Adding Keyword with Count of Occurrence in Sheet2 to Existing ExcelFile from Sheet1 with Pandas Python Introduction In this article, we will explore how to add a new column to an existing Excel file using pandas and Python. We will also discuss how to count the occurrence of keywords in a specific column and display them in another column. Overview of Pandas Pandas is a powerful library for data manipulation and analysis in Python.

Optimizing Levenshtein Distance Calculation for Large DataFrames Introduction In this article, we will explore the optimization of Levenshtein distance calculation for large dataframes. The Levenshtein distance is a measure of the minimum number of single-character edits (insertions, deletions or substitutions) required to change one word into the other. Levenshtein distance calculation can be computationally expensive, especially when dealing with large datasets. In this article, we will discuss various approaches to optimize Levenshtein distance calculation and provide a comprehensive example using NumPy and Cython.

Understanding and Implementing Rownumber to Select Non-Duplicate Rows from a Table In this article, we will explore how to use the ROW_NUMBER function in SQL Server to select non-duplicate rows from a table. We will also discuss the error that occurs when trying to calculate date difference between two dates of different data types. Introduction The ROW_NUMBER function is used to assign a unique number to each row within a partition of a result set.

Slicing Data for Each Unique ID in Python Introduction In this article, we will explore how to slice data for each unique ID in Python. We will start by understanding the problem and then move on to providing a solution using loops. We have been given a dataset with an id column and a val column. The task is to slice the data for each unique id based on the length of val.

ValueError: Could Not Convert String to Float: ‘130.4,120.6,110.9’ In this article, we will delve into the error ValueError: could not convert string to float: '130.4,120.6,110.9' and explore its causes and solutions. Understanding ValueError A ValueError is an exception in Python that is raised when a function or operation cannot handle certain types of data. In this case, the error occurs when trying to convert a string to a float. What are Floats?

Render a Conditional Panel with a Parameter Passed from the Server If you want to render a conditional panel (conditionalPanel) that displays based on a parameter passed from the server, you can use renderConditionalPanel in R Shiny. Using renderUI and renderValue You can also achieve this using renderUI and renderValue. Here’s an example: library(shiny) # --- Demo Module --- basicMod_ui <- function(id) { ns <- NS(id) tagList( textOutput(ns("text")), selectInput(ns("column"), "Select Column", choices = NULL, multiple = TRUE), conditionalPanel("input.

Querying Croud Repository to Get Last 10 Different Firms for a Customer As a backend developer, it’s common to encounter the need to retrieve specific data from a database while minimizing the impact on performance. In this blog post, we’ll explore how to efficiently query a Crowd Repository to get the last 10 different firms that a customer has transferred money with, without retrieving all database rows. Introduction Crowd is a popular open-source tool for managing crowdsourced tasks and workflows.

Understanding Scoped Services in ASP.NET Core and Their Interactions with Singletons Introduction to Dependency Injection in ASP.NET Core In ASP.NET Core, dependency injection (DI) is a powerful feature that allows developers to decouple their applications from specific implementations of interfaces or abstract classes. The Microsoft.Extensions.DependencyInjection package provides the foundation for building applications with DI, and its services are used throughout this article. When building an application using DI in ASP.NET Core, one must understand how the different lifetime scopes (Transient, Scoped, Singleton) work together to provide services to components within the application.

Calculating Mean and Standard Deviation of Multiple Dataframes at One Go As a data analyst or scientist working with large datasets, you often encounter situations where you need to perform calculations on multiple dataframes simultaneously. In this article, we will explore how to calculate the mean and standard deviation of multiple pandas dataframes using Python. Overview of Pandas Library Pandas is a powerful library in Python that provides high-performance, easy-to-use data structures and data analysis tools.

Understanding the Problem and Solution The problem presented is an AttributeError caused by trying to call the replace() method on a column name that doesn’t exist. In this case, the column name has been modified to include the _0_ suffix after using the flatten_json library to flatten a JSON object. Background: Understanding Pandas DataFrames and Column Names In pandas, dataframes are represented as 2D tables where each row represents a single observation and each column represents a variable.