import pandas as pd import numpy as np # Create DataFrame data = { 'id': [13, 16, 17, 18, 19], 'start_date': ['2018-09-01', '1999-11-01', '2018-10-01', '2019-01-01', '2009-11-01'], 'end_date': ['2021-12-31', '2022-12-31', '2020-09-30', '2021-02-28', '2022-10-31'] } df = pd.DataFrame(data) # Define target year year = 2020 # Create date range for the target year rng2020 = pd.date_range(start='2020-01-01', end='2020-12-31', freq='M') # Calculate months worked in each row df['months'] = df.apply(lambda x: len(np.intersect1d(pd.date_range(start=x['start_date'], end=x['end_date'], freq='M'), rng2020)), axis=1) # Drop rows with no months worked df.

Understanding Oracle Explain Plan and Hints Introduction When working with databases, it’s essential to understand how the optimizer chooses plans for queries. The explain plan provides insight into the optimizer’s decision-making process, which can help improve query performance. However, sometimes you want to take control of the optimization process by specifying hints. In this article, we’ll explore the details of Oracle Explain Plan and Hints. Oracle Explain Plan Overview The explain plan is a summary of how the optimizer chooses a query execution plan.

Understanding Pie Charts and Animation in iOS 7 ===================================================== In this article, we will explore how to draw a pie chart with animation in iOS 7. We will cover the basics of pie charts, how to implement animation in iOS 7, and provide code examples using CocoaControls. What are Pie Charts? A pie chart is a type of graphical representation that shows how different categories contribute to an entire group. It is commonly used to display data as a circle divided into sectors, with each sector representing a specific category.

Hooking into Private Functions in DYLibs using MobileSubstrate Introduction MobileSubstrate is a popular tool for injecting code into iOS and iPadOS applications, allowing developers to create custom hooks, intercept system calls, and even tamper with app behavior. One of the most common use cases for MobileSubstrate is hooking into private functions in DYLIBs (Dynamic Link Libraries). However, as you’ve discovered, dealing with mangled function names and return types can be a challenge.

Understanding Static Unique Identifiers in SQL Views SQL views are a powerful tool for simplifying complex queries and providing a layer of abstraction between the data and the user. However, sometimes we need to add an additional layer of uniqueness to our views, which can be challenging when dealing with large datasets. In this article, we’ll explore the concept of static unique identifiers in SQL views, how they work, and provide solutions for implementing them.

Writing a SQL ResultSet to a CSV File When working with databases, it’s often necessary to export the results of a query to a file for further analysis or processing. In this article, we’ll explore how to write a SQL ResultSet to a CSV (Comma Separated Values) file. Understanding the Basics of SQL and ResultSet Before diving into the code, let’s quickly review the basics of SQL and ResultSet. SQL (Structured Query Language) is a standard language for managing relational databases.

Understanding Image Editing with Multi-Changeable Areas on iPhone Introduction Image editing has become an essential feature in modern mobile applications, allowing users to manipulate and enhance their digital content. One specific use case is the ability to select and edit different areas of an image simultaneously. In this article, we will explore how to achieve this feature using Titanium Appcelerator for an iPhone application. Background Titanium Appcelerator provides a powerful framework for building cross-platform mobile applications.

Understanding dplyr::starts_with() and Its Applications in Data Manipulation In this article, we will delve into the usage of dplyr::starts_with() and explore its applications in data manipulation. The function is a part of the dplyr package, which is a popular R library used for data manipulation and analysis. Introduction to dplyr Package The dplyr package was introduced by Hadley Wickham in 2011 as an extension to the ggplot2 package. The primary goal of the dplyr package is to provide a consistent and efficient way of performing common data operations such as filtering, sorting, grouping, and transforming.

Constant Column Value in Select Query Output: A PostgreSQL and SQL Solutions In a recent Stack Overflow question, a user was faced with an issue where they wanted to add a constant column value to their select query output. The goal was to display a specific product name alongside the aggregated sum of size values from a table. However, when there were no rows in the table, the desired empty row should be displayed instead.

Symbolic Computation Using Regex and Eval() in Python In this blog post, we will explore the use of regular expressions (regex) and the eval() function in Python to perform symbolic computation on financial models. We will delve into the details of how regex can be used to parse and evaluate mathematical expressions, and how this can be applied to build a generic cash flow model. Introduction Symbolic computation is a powerful technique that allows us to perform calculations using mathematical expressions rather than numerical values.