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The Waiting Time Paradox, or, Why Is My Bus Always Late?
Image Source: Wikipedia License CC-BY-SA 3.0 If you, like me, frequently commute via public transit, you may be familiar with the following situation: You arrive at the bus stop, ready to catch your bus: a line that advertises arrivals every 10 minutes. You glance at your watch and note the time...
Python Data Science Handbook by Jake VanderPlas Blog15 min read
Simulating Chutes & Ladders in Python
This weekend I found myself in a particularly drawn-out game of Chutes and Ladders with my four-year-old. If you've not had the pleasure of playing it, Chutes and Ladders (also sometimes known as Snakes and Ladders) is a classic kids board game wherein players roll a six-sided die to advance…
Python Data Science Handbook by Jake VanderPlas Blog21 min read
Optimization of Scientific Code with Cython: Ising Model
Python is quick and easy to code, but can be slow when doing intensive numerical operations. Translating code to Cython can be helpful, but in most cases requires a bit of trial and error to achieve the optimal result.
Python Data Science Handbook by Jake VanderPlas Blog6 min readInstalling Python Packages from a Jupyter Notebook
In software, it's said that all abstractions are leaky, and this is true for the Jupyter notebook as it is for any other software. I most often see this manifest itself with the following issue: I installed package X and now I can't import it in the notebook. Help!
Python Data Science Handbook by Jake VanderPlas Blog21 min read
Exploring Line Lengths in Python Packages
This week, Twitter upped their single-tweet character limit from 140 to 280, purportedly based on this interesting analysis of tweet lengths published on Twitter's engineering blog.
Python Data Science Handbook by Jake VanderPlas Blog11 min readExposing Python 3.6's Private Dict Version
/*! * * IPython notebook * */ /* CSS font colors for translated ANSI colors. */ .ansibold { font-weight: bold; } /* use dark versions for foreground, to improve visibility */ .ansiblack { color: black; } .ansired { color: darkred; } .ansigreen { color: darkgreen; } .ansiyellow { color: #c4a000; }…
Python Data Science Handbook by Jake VanderPlas Blog8 min read
A Practical Guide to the Lomb-Scargle Periodogram
This week I published the preprint of a manuscript that started as a blog post, but quickly out-grew this medium: Understanding the Lomb-Scargle Periodogram. Figure 24 from Understanding the Lomb-Scargle Periodogram.
Python Data Science Handbook by Jake VanderPlas Blog5 min read
Group-by From Scratch
I've found one of the best ways to grow in my scientific coding is to spend time comparing the efficiency of various approaches to implementing particular algorithms that I find useful, in order to build an intuition of the performance of the building blocks of the scientific Python ecosystem.
Python Data Science Handbook by Jake VanderPlas Blog9 min read
Triple Pendulum CHAOS!
Earlier this week a tweet made the rounds which features a video that nicely demonstrates chaotic dynamical systems in action: A visualization of chaos: 41 triple pendulums with very slightly different initial conditions pic.twitter.com/CTiABFVWHW— Fermat's Library (@fermatslibrary) March 5, 2017…
Python Data Science Handbook by Jake VanderPlas Blog8 min readReproducible Data Analysis in Jupyter
Jupyter notebooks provide a useful environment for interactive exploration of data. A common question I get, though, is how you can progress from this nonlinear, interactive, trial-and-error style of exploration to a more linear and reproducible analysis based on organized, packaged, and tested…
Python Data Science Handbook by Jake VanderPlas Blog3 min readConda: Myths and Misconceptions
I've spent much of the last decade using Python for my research, teaching Python tools to other scientists and developers, and developing Python tools for efficient data manipulation, scientific and statistical computation, and visualization.
Python Data Science Handbook by Jake VanderPlas Blog19 min readAnalyzing Pronto CycleShare Data with Python and Pandas
This week Pronto CycleShare, Seattle's Bicycle Share system, turned one year old. To celebrate this, Pronto made available a large cache of data from the first year of operation and announced the Pronto Cycle Share's Data Challenge, which offers prizes for different categories of analysis.
Python Data Science Handbook by Jake VanderPlas Blog17 min readOut-of-Core Dataframes in Python: Dask and OpenStreetMap
In recent months, a host of new tools and packages have been announced for working with data at scale in Python. For an excellent and entertaining summary of these, I'd suggest watching Rob Story's Python Data Bikeshed talk from the 2015 PyData Seattle conference.
Python Data Science Handbook by Jake VanderPlas Blog12 min readFrequentism and Bayesianism V: Model Selection
Last year I wrote a series of posts comparing frequentist and Bayesian approaches to various problems: In Frequentism and Bayesianism I: a Practical Introduction I gave an introduction to the main philosophical differences between frequentism and Bayesianism, and showed that for many common…
Python Data Science Handbook by Jake VanderPlas Blog26 min readLearning Seattle's Work Habits from Bicycle Counts
Last year I wrote a blog post examining trends in Seattle bicycling and how they relate to weather, daylight, day of the week, and other factors. Here I want to revisit the same data from a different perspective: rather than making assumptions in order to build models that might describe the data,…
Python Data Science Handbook by Jake VanderPlas Blog13 min readThe Model Complexity Myth
An oft-repeated rule of thumb in any sort of statistical model fitting is "you can't fit a model with more parameters than data points". This idea appears to be as wide-spread as it is incorrect.
Python Data Science Handbook by Jake VanderPlas Blog21 min read
Fast Lomb-Scargle Periodograms in Python
Image source: astroML. Source code here Edit, Spring 2017: For an in-depth guide to the practical use of Lomb-Scargle periodograms, see the paper discussed in A Practical Guide to the Lomb-Scargle Periodogram.
Python Data Science Handbook by Jake VanderPlas Blog17 min readOptimizing Python in the Real World: NumPy, Numba, and the NUFFT
Donald Knuth famously quipped that "premature optimization is the root of all evil." The reasons are straightforward: optimized code tends to be much more difficult to read and debug than simpler implementations of the same algorithm, and optimizing too early leads to greater costs down the road.
Python Data Science Handbook by Jake VanderPlas Blog17 min read
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