When it comes to data science and machine learning processing, the very thing that comes to the forefront is the necessity to process the data. The data is collected from a variety of sources, both external and internal and is usually unstructured. This means there can be errors and discrepancies throughout the ml datasets. This can affect the final result and can render the entire process of machine learning useless. This is the reason why data processing is so crucial.
What is data processing?
The data processing of large data sets includes removing duplicate characters and elements. It also removes the errors that can affect the consistency of the data. Therefore it is necessary to identify the errors, fill in the gaps in the data and remove the bugs from the humongous datasets. When all these things are changed, the data sets become complete, accurate, and useable by the various machine learning algorithms.
What is VAEX?
Vaex is out of core frames python library that is used for data processing of huge datasets. The library can process N-dimensional grid by running various statistical operations like sum, mean, count, etc. the library can help visualise and explore the data sets at a speed of one billion rows/second of data. Memory mapping is something that Vaex uses for less memory error. Also, the library uses parallel execution out of core that makes evaluating the data effortlessly.
Steps of processing data
The process of processing huge datasets using vaex is:
– Firstly, the library vaex is to be installed along with python 7.25.0.
– The next step is to import the vaex library.
– Now, the data set is imported and loaded to the frame.
– After the data sets are loaded into the data frame, the data is visualized. This happens within a few seconds.
Using vaex is quite easy, and one can excel at it with regular usage. Whenever there is a large amount of data, this python library can be the best way to visualize the data within seconds.