numpy array.
• To do this task we are going to use the numpy.expand_dims() function. In Python, this function is used to add a new dimension in a numpy array or we can say it will expand the array by adding a new axis within this function.
• This method is available in the numpy module package and it takes two parameters for expanding array shape and returns a new array with an extra dimension.

Syntax:

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

numpy.expand
            (
             a,
             axis
            )

• It consists of a few parameters:
  • a: This parameter indicates the input array
  • axis: This parameter represents the position where a axis to be added

Example:

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

Source Code:

import numpy as np

new_arr = np.array([15,17,29,17,18])
print("One-dimensional array shape:",new_arr.shape)
result = np.expand_dims(new_arr, axis = 0)
print("Added new dimension:",result.shape)

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.

Read: Python NumPy argsort

Python numpy add two arrays

• In this section, we will discuss how to add two NumPy arrays in Python.
• By using the numpy.add() function, we can easily solve this problem and get the solution of the addition of two arrays. In Python, the numpy.add() function is used to add the values or elements in numpy arrays.
• This method will help the user to add first array elements with second array elements and returns a new array.

Syntax:

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

numpy.add
         (
          x1,
          x2,
           /,
          out=None,
          *,
          where=True,
          casting='same_kind',
          order='K',
          dtype=None,
          subok=True
          )

Example:

import numpy as np

new_arr1=np.array([78,189,219,456])
new_arr2=np.array([43,58,932,178])
new_result= np.add(new_arr1,new_arr2)
print("adding two arrays:",new_result)

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.

Read: Python NumPy Indexing

Python numpy add row to array

• In this section, we will discuss how to add a row in the Python numpy array.
• To do this task we are going to apply the np.vstack() method for adding the new row in an existing array. In Python, this function is used to add the sequence of input arrays row-wise and make them in a one-dimensional array.
• This method is available in the NumPy package module and always returns the array stacking the given arrays.

Syntax:

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

numpy.vstack
            (
             tup
            )

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:

import numpy as np

new_array = np.array([[23,27,29],[67,34,98]])
add_row = np.array([45,94,117])
result = np.vstack([new_array,add_row])
print("Adding new row:",result)

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

Read: Python Numpy Not Found – How to Fix

Python numpy add multiple arrays

• In this Program, we will discuss how to add multiple NumPy arrays in Python.
• By using the np.vstack() function we can easily perform this particular task and adding multiple arrays in Python.
• In Python, the np.stack() is used for adding new elements row-wise in an array. For example, suppose we have a list that contains integer values. Now we have to add those lists into np.vstack() function and it will return into numpy array.

Example:

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

Source Code:

import numpy as np

new_arr1 = [15, 78, 92]
new_arr2 = [167, 897, 923]
new_arr3 = [423, 107, 289]
new_result = (new_arr1, new_arr2, new_arr3)
d = np.vstack(new_result)
print("adding multiple arrays:",d)

You can refer to the below Screenshot

Read: Python NumPy Delete

Python numpy add element to list

• In this Program, we will discuss how to add elements to list in Python.
• To perform this particular task we are going to use the append() function for adding new elements in a list. This method takes only one argument and does not return any value.
• This method is available in the Python module package and it does not create any new list. It modifies only the original list. For example, suppose we have a list that contains only country name strings. Now we want to update this list and add some new country names to it. For this, we are going to apply the list.append() function.

Syntax:

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

list.append
           (
            item
           )

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

Source Code:

Country_name = ["France", "Japan","Russia"]

Country_name.append("China")
print("Added new element in list:",Country_name)

Here is the execution of the following given code

Also, check: Python NumPy Minimum tutorial

Python numpy array add element at beginning

• In this section, we will discuss how to add an element at beginning in the NumPy array Python.
• To do this task we are going to use the numpy.insert() function for adding elements at the beginning of the array. In Python the numpy.insert() function is used to insert elements in an array along with the axis. If the axis is not defined then by default the array is flattened.
• In this example, we have created a simple array by using the np.array() function. After that, we declared a variable ‘new_output’ and assign a numpy.insert() function. Within this function, we have passed an array and index number ‘0’ as an argument.

Example:

import numpy as np

new_values=np.array([[156,278,945,178,923,190]])
new_output=np.insert(new_values,0,78)
print("Add new element at beginning:",new_output)

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.

Read: Python NumPy Stack

Python np.add.reduce

• In this section, we will discuss how to use the np.add.reduce() function in Python.
• In Python this function is used to reduce by applying the universal function like add, multiply, subtract, divide and it is denoted by unfunc.reduce.
• In this program, we have to reduce the array’s dimension and produce a set of scalers as output.
• In this method, the np.add() function takes two arguments and returns one output parameter.

Syntax:

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

ufunc.reduce
            (
             array,
             axis=0,
             dtype=None,
             out=None,
             keepdims=False,
             initial=<no value>,
             where=True
            )

• It consists of a few parameters
  • array: This parameter indicates the array which we have to work on it.
  • axis: By default, it takes the ‘0’ value that represents a reduction over the first dimension of the numpy array. If this argument takes no value then a reduction is performed over all the axes.
  • dtype: This type represents the intermediate results.
  • Out: This is an optional parameter and it takes none value that represents the location in which the result is stored. And if it is not provided any value then a freshly allocated array is returned.
  • keepdims: This is also an optional parameter and by default, it is set as ‘false’ which are reduced are right in the result as a dimension.

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:

import numpy as np

new_arr=np.array([[2,3],
                [14,27],
                [56,78]])
print("Creation of array:",new_arr)
result1=np.add.reduce(new_arr, 0)
result2=np.add.reduce(new_arr,1)
print("Reduction of array along axis=0:",result1)
print("Reduction of array along axis=1:",result2)

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

Read: Python Numpy Factorial

Python numpy sum of squares

• In this Program, we will learn how to find the sum of squares in NumPy Python.
• In this program, we have to get the squares of elements and add them. To do this task we are going to use the np.sum() function. In Python, this function is used to sum all elements over a given axis.
• This method is available in the numpy package module and it will each row and column of a given array.
• To get detail information about this topic you can refer our article Python NumPy Sum

Source Code:

import numpy as np

new_arr=np.array([[16,29],
                [78,18],
                [25,36]])
result = np.sum(new_arr**2,axis=0)
print("Column-wise sum of square elements:",result)

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].

Read: Python NumPy round

Python np.add.at

• Here we can see how to use the numpy.add.at() function in Python. In this example, I used to provide an index for the numpy array, and for addition ufunc, this function is equivalent to a[indices] +=b.
• This method is available in the numpy package module and this method will only increment the first value the reason behind is buffering.

Syntax:

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

ufunc.at
        (
         a,
         indices,
         b=None,
          /
        )

• It consists of a few parameters
  • a: This parameter indicates the input array to perform some operation.
  • indices: The indices can be a tuple of numpy array like slice objects.

Example:

import numpy as np

new_array1 = np.array([17, 21, 67, 98])
new_array2 = np.array([21, 67])
np.add.at(new_array1, [0, 1], new_array2)
print(new_array1)

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

Read: Python Numpy unique

Python np.savetxt append

• In this section, we will discuss how to use the np.savetxt() function in Python.
• By using np.savetxt we can easily be appending the input array. In Python the numpy.savetxt() function is used to save a numpy array to a CSV or text file with different delimiters.
• This method is available in the NumPy module package and takes two arguments which are the file name and array name that need to be saved in our CSV file.

Syntax:

Here is the Syntax of numpy.savetxt() function

numpy.savetxt
             (
              fname,
              X,
              fmt='%.18e',
              delimiter= ' ',
              newline='\n',
              header= ' ',
              footer= ' ',
              comments='#',
              encoding=None
             )

• It consists of a few parameters
  • fname: This parameter represents the final name and the filename ends in .gz.
  • X: It is used to contain array data in a text or CSV file.
  • fmt: By default it takes ‘%.18e’ value and it indicates the sequence of formats or the multi format string.
  • delimiter: This parameter indicates the separating columns ‘,’. It is an optional parameter.
  • newline: It is used for separating the lines.
  • header: This parameter will be written at the beginning of the file.
  • footer: This parameter will be written at the end of the file.

Example:

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

Source Code:

import numpy as np        

m=open('final.csv','a')
new_arr1 = np.array([15, 16, 19])
new_arr2 = np.array([21, 28, 31])
new_arr3 = np.array([67, 56, 87])
np.savetxt(m, new_arr1,  delimiter=", ",fmt='%d')
m.write("\n")
np.savetxt(m, new_arr2,  delimiter=", ",fmt='%d')
m.write("\n")
np.savetxt('final.csv', new_arr3,  delimiter=", ", fmt='%d')
m.write("\n")
m.close()

Here is the execution of the following given code

CSV File Screenshot

Read: Python NumPy Data types

Python add numpy array to dictionary

• In this Program, we will discuss how to add a numpy array to dictionary in Python.
• To perform this particular task we are going to use the concept of zip() function. In Python this function is used to combine items from given iterables like lists and arrays.

Syntax:

Here is the Syntax of Python dictionary zip() function

zip(*iterable)

Example:

import numpy as np

new_array1 = np.array([67, 145, 28, 190])
new_array2 = np.array([93,89,43,109])
result= dict(zip(new_array1,new_array2))
print("Added numpy values to dictionary:",result)

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

Read: Python NumPy 2d array

Python add numpy array to dataframe

• Here we can see how to add numpy arrays in Python Pandas dataframe.
• In this example, we are going to use the np.array() function for creating an array and then create a list ‘new_indices’ that indicates the index number.
• Now use the pd.dataframe() function to add the arrays into the dataframe. In Python Pandas dataframe is a two-dimensional and it is a tabular form (rows and columns.

Syntax:

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

pandas.dataframe
                (
                 data=None,
                 index=None,
                 columns=None,
                 dtype=None,
                 copy=None
                )

• It consists of a few parameters
  • data: This parameter indicates different data forms like array, lists, dictionary etc.
  • index: By default it is an optional parameter and it is used for resulting.
  • dtype: This parameter represents the data type of each column.

Example:

import pandas as pd
import numpy as np

new_arr = np.array([['George', 178], ['John', 456],
				['William', 34], ['Smith', 16],['Peter',78]])

new_indices = [2,3,4,5,6]

new_output = pd.DataFrame(data = new_arr,
				index = new_indices,
				)
print(new_output)

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

Read: Python NumPy 3d array

Python numpy add gaussian noise

• In this section, we will discuss how to add gaussian noise in NumPy array Python.
• To perform this particular task we are going to use the np.random.normal() function and pass integer values to it. In this example ‘1’ means the normal distribution starts from and ‘2’ represents the standard deviation.

Syntax:

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

random.normal
             (
              loc=0.0,
              scale=1.0,
              size=None
             )

Example:

import numpy as np

new_array = np.random.normal(1,2,50)
print("Adding gaussian noise:",new_array)

Here is the implementation of the following given code

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

• Python NumPy Split + 11 Examples
• Python NumPy Savetxt + Examples
• Python NumPy Normalize + Examples
• Python NumPy max with examples
• Python NumPy Matrix Multiplication

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.

• Python numpy add element to array
• Python numpy add column to array
• Python numpy add dimension
• Python numpy add two arrays
• Python numpy add row to array
• Python numpy add multiple arrays
• Python numpy add element to list
• Python numpy sum where
• Python numpy append not working
• Python numpy array add element at beginning
• Python np.add.reduce
• Python numpy sum of squares
• Python np.add.at
• Python np.savetxt append
• Python add numpy array to dictionary
• Python add numpy array to dataframe
• Python numpy add gaussian noise