We at Win-Vector LLC have some big news. We are finally porting a streamlined version of our R vtreat variable preparation package to Python. vtreat is a great system for preparing messy data for suprevised machine learning. The new implementation is based on Pandas, and we are experimenting with pushing the sklearn.pipeline.Pipeline APIs to their … Continue reading Big News: Porting vtreat to Python

# Category: Python

## Big News: Porting vtreat to Python

We at Win-Vector LLC have some big news. We are finally porting a streamlined version of our R vtreat variable preparation package to Python. vtreat is a great system for preparing messy data for suprevised machine learning. The new implementation is based on Pandas, and we are experimenting with pushing the sklearn.pipeline.Pipeline APIs to their … Continue reading Big News: Porting vtreat to Python

## Distribution of quadratic residues

Let p be an odd prime number. If the equation x² = n mod p has a solution then n is a square mod p, or in classical terminology, n is a quadratic residue mod p. Half of the numbers between 0 and p are quadratic residues and half are not. The residues are distributed […]

## Data Science Jobs Report 2019: Python Way Up, Tensorflow Growing Rapidly, R Use Double SAS

In my ongoing quest to track The Popularity of Data Science Software, I’ve just updated my analysis of the job market. To save you from reading the entire tome, I’m reproducing that section here. Continue reading →

## Free Video Lecture: Vectors for Programmers and Data Scientists

We have just released two new free video lectures on vectors from a programmer’s point of view. I am experimenting with what ideas do programmers find interesting about vectors, what concepts do they consider safe starting points, and how to condense and present the material. Please check the lectures out. Vectors for Programmers and Data … Continue reading Free Video Lecture: Vectors for Programmers and Data Scientists

## Bessel function crossings

The previous looked at the angles that graphs make when they cross. For example, sin(x) and cos(x) always cross with the same angle. The same holds for sin(kx) and cos(kx) since the k simply rescales the x-axis. The post ended with wondering about functions analogous to sine and cosine, such as Bessel functions. This post […]

## Calling Python from Mathematica

The Mathematica function ExternalEvalute lets you call Python from Mathematica. However, there are a few wrinkles. I first pasted in an example from the Mathematica documentation and it failed. ExternalEvaluate[ “Python”, {“def f(x): return x**2”, “f(3)”} ] It turns out you (may) have to tell Mathematica where to find Python. I ran the following, tried […]

## Piping is Method Chaining

What R users now call piping, popularized by Stefan Milton Bache and Hadley Wickham, is inline function application (this is notationally similar to, but distinct from the powerful interprocess communication and concurrency tool introduced to Unix by Douglas McIlroy in 1973). In object oriented languages this sort of notation for function application has been called … Continue reading Piping is Method Chaining

## Squircle perimeter and the isoparametric problem

If you have a fixed length of rope and you want to enclose the most area inside the rope, make it into a circle. This is the solution to the so-called isoparametric problem. Dido’s problem is similar. If one side of your bounded area is given by a straight line, make your rope into a […]

## Data Science Software Used in Journals: Stat Packages Declining (including R), AI/ML Software Growing

In my neverending quest to track The Popularity of Data Science Software, it’s time to update the section on Scholarly Articles. The rapid growth of R could not go on forever and, as you’ll see below, its use actually declined … Continue reading →

## Starting With Data Science: A Rigorous Hands-On Introduction to Data Science for Engineers

Starting With Data Science A rigorous hands-on introduction to data science for engineers. Win Vector LLC is now offering a 4 day on-site intensive data science course. The course targets engineers familiar with Python and introduces them to the basics of current data science practice. This is designed as an interactive in-person (not remote or … Continue reading Starting With Data Science: A Rigorous Hands-On Introduction to Data Science for Engineers

## An infinite product challenge

Gil Kalai wrote a blog post yesterday entitled “Test Your Intuition (or knowledge, or programming skills) 36.” The challenge is to evaluate the infinite product I imagine there’s an elegant analytical solution, but since the title suggested that programming might suffice, I decided to try a little Python. I used primerange from SymPy to generate […]

## Implementing the ChaCha RNG in Python

My previous post talked about the ChaCha random number generator and how Google is using it in a stream cipher for encryption on low-end devices. This post talks about how to implement ChaCha in pure Python. First of all, the only reason to implement ChaCha in pure Python is to play with it. It would […]

## Gartner’s 2019 Take on Data Science Software

I’ve just updated The Popularity of Data Science Software to reflect my take on Gartner’s 2019 report, Magic Quadrant for Data Science and Machine Learning Platforms. To save you the trouble of digging through all 40+ pages of my report, here’s just the updated section: Continue reading →

## Computing Legendre and Jacobi symbols

In a earlier post I introduce the Legendre symbol where a is a positive integer and p is prime. It is defined to be 0 if a is a multiple of p, 1 if a has a square root mod p, and -1 otherwise. The Jacobi symbol is a generalization of the Legendre symbol and uses the same notation. It […]

## Exploring the sum-product conjecture

Quanta Magazine posted an article yesterday about the sum-product problem of Paul Erdős and Endre Szemerédi. This problem starts with a finite set of real numbers A then considers the size of the sets A+A and A*A. That is, if we add every element of A to every other element of A, how many distinct sums are there? If we […]

## R Tip: Use Inline Operators For Legibility

R Tip: use inline operators for legibility. A Python feature I miss when working in R is the convenience of Python‘s inline + operator. In Python, + does the right thing for some built in data types: It concatenates lists: [1,2] + [3] is [1, 2, 3]. It concatenates strings: ‘a’ + ‘b’ is ‘ab’. … Continue reading R Tip: Use Inline Operators For Legibility

## Projecting Unicode to ASCII

Sometimes you need to downgrade Unicode text to more restricted ASCII text. For example, while working on my previous post, I was surprised that there didn’t appear to be an asteroid named after Poincaré. There is one, but it was listed as Poincare in my list of asteroid names. Python module I used the Python module unidecode […]

## Timing the Same Algorithm in R, Python, and C++

While developing the RcppDynProg R package I took a little extra time to port the core algorithm from C++ to both R and Python. This means I can time the exact same algorithm implemented nearly identically in each of these three languages. So I can extract some comparative “apples to apples” timings. Please read on … Continue reading Timing the Same Algorithm in R, Python, and C++

## Groups of order 2019

How many groups have 2019 elements? What are these groups? 2019 is a semiprime, i.e. the product of two primes, 3 and 673. For every semiprime s, there are either one or two distinct groups of order s. As explained here, if s = pq with p > q, all groups of order s are isomorphic if q is not a factor of p-1. […]