After using pandas for over three years, here are the 30 methods I have used almost all the time — Pandas is undoubtedly one of the best libraries ever built in Python for tabular data-wrangling and processing tasks. Being open-source, numerous developers from different parts of the world have contributed to its development and brought it to where it is today — supporting hundreds of methods for various tasks.

A step towards data analysis run-time optimization — The motivation to design and build real-world applicable machine learning models has always intrigued Data Scientists to leverage optimized, efficient, and accurate methods at scale. Optimization plays a foundational role in sustainably delivering real-world and user-facing software solutions. While I understand that not everyone is building solutions at scale, awareness…

Putting Pareto’s Principle to work on the Pandas library — Mastering an entire Python library like Pandas can be challenging for anyone. However, if we take a step back and think, do we really need to be aware of every minute detail of a specific library, especially when we live in a world governed by Pareto’s Principle? …

It’s time to say goodbye to pd.to_csv() and pd.read_csv() — Building an end-to-end data-driven pipeline is challenging and demanding. Having been there myself, the process is extremely tedious, and one may inevitably end up with numerous intermediate files. While these files are usually meant to serve as checkpoints or assist further modules in the pipeline, one might be unknowingly compromising…

Comparing Streamlit and Taipy on various parameters. — In two of my recent articles, I presented a comprehensive overview of Taipy — an open-source tool that streamlines the creation, management, and execution of reliable data-driven pipelines with low code. It comprises of two parts:

Build web apps in no time — Today, when more and more businesses are driving critical decisions based on data, effective communication of insights driving these key decisions holds immense importance. In this process, businesses often need effective low-code tools, such as creating dashboards/web apps to present data to stakeholders, for instance, in a way that allows…

Towards helping data teams build, run and manage data pipelines effortlessly. — Motivation Building reliant, scalable, efficient and production-ready data pipelines is vital to many modern businesses today to manage and visualize data effectively and make data-driven business decisions. What’s more, teams are often required to integrate the data pipeline with a full-fledged GUI application that offers a seamless interaction.

A self-curated collection of Python and Data Science tips to level up your data game. — Being a data scientist demands expertise in plenty of areas. You need to be good at using appropriate tools, like Pandas, NumPy, Sklearn, etc. These are indispensable to the development life cycle of many data-driven projects, making them essential skills to begin/maintain a career in data science. What’s more…

Two cool ways to create dummy datasets. — Usually, for executing/testing a pipeline, we need to provide it with some dummy data. However, finding a good dataset can be a challenging task, especially when you need a specific type of data. Here, looking for open-source datasets can get time-consuming. …