Pandas OR statement ending in series contains

Question

I have a DataFrame df that has columns type and subtype and about 100k rows, I'm trying to classify what kind of data df contains by checking type / subtype combinations. While df can contain many different combinations there are particular combinations that only appear in certain data types. To check if my objects contains any of these combinations I'm currently doing:

typeA = ((df.type == 0) & ((df.subtype == 2) | (df.subtype == 3) | 
         (df.subtype == 5) | (df.subtype == 6))) | 
         ((df.type == 5) & ((df.subtype == 3) | (df.subtype == 4) | (df.subtype == 7) | 
         (df.subtype ==  8)))
A = typeA.sum()

Where typeA is a long Series of Falses that might have some Trues, if A > 0 then I know it contained a True. The problem with this scheme is that if the first row of the df produces a True it still has to check everything else. Checking the whole DataFrame is faster then using a for loop with a break, but I'm wondering if there is a better way to do it.

Thanks for any suggestions.

Answer 1

use Pandas crosstab:

import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.randint(0, 10, size=(100, 2)), columns=["type", "subtype"])
counts = pd.crosstab(df.type, df.subtype)

print counts.loc[0, [2, 3, 5, 6]].sum() + counts.loc[5, [3, 4, 7, 8]].sum()

the result is same as:

a = (((df.type == 0) & ((df.subtype == 2) | (df.subtype == 3) | 
         (df.subtype == 5) | (df.subtype == 6))) | 
         ((df.type == 5) & ((df.subtype == 3) | (df.subtype == 4) | (df.subtype == 7) | 
         (df.subtype ==  8))))
a.sum()

Answer 2

In pandas 0.13 (soon to be released) you can pass this as a query, which will use numexpr, which should be more efficient for your usecase:

df.query("((df.type == 0) & ((df.subtype == 2) | (df.subtype == 3) | 
         (df.subtype == 5) | (df.subtype == 6))) | 
         ((df.type == 5) & ((df.subtype == 3) | (df.subtype == 4) | (df.subtype == 7) | 
         (df.subtype ==  8)))")

Note: I would probably clean up the indentation to make this more readable (you can also replace df.type with type in most cases:

df.query("((type == 0) & ((subtype == 2)"
                        "|(subtype == 3)"
                        "|(subtype == 5)"
                        "|(subtype == 6)))"
        "|((type == 5) & ((subtype == 3)"
                        "|(subtype == 4)"
                        "|(subtype == 7)"
                        "|(subtype ==  8)))")

Update: It may be able to do this more efficiently, certainly more concisely, using the "in" syntax:

df.query("(type == 0) & (subtype in [2, 3, 5, 6])"
        "|(type == 5) & (subtype in [3, 4, 7, 8])")