Python Data Science Handbook, 2nd Edition

Chapter 3. Debugging and Profiling

In addition to the enhanced interactive tools discussed in the previous chapter, Jupyter provides a number of ways to explore and understand the code you are running, such as by tracking down bugs in the logic or unexpected slow execution. This chapter will discuss some of these tools.

Errors and Debugging

Code development and data analysis always require a bit of trial and error, and IPython contains tools to streamline this process. This section will briefly cover some options for controlling Python’s exception reporting, followed by exploring tools for debugging errors in code.

Controlling Exceptions: %xmode

Most of the time when a Python script fails, it will raise an exception. When the interpreter hits one of these exceptions, information about the cause of the error can be found in the traceback, which can be accessed from within Python. With the %xmode magic function, IPython allows you to control the amount of information printed when the exception is raised. Consider the following code:

In [1]: def func1(a, b):
            return a / b

        def func2(x):
            a = x
            b = x - 1
            return func1(a, b)

In [2]: func2(1)
ZeroDivisionError                         Traceback (most recent call last)
<ipython-input-2-b2e110f6fc8f> in <module>()
----> 1 func2(1)

<ipython-input-1-d849e34d61fb> in func2(x)
      5     a = x
      6     b = x - 1
----> 7     return func1(a, b)

<ipython-input-1-d849e34d61fb> in func1(a, b)
      1 def func1(a, b):
----> 2     return a / b
      3
      4 def func2(x):
      5     a = x

ZeroDivisionError: division by