Understanding PL/SQL Error ORA-00942: Table or View Does Not Exist Inside Function ORA-00942 is a common error encountered by many developers when working with PL/SQL functions. In this article, we will delve into the reasons behind this error and explore the necessary steps to resolve it. What Causes ORA-00942? ORA-00942 occurs when a SELECT statement is executed inside a PL/SQL function without proper privileges. The error message indicates that the table or view being referenced does not exist in the current context of the database session.

Merging Pandas Dataframes, Keeping All Rows and Columns, Without Duplicates Introduction In this article, we’ll explore how to merge two Pandas DataFrames while keeping all rows and columns from both dataframes without duplicates. We’ll also discuss common pitfalls and solutions to avoid errors. Background Pandas is a powerful library for data manipulation and analysis in Python. It provides an efficient way to handle structured data, including tabular data like spreadsheets or SQL tables.

Introduction to Updating Max Value in PostgreSQL Overview of the Problem and Solution In this article, we will explore a common problem that arises when updating values based on data from another table. Specifically, we’ll discuss how to update the maximum value between two columns in one table based on the count of rows from another table. We have two tables: license and device. The device table has multiple records for a single merchant, represented by the unique merchant_id column.

Understanding Location Aware Notifications on iPhone Introduction Location aware notifications are a crucial feature for many iOS applications. They allow developers to send notifications to users when they enter or leave specific regions, such as their home or office. In this article, we will delve into the world of location aware notifications on iPhone and explore common mistakes that can prevent them from working properly. Background To understand how location aware notifications work on iPhone, it’s essential to know a bit about the underlying technology.

Understanding PickerInput and Limiting Multiple Choices in Shiny Apps ===================================================== In this article, we will delve into the world of pickerInput() from the shinyWidgets package and explore how to limit the number of choices made when using multiple selections. We’ll examine the available options, common pitfalls, and provide a step-by-step guide on how to achieve our goal. Introduction pickerInput() is a powerful widget provided by the shinyWidgets package in R that allows users to select values from a list of choices.

Understanding the Problem with Duplicate Records after Using WITH AS In recent weeks, I have come across several questions on Stack Overflow regarding a common issue when using the WITH statement to retrieve data from multiple tables. Specifically, users are struggling to get duplicate records in their results after combining data from multiple queries using WITH AS. In this article, we’ll delve into the problem and its solution. What is the Problem?

Using Pandas to Handle Missing Values Missing values are a common problem in data analysis. They can arise due to various reasons such as data entry errors, missing observations, or incorrect assumptions about the data. In this blog post, we will explore how to handle missing values using the pandas library in Python. Introduction to Pandas Pandas is a popular library for data manipulation and analysis in Python. It provides data structures and functions that make it easy to work with structured data, such as tabular data.

Creating a Floating Sidebar in Shiny Dashboard with Leaflet Introduction Shiny dashboard is a popular framework for building interactive dashboards using R. One of its key features is the ability to create custom UI components, including sidebars. In this article, we will explore how to create a floating sidebar that floats on top of a leaflet map in a Shiny app. Background Leaflet is a powerful library for creating interactive maps in R.

Working with Time Series Data in Python using pandas and resampling =========================================================== In this article, we’ll explore how to work with time series data in Python using the pandas library. We’ll cover topics such as date manipulation, resampling, and applying calculations to series of numbers while handling maximum limits. Overview of pandas and its Role in Time Series Data pandas is a powerful open-source library for data analysis in Python. It provides high-performance, easy-to-use data structures and functions for manipulating numerical data.

Function that i want to subtract R In this article, we will explore how to create a reusable function in R that can be used to compare prices at different lags and leads without having to rewrite the formula every time. Background R is a popular programming language for statistical computing and data visualization. It has a vast array of libraries and functions that make it easy to perform various tasks such as data analysis, machine learning, and data visualization.