Though more complicated as it requires programming knowledge, Python allows you to perform any manipulation, transformation, and visualization of your data. It is ideal for data scientists.

This is a collection of my personal notes for Data Visualization in Python. Originally I had kept these in a collection of Jupyter notebooks, but it will be much more useful to just put them online so ...

Access to Rich Python Libraries: Utilize a vast ecosystem of Python libraries for data manipulation, statistical modeling, and data visualization, all available within Excel. The integration of ...

Choosing the right way to visualize your data makes the difference between telling a clear, compelling story or creating cognitive overload. Here's how to pick.

Compound graphs, a frequently encountered type of data set, have a hierarchical tree structure with parent-child relations (‘inclusion’ relations) and non-hierarchical relations between leaf nodes ...

Python and Excel can only really talk to each other through limited functions—xl() and =PY()—that can only return code results, not macros, VBA code, or other data, Microsoft claims.

Instead of telling people about a story, data, or information, show them. Here are tips to make your data visualization more engaging and effective.

Employ data manipulation libraries like pandas in Python or dplyr in R to preprocess and clean large datasets before visualization. Consider using data streaming techniques for real-time data ...