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Word documents contain formatted text wrapped within three object levels. Lowest level − Run objects, Middle level − Paragraph objects and Highest level − Document object. So, we cannot work with these documents using normal text editors. But we can manipulate these word documents in Python using the python-docx module. Installation The first step is to install this third-party module python-docx. You can use pip ? pip install python-docx Important: After installation, import docx NOT python-docx. Use docx.Document class to start working with the word document. Creating a Basic Word Document ... Read More
Python offers multiple ways to initialize lists, but their performance varies significantly. Understanding which method is fastest helps write more efficient code. This article compares four common list initialization methods: for loops, while loops, list comprehensions, and the star operator. Performance Comparison of List Initialization Methods Let's benchmark four different approaches to initialize a list with 10, 000 zeros and measure their execution times − import time # Initialize lists to store execution times for_loop_times = [] while_loop_times = [] list_comp_times = [] star_operator_times = [] # Run 500 iterations for reliable averages for ... Read More
While developing an application, there are many scenarios when we need to operate on strings and convert them into mutable data structures like lists. Python provides several ways to merge string representations into a single list. Using ast.literal_eval() The ast.literal_eval() method safely evaluates string literals containing Python expressions. This is the recommended approach for converting string representations to lists ? import ast # Initialization of strings str1 = "'Python', 'for', 'fun'" str2 = "'vishesh', 'ved'" str3 = "'Programmer'" # Initialization of list result_list = [] # Extending into single list for x in ... Read More
Lists are fundamental containers in Python programming used in day-to-day development. When dealing with nested structures like tuple lists of lists, knowing different iteration methods becomes essential for efficient data manipulation. Using zip_longest() with List Comprehension The zip_longest() function from itertools allows iteration across multiple lists of different lengths, filling missing values with None ? # using itertools.zip_longest from itertools import zip_longest # initialising list of lists test_list = [ [('11'), ('12'), ('13')], [('21'), ('22'), ('23')], [('31'), ('32'), ('33')] ] # printing ... Read More
Dictionary is a collection which is unordered, changeable and indexed. In Python, dictionaries are written with curly brackets, and they have keys and values. Inverting a dictionary means swapping keys and values, so the original values become the new keys and vice versa. Using Dictionary Comprehension The most Pythonic way to invert a dictionary is using dictionary comprehension ? # initialising dictionary ini_dict = {101: "vishesh", 201: "laptop"} # print initial dictionary print("initial dictionary:", ini_dict) # inverse mapping using dict comprehension inv_dict = {v: k for k, v in ini_dict.items()} # print final ... Read More
When working with lists containing numeric values, you often need to format them for better readability or presentation. Python provides several methods to format list elements, including list comprehension with string formatting, map() function, and modern f-string formatting. Using List Comprehension with % Formatting The traditional approach uses list comprehension with percentage formatting ? numbers = [100.7689454, 17.232999, 60.98867, 300.83748789] # Format to 2 decimal places using % formatting formatted = ["%.2f" % elem for elem in numbers] print(formatted) ['100.77', '17.23', '60.99', '300.84'] Using map() Function The map() function ... Read More
Flattening a 2D list means converting a nested list structure into a single-dimensional list. Python provides several methods to accomplish this task, each with different performance characteristics and use cases. Using itertools.chain.from_iterable() The chain.from_iterable() function efficiently flattens a 2D list by chaining all sublists together − from itertools import chain nested_list = [[1, 2, 3], [3, 6, 7], [7, 5, 4]] print("Initial list:", nested_list) ... Read More
Finding indices of specific values in a list is a common task in Python programming. While the index() method finds the first occurrence, there are several ways to find all indices where a particular value appears in a list. Using enumerate() The enumerate() function returns both index and value, making it perfect for this task ? # initializing list numbers = [1, 3, 4, 3, 6, 7] print("Original list :", numbers) # using enumerate() to find indices for 3 indices = [i for i, value in enumerate(numbers) if value == 3] print("Indices of 3 :", ... Read More
A triplet is a group of three consecutive elements from a list. Creating triplets from a given list means extracting all possible combinations of three adjacent elements. Python provides several approaches to accomplish this task efficiently. Using List Comprehension List comprehension provides a concise way to create triplets by slicing the list ? # List of words words = ['I', 'am', 'Vishesh', 'and', 'I', 'like', 'Python', 'programming'] # Using list comprehension to create triplets triplets = [words[i:i + 3] for i in range(len(words) - 2)] print("Triplets using list comprehension:") print(triplets) Triplets ... Read More
When working with numerical data, you often need to convert arrays of string numbers to floating-point arrays for mathematical operations. NumPy provides several efficient methods to perform this conversion. Using astype() Method The astype() method is the most common way to convert data types in NumPy arrays − import numpy as np # Create array of string numbers string_array = np.array(["1.1", "1.5", "2.7", "8.9"]) print("Initial array:", string_array) # Convert to array of floats using astype float_array = string_array.astype(np.float64) print("Final array:", float_array) print("Data type:", float_array.dtype) Initial array: ['1.1' '1.5' '2.7' '8.9'] Final ... Read More