#Klib library python series
Next week, I’ll write another column, inspired by many of the replies on tips and tricks you can use to reduce the file size of large datasets and speed up their computation with tools like Numba or Cython, so stay tuned for that, including a few ideas which were tweeted by time series that fit in memory, Pandas is usually a fairly conventional choice. I recently tweeted about some of the Python libraries I’ve found useful for working with large datasets (in particular time series), and the thread got a lot of interest and lots of great suggestions, so I’ve elaborated on it below. I’m also going to be teaching a more general Python course, Python for finance for QDC ( sign up here). Over the past few weeks I’ve been preparing a course on Python, alt data/NLP and large datasets, which I’ll be teaching at Queen Mary University of London (and if you’d like me to teach it at your firm let me know!). Inevitably the whole process of preparing a course or writing a book, means I learn lots of stuff in the process which I haven’t yet picked up on the job.
However, when I’m teaching (or when I’ve be coauthoring The Book of Alternative Data), the objective is go over a subject in a wide ranging way, rather than snippets here and there. I’ll learn what I need to do to get the task done. When I’m researching the markets or coding something, I’m generally pretty focused at the task at hand. It’s obviously a bit of cliche, but I have to admit it’s one of things which really does ring true. Hello Test Div 2 Change Background document.getElementById( " myBtn").People say the best way to learn something is to teach it. remove(): This function removes the style from document head.unset(property,): This function facilitates the ability to remove a style property.set(property, value) or set(): This function facilitates the ability to set a style property.node: A reference to the newly created style element in the document head.If debugMode is passed and is true then the debugger console will log the generated CSS.
There is also a second optional boolean parameter named debugMode. It takes the css selector as the parameter. KLib-CSS provides a global object constructor named "css". Link to article about: kLib (main library) Features
#Klib library python code
KLib-CSS also provides shorthand for vendor prefixing, making the life of the coder easier and improving the code readability. Since we are setting CSS via JavaScript we are able to use variables for writing common styles. So, it is possible to change the stylesheet on the fly thus making the coding of cool effects very easy. KLib-CSS provides an approach for scripting CSS dynamically. So you can use it even if you have not included the kLib main library. Although, I have decided to bundle it with the kLib libraries but, this micro-library has no dependency on the kLib main library. Inspired by this idea I started writing kLib-css.js. It would have been very cool to be able to use this kind of approach on the client-side. This limits its benefits to the server-side. Please see its home page for more info.). This was an added bonus (there are many other cool features in SASS. SASS allows us to use variables for setting CSS values. Although it does not have any direct solution but I was able to use the mixins to create template for every CSS3 property that had vendor prefixes and then use them. After an hour of testing many of the solutions available online I chose SCSS/SASS. Whenever writing CSS with all the vendor prefixes I thought there must be a better way to do this.
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