Summing arrays in python

Python NumPy Sum + Examples

In this Python NumPy tutorial, we will discuss Python numpy sum with a few examples like below:

• Python NumPy summary statistics
• Python NumPy sum of columns
• Python NumPy sum each row
• Python NumPy sum function
• Python NumPy sum ignore nan
• Python NumPy sum two arrays
• Python NumPy sum of squares
• Python NumPy sum 3d array

Python NumPy sum

• In this section, we will learn about the python numpy sum.
• Numpy.sum() function is available in the numpy libraries of Python.
• This function is used to sum all elements, the sum of each row, and the sum of each column of a given array.
• We can also specify dtype to specify the returned output datatype.

Here is the syntax of numpy.sum()

numpy.sum( arr, axis, dtype, out )

1. arr: Input array
2. axis: axis along which we want to calculate the sum value.
3. dtype: We can specify the returned output datatype.
4. out: We want to place the result.

Let’s take an example to check how to sum elements in an array.

import numpy as np arr = np.array([[3, 6], [9, 2]]) sum_arr = np.sum(arr) print(sum_arr)

Here is the Screenshot of the following given code.

This is an example of Sum in Python NumPy.

Python numpy summary statistics

Summary statistics describe a variable value and its spread. For example, imagine you work for a company that monitors parent’s heath in real-time and you need to build a script that detects dangerous anomalies.

• You could generate summary statistics in micro batch and then calculate the mean the max and standard deviation of incoming data that is generated from the monitoring device with there summary statistics.
• You generate automatic alerts when you unusually high or low data points are generated by the device.
• Numpy has quite a few useful statistical function for calculating
  • Sum
  • Median
  • Mean
  • Max

  Let’s take an example to check summary statistics

  import numpy as np import sys arr = np.array([[3, 6], [9, 2]]) a=np.sum(arr) # sum of elements print(a) b=np.mean(arr) # mean of array print(b) c=np.min(arr) #minimum number in array print(c) d= np.max(arr) # maximum number in array print(d) e = np.std(arr) # standard deviation print(e)

  Here is the screenshot of the following given code

  

  Python numpy sum of columns

  • In this section, we will learn about the python numpy sum of columns.
  • Use the numpy.sum() function to find the sum of columns of a matrix.
  • This function is used to the sum of each column of a given array.

  Here is the syntax of numpy.sum()

  1. arr: Input array
  2. axis: axis along which we want to calculate the sum value.
  3. out: We want to place the result.

  Let’s take an example to check how to sum of columns in an array

  import numpy as np a =np.array([[2,3,4],[4,5,7],[8,9,2]]) new_column = a.sum(axis=0) #column sums print(new_column)

  Here is the Screenshot of the following given code

  

  This is how to do some of columns in Python NumPy.

  Python numpy sum each row

  • In this section, we will learn about the python numpy sum of rows.
  • Use the numpy.sum() function to find the sum of rows of a matrix.
  • This function is used to the sum of each row of a given array.

  Here is the syntax of numpy.sum()

  1. arr: Input array
  2. axis: axis along which we want to calculate the sum value.
  3. out: We want to place the result.

  Let’s take an example to check how to sum of rows in an array

  import numpy as np a =np.array([[4,6,7],[7,5,7],[8,9,2]]) new_row = a.sum(axis=1) #rows sums print(new_row)

  Here is the Screenshot of following given code

  

  This is how to do sum each row in Python NumPy.

  Python numpy sum function

  • In this section, we will learn about the python numpy sum.
  • Numpy.sum() function is available in the numpy libraries of Python.
  • This function is used to sum all elements, the sum of each row, and the sum of each column of a given array.
  • We can also specify dtype to specify the returned output datatype.

  Here is the syntax of numpy.sum()

  numpy.sum( arr, axis, dtype, out )

  1. arr: Input array
  2. axis: axis along which we want to calculate the sum value.
  3. dtype: We can specify the returned output datatype.
  4. out: We want to place the result.

  Let’s take an example to check how to sum an elements in an array.

  import numpy as np arr = np.array([[2, 3], [6, 7]]) sum_arr = np.sum(arr) print(sum_arr)

  Here is the Screenshot of following given code

  

  This is how to use Python NumPy Sum() function.

  Python numpy sum ignore nan

  • In this section, we will learn about the python numpy sum ignore nan.
  • nansum() function can be used to calculate the sum of the array ignoring the nan value.
  • This function is used to sum all elements, the sum of each row, and the sum of each column of a given array.

  Here is the syntax of sum ignore nan

  numpy.nansum( arr, dtype=None, )

  import numpy as np a = np.nansum([1, np.nan]) a = np.array([[2, 3], [4, np.nan]]) b = np.nansum(a) print(b)

  Here is the Screenshot of following given code

  

  Python numpy sum two arrays

  • In this section, we will learn about the python numpy sum of two arrays.
  • Numpy.sum() function is available in the NumPy libraries of Python.
  • This function is used to sum all elements, the sum of each row, and the sum of each column of a given array.
  • We can also specify dtype to specify the returned output datatype.

  Here is the syntax of numpy.sum()

  numpy.sum( arr, axis, dtype, out )

  Let’s take an example to check how to sum an elements in an array.

  import numpy as np arr = np.array([[2, 3], [4, 5]]) sum_arr = np.sum(arr) print(sum_arr)

  Here is the Screenshot of following given code

  

  This is how to sum two arrays using Python NumPy.

  Python numpy sum of squares

  • In this section, we will learn about the python numpy sum of squares.
  • Numpy. square() function helps the user to calculate the square value of each element in the array.
  • This function is used to sum all elements, the sum of each row, and the sum of each column of a given array.

  Here is the syntax of numpy.square()

  numpy.square( arr, axis, dtype, out )

  Let’s take an example to check sum of square

  import numpy as np arr = np.array([[2, 3], [4, 5]]) squa_arr = np.square(arr) print(squa_arr)

  Here is the Screenshot of following given code

  

  Python numpy sum 3d array

  • In this section, we will learn about the python NumPy sum 3d array.
  • Numpy. sum() function is available in the NumPy libraries of Python.
  • This function is used to sum all elements, the sum of each row, and the sum of each column of a given array.
  • We can also specify dtype to specify the returned output datatype.

  Here is the syntax of numpy.sum()

  numpy.sum( arr, axis, dtype, out )

  1. arr: Input array
  2. axis: axis along which we want to calculate the sum value.
  3. dtype: We can specify the returned output datatype.
  4. out: We want to place the result.

  Let’s take an example to check how to sum an elements in an 3d array.

  import numpy as np a = np.arange(0,27,1) #3 matrix layer 3 rows and columns a = a.reshape(3,3,3) b = a.sum(axis=1) print(b) 

  Here is the screenshot of following given code

  

  This is how to sum 3d array using Python NumPy.

  You may like the following Python tutorials:

  In this Python tutorial, we will discuss Python numpy sum and also cover the below examples:

  • Python numpy summary statistics
  • Python numpy sum of columns
  • Python numpy sum each row
  • Python numpy sum function
  • Python numpy sum ignore nan
  • Python numpy sum two arrays
  • Python numpy sum of squares
  • Python numpy sum 3d array

  I am Bijay Kumar, a Microsoft MVP in SharePoint. Apart from SharePoint, I started working on Python, Machine learning, and artificial intelligence for the last 5 years. During this time I got expertise in various Python libraries also like Tkinter, Pandas, NumPy, Turtle, Django, Matplotlib, Tensorflow, Scipy, Scikit-Learn, etc… for various clients in the United States, Canada, the United Kingdom, Australia, New Zealand, etc. Check out my profile.

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  numpy.sum() in Python

  

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  Python numpy sum() function syntax

  sum(array, axis, dtype, out, keepdims, initial) 

  • The array elements are used to calculate the sum.
  • If the axis is not provided, the sum of all the elements is returned. If the axis is a tuple of ints, the sum of all the elements in the given axes is returned.
  • We can specify dtype to specify the returned output data type.
  • The out variable is used to specify the array to place the result. It’s an optional parameter.
  • The keepdims is a boolean parameter. If this is set to True, the axes which are reduced are left in the result as dimensions with size one.
  • The initial parameter specifies the starting value for the sum.

  Python numpy sum() Examples

  Let’s look at some of the examples of numpy sum() function.

  1. Sum of All the Elements in the Array

  If we pass only the array in the sum() function, it’s flattened and the sum of all the elements is returned.

  import numpy as np array1 = np.array( [[1, 2], [3, 4], [5, 6]]) total = np.sum(array1) print(f'Sum of all the elements is ') 

  Output: Sum of all the elements is 21

  2. Sum of Array Elements Along the Axis

  If we specify the axis value, the sum of elements along that axis is returned. If the array shape is (X, Y) then the sum along 0-axis will be of shape (1, Y). The sum along 1-axis will be of shape (1, X).

  import numpy as np array1 = np.array( [[1, 2], [3, 4], [5, 6]]) total_0_axis = np.sum(array1, axis=0) print(f'Sum of elements at 0-axis is ') total_1_axis = np.sum(array1, axis=1) print(f'Sum of elements at 1-axis is ') 

  Sum of elements at 0-axis is [ 9 12] Sum of elements at 1-axis is [ 3 7 11] 

  3. Specifying Output Data Type of Sum

  import numpy as np array1 = np.array( [[1, 2], [3, 4]]) total_1_axis = np.sum(array1, axis=1, dtype=float) print(f'Sum of elements at 1-axis is ') 

  Output: Sum of elements at 1-axis is [3. 7.]

  4. Initial Value for the Sum

  import numpy as np array1 = np.array( [[1, 2], [3, 4]]) total_1_axis = np.sum(array1, axis=1, initial=10) print(f'Sum of elements at 1-axis is ') 

  Output: Sum of elements at 1-axis is [13 17] Reference: API Doc

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