Python Data Science ErrorsPython is a general-purpose, interpreted, object-oriented coding language with dynamic semantics. Pandas is a popular Python library , particularly for data science. You may make certain mistakes while coding a program that lead to errors when you try to execute it. Python interpreter throws an error as soon as it encounters an unhandled error .
Once you understand why you get certain types of errors , it become much easier to rectify. The process of catching and fixing errors is called debugging. So without further ado, let us get started with the most common errors while you using data science libraries and how to prevent them.
- ImportError: No module named pandas
- What is SettingWithCopyWarning?
- UnicodeDecodeError while reading CSV file
- How to fix CParserError: Error tokenizing data
- ValueError: cannot reindex from a duplicate axis
- How to fix "Unnamed: 0" column in a pandas DataFrame
- ValueError: cannot convert float NaN to integer
- ValueError: Unknown label type: 'unknown'
- ValueError: Length of values does not match length of index
- ValueError: The truth value of an array with more than..