## Unrolling the ROC Curve

Most readers of this blog are likely familiar with the use of the ROC (Receiver Operating Characteristic) curve (or, at least, the area under that curve) for evaluating the quality of binary decision processes. One example of such a process is a binary classification model; another example is an A/B […]

## Including ggplot2 Plots in Python Notebooks

For an article on A/B testing that I am preparing, I asked my partner Dr. Nina Zumel if she could do me a favor and write some code to produce the diagrams. She prepared an excellent parameterized diagram generator. However being the author of the book Practical Data Science with […]

## Schemas for Python Data Frames

The Pandas data frame is probably the most popular tool used to model tabular data in Python. For in-memory data, Pandas serves a role that might normally fall to a relational database. Though, Pandas data frames are typically manipulated through methods, instead of with a relational query language. One can […]

## Detecting Data Differences Using the Sphering Transform

Many people who work with data are familiar with Principal Components Analysis (PCA): it’s a linear transformation technique that’s commonly used for dimension reduction, as well as for the orthogonalization of data prior to downstream modeling or analysis. In this article, we’ll talk about another PCA-style transformation: the sphering or […]

## Some of the Perils of Time Series Forecasting

I’ve recently released a couple of articles on time series forecasting that I want to re-share: A Time Series Apologia Forecasting in Aggregate Versus in Detail Roughly I am trying to point out alternatives to rushing to ARIMA without trying additional methods. ARIMA is great at handing the issues of […]

## A Pandas/Polars Rosetta Stone

Dr. Nina Zumel just shared a nice Pandas/Polars Rosetta Stone. She has a list of the common needed data wrangling operations, and how they are realized in Pandas and Polars. This can help with the data wrangling in your projects. Please check it out!

## Doing Better than the Average

The standard way to estimate the an expected value of a population from a sample of values v1 … vn is to compute the average (1/n) sumi = 1…nvi. It is well known in statistics that for grouped data, there are other estimators that can have smaller expected square error. […]

## Short Data Science Video: Parameterized Juypter Notebooks

I am sharing a new short data science video: Parameterized Juypter Notebooks. It is an example from the wvpy package showing how to programmatically re-run the same notebook with many different inputs. If you are doing data science in Python, this may help you with your projects. link

## Yet Another Data Transform Tutorial

I am sharing yet another data transform tutorial here! It is about coordinatized data, the larger theory encompassing pivot and un-pivot. The example is in Python, but we also supply a similar package for R users.

## Data Algebra over Polars Ready for Production Use

The data algebra is a system for composing data manipulation tasks in Python. In the data algebra, operator pipelines (or even directed acyclic graphs) are the primary objects. Applying operations composes small data pipelines into larger ones. This allows the fluid specification, inspection, and sharing of data processing and data […]