A violation of Python syntax that causes a program to fail. A syntax error means that the code does not follow the correct structure and rules of the language.

Syntax Errors

A semantic error, also known as a logic error, occurs when a program runs successfully without any errors, but the outcome of the code is not what the programmer had intended. 

Semantic Errors

Runtime errors occur when the syntax of a program is correct, but a statement within the program prevents the interpreter from executing the code. These are also known as exceptions. For example, dividing by zero, performing type conversions on values that can not be converted, or performing operations on the wrong data type will all lead to runtime errors.

Runtime Errors

In Python, all exceptions must be instances of a class that derives from BaseException. In a try statement with an except clause that mentions a particular class, that clause also handles any exception classes derived from that class (but not exception classes from which it is derived). Two exception classes that are not related via subclassing are never equivalent, even if they have the same name.

The built-in exceptions listed below can be generated by the interpreter or built-in functions. Except where mentioned, they have an “associated value” indicating the detailed cause of the error. This may be a string or a tuple of several items of information (e.g., an error code and a string explaining the code). The associated value is usually passed as arguments to the exception class’s constructor.

User code can raise built-in exceptions. This can be used to test an exception handler or to report an error condition “just like” the situation in which the interpreter raises the same exception; but beware that there is nothing to prevent user code from raising an inappropriate error.

The built-in exception classes can be subclassed to define new exceptions; programmers are encouraged to derive new exceptions from the Exception class or one of its subclasses, and not from BaseException. More information on defining exceptions is available in the Python Tutorial under User-defined Exceptions.

Built-In Exceptions

Many problems in your program will lead to an error message.

Most common error messages:
- SyntaxError
- TypeError
- NameError
- ValueError

Less common error messages:
- AttributeError
- TimeLimitError
- IndexError
- IndentationError
- ExternalError
- UnboundLocalError
- ImportError
- ZeroDivisionError
- KeyError
- IOError

Error Messages

- Syntax Errors
- Semantic Errors
- Runtime Errors

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- General Tips & Tricks
- Common Syntax Errors
- Error types
- Error Messages

Debugging in Python

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- Missing comma after dictionary entry
- Confusing assignment operator (=) and comparison operator (==)
- Using Python keyword where unintended*
- Missing parentheses, brackets, or quotes**
- Adding extra parentheses or brackets
- Missing a colon at the end of a for, while, or if statement
- Improper indentation

*Note: The keyword library can be used to check if something is a Python keyword. To demonstrate,
```python
import keyword as kw
kw.iskeyword('class') # returns True
```

**Note: To initialize a string containing the quote character (e.g. English contractions), you can escape the quote symbol using a backslash:
```python
message = 'can\'t'
print(message) # prints "can't"
```

Common Syntax Errors in Python

- Look for the line number in the error message to find where the interpreter believes the error is coming from. Trace backwards if error doesn't stem from that line
- Understand the different types of errors (Syntax, Runtime, Semantic) 
- Use print statements to check the values of variables and if a block of code is being run, especially when your code has if-statements or loops
- Debug code in blocks as you go. This will make it easier to find more subtle semantic issues instead of having to look through your entire program when debugging at the end
-Usage of Break Points can be very useful if your IDE allows for it. This way you can run the program up to the break point and step through the code step by step checking the value of variables and comparing it to what you expect it to be. If there is a miss-match then you can trace it back to the line where the program deviated from your intended functionality. 

-When dealing with large amounts of data, use mocking to test your code as you build bigger projects. Mocking is the action of running smaller, more palpable data sets through code to more easily see errors in code designed for larger amounts of data. 

General Tips for Debugging

Error Types

```pdb``` is a built-in python debugging tool. It is a very simple and effective tool. To start debugging session, we write the following line of code:
```python
import pdb; pdb.set_trace()
``` 
When this line is reached, python will stop the execution and start an interactive debugging session in the console.

Python 3.7 brings the ability to use breakpoint() rather than 
import pdb;pdb.set_trace()

Python built-in debugger "pdb"

Python provides a construct called the try-except construct that allows a programmer to capture a runtime error and give the programmer the option to handle that error.

```python
try:
     #code
except (insert_error_name):
     #code to run when faced this error
```

Try and except blocks allows for multiple different except blocks to handle a variety of errors. Exceptions can also be raised intentionally to test try and except functionality. 

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