Understanding Web-Scraping from Fragments (#) Menu Web-scraping is the process of extracting data from websites using specialized algorithms and software. In this article, we will explore how to web-scrape data from fragments menu marked with #. Specifically, we’ll discuss a common issue when working with such menus and provide a solution using R and several popular libraries. Introduction Web-scraping can be challenging due to the dynamic nature of websites. Some websites use JavaScript to load content dynamically, making it difficult for web-scrapers to retrieve data.

Updating Table in PostgreSQL: Matching Count of Column and Updating Based on Condition In this article, we will explore the concept of updating a table in PostgreSQL based on certain conditions. Specifically, we will focus on how to match the count of a column with a specific threshold value. We will also discuss how to update the table accordingly. Understanding the Problem Statement The problem statement involves updating a table in PostgreSQL where the number of rows for a particular column is greater than 2.

Fast Matrix Calculation in R In this article, we’ll explore how to efficiently calculate the similarity between two large document term matrices (DTMs) in R. Introduction The goal of natural language processing and text analysis is often to compare the similarity or dissimilarity between documents. One common approach is to use the document-term matrix (DTM), which represents the frequency of each word in a document as rows and columns, respectively. When comparing two DTMs, we can calculate the similarity by taking into account both the presence and absence of terms.

Understanding String Comparison and Extraction in Python DataFrames =========================================================== In this article, we will explore how to compare two series of strings in a Pandas DataFrame and store the difference in a new column. We will also discuss methods for improving performance when dealing with large datasets. Introduction When working with dataframes that contain string values, it’s often necessary to compare these strings for differences. In this article, we’ll focus on comparing two series of strings from a Pandas DataFrame and storing the result in a new column.

Integrating Twitter Sharing into an iPhone App In today’s digital age, social media sharing has become a crucial aspect of mobile app development. One popular platform for sharing content is Twitter, with over 440 million monthly active users worldwide. In this article, we will delve into the process of integrating Twitter sharing functionality into an iPhone app. Background and History of MGTwitterEngine The first step in understanding how to integrate Twitter sharing into our iPhone app is to learn about the popular library used for this purpose: MGTwitterEngine.

Loading Keras Models into RMarkdown Files and Predicting with Knit As a data scientist, working with machine learning models is an essential part of the job. When you’ve trained a model using a deep learning framework like TensorFlow or Keras, saving it in a file format that can be easily loaded and used for predictions is crucial. In this article, we’ll explore how to load a Keras model into an RMarkdown file and make predictions using the knit function.

Understanding the Issue with Facebook Permission Dialog in iPhone App Facebook provides a SDK for iOS that allows developers to integrate their app with Facebook features such as login, sharing, and permission requests. In this article, we will delve into the issue of getting a blank Facebook permission dialog in an iPhone app and explore the possible reasons behind it. Introduction to Facebook SDK for iOS The Facebook SDK for iOS is a set of tools that makes it easy to integrate Facebook features into an iOS app.

Sorting Columns in Pandas DataFrame Sorting columns in a pandas DataFrame can be achieved by using the sort_values function, which allows you to specify multiple columns for sorting. In this article, we will explore how to sort two or more columns while maintaining the original order of one column. Problem Statement Suppose we have a DataFrame with an id, date, and price column. We want to sort the ids in ascending order, then sort the dates while keeping the ids sorted.

Working with CSV Data in Python: A Guide to Importing Specific Rows As a data analyst or scientist, working with CSV (Comma Separated Values) files is an essential skill. One common task that arises while working with such files is importing specific rows based on certain conditions. In this article, we will explore how to achieve this using the popular Python library Pandas. Understanding the Problem The question at hand involves importing a specific row from a CSV file containing data on yields of different government bonds of varying maturities.

Creating New DataFrames Based on Existing GroupBy Operations In this article, we will explore how to create new dataframes based on existing groupby operations. We will take the example of creating a new column in a dataframe and then using that column to create a new dataframe with extreme elements. Understanding GroupBy Operations Before we dive into the solution, let’s quickly review what groupby operations are. In pandas, groupby is a powerful tool used for dividing data into groups based on one or more columns.