Creating a new column based on if-elif-else condition

Question

I have a DataFrame df:

    A    B
a   2    2 
b   3    1
c   1    3

I want to create a new column based on the following criteria:

if row A == B: 0

if rowA > B: 1

if row A < B: -1

so given the above table, it should be:

    A    B    C
a   2    2    0
b   3    1    1
c   1    3   -1 

For typical if else cases I do np.where(df.A > df.B, 1, -1), does pandas provide a special syntax for solving my problem with one step (without the necessity of creating 3 new columns and then combining the result)?

Answer 1

To formalize some of the approaches laid out above:

Create a function that operates on the rows of your dataframe like so:

def f(row):
    if row['A'] == row['B']:
        val = 0
    elif row['A'] > row['B']:
        val = 1
    else:
        val = -1
    return val

Then apply it to your dataframe passing in the axis=1 option:

In [1]: df['C'] = df.apply(f, axis=1)

In [2]: df
Out[2]:
   A  B  C
a  2  2  0
b  3  1  1
c  1  3 -1

Of course, this is not vectorized so performance may not be as good when scaled to a large number of records. Still, I think it is much more readable. Especially coming from a SAS background.

Edit

Here is the vectorized version

df['C'] = np.where(
    df['A'] == df['B'], 0, np.where(
    df['A'] >  df['B'], 1, -1))