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Pandas is a Python library that is used for data manipulation and analysis of structured data. The cut() and qcut() methods of pandas are used for creating categorical variables from numerical data. The cut() method splits numerical data into discrete intervals based on value ranges, while qcut() splits data into quantiles with equal frequencies. In this article, we will understand the functionalities of both methods with practical examples. The cut() Function The cut() function divides a continuous variable into discrete bins or intervals based on specified criteria. It creates groups or categories of data based on the range ... Read More
The apply() function in pandas is used to apply a custom function to a DataFrame or Series. By default, apply() returns a new DataFrame or Series, but you can modify the original data in-place using specific techniques. In this article, we'll explore how to achieve in-place modifications with apply(). Syntax DataFrame.apply(func, axis=0) Series.apply(func) The axis parameter determines whether to apply the function row-wise (axis=1) or column-wise (axis=0). The func can be a built-in function, lambda function, or custom function. Default Behavior − Creating New Objects When using apply() without in-place assignment, it returns ... Read More
Web scraping paragraphs is a common task in data extraction and content analysis. Beautiful Soup is a Python library that allows us to parse HTML and XML documents effortlessly. It provides a convenient way to navigate and search the parsed data, making it an ideal choice for web scraping tasks. In this article, we will learn how to scrape paragraphs using Beautiful Soup with practical examples. Installing Required Libraries Before scraping paragraphs, we need to install the necessary libraries. Open your terminal or command prompt and run the following command to install BeautifulSoup and requests ? ... Read More
The data of local HTML files can be extracted using Beautiful Soup and Python file handling techniques. Beautiful Soup allows us to parse HTML documents and navigate their structure, while file handling enables us to read HTML content from local files. By combining these tools, we can extract valuable data from HTML files stored on our computers. Prerequisites Before scraping data from local HTML files, ensure you have Python installed on your machine. Additionally, basic knowledge of Python programming and HTML structure is recommended. Installing Python Libraries To extract data from HTML files, we'll use the ... Read More
Google Maps is a powerful tool that provides a vast amount of geospatial data, including locations, addresses, reviews, ratings, and more. Being able to extract this data programmatically can be immensely useful for various applications such as business analysis, research, and data-driven decision-making. In this article, we will explore how to scrape data from Google Maps using Python. Important Note: Web scraping Google Maps may violate their Terms of Service. For production applications, consider using the Google Places API instead, which is the official and recommended approach. Installing Required Libraries To begin with, we need ... Read More
Web scraping is a powerful technique used to extract data from websites. One popular library for web scraping in Python is BeautifulSoup. BeautifulSoup provides a simple and intuitive way to parse HTML or XML documents and extract the desired information. In this article, we will explore how to scrape all the text from the tag of a web page using BeautifulSoup in Python. Algorithm The following algorithm outlines the steps to scrape all text from the body tag using BeautifulSoup ? Import the required libraries: We need to import the requests ... Read More
In this article, we will explore various methods to find tuple records that start with a specific prefix using Python. This is useful when working with datasets where you need to filter records based on string patterns. A tuple is an immutable sequence whose values cannot be changed once assigned. A prefix tuple record refers to tuples where the first element (typically a string) starts with a common prefix. Problem Statement Given a list of tuples and a prefix string, we need to find all tuples whose first element starts with that prefix ? records ... Read More
In this article, we will learn how to find the prefix frequency in a string list using Python. Finding prefix frequency helps in analyzing patterns and distribution of word usage in text data. We'll explore five different approaches, each with its own advantages for different use cases. Method 1: Using Simple for Loop The most straightforward approach uses a counter variable and iterates through each string ? def find_prefix_freq(strings, prefix): count = 0 for string in strings: if string.startswith(prefix): ... Read More
In this article, we will learn about the Power Function Distribution in statistics using Python. We will explore various methods to analyze and visualize this distribution, including generating random samples, calculating probability functions, and creating visual representations. What is Power Function Distribution? The Power Function Distribution is a continuous probability distribution commonly used to model data where smaller values are more frequent than larger ones. It's particularly useful for analyzing rare events, identifying outliers, and making predictions about extreme values. The distribution has the probability density function: PDF: f(x) = α * x^(α-1) for 0 ≤ x ... Read More
In this article we will learn about Power Log-Normal Distribution, its applications and uses. We will analyze the distribution using different methods including PDF, CDF, and visualization techniques. Power Log-Normal Distribution Power Log-Normal Distribution is a variation of the log-normal distribution obtained by applying a power transformation. While power normal distribution modifies the normal distribution, power log-normal distribution modifies the log-normal distribution. Both distributions have power parameters that define their shape characteristics. Random Number Generation We can generate random numbers from the power log-normal distribution using the scipy.stats module ? from scipy.stats import powerlognorm ... Read More