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

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)) 

df.loc[df['A'] == df['B'], 'C'] = 0
df.loc[df['A'] > df['B'], 'C'] = 1
df.loc[df['A'] < df['B'], 'C'] = -1

Easy to solve using indexing. The first line of code reads like so, if column A is equal to column B then create and set column C equal to 0.

For this particular relationship, you could use np.sign:

>>> df["C"] = np.sign(df.A - df.B)
>>> df
   A  B  C
a  2  2  0
b  3  1  1
c  1  3 -1

Lets say above one is your original dataframe and you want to add a new column 'old'

If age greater than 50 then we consider as older=yes otherwise False

step 1: Get the indexes of rows whose age greater than 50

row_indexes=df[df['age']>=50].index

step 2: Using .loc we can assign a new value to column

df.loc[row_indexes,'elderly']="yes"

same for age below less than 50

row_indexes=df[df['age']<50].index

df[row_indexes,'elderly']="no"