Introduction to Oracle PL SQL String Functions Oracle PL SQL is a powerful language used for managing and manipulating data in an Oracle database. One of the most commonly used functions in Oracle PL SQL is the string function, which is used to manipulate strings stored in columns or variables. In this article, we will discuss the string functions available in Oracle PL SQL, specifically focusing on how to extract names and codes from a given string.

Pandas Arithmetic Between Two Different Sized Dataframes Given Common Columns Pandas is a powerful library used for data manipulation and analysis in Python. One of its key features is the ability to perform arithmetic operations between two different sized dataframes given common columns. In this article, we will explore how to achieve this using pandas. Introduction When working with large datasets, it’s common to have multiple dataframes that share some common columns.

Understanding the Photos App on iPhone and Storing CGImages The Photos app on an iPhone is a powerful tool that allows users to store, edit, and share their photos. As a developer, you may need to integrate this app into your own applications or use its features in your code. In this article, we will explore how to store CGImages in the Photos app. Background The Photos app on iPhone uses a combination of technologies such as Core Image, Core Graphics, and UIKit to provide its functionality.

Understanding the pandas groupby and apply Functions In this article, we will delve into the world of pandas data manipulation. Specifically, we’ll explore how to use the groupby function in conjunction with the apply method to apply a function to each group in a DataFrame, and how to transform the output into a Series while retaining the original index. Introduction to Grouping and Applying Functions The groupby function is a powerful tool for grouping DataFrames by one or more columns.

Understanding the libxml/tree.h File Not Found Error When Archiving a Project in Xcode When working with third-party libraries like libxml in an Xcode project, it’s common to encounter errors during archiving or distribution. In this article, we’ll delve into the specifics of the libxml/tree.h file not found error that occurs when trying to archive a project for release. Introduction to libxml and TouchXML Before diving into the solution, let’s quickly review what libxml and TouchXML are.

Replacing Duplicates in MultiIndex Series Using Pandas In this article, we will explore the various ways to replace duplicates in a multi-index series while maintaining specific conditions. We’ll delve into different techniques and provide code examples using Python and the popular pandas library. Introduction Pandas is a powerful data manipulation library for Python that provides efficient data structures and operations for analyzing data. One common operation when working with pandas dataframes is to handle duplicates.

Converting Date Formats in C#: Understanding the ToString Method and Format Strings As a developer, working with dates and times can be challenging, especially when different systems or databases use varying formats. In this article, we will delve into the world of date formatting in C#, exploring the ToString method and format strings. We’ll examine how to convert SQL Server date formats to a consistent C# format. Introduction When working with dates and times, it’s essential to ensure consistency across different systems or databases.

pivot_table - TypeError: ‘>’ not supported between instances of ‘str’ and ‘int’ In this blog post, we will discuss a common error encountered when using the pivot_table function in pandas. The error, TypeError: '>' not supported between instances of 'str' and 'int', occurs when the pivot_table function tries to perform an operation that combines a string with an integer or float value. Understanding the Error The error message indicates that there is a problem comparing a string ('>') with an integer or float ('5').

Understanding Pandas: Counting Unique Values in a DataFrame Introduction to Pandas and the Problem at Hand Pandas is a powerful library in Python for data manipulation and analysis. One of its most useful features is handling DataFrames, which are two-dimensional tables of data with rows and columns. In this article, we’ll delve into counting unique values in a DataFrame using various methods. We’re given a sample DataFrame d with some missing values (NaN).

Understanding the Problem and Required Output In this article, we will explore how to generate all possible sums of values from a given data frame. We are provided with a sample dataset dat containing two columns: LOOKUP and VALUE. The LOOKUP column holds an index number, while the VALUE column contains a string associated with that index. The problem asks us to find all possible combinations of sums using these values and output them in a new data frame.