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Python Articles
Page 29 of 855
How to clear screen in python?
When working with Python programs, you may need to clear the terminal screen programmatically to improve output readability. While you can manually clear the screen with Ctrl + L, Python provides ways to clear the screen automatically within your script. Python uses the os.system() function to execute system commands for clearing the screen. The command differs between operating systems: 'clear' for Unix/Linux/macOS and 'cls' for Windows. Basic Screen Clearing Function Here's how to create a cross-platform screen clearing function ? import os def clear_screen(): # For Unix/Linux/macOS (os.name is 'posix') ...
Read MoreFinding Mean, Median, Mode in Python without Libraries
Mean, Median and Mode are very frequently used statistical functions in data analysis. Python provides built-in functions and simple algorithms to calculate these measures without external libraries. Finding Mean Mean of a list of numbers is also called average of the numbers. It is found by taking the sum of all the numbers and dividing it by the count of numbers. In the below example we apply the sum() function to get the sum of the numbers and the len() function to get the count of numbers ? Example numbers = [21, 11, 19, 3, ...
Read MoreFind the k most frequent words from data set in Python
If there is a need to find the k most frequent words in a data set, Python can help us achieve this using the collections module. The collections module has a Counter class which counts the frequency of words after we supply a list of words to it. We also use the most_common() method to find the specified number of most frequent words. Basic Approach Using Counter In the below example we take a paragraph, create a list of words using split(), then apply Counter() to count word frequencies. Finally, most_common() returns the top k most frequent words ...
Read MoreFind all the numbers in a string using regular expression in Python
Extracting numbers from text is a common requirement in Python data analytics. Regular expressions provide a powerful way to define patterns for matching digits, decimal numbers, and numbers with signs. Basic Number Extraction The re.findall() function extracts all occurrences of a pattern from a string. The pattern r'\d+' matches one or more consecutive digits ? import re text = "Go to 13.8 miles and then -4.112 miles." numbers = re.findall(r'\d+', text) print(numbers) ['13', '8', '4', '112'] Note that this pattern extracts only digits, splitting decimal numbers and ignoring signs. ...
Read MoreCount frequencies of all elements in array in Python using collections module
Python lists allow duplicate elements, so we often need to count how many times each element appears. The frequency of elements indicates how many times an element occurs in a list. The Counter class from the collections module provides an efficient way to count element frequencies. Syntax Counter(iterable) Where iterable is any Python iterable like a list, tuple, or string. Basic Example The Counter() function returns a dictionary-like object with elements as keys and their counts as values − from collections import Counter days = ['Mon', 'Tue', 'Wed', 'Mon', 'Mon', ...
Read MoreCount distinct elements in an array in Python
In Python lists, we often encounter duplicate elements. While len() gives us the total count including duplicates, we sometimes need to count only the distinct (unique) elements. Python provides several approaches to accomplish this task. Using Counter from collections The Counter class from the collections module creates a dictionary where elements are keys and their frequencies are values. We can use its keys() method to get distinct elements ? from collections import Counter days = ['Mon', 'Tue', 'Wed', 'Mon', 'Tue'] print("Length of original list:", len(days)) distinct_elements = Counter(days).keys() print("List with distinct elements:", list(distinct_elements)) print("Length ...
Read MoreBackward iteration in Python
Sometimes we need to iterate through a list in reverse order − reading the last element first, then the second-to-last, and so on. Python provides three common approaches: range() with negative step, slice notation [::-1], and the reversed() built-in function. Using range() with Negative Step Start from the last index and step backwards by -1 until index 0 ? days = ['Mon', 'Tue', 'Wed', 'Thu'] for i in range(len(days) - 1, -1, -1): print(days[i]) Thu Wed Tue Mon range(3, -1, -1) generates indices 3, 2, 1, ...
Read Moreappend() and extend() in Python
The append() and extend() methods both add elements to a Python list, but they behave differently. append() adds its argument as a single element (even if it's a list), while extend() adds each element of an iterable individually. append() Adds the argument as one element to the end of the list. List length increases by exactly 1, regardless of the argument type. days = ['Mon', 'Tue', 'Wed'] print("Original:", days) # Append a single element days.append('Thu') print("After append('Thu'):", days) # Append a list — added as a nested list days.append(['Fri', 'Sat']) print("After append(['Fri', 'Sat']):", days) ...
Read MoreAdd a row at top in pandas DataFrame
Adding a row at the top of a Pandas DataFrame is a common operation when you need to insert headers, summary rows, or new data at the beginning. There are several methods to achieve this − using pd.concat(), loc[] with index manipulation, or iloc slicing. Create a Sample DataFrame import pandas as pd df = pd.DataFrame({ 'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 35], 'City': ['Delhi', 'Mumbai', 'Pune'] }) print("Original DataFrame:") print(df) Original DataFrame: Name ...
Read MoreAbsolute and Relative frequency in Pandas
In statistics, frequency indicates how many times a value appears in a dataset. Absolute frequency is the raw count, while relative frequency is the proportion (count divided by total observations). Pandas provides built-in methods for calculating both. Absolute Frequency Using value_counts() The simplest way to count occurrences of each value ? import pandas as pd data = ["Chandigarh", "Hyderabad", "Pune", "Pune", "Chandigarh", "Pune"] df = pd.Series(data).value_counts() print(df) Pune 3 Chandigarh 2 Hyderabad 1 dtype: int64 ...
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