As a trainer and content creator, I regularly generate fake datasets for exercises. This task can be more challenging than it sounds. Creating variables that follow a specific pattern or distribution was difficult with earlier random data distribution tools. Additionally, crafting a dataset that “feels” real for a given industry, ...

Whether you’re a seasoned financial analyst or a data-proficient professional, understanding the calculation of investment returns can help assess the profitability and risks of various assets. This post explores three core methods for calculating investment returns: normalized returns, daily returns, and logarithmic daily returns. The integration of Python within ...

Upsampling time series data involves increasing the frequency of your data points by filling in gaps to transform a lower-frequency dataset into a higher-frequency one. This process is often necessary when aligning datasets with different frequencies or when a more granular view of the data is required. For instance, in ...

Data analysts should be proactive about missing values in their data, as these can significantly impact analysis outcomes. First of all, many algorithms such as linear and logistic regression as well as decision trees require complete datasets and cannot inherently process missing data. But beyond statistical modeling and machine learning, ...

Recently, I encountered a task in Excel where I needed to identify the top N values within each group. Generally, I’ve found that working with groups in Excel can be less than user-friendly for many tasks, and I was hopeful that some of the newer dynamic array functions might ...

One of the greatest wins for Excel users learning Python is access to a comprehensive suite of data visualization tools. However, compared to Excel, the default plots produced with Python in Excel can seem somewhat static. For example, there’s no straightforward way to add tooltips or interactive elements that ...

Monte Carlo simulations leverage probability and randomness to simulate processes multiple times, exploring a wide range of possible outcomes. This approach can illuminate the inherent uncertainty and variability in business processes and outcomes. Integrating Python’s capabilities for Monte Carlo simulations into Excel enables the modeling of complex scenarios, from ...

Working with Python code becomes significantly more manageable when objects are assigned clear, sensible names, and Excel is no different. This blog post covers how to create named ranges and tables in Excel using openpyxl. To begin this demonstration, we will import several modules from openpyxl, along with a sample ...

Several packages facilitate working with Python and Excel, but openpyxl stands out as an excellent choice due to its comprehensive features for reading and writing workbooks. In this blog post, we will explore how to get started with creating Excel workbooks from scratch and modifying existing ones using openpyxl. For ...