I have this simple function with 2 columns. What I’m trying to do is to check what group has a number of nan and change it to a new desired value. Here’s a code snippet:
def twod_array(): data = {"group": [-1, 0, 1, 2, 3], 'numbers': [[2], [14, 15], [16, 17], [19, 20, 21], [np.nan]], } df = pd.DataFrame(data=data) new_group_number = 100 df.loc[4, "group"] = new_group_number return df
Before: This is how the data looks like, you can assume numbers are sorted.
group numbers 0 -1 [2] 1 0 [14, 15] 2 1 [16, 17] 3 2 [19, 20, 21] 4 3 [nan]
In my example I know where nan and since it was at position 4, I was able to use loc to change it to a 100, like this:
group numbers 0 -1 [2] 1 0 [14, 15] 2 1 [16, 17] 3 2 [19, 20, 21] 4 100 [nan]
What if I don’t know where the nan is? How can I know which group to update? All that comes to my mind is nested for loop which I would rather avoid… Any suggestions here?
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Answer
You could replace
df.loc[4, "group"] = new_group_number
with
idx = df.numbers.apply(lambda l: any(pd.isna(e) for e in l)) df.loc[idx, 'group'] = new_group_number