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Python data science libraries, such as NumPy, Pandas, and others are used by Data scientists to perform fast, modular, and efficient data analysis. We can use the methods and functions of these libraries to perform certain tasks on our data. For example, if we want to create a new column based on particular conditions various methods are used in Python. In this guide, you will be able to create a DataFrame column based on the condition using the following methods: “List Comprehension” “Numpy.where()” Method “Numpy.select()” Method “Numpy.apply()” Method “DataFrame.map()” Method View the full article

Python doesn’t have built-in arrays that can be used for representing the matrices. However, to represent the matrices in Python, the NumPy package is used to create arrays. Suppose the user wants to perform multiplication or element-wise multiplication on the arrays. To do this, the NumPy library provides a method “multiply(),” and this same function can be used to perform element-wise multiplication as well. This post will illustrate the use of the multiply() method to perform array multiplication and element-wise multiplication. How to Use “multiply()” Method of NumPy? As explained above, this method is used to perform multiplication on arrays created through the NumPy package. Simply go over its syntax given below: numpy.multiply(array1, array2, optionsParam) In this syntax: “array1” denotes the first array in the multiplication. “array2” denotes the second array in the multiplication. “optionsParams” include different options that can be used to fine-tune the multiplication process. Note: To learn about the optional parameters in the multiply() method, check out the official documentation from NumPy! Example 1: Element Wise Multiplication With a Scalar Value To perform scalar multiplication on every element of an array one by one, start by importing the numpy library into your program: import numpy After that, create a new array using the array() method from the numpy library: array1 = numpy.array([[1,2,3], [4,5,6]]) Use the multiply() and pass a scalar value in the first argument and the array in the second argument: result =numpy.multiply(4,array1) Lastly, print out the result onto the terminal by using the print() method: print(result) The complete code snippet for this example is as: import numpy array1 = numpy.array([[1,2,3],[4,5,6]]) result =numpy.multiply(4,array1) print(result) When this code is executed, it produces the following result: It is clear from the output image above that every element was multiplied with a scalar value. Example 2: Element Wise Multiplication Between two Arrays Start by importing the numpy library and creating two different arrays using the array() method: import numpy array1 = numpy.array([[1,2,3],[4,5,6]]) array2 = numpy.array([[4,5,6],[1,9,3]]) After that, pass both of the arrays in the arguments of the multiply() method and print out the result using the following lines: result = numpy.multiply(array1,array2) print(result) Once this code is executed, it produces the following result: The output verifies that both matrices/arrays have been multiplied using element-wise multiplication. Alternative: Use the “*” Operator Alternatively, the user can simply use the asterisk symbol instead of using the multiply() method as it is considered a short-hand operator for the multiply() method. To demonstrate this, simply take the following code: import numpy array1 = numpy.array([[1,2,3],[4,5,6]]) array2 = numpy.array([[4,5,6],[1,9,3]]) result = array1 * array2 print(result) Running the above code will produce the following result: The asterisk operator produced the same results as the multiply() method. Conclusion To perform element-wise multiplication on matrices created with the NumPy library, the user can utilize the multiply() method. This multiply() method takes two mandatory arguments, which are the arrays to be multiplied or a scalar and an array. Alternatively, the user can use the asterisk operator “*” to get the same results. View the full article

“Numpy eigenvalues are the function in the Python script which allows us to compute the eigenvalues for a given matrix. Eigenvalues have a vast number of applications in the field of machine learning, datasets, and control systems. These values define the system stability in control systems, help in extracting the features such as dimensionality reduction, and also allow to find of the best fit line for the data using the machine learning algorithms. Numpy belongs to those available packages that are provided by python for dealing with various functions that are relevant to nd-arrays and matrices. To compute the eigenvalues for any nd-array, we use the built-in function provided by the numpy package “numpy. linalg ()”. We may compute the eigenvectors for the eigenvalues using the same formula since they are interrelated.” Procedure This article comprises all the details to implement the numpy eigenvalue function in the python script. The article first gives a brief introduction to the eigenvalue and the numpy library, and then it shows the method of implementation of this function on the distinguished examples. To work with this function, we have to download the python compiler and install and import the numpy packages. Syntax The syntax to call the function of numpy eigenvalue is quite simple and is given as follows: $ numpy. linalg.eig() This function takes in any matrix or nd-array that is square in nature and returns the eigenvalues and the eigenvectors for that matrix. A multi-dimensional array is known as the square matrix, and this matrix represents all the information related to the system or the data set. Now that we have learned about the syntax for this function call, so now we should try implementing this function on the various examples. Example # 01 To compute the eigenvalues of any system, we should know its matrix. So, we will hypothetically define a square matrix or 2d (two-dimensional) array since the matrix and the nd-array are almost the same, but their declaration method varies a little bit from each other. To create an nd-array or matrix for the system, we will first import the Numpy library as the “np” so that we can utilize this name, where we will be required to call the Numpy. After importing the numpy, we will now step forward and will declare and initialize a 2d- array with the values or its elements as “[2, 2], [4, 4]”. This declaration will be done by calling the “np. array()” method, and then we will pass these values to this function as its parameter and will save the results in some variable “a”. This variable “a” now has the system matrix stored in it. After initializing the 2d-array, we will now compute eigenvalues and eigenvectors by calling the function “np”. linalg. eig(array)”.To this function, we will pass the nd-array that we have already created, and it will return the two parameters one eigenvalue, which we will store in the variable as “eigenvalue”, and the second parameter would be eigenvectors which would be then stored in the variable, as “evec” and then we will display these two parameters by calling the print function as “print (name of the parameter)”. We have described this whole explained example in the form of the python code in the following figure, and now we will try to execute this script to verify whether our code is built correctly or not. import numpy as np a = np.array([[2, 2], [4, 4]]) eigenvalue, evec = np.linalg.eig(a) print(" The Eigen values:\n", eigenvalue ) print("The eigenvectors :\n", evec) After the execution of the code, for example, number 1, the code build was created successfully, and the code returned and displayed the two parameters for the system’s nd-array as the “eigenvector” and “eigenvalues” that can be seen in the snippet of the output. Example # 02 The previous example has taken a square matrix of the order 2×2, which is, in fact, the 2-d array. So, in this example, we will try to upgrade the concept a step further and will compute the eigenvalues for the system having the system matrix of order 3×3. To begin with this example, we will create a new project in the Python compiler, and then we will import the basic python libraries and the packages that we will require to work with later in the project. We will install the Numpy package from the python libraries, and then from these installed packages, we will import the numpy as the word “np”. Now we will use this np instead of Numpy in the code. Let us move further and create a 3-dimensional array for the system. A 3-dimensional array consists of three rows and three columns. We will call the function “np. array()” and pass the elements to the array in the 3×3 order as “[2, 2, 3], [3, 2, 4], [5, 4, 6]”. Once the 3-d array is initialized, then we will try to find the eigenvalues for this array, and we will again call the function as we have done in the previous example as “np.linalg.eig(array)”. To the function, we will pass the array, and it will return the eigenvalues and the vectors for the system’s matrix. import numpy as np array = np.array([[2, 2, 3], [3, 2, 4], [5, 4, 6]]) eigenvalue, evec = np.linalg.eig(array) print(" The Eigen values:\n", eigenvalue ) print("The eigenvectors :\n", evec) The above figure represents the code snippet of the scenario that we have just discussed in the second example of this article in the form of the python script. We can simply copy this code and try to run it on our compilers and check the output. The output of the code has returned exactly the two parameters that are eigenvalues and eigenvectors, in the form of the complex numbers that we wanted to be computed for our system’s matrix, which was a square matrix of 3×3 dimensions (3d- array). Conclusion We will summarize the article by once again reviewing the steps that we have taken in this article. We have given a brief history of the concept of eigenvalues with numpy packages. Then we discussed the syntax for the implementation of the eigenvalues using the Numpy package, and finally, we explained and implemented in detail the eigenvalues for the nd-arrays or the system matrices. View the full article

Numpy is a Python package that is used to do scientific computations. It offers high-performance multidimensional arrays as well as the tools needed to work with them. A NumPy array is a tuple of positive integers that indexes a grid of values (of the same type). Numpy arrays are quick and simple to grasp, and they allow users to do calculations across vast arrays. NumPy has a wide range of methods that can be used in various situations. Set_printoptions() is an example of a numerical range-based function. The set_printoptions() function in Python is used to control how floating-point numbers, arrays, and other NumPy objects are printed. The set_printoptions() method will be discussed in-depth and with examples in this article. What Is the Set_printoptions() Method in Python? We can get custom printing options with the numpy.set_printoptions() method of Python, such as setting the precisions of floating values. To display each entry in the array with precise digits of precision, call numpy.set_printoptions (precision=None, suppress=None). Set suppress to True to disable scientific notation when it is presented. NumPy uses up to 8 digits of precision by default, and scientific notation is not suppressed. What Is the Syntax of Set_printoptions() Method? The set_printoptions() method’s syntax is given below. The set_printoptions() method has the following parameters in its syntax. precision: The default value for this parameter is 8, which reflects the number of digits of precision. threshold: Instead of full repr, this reflects the total amount of array members that trigger summarization. This is an optional field with a value of 1000 as the default. edgeitems: This reflects the total number of array objects at the start and the end of each dimension. This is a three-digit field that is optional. suppress: A Boolean value is required. If True, the function will always use fixed-point notation to output floating-point integers. The numbers that are equal to zero in the present precision will print as zero in this situation; when the absolute value of the smallest is <1e-4 or the ratio of the largest absolute value to the minimum is >1e3, the scientific notation is used if False. This is also an optional parameter with the value False as the default. Now that you have a basic grasp of the set_printoptions method’s syntax and operation, it’s time to look at some examples. The provided examples will show you how to use the set_printoptions() method to print numpy arrays with precision. Example 1 To help you understand how to use the set_printoptions() function below is an example program. The arange and set_printoptions functions from the numpy module are used in the code below. After that, we used a precision value of 5, a threshold value of 5, an edgeitems value of 4, and a suppress value of True to implement the set_printoptions() function. Our code’s printing option is configured with this command. We used the arange() function to build an array object ‘arr’ containing integers ranging from 1 to 11 in the second final line of the code. Finally, the array object ‘arr’ has been printed. from numpy import set_printoptions, arange set_printoptions(precision=5, threshold=5, edgeitems=4, suppress=True) arr = arange(12) print(arr) As you can see, the integers 1 to 11 are printed using the above-mentioned program code. Example 2 Another NumPy sample code to construct an array with scientific notation numbers can be found here. We set the precision value to 8 in this example and printed the array in this code. Let’s just have a look at each line of the code one by one. This way, you’ll have a better understanding of what this code performs. We began by importing the numpy module, which is required to build and run this program code. Following that, we constructed the array and saved it in the variable ‘n.’ Following that, we printed the message ‘Precision value is set to 8′ to benefit the readers’ understanding. After that, we used the set_printoptions() method to set the precision to 8 and print the array in the same way. import numpy as np n = np.array([1.3e-6, 1.2e-5, 1.1e-4]) print("Precision value is set to 8:") np.set_printoptions(suppress=True, precision=8) print(n) The typed message is displayed first, followed by the array values, which are presented according to the set precision, which in our case is 8. Example 3 We’ve created a NumPy program code to display NumPy array elements of floating values with specified precision in the third and final example of this post. The numpy module is imported first in the program code, and an array (named arr) is generated with the various floating values. These include [0.56448929, 0.12343222, 0.5643783, 0.8764567, 0.34567826, 0.34562654, 0.23452456, 0.86342567, 0.09423526, 0.25617865], 0.34567826, 0.34562654, 0.23452456, 0.86342567, 0.09423526, 0.25617865]. Following that, the message (Precision value is set to 4) is displayed, informing the readers of the specified value of precision. Finally, the precision value is passed to the set_printoptions() function, and the array is updated and presented. import numpy as np arr =np.array([ 0.56448929, 0.12343222, 0.5643783, 0.8764567, 0.34567826, 0.34562654, 0.23452456, 0.86342567, 0.09423526, 0.25617865]) print("Precision value is set to 4:") np.set_printoptions(precision=4) print(arr) The message and precise array values are displayed in the output image. See the image below. Conclusion The set_printoptions() function of Python was covered in this post. It is often used by programmers to modify the printing of Numpy arrays. Here you’ll find all the details as well as sample programs that you may use on your own. This will make it easy for you to comprehend the entire issue. This article contains all you need to know, from definition to syntax to examples. If you’re new to this notion and need a step-by-step guide to getting started, go no further than this article. View the full article

Numpy is a Python package that is used to do scientific computations. It offers high-performance multidimensional arrays as well as the tools needed to work with them. A NumPy array is a tuple of positive integers that indexes a grid of values (of the same type). Numpy arrays are quick and simple to grasp, and they allow users to do calculations across vast arrays. NumPy has a wide range of methods that can be used in various situations. Set_printoptions() is an example of a numerical range-based function. The set_printoptions() function in Python is used to control how floating-point numbers, arrays, and other NumPy objects are printed. The set_printoptions() method will be discussed in-depth and with examples in this article. View the full article

In the previous tutorial, we have discussed some basic concepts of NumPy in Python Numpy Tutorial For Beginners With Examples. In this tutorial, we are going to discuss some problems and the solution with NumPy practical examples and code. As you might know, NumPy is one of the important Python modules used in the field of data science and machine learning. As a beginner, it is very important to know about a few NumPy practical examples. Numpy Practical Examples Let’s have a look at 7 NumPy sample solutions covering some key NumPy concepts. Each example has code with a relevant NumPy library and its output. How to search the maximum and minimum element in the given array using NumPy? Searching is a technique that helps finds the place of a given element or value in the list. In Numpy, one can perform various searching operations using the various functions that are provided in the library like argmax, argmin, etc. numpy.argmax( )This function returns indices of the maximum element of the array in a particular axis. Example: import numpy as np # Creating 5x4 array array = np.arange(20).reshape(5, 4) print(array) print() # If no axis mentioned, then it works on the entire array print(np.argmax(array)) # If axis=1, then it works on each row print(np.argmax(array, axis=1)) # If axis=0, then it works on each column print(np.argmax(array, axis=0)) Output: [[ 0 1 2 3] [ 4 5 6 7] [ 8 9 10 11] [12 13 14 15] [16 17 18 19]] 19 [3 3 3 3 3] [4 4 4 4] Similarly one can use numpy.argmin( ) to return indices of the minimum element of the array in a particular axis. How to sort the elements in the given array using Numpy? Sorting refers to arrange data in a particular format. Sorting algorithm specifies the way to arrange data in a particular order. In Numpy, one can perform various sorting operations using the various functions that are provided in the library like sort, argsort, etc. numpy.sort( )This function returns a sorted copy of an array. Example: import numpy as np array = np.array([ [3, 7, 1], [10, 3, 2], [5, 6, 7] ]) print(array) print() # Sort the whole array print(np.sort(array, axis=None)) # Sort along each row print(np.sort(array, axis=1)) # Sort along each column print(np.sort(array, axis=0)) Output: [[ 3 7 1] [10 3 2] [ 5 6 7]] [ 1 2 3 3 5 6 7 7 10] [[ 1 3 7] [ 2 3 10] [ 5 6 7]] [[ 3 3 1] [ 5 6 2] [10 7 7]] numpy.argsort( )This function returns the indices that would sort an array. Example: import numpy as np array = np.array([28, 13, 45, 12, 4, 8, 0]) print(array) print(np.argsort(array)) Output: [28 13 45 12 4 8 0] [6 4 5 3 1 0 2] How to find the mean of every NumPy array in the given list? The problem statement is given a list of NumPy array, the task is to find mean of every NumPy array. Using np.mean( )import numpy as np list = [ np.array([3, 2, 8, 9]), np.array([4, 12, 34, 25, 78]), np.array([23, 12, 67]) ] result = [] for i in range(len(list)): result.append(np.mean(list[i])) print(result) Output: [5.5, 30.6, 34.0] How to add rows and columns in NumPy array? The problem statement is given NumPy array, the task is to add rows/columns basis on requirements to numpy array. Adding Row using numpy.vstack( ) import numpy as np array = np.array([ [3, 2, 8], [4, 12, 34], [23, 12, 67] ]) newRow = np.array([2, 1, 8]) newArray = np.vstack((array, newRow)) print(newArray) Output: [[ 3 2 8] [ 4 12 34] [23 12 67] [ 2 1 8]] Adding Column using numpy.column_stack( ) import numpy as np array = np.array([ [3, 2, 8], [4, 12, 34], [23, 12, 67] ]) newColumn = np.array([2, 1, 8]) newArray = np.column_stack((array, newColumn)) print(newArray) Output: [[ 3 2 8 2] [ 4 12 34 1] [23 12 67 8]] How to reverse a NumPy array? The problem statement is given NumPy array, the task is to reverse the NumPy array. Using numpy.flipud( )import numpy as np array = np.array([3, 6, 7, 2, 5, 1, 8]) reversedArray = np.flipud(array) print(reversedArray) Output: [8 1 5 2 7 6 3] How to multiply two matrices in a single line using NumPy? The problem statement is given two matrices and one has to multiply those two matrices in a single line using NumPy. Using numpy.dot( )import numpy as np matrix1 = [ [3, 4, 2], [5, 1, 8], [3, 1, 9] ] matrix2 = [ [3, 7, 5], [2, 9, 8], [1, 5, 8] ] result = np.dot(matrix1, matrix2) print(result) Output: [[19 67 63] [25 84 97] [20 75 95]] How to print the checkerboard pattern of nxn using NumPy? The problem statement is given n, print the checkerboard pattern for a nxn matrix considering that 0 for black and 1 for white. Solution: import numpy as np n = 8 # Create a nxn matrix filled with 0 matrix = np.zeros((n, n), dtype=int) # fill 1 with alternate rows and column matrix[::2, 1::2] = 1 matrix[1::2, ::2] = 1 # Print the checkerboard pattern for i in range(n): for j in range(n): print(matrix[i][j], end=" ") print() Output: 0 1 0 1 0 1 0 1 1 0 1 0 1 0 1 0 0 1 0 1 0 1 0 1 1 0 1 0 1 0 1 0 0 1 0 1 0 1 0 1 1 0 1 0 1 0 1 0 0 1 0 1 0 1 0 1 1 0 1 0 1 0 1 0 View the full article