Based on our record, Pandas seems to be a lot more popular than Fityk. While we know about 198 links to Pandas, we've tracked only 2 mentions of Fityk. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Process as to retrieve the interesting bits? AWK and grep. Process as in curve fitting? fityk. Process as in running statistical tests? past4. But very often, it is not an "either a), or b)" but a combination of "a) and b)". Source: about 1 year ago
On the other hand - your question does not share what you eventually intend to do - if you want to plot the PXRD recorded to perform a baseline correction, pick the peaks, export the data, etc. Fityk may be an interesting tool equally useful outside crystallography (J. Appl. Cryst. 2010, 43, 1126-1128, DOI 10.1107/S0021889810030499 with 1.8k+ citations so far.) It runs on Linux, Windows, Mac; its actions can be... Source: about 1 year ago
Python is a natural fit for serverless development. It boasts a vast array of libraries, including Powertools for AWS and robust libraries for data engineers. Its versatility and excellent developer experience make it a top choice for serverless projects, offering a seamless and enjoyable development experience. - Source: dev.to / 17 days ago
In data analysis, managing the structure and layout of data before analyzing them is crucial. Python offers versatile tools to manipulate data, including the often-used Pandas reset_index() method. - Source: dev.to / 11 days ago
Dash is a Python framework that enables you to build interactive frontend applications without writing a single line of Javascript. Internally and in projects we like to use it in order to build a quick proof of concept for data driven applications because of the nice integration with Plotly and pandas. For this post, I'm going to assume that you're already familiar with Dash and won't explain that part in detail.... - Source: dev.to / 2 months ago
Last year I worked through the challenges using VisiData, Datasette, and Pandas. I walked through my thought process and solutions in a series of posts. - Source: dev.to / 5 months ago
Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks. - Source: dev.to / 5 months ago
NumeRe - Framework for numerical computations, data analysis and visualisation.
NumPy - NumPy is the fundamental package for scientific computing with Python
SciDaVis - SciDAVis is a free application for Scientific Data Analysis and Visualization.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
QtiPlot - QtiPlot is a professional scientific data analysis and visualisation solution.
OpenCV - OpenCV is the world's biggest computer vision library