Why is python used for data cleaning in data science?

Data scientists must clean and convert massive data sets so they can work with them. It’s critical to remove meaningless outliers, malformed records, missing values, inconsistent formatting, and other redundant data for improved results.

Matplotlib, Pandas, Numpy, Keras, and SciPy are some of the most popular Python packages for data cleaning and analysis. These libraries are used to load and clean data so that effective analysis may be carried out. For instance, a CSV file entitled “Student” contains information on an institute’s students, such as their names, standard, address, phone number, grades, and marks.