Pandas creating a new variable based on two existing variables

Question

I have the following code I think is highly inefficient. Is there a better way to do this type common recoding in pandas?

df['F'] = 0
df['F'][(df['B'] >=3) & (df['C'] >=4.35)] = 1
df['F'][(df['B'] >=3) & (df['C'] < 4.35)] = 2
df['F'][(df['B'] < 3) & (df['C'] >=4.35)] = 3
df['F'][(df['B'] < 3) & (df['C'] < 4.35)] = 4

Answer 1

Use numpy.select and cache boolean masks to variables for better performance:

m1 = df['B'] >= 3
m2 = df['C'] >= 4.35
m3 = df['C'] < 4.35
m4 = df['B'] < 3

df['F'] = np.select([m1 & m2, m1 & m3, m4 & m2, m4 & m3], [1,2,3,4], default=0)

Answer 2

In your specific case, you can make use of the fact that booleans are actually integers (False == 0, True == 1) and use simple arithmetic:

df['F'] = 1 + (df['C'] < 4.35) + 2 * (df['B'] < 3)

Note that this will ignore any NaN's in your B and C columns, these will be assigned as being above your limit.