In Python, SciPy is a free and open-source Python library used for scientific computing and technical computing. Check out our practical tutorials on SciPy. Talk to our consultants if you still have questions.
Recently, I was working on an image analysis project where I needed to read and manipulate images using Python. This is when I discovered the power of SciPy’s ndimage module and its imread function. The ndimage module offers a comprehensive suite of image processing tools, with imread being particularly useful for reading images as NumPy … Read more >>
When I was working on an optimization problem where I needed to find the global minimum of a complex function with multiple variables. Traditional optimization methods kept getting stuck in local minima, and I needed something more robust. That’s when I turned to SciPy’s differential evolution algorithm. In this article, I’ll walk you through how … Read more >>
linprog in SciPy solves linear programming problems: minimise a linear objective subject to linear constraints. Three things about it surprise people, and all three are in the first example: it only minimises, every variable is non-negative by default, and constraints must be written as <=. I solved each of these on SciPy 1.18.1 on Python … Read more >>
Recently, I was working on a machine learning project that involved a large dataset with mostly zero values. When I tried to process this data using regular NumPy arrays, my computer almost crashed due to memory limitations. That’s when I discovered the power of sparse matrices in SciPy, particularly the LIL (List of Lists) matrix … Read more >>
Recently, I was working on a data analysis project where I needed to estimate the probability density function of a dataset. The issue is, traditional histograms weren’t giving me the smooth representation I needed. This is where Gaussian Kernel Density Estimation (KDE) from SciPy came to the rescue. In this article, I’ll cover everything you … 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 project where I needed to process audio signals and remove unwanted noise. As I explored different solutions, I found that SciPy’s IIR filters were incredibly powerful for this task. In this article, I’ll share my hands-on experience with SciPy’s IIR filters, showing you how to implement them effectively in … Read more >>
Recently, I was working on a signal processing project where I needed to remove noise from sensor data collected from a traffic monitoring system in downtown Chicago. The challenge was filtering out random fluctuations while preserving the important traffic pattern information. This is where the Butterworth filter in SciPy came to my rescue. In this … 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 >>
SciPy curve fit takes a model function and some data, then finds the parameter values that make the model match. It lives in SciPy‘s optimize module: popt comes back with the fitted parameters and pcov with their covariance. The one thing that goes wrong more than anything else is RuntimeError: Optimal parameters not found, which … Read more >>