Employing Various Data Cleaning Techniques to Achieve Better Data Quality using Python

In the world of information, data plays a key role in all fields of Science, Engineering and Technology. Many important industries and services constantly deal with large volume of data. Important decisions that determine the future of organizations are taken from the data such gathered. However, oftentimes, the data thus obtained is replete with inaccuracies. No data set is error free, therefore a host of tools are developed to deal with issues such as, missing data, miscoding, null sets, spelling mistakes and duplicate entries. Therefore, Data Cleaning plays a significant role in guaranteeing that the consequent processes for analysis of data are run efficiently and quickly. Inaccurate data may lead to unpredictable and unusable conclusion, thus cleaning data forms an important part of grooming data prior to analyze it. The existing techniques which include using various programs such as Excel are intensive and complex to use - which consume more time and demand a wide knowledge of the use of formulas. This paper explores with the different techniques & tools available for cleaning in the market were summarized with their application using Python. The paper intends to discuss the various issues that can arise during the application of these techniques and methods to overcome such hurdles. Data cleaning techniques using Python are more efficient and quicker compared to other classical methods, this leading to more accurate and cleaner data that can be processed.