There are various data cleaning methods in Python

There are various data cleaning methods in Python depending on the type of data and what needs to be cleaned. Some common methods include using built-in string methods such as replace() or strip(), regular expressions (regex), and using data manipulation libraries such as pandas.

For example, if you want to replace a certain substring in a string with another, you can use the replace() method:

text = "Hello, world!"
cleaned_text = text.replace("world", "Python")
print(cleaned_text)

If you want to remove whitespace from the beginning and end of a string, you can use the strip() method:

text = "   hello     "
cleaned_text = text.strip()
print(cleaned_text)

If you want to perform more complex data cleaning tasks on tabular data, you can use the pandas library to perform operations such as filtering, sorting, and adjusting data values.

For more advanced data cleaning tasks such as handling missing data, outliers, or data normalization, there are also specific libraries like numpy and sklearn which can be used.

Overall, there are a wide range of data cleaning methods available in Python depending on the requirements of the task at hand.