For performance, you might be better off working with NumPy array and using np.where
-
a = df.values # Assuming you have two columns A and B
df['C'] = np.where(a[:,1]>5,a[:,0],0.1*a[:,0]*a[:,1])
Runtime test
def numpy_based(df):
a = df.values # Assuming you have two columns A and B
df['C'] = np.where(a[:,1]>5,a[:,0],0.1*a[:,0]*a[:,1])
Timings -
In [271]: df = pd.DataFrame(np.random.randint(0,9,(10000,2)),columns=[['A','B']])
In [272]: %timeit numpy_based(df)
1000 loops, best of 3: 380 μs per loop
In [273]: df = pd.DataFrame(np.random.randint(0,9,(10000,2)),columns=[['A','B']])
In [274]: %timeit df['C'] = df.A.where(df.B.gt(5), df[['A', 'B']].prod(1).mul(.1))
100 loops, best of 3: 3.39 ms per loop
In [275]: df = pd.DataFrame(np.random.randint(0,9,(10000,2)),columns=[['A','B']])
In [276]: %timeit df['C'] = np.where(df['B'] > 5, df['A'], 0.1 * df['A'] * df['B'])
1000 loops, best of 3: 1.12 ms per loop
In [277]: df = pd.DataFrame(np.random.randint(0,9,(10000,2)),columns=[['A','B']])
In [278]: %timeit df['C'] = np.where(df.B > 5, df.A, df.A.mul(df.B).mul(.1))
1000 loops, best of 3: 1.19 ms per loop
Closer look
Let's take a closer look at NumPy's number crunching capability and compare with pandas into the mix -
# Extract out as array (its a view, so not really expensive
# .. as compared to the later computations themselves)
In [291]: a = df.values
In [296]: %timeit df.values
10000 loops, best of 3: 107 μs per loop
Case #1 : Work with NumPy array and use numpy.where :
In [292]: %timeit np.where(a[:,1]>5,a[:,0],0.1*a[:,0]*a[:,1])
10000 loops, best of 3: 86.5 μs per loop
Again, assigning into a new column : df['C']
would not be very expensive either -
In [300]: %timeit df['C'] = np.where(a[:,1]>5,a[:,0],0.1*a[:,0]*a[:,1])
1000 loops, best of 3: 323 μs per loop
Case #2 : Work with pandas dataframe and use its .where
method (no NumPy)
In [293]: %timeit df.A.where(df.B.gt(5), df[['A', 'B']].prod(1).mul(.1))
100 loops, best of 3: 3.4 ms per loop
Case #3 : Work with pandas dataframe (no NumPy array), but use numpy.where
-
In [294]: %timeit np.where(df['B'] > 5, df['A'], 0.1 * df['A'] * df['B'])
1000 loops, best of 3: 764 μs per loop
Case #4 : Work with pandas dataframe again (no NumPy array), but use numpy.where
-
In [295]: %timeit np.where(df.B > 5, df.A, df.A.mul(df.B).mul(.1))
1000 loops, best of 3: 830 μs per loop