Data Scientists have to clean and transform huge data sets into a form that they can work with. It is important to deal with 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 instance, you might decide to remove outliers that are beyond a certain standard deviation from the mean of a numerical column.
mean = df['Price'].mean()
std = df['Price'].std()
threshold = mean + (3 * std)  # Set a threshold for outliers
df = df[df['Price'] < threshold]  # Remove outliers
Hence, this is how the process of data cleaning is done using python libraries in the field of data science.
Learn more about Data Cleaning in a Data Science Tutorial!