Stefanie Molin's new book, “Hands-On Data Analysis with Pandas" is about using the powerful pandas library to get started with machine learning in Python.

The simplest form of regression in Python is, well, simple linear regression. With simple linear regression, you're trying to ...

Pandas is a necessary component of the data science life cycle (Python data analysis). It is the most well-known and widely used Python package for data research, along with NumPy in matplotlib.

It is a handy tool for keeping a record of data explorations, creating charts, styling text and sharing the results of that work. For data analysis, the cornerstone package in Python is “Pandas”.

The data manipulation API resembles Pandas, but adds .rows() and .cols() accessors to make it easy to do things like sort a DataFrame, filter by column values, alter data according to criteria, or ...

Still using Excel for your data analysis? Learn how to leverage Python so you can work with larger datasets and automate repetitive tasks.

If you want to master, or even just use, data analysis, Python is the place to do it. Python is easy to learn, it has vast and deep support, and most every data science library and machine ...

Python libraries like Pandas, NumPy, SciPy, and Matplotlib streamline data cleaning, statistical analysis, and visualization directly within Excel.

Python’s Pandas library allows for advanced data manipulation, statistical analysis, and exploration directly within Excel, streamlining workflows.