Why is Python used for Data Cleaning in Data Science?

Data Scientists have to clean and transform the huge data sets in a form that they can work with. It is important to deal with the redundant data for better results by removing nonsensical outliers, malformed records, missing values, inconsistent formatting, etc.

Python libraries such as Matplotlib, Pandas, Numpy, Keras, and SciPy are extensively used for Data cleaning and analysis. These libraries are used to load and clean the data and do effective analysis. For example, a CSV file named “Student” has information about the students of an institute like their names, standard, address, phone number, grades, marks, etc.