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Articles related to "time"


Beginner's Guide to Abstraction

  • You'll know you've made the wrong abstraction when after a while the interface has been expanded to support various optional flags, each for a different use case, and you need to be a genius to reason about what the code will actually do for a given use case.
  • If you want to know how the average method behaves when you're dealing with an array of strings with no nil values, you have to read through the first if condition which has nothing to do with your use case before reaching the code that does.
  • Note that looking at the Good variant, it's clear that the behaviour is quite different from one use case to the next, but that is not at all clear in the Bad variant because the method calls all look so simple and it was anybody's guess how much code inside average applied to each use case.

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Why alternative data is here to stay

  • Alternative data sources, typically the preserve of equity and commodity analysts, has flown the coop through COVID-19, and is now a critical input to global macro.
  • Global macro, which can been adequately defined as the analysis and prediction of economic, political and financial market developments, has also seen some early movers adopt Alt-Data.
  • Those who embraced Alt-Data early on during COVID-19, were able to manage through the volatility of March in the context of quantifiable risk.
  • The moves were extreme, but Alt-Data, both in terms of COVID-19 and measures of social mobility, helped to fill the void and empower decision making.
  • Alternative data sources, typically the preserve of equity and commodity analysts, has flown the coop through COVID-19, and is now a critical input to global macro investing, writes Grant Wilson.

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Lindsay Lohan Is Back, But... Why? – CCN.com – Op-ed %

  • So, Lindsay Lohan has surfaced again–the only problem is, I don’t know why.
  • Whatever you find to be synonymous with the name Lindsay Lohan is likely dependent on your demographic.
  • And during all these tough times I feel like I’ve learned to understand the simplicity of life and birthdays.
  • I was thinking about how many birthdays I’ve had that are just like so extravagant and how lucky we all are to just have the people around us that we love and the people in our lives that we love and how simple is key.
  • In quite unfortunate timing (especially considering she’d waited almost a decade), the lockdown lined up with the single’s early April street date.
  • Naturally, the people interviewing her wanted to know what Lindsay Lohan’s been doing for the last decade.

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The leap from a discrete to a continuous probability distribution

  • Unlike a discrete random variable that has a finite or countably infinite set of possible outcome values, a continuous case deals with a set of values that is not countable.
  • So, we can conclude that in case of continuous random variables the difference between two consecutive possible outcomes would be infinitesimally small and it is so small that it does not hold any true meaning.
  • But for the time being, we need to pause and ponder that unlike a discrete distribution where we had a mass function, throwing at us the probability values of each possible outcome; in continuous case using the same function may throw incorrect values at us.
  • Continuing our example of the continuum where X takes any value between 10 to 15, we would get infinite slices on the X-axis and corresponding individual probability of close to zero.

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Statistics measures are the best starter

  • Statistics contains many basic concepts including descriptive statistics which summarize data from a sample using indexes such as the mean or standard deviation, and inferential statistics, which draw conclusions from data that are subject to random variation.
  • Probability theory, in addition to explaining random phenomena, examines phenomena that are not necessarily random, but by repeating the test many times, the results follow a specific pattern.
  • While regression analysis is often employed in such a way as to test theories that the current values of one or more independent time series affect the current value of another time series, this type of analysis of time series is not called “time series analysis”, which focuses on comparing values of a single time series or multiple dependent time series at different points in time.

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Speed up your Pandas Processing with Swifter

  • In this article, I want to show you a simple package to speed up your Pandas processing called Swifter.
  • Swifter is a package that tries to efficiently apply any function to a Pandas Data Frame or Series object in the quickest available method.
  • The time to execute the function to each data takes around 42.9 ms for an apply attribute by Pandas.
  • As we can see above, Swifter processes the data way faster compared to the normal Pandas apply function.
  • As we can see above, it takes much longer using Swifter compared to the regular Pandas apply function.
  • The execution time now takes only 11 ms with the vectorized function, which saves so much time compared to the normal apply function.
  • This is why it is advisable to use the vectorized function when we are processing data with Swifter.

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The leap from a discrete to a continuous probability distribution

  • Unlike a discrete random variable that has a finite or countably infinite set of possible outcome values, a continuous case deals with a set of values that is not countable.
  • So, we can conclude that in case of continuous random variables the difference between two consecutive possible outcomes would be infinitesimally small and it is so small that it does not hold any true meaning.
  • But for the time being, we need to pause and ponder that unlike a discrete distribution where we had a mass function, throwing at us the probability values of each possible outcome; in continuous case using the same function may throw incorrect values at us.
  • Continuing our example of the continuum where X takes any value between 10 to 15, we would get infinite slices on the X-axis and corresponding individual probability of close to zero.

save | comments | report | share on


Statistics measures are the best starter

  • Statistics contains many basic concepts including descriptive statistics which summarize data from a sample using indexes such as the mean or standard deviation, and inferential statistics, which draw conclusions from data that are subject to random variation.
  • Probability theory, in addition to explaining random phenomena, examines phenomena that are not necessarily random, but by repeating the test many times, the results follow a specific pattern.
  • While regression analysis is often employed in such a way as to test theories that the current values of one or more independent time series affect the current value of another time series, this type of analysis of time series is not called “time series analysis”, which focuses on comparing values of a single time series or multiple dependent time series at different points in time.

save | comments | report | share on


Speed up your Pandas Processing with Swifter

  • In this article, I want to show you a simple package to speed up your Pandas processing called Swifter.
  • Swifter is a package that tries to efficiently apply any function to a Pandas Data Frame or Series object in the quickest available method.
  • The time to execute the function to each data takes around 42.9 ms for an apply attribute by Pandas.
  • As we can see above, Swifter processes the data way faster compared to the normal Pandas apply function.
  • As we can see above, it takes much longer using Swifter compared to the regular Pandas apply function.
  • The execution time now takes only 11 ms with the vectorized function, which saves so much time compared to the normal apply function.
  • This is why it is advisable to use the vectorized function when we are processing data with Swifter.

save | comments | report | share on


The leap from a discrete to a continuous probability distribution

  • Unlike a discrete random variable that has a finite or countably infinite set of possible outcome values, a continuous case deals with a set of values that is not countable.
  • So, we can conclude that in case of continuous random variables the difference between two consecutive possible outcomes would be infinitesimally small and it is so small that it does not hold any true meaning.
  • But for the time being, we need to pause and ponder that unlike a discrete distribution where we had a mass function, throwing at us the probability values of each possible outcome; in continuous case using the same function may throw incorrect values at us.
  • Continuing our example of the continuum where X takes any value between 10 to 15, we would get infinite slices on the X-axis and corresponding individual probability of close to zero.

save | comments | report | share on