So here we have the average stock data displayed on the 31st of each month. Below is a complete list of the offset value

If we want a STRONG margin of safety (we don’t know all the circumstances in which our game will run), we may consider 3 ms as the maximum time available for the execution of each loop in the game.

So what are the significant effects of calculating a moving average or using this rolling method? Our data becomes a lot less noisy and becomes more reflective of the trend than the actual data. Let’s plot this out. We shall plot the original data and then the rolling data for 30 days in the same graph.

The blue line is the original noisy data, while the orange line with a 30-day rolling window ends up having not as much noise as the blue line. Now, once you run this code, an important aspect to keep in mind is that the first 29 days aren’t going to have that orange line because there wasn’t enough data actually to calculate that rolling mean.

The terrain list is the large list, containing 10 thousand (100x100) squareObjects. Each squareObject tells everything about the square of the terrain that it represents, including the creature and the missiles that may be over it.

There’s quite a bit of noise here because this is the daily data. It would be nice to average this out by a week, which is where a rolling mean comes in. A rolling mean or Moving average is a transformation method that tends to average out this noise from the data. The idea is simple. Split the data into windows, and the data in each of these windows is then aggregated by some function like mean(), median(), count(), etc.

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t why? Oh, there are many uses for that. It’s common to store hierarchical data in SQL and recursive queries are a convenient way to extract information from such graphs. Organizational structure, application menu structure, a set of tasks with sub-tasks in the project, links between web pages, breakdown of an equipment module into parts and sub-parts are examples of the hierarchical data. The post will not go into great details of those many use cases rather look at two toy examples to understand the concept - the simplest possible case of recursion on numbers and querying data from the family tree.

Pandas is a powerful library with a lot of inbuilt functions for analyzing time-series data. This article saw how Python’s pandas’ library could be used for wrangling and visualizing time series data. We also performed tasks like time sampling, time-shifting, and rolling on the stock data. These are usually the first steps in investigating any time series data. Going forward, we could use this data in several ways. One way could be to perform a basic financial analysis by calculating the daily percentage change in stocks to get an idea about the stock price volatility. Another way would be to use this data to predict Maruti’s stock price for the next few days by employing Machine Learning Techniques. Whatever assignment you choose, the preliminary steps shown in this article would come in handy.

Sometimes, it is desirable to shift or move the data forward or backward in time. This shifting is done along a time index by the desired number of time-frequency increments. Here is the original dataset without any shiftings.

Time series data can be noisy, and as a result, it becomes difficult to gauge the trend or pattern due to the high fluctuations. For instance, here is the visualization of the VWAP price of the Maruti stock over the years.

Why Chrome? Because it is by far the fastest (at the time of writing this article). Therefore, if the code doesn’t work in Chrome, we know it is not going to work anywhere else.

An application running at 60 FPS means that it has less than 16,666 ms (1 second / 60) to complete the execution of each loop. But since the browser and the operating system have other tasks to perform, in practice we have half of this time.

The image at the beginning of the article is a good example of the type of game I used as inspiration for the tests. A 2.5D adventure like game, third person view, which the player’s avatar is always positioned at the center of the canvas. The game environment is a labyrinth of 100x100 squares with lakes, creatures, and many trees and many walls.

In this article, we check whether the browser is capable of running a sprite game at 60 FPS (frames per second) or not. We discuss architecture options, run performance tests and discover a HUGE catch.

To shift our data forward, we will pass the desired number of periods (or increments) through the shift() function, which in this case, needs to be a positive value. Let’s move our data forward by one period or index, which means that all values which earlier corresponded to row N will now belong to row N 1. Here is the output:

Pandas is a powerful library with a lot of inbuilt functions for analyzing time-series data. This article saw how Python’s pandas’ library could be used for wrangling and visualizing time series data. We also performed tasks like time sampling, time-shifting, and rolling on the stock data. These are usually the first steps in investigating any time series data. Going forward, we could use this data in several ways. One way could be to perform a basic financial analysis by calculating the daily percentage change in stocks to get an idea about the stock price volatility. Another way would be to use this data to predict Maruti’s stock price for the next few days by employing Machine Learning Techniques. Whatever assignment you choose, the preliminary steps shown in this article would come in handy.