Why does using "==" return a Series instead of bool in pandas?

It is testing each element of data.categ for equality with cat. That produces a vector of True/False values. This is passed as in indexer to data[], which returns the rows from data that correspond to the True values in the vector.

To summarize, the whole expression returns the subset of rows from data where the value of data.categ equals cat.

(Seems possible the whole operation could be done more elegantly using data.groupBy('categ').apply(someFunc).)

Yes, it is a test. Boolean expressions are not restricted to if statements.

It looks as if data is a data frame (PANDAS). The expression used as a data frame index is how PANDAS denotes a selector or filter. This says to select every row in which the fieled categ matches the variable cat (apparently a pre-defined variable). This collection of rows becomes a new data frame, subset.

It creates a boolean series with indexes where data.categ is equal to cat , with this boolean mask, you can filter your dataframe, in other words subset will have all records where the categ is the value stored in cat.

This is an example using numeric data

np.random.seed(0)
a = np.random.choice(np.arange(2), 5)
b = np.random.choice(np.arange(2), 5)
df = pd.DataFrame(dict(a = a, b = b))


df[df.a == 0].head()

#   a   b
# 0 0   0
# 2 0   0
# 4 0   1

df[df.a == df.b].head()

#   a   b
# 0 0   0
# 2 0   0
# 3 1   1