Apply an if/then condition over all elements of a Pandas dataframe

Question

I need to apply a very simple if/then function to every element in a Pandas Dataframe.

If the value of any element is over 0.5, I need to return a 1. Otherwise, I need to return a 0.

This seemed really simple with a lambda function, but every time I try I get an error: 'ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()' 'ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()'

So far I have:

df_new = df.apply(lambda x: 1 if x > 0.5 else 1)

I'd be grateful for any help.

Answer 1

You should use applymap instead because you want the operation to be performed on each element in your dataframe, not each column, which is what apply does.

df = pd.DataFrame({"A": [0.1, 0.2, 0.5, 0.6, 0.7],
                  "B": [0.75, 0.85, 0.2, 0.9, 0.0],
                  "C": [0.2, 0.51, 0.49, 0.3, 0.1]})

print(df)

      A        B       C
0   0.1     0.75    0.20
1   0.2     0.85    0.51
2   0.5     0.20    0.49
3   0.6     0.90    0.30
4   0.7     0.00    0.10

df_new = df.applymap(lambda x: 1 if x > 0.5 else 0)

print(df_new)

    A   B   C
0   0   1   0
1   0   1   1
2   0   0   0
3   1   1   0
4   1   0   0

Answer 2

If it is something binary, you could use the following:

df = (df > 0.5).astype(int)

The code in the parentheses would generate a dataframe of bools, which would then be converted to the ints 1 and 0 (1 for True, 0 for False)