Recently, I was working on a data analysis project where I needed to determine if two groups of data were statistically different from each other. The question was simple: Does our new machine learning algorithm perform better than the traditional one? That’s when I turned to SciPy’s ttest_ind function. In this article, I’ll walk you … Read more >>
When working with data analysis in Python, understanding the shape and distribution of your data is crucial. One key metric that helps us describe data distribution is skewness, which measures the asymmetry of a probability distribution. As a Python developer who has spent years analyzing data, I’ve found SciPy’s skew functions to be invaluable tools … Read more >>
I recently worked on a data science project focused on analyzing rare events that occur within fixed time intervals. Specifically, I aimed to model customer arrivals at a coffee shop during the morning rush hour. The challenge involved predicting how staffing needs would change based on varying customer arrival rates. This is where the Poisson … Read more >>
scipy.stats.norm is the normal distribution, and every method takes the same two arguments: loc is the mean and scale is the standard deviation. Passing a variance there is the single most common mistake, and it fails silently. All numbers below are real output from Python 3.12.5, SciPy 1.18.1, NumPy 2.5.3. scipy.stats.norm pdf, cdf and ppf … Read more >>
Recently, I was working on a data analysis project where I needed to determine which statistical distribution best fit my dataset. This is a common challenge in data science, particularly when attempting to make predictions or uncover underlying patterns in your data. The good news is that Python’s SciPy library makes this process simple with … Read more >>
Recently, I was working on a machine learning project where I needed to calculate distances between multiple data points efficiently. The challenge was computing distances between two collections of inputs without writing nested loops. That’s when I discovered SciPy’s spatial distance cdist function. In this article, I’ll share how to use the cdist function to … Read more >>
Recently, I was working on a machine learning project where I needed to process a large dataset with mostly zero values. The regular NumPy arrays were consuming too much memory and slowing down my computations. The issue is, dense matrices aren’t efficient for sparse data. So we need a specialized data structure. In this article, … Read more >>
Recently, I was working on a data analysis project that required dealing with noisy sensor data. The issue is that raw data often contains random fluctuations, which make it difficult to identify underlying patterns. So we need effective smoothing techniques. In this article, I’ll cover several simple ways you can use SciPy to smooth your … Read more >>
SciPy gives you three doors into root finding, and picking the wrong one is why scipy.optimize.root feels awkward for simple problems. Most people land here with a single equation, so it’s one of the first two they want. I reach for brentq unless there’s a reason not to. Everything that follows was run on SciPy … Read more >>
While I was working on a project that required solving differential equations to model a real-world physical system. The challenge was finding a simple yet useful way to solve these equations in Python. That’s when I discovered the scipy.integrate.odeint function, a game-changer for solving ordinary differential equations (ODEs) numerically. In this article, I’ll cover several … Read more >>