How do I create a new dataframe column based on two other columns?

I want to create a binary column which indicates 1 if the values of both columns in the following table are within the same range. For example, if the value on cat_1 is between 5-10 and the value in cat_2 is also between 5-10 then it should indicate 1, otherwise, it should be 0.

| cat_1.   | cat_2.         | [5-10] (new column to be created|
| -------- | -------------- | --------------------------------|
| 5        | 10             |1.                               |
| 7        | 9.             |1                                |
| 1        | 7.             |0                                |

So far, I have tried the following code but it return an error:

df.loc[((df['cat_1l'] >= 5 & df['cat_1'] <= 10) 
       & (df['cat_2'] >= 5 & result['cat_2'] <= 10)), '[5-10]' = 1

and here is the error:

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

Answer

The reason why you’re getting an error is that evaluation of & has priority over >=. To fix your snippet, add parentheses around column comparisons:

df.loc[((df['cat_1l'] >= 5) & (df['cat_1'] <= 10) 
       & (df['cat_2'] >= 5) & (result['cat_2'] <= 10)), '[5-10]' = 1

Even better, it is preferred to define the new column as a whole, without subsetting using .loc. Consider e.g.:

df['[5-10]'] = df['cat1'].between(5, 10) & df['cat_2'].between(5, 10)