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Sometimes we may have a need to refine a given list by eliminating duplicate substrings from delimited strings. This can be achieved by using a combination of various methods available in Python's standard library like set(), split(), and list comprehensions. Using set() and split() The split() method segregates the elements for duplicate checking, and the set() method stores only unique elements from each string. This approach maintains grouping by original string ? Example # initializing list strings = ['xy-xy', 'pq-qr', 'xp-xp-xp', 'dd-ee'] print("Given list:", strings) # using set() and split() result = [set(sub.split('-')) ... Read More
When building web applications, you may notice that clicking on buttons often leaves an outline or "focus ring" around them. This can be helpful for accessibility, but sometimes it's not desirable for specific designs. In this article, we'll explore several ways to remove the focus ring from buttons when they are clicked, without sacrificing accessibility for keyboard users. Syntax /* CSS approach */ button:focus { outline: none; } /* Or using focus-visible for better accessibility */ button:focus-visible { outline: 2px solid color; } Method 1: Using ... Read More
Multiplying elements in a list of lists (nested list) with corresponding elements from another list is a common operation in data analysis. Python provides several approaches to perform this custom multiplication efficiently. Using Nested Loops In this approach, we use two nested for loops. The outer loop iterates through each sublist, while the inner loop multiplies each element in the sublist with the corresponding multiplier ? nested_list = [[2, 11, 5], [3, 2, 8], [11, 9, 8]] multipliers = [5, 11, 0] # Original list print("The given list:", nested_list) print("Multiplier list:", multipliers) # Using ... Read More
Centering the middle item in a layout while ensuring it stays centered even when other items are removed is a common design challenge. This article explores CSS techniques to center the middle item using methods that maintain its position regardless of adjacent elements. Method 1: Using Flexbox with Auto Margins Flexbox offers a straightforward way to center an item within a container. By setting the middle item's margin property to auto, it remains perfectly centered without depending on adjacent items. Example ... Read More
Sometimes when creating a matrix using Python, we may need to control how many times a given element is repeated in the resulting matrix. In this article, we will see how to create a matrix with a required number of elements when the elements are given as a list. Using zip() and List Comprehension We declare a list with elements to be used in the matrix. Then we declare another list which will hold the number of occurrences of the element in the matrix. Using the zip() function with list comprehension, we can create the resulting matrix ? ... Read More
Pandas is a widely used Python library for data processing and analysis. In this article, we will see how to create pandas DataFrame from Python dictionaries containing lists as values. Creating DataFrame from Dictionary with Lists A dictionary with lists as values can be directly converted to a DataFrame using pd.DataFrame(). Each key becomes a column name, and the corresponding list becomes the column data. Example Let's create a DataFrame from a dictionary containing exam schedule information ? import pandas as pd # Dictionary for Exam Schedule exam_schedule = { ... Read More
Flexbox is a powerful layout tool that allows us to align items within a container dynamically. However, sometimes you may want a flex item to take up only as much width as its content, rather than stretching to fill the available space within the container. In this article, we'll go over different techniques to make specific items within a flex container take up only the space needed for their content, without stretching to fill the available space. Syntax /* Method 1: Using align-self */ .item { align-self: flex-start; } /* Method 2: ... Read More
Pandas DataFrames allow you to add new columns based on conditions applied to existing data. This is useful for categorizing, labeling, or transforming data based on specific criteria. Creating the Base DataFrame Let's start with a simple exam schedule DataFrame ? import pandas as pd # Lists for Exam subjects and Days days = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri'] subjects = ['Chemistry', 'Physics', 'Maths', 'English', 'Biology'] # Dictionary for Exam Schedule exam_data = {'Exam Day': days, 'Exam Subject': subjects} # ... Read More
When creating web pages, centering a background image in a div is a common design requirement. There are several CSS methods available, each suited for different scenarios. This article demonstrates various techniques to center background images inside divs regardless of screen size or container dimensions. Syntax /* Method 1: Background Position */ .container { background-image: url('image.jpg'); background-position: center; background-repeat: no-repeat; } /* Method 2: Flexbox */ .container { display: flex; justify-content: center; ... Read More
A Pandas DataFrame can be created from a dictionary where values are lists of equal length. This approach is useful when you have related data stored in separate lists and want to combine them into a tabular structure. Using Separate Lists and Dictionary In this approach, we declare lists individually and then use each list as a value for the appropriate key inside a dictionary. Finally, we apply pd.DataFrame() to convert the dictionary into a DataFrame ? Example import pandas as pd # Lists for Exam schedule days = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri'] ... Read More