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Pocket reading list : Week 3 of Jan

The missing 11th of the month : For those of you who read xkcd religiously, you would've come across this comic on the calendar of meaningful dates. (Those of you who don't read xkcd religiously should and those of you who dont read xkcd should start!). The article on the other hand does a brilliant job at understanding one peculiar feature of the xkcd comic - the fact that the 11th of any month seems to be less common in the literature than any of the other dates of the month! And the reason behind it is pretty interesting!

Minorities exploited by Warren Buffett’s mobile-home empire : So, apparently blacks and minorities are exploited in the United States. While this might not be news for most of you, this is just another example of how the mobile-home industry in certain parts of the US takes advantage of language difference or through hostile behavior lends blacks and minorities loans at higher-than-normal rate of interest.

The Lawyer Who Became DuPont’s Worst Nightmare : A corporate lawyer takes on a case for the better good and goes against a chemical giant, DuPont. This is as real as it gets to a real life David and Goliath story. Also, note that DuPont knowingly used a material, PFOA,  in their manufacturing process that were harmful to humans and were later found to cause serious health problems for those who come in contact with the material. A must read.

The Most Amazing Lie in History : For those of you who are familiar with recent human history, you know that Hitler was defeated by the Allied forces, starting with the D-Day invasion on beaches of Normandy. There are a good number of movies, documentaries and TV series based on it (my personal favorite being Band of Brothers) but I digress. Apparently, the Allied commanders weren't so sure of the success of the invasion and tried throwing Hitler's army off their scent. This is the story of how a lonely spy helped saved the lives of millions of Allied soldiers, working as a double spy.

A Fighter’s Hour of Need : I don't exactly understand why boxing is such a huge commercial sport, on which a lot of bets ride. I mean I understand guys beating on each other till they're senseless or till one of them is on the floor. I guess people will hedge bets on pretty much anything. Again, I digress. Boxing, like american football, can cause serious head trauma to the players and the players in the games are, apparently, monitored for signs of concussion after the games. This is such a story which didn't work out so well for one of the boxers.

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Pandas download statistics, PyPI and Google BigQuery - Daily downloads and downloads by latest version

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Adaptive step size Runge-Kutta method

I am still trying to implement an adaptive step size RK routine. So far, I've been able to implement the step-halving method but not the RK-Fehlberg. I am not able to figure out how to increase the step size after reducing it initially.

To give some background on the topic, Runge-Kutta methods are used to solve ordinary differential equations, of any order. For example, in a first order differential equation, it uses the derivative of the function to predict what the function value at the next step should be. Euler's method is a rudimentary implementation of RK. Adaptive step size RK is changing the step size depending on how fastly or slowly the function is changing. If a function is rapidly rising or falling, it is in a region that we should sample carefully and therefore, we reduce the step size and if the rate of change of the function is small, we can increase the step size. I've been able to implement a way to reduce the step size depending on the rate of change of …