Published on July 19, 2021 in Towards Data Science Read data directly from the clipboard without saving it first When I write about a library or a new concept, I typically like to showcase its working via examples. The source of datasets that I use in my articles varies widely. Sometimes I create simple toy datasets, while on other … Continue reading From clipboard to DataFrame with Pandas
Some tips and tricks to make the most of your Github experience Originally published here This article compiles some useful tips and hacks that I have discovered over time while using Github. These have been gathered from various sources over time. I have filtered out the ones that were too familiar to avoid repetition. … Continue reading A Tour of 10 Useful Github Features
Important caveats to be kept in mind when encoding data with pandas.get_dummies() Handling categorical variables forms an essential component of a machine learning pipeline. While machine learning algorithms can naturally handle the numerical variables, the same is not valid for their categorical counterparts. Although there are algorithms like LightGBM and Catboost that can inherently handle the categorical variables, it is … Continue reading Beware of the Dummy variable trap in pandas
An open-source package for decision tree visualization and model interpretation Originally published here It is rightly said that a picture is worth a thousand words. This axiom is equally applicable for machine learning models. If one can visualize and interpret the result, it instills more confidence in the model’s predictions. Visualizing how a machine learning … Continue reading A better way to visualize Decision Trees with the dtreeviz library
My tryst with the pandas’ library continues. Of late, I have been trying to look deeper into this library and consolidating some of the pandas’ features in byte-sized articles. I have written articles on reducing memory usage while working with pandas, converting XML files into a pandas dataframe easily, getting started with time series in pandas, and many more. In this article, … Continue reading A hands-on guide to ‘sorting’ dataframes in Pandas
Managing large datasets on Kaggle without fearing about the out of memory error Image by user Datatable is a Python package for manipulating large dataframes. It has been created to provide big data support and enable high performance. This toolkit resembles pandas very closely but is more focused on speed. It supports out-of-memory datasets, multi-threaded data processing, … Continue reading Using Python’s datatable library seamlessly on Kaggle
Breaking the Jargons 