Python Articles - Page 785 of 929

Sometimes when dealing with the binary representation of numbers we may be needed to find out how many continuous 1’s are present in the number. This article shows two ways how we can find out that.Using Split and MapThe split function in python can be used to split the given string into multiple strings. We split it by zeros and the map function is used to find the maximum length among the splits generated.Example Live Demodata = '11110000111110000011111010101010101011111111' def Max_len_cons_1(data): print ("Maximum Number of consecutive one's: ", max(map(len, data.split('0'))) ) Max_len_cons_1(data)OutputRunning the above code gives us the following result −Maximum Number ... Read More

Python programming language uses both * and ** on different contexts. In this article we will see how these two are used and what the respective useful scenarios.As an Infix OperatorWhen * is used as infix operator, it basically gives the mathematical product of numbers. IN the below example we take integers. Floats and complex numbers to multiply and get the results.Example Live Demo# Integers x = 20 y = 10 z = x * y print(z, "") # Floats x1 = 2.5 y1 = 5.1 z1 = x1 * y1 print(z1, "") # Complex Numbers x2 = 4 ... Read More

Statistics is fundamental to learn ml and AI. As Python is the language of choice for these Technologies, we will see how to write Python programs which incorporate statistical analysis. In this article we will see how to create graphs and charts using various Python modules. This variety of charts help us in analyzing the data quickly and deriving insides are conclusions graphically.Data PreparationWe take the data set containing the data about various seeds. This data set is available at kaggle in the link shown in the program below. It has eight columns which will be used to cerate various ... Read More

Every business depends on customer's loyalty. The repeat business from customer is one of the cornerstone for business profitability. So it is important to know the reason of customers leaving a business. Customers going away is known as customer churn. By looking at the past trends we can judge what factors influence customer churn and how to predict if a particular customer will go away from the business. In this article we will use ML algorithm to study the past trends in customer churn and then judge which customers are likely to churn.Data PreparationAs an example will consider the Telecom ... Read More

Frauds are really in many transactions. We can apply machine learning algorithms to lies the past data and predict the possibility of a transaction being a fraud transaction. In our example we will take credit card transactions, analyse the data, create the features and labels and finally apply one of the ML algorithms to judge the nature of transaction as being fraud or not. Then we will find out the accuracy, precision as well as f-score of the model we are chosen.Preparing the DataWe in this step we read the source data, study the variables present in it and have ... Read More

Python allows XML data to be read and processed through its inbuilt module called expat. It is a non-validating XML parser. it creates an XML parser object and captures the attributes of its objects into various handler functions. In the below example we will see how the various handler functions can help us read the XML file as well as give the attribute values as the output data. This generated data can be used for the processing.Exampleimport xml.parsers.expat # Capture the first element def first_element(tag, attrs):    print ('first element:', tag, attrs) # Capture the last element def last_element(tag):   ... Read More

Census data is the source of information collected by the government to understand the population and its characteristics. It consists of details such as age, gender, education, and housing. This helps the government in understanding the current scenario as well as planning for the future. In this article, we are going to learn how to analyze the census data in Python. Python, with its libraries like pandas, numpy, and matplotlib, is widely used for analyzing census data. Analyzing Census Data Here, we are going to use the sample data that consists of the census data stored in ... Read More

Suppose we have an integer array called nums, we have to find the contiguous subarray within an array (containing at least one number) which has the largest product. So if the array is [2, 3, -2, 4], the output will be 6, as contiguous subarray [2, 3] has max product.To solve this, we will follow these steps −max_list := list of size nums, and fill with 0min_list := list of size nums, and fill with 0max_list[0] := nums[0] and min_list[0] := nums[0]for i in range 1 to length of numsmax_list[i] = max of max_list[i - 1]*nums[i], min_list[i - 1]*nums[i] and ... Read More

Suppose we have the inorder and postorder traversal sequence of a binary tree. We have to generate the tree from these sequences. So if the postorder and inorder sequences are [9, 15, 7, 20, 3] and [9, 3, 15, 20, 7], then the tree will be −Let us see the steps -Suppose the method is called buildTree with preorder and inorder listsroot := last node from the postorder, and delete first node from postorderroot_index := index of root.val from the inorder listleft or root := buildTree(subset of inorder from root_index + 1 to end, postorder)right or root := buildTree(subset of ... Read More

Suppose we have the inorder and preorder traversal sequence of a binary tree. We have to generate the tree from these sequences. So if the preorder and inorder sequences are [3, 9, 20, 15, 7] and [9, 3, 15, 20, 7], then the tree will be −Let us see the steps −Suppose the method is called buildTree with preorder and inorder listsroot := first node from the preorder, and delete first node from preorderroot_index := index of root.val from the inorder listleft or root := buildTree(preorder, subset of inorder from 0 to root_index)right or root := buildTree(preorder, subset of inorder ... Read More