![]() When we do this, we can see that our variables are all present. If you supply a module or class, it will list the attributes of the module or the class (and its subclasses). When you invoke this function with no arguments, it will return a list of all the variable names in the local scope. One function to consider is the dir builtin. Or maybe we edited the cell a few times and re-executed it, and changed some variable names. But how do we find objects that we don’t know exist? Maybe we deleted the cell that created the values, or if we’re using an IPython command line, our history is not visible anymore for that code. OK, this above method is obvious, we can view items that we know exist. 'a variable' But what if the code is gone? If using the default interpreter, display is not available, but executing any variable will show you the value (based on its _repr_ method). A cell will display the value of the last row in the cell (unless you append a at the end of the line). Now once a cell with the above Python code has been executed, I can inspect the state of my current session by either executing a single cell with one of the variables, in it, or using the IPython display function. Note that this code example uses Jupyter 6.15 with IPython 7.19.0 as the kernel.įirst, let’s make some data. Let’s walk through a few ways to do this, from the simple to more complex. If a developer doesn’t want to continually restart their interpreter, they can also view what the current state is. This will make sense for small notebooks where you can quickly visualize all the code and verify it on inspection.īut this may not be practical for all cases. If you just run a new Python instance with the saved code, no hidden state can exist and the output will be consistent. This mimics the more typical development of unit testing or running scripts from the command line (or in an IDE integration). If your notebook is small, and runs quickly, you can always restart your kernel and run all the code again. Practically, you can work towards this goal in a couple of ways. In order for a notebook to reflect reality, it should contain valid code that can be executed in order to produce consistent results. This causes some obvious problems, first for the current session of the notebook, and second for any future invocations of the notebook. Variables, functions, classes, and any other code will continue to exist and possibly affect code in other cells. Once a cell is deleted or modified and re-executed, the hidden state from that cell remains in the current session. It can be common to modify a call, execute it multiple times, and even delete it. This often happens during development and exploration. This hidden state can show up in a few ways, but one common way is by executing notebook cells out of order. A very common problem raised is the idea of hidden state in a notebook. Many developers think that using notebooks can promote some bad habits, cause confusion, and result in ugly code. Bring up the subject of Jupyter notebooks around Python developers and you’ll likely get a variety of opinions about them.
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