How to deal with no matches Boolean Mask

What is the best way to deal with the following:
Let’s say I am inspecting a dataframe and the field doesn’t have any matches. When that happens, it throws an error. What’s the best way to deal with that? Using try()? If so, how would one do it?

For example, let’s say I have a dataframe with a column called ‘out_in_state’ and I was looking for rows where that column had an oos (out of state) flag.


If there are no rows matching that, it will throw an error. How should I deal with that?



The scenario you provided doesn’t actually throw an error:

>>> import pandas as pd
>>> df = pd.DataFrame({"index": ['one', 'two', 'three'],
...                    "flag": ['f', 'f', 'f']})
>>> df
  flag  index
0    f    one
1    f    two
2    f  three
>>> selection = df[df['flag'] == 't']
>>> selection
Empty DataFrame
Columns: [flag, index]
Index: []

There’s no error, there’s just an empty dataframe. If you have subsequent code that expects there to be a non-empty dataframe, you might get an error then. One way to handle this is with try/except, but you can also just check the shape of the df:

if selection.shape[0] == 0:
    # insert whatever you want to do here

So, I should have clarified that is wasn’t an error I was receiving. It was a “future warning” message. The message is similar to the SetCopyWithWarning logic. It doesn’t stop anything from working, but (as it is mentioned in the training) is something that shouldn’t be ignored either.

It is really an issue that is occurring because the dataframe doesn’t have any rows that match what is being “searched” on by the boolean mask.

My question is, how to deal with that? Should I use a try() function? Is there a better way to handle that?

I am trying encapsulate it in a function and I want the function to operate (without presenting that message) regardless of whether there are rows that match based on the boolean mask or not.

A warning by itself isn’t necessarily something to be worried about, but a FutureWarning indicates that something is likely going to change in a future version of pandas. This might mean you don’t need to change your code now, or even that your code will break if there is a change, but that future behavior might change.

Can you share the warning so I can better understand what’s happening? I’m operating in the dark a little as to what the underlying issue is here.