Iteration order with pandas groupby on a pre-sorted DataFrame

The Situation

I'm classifying the rows in a DataFrame using a certain classifier based on the values in a particular column. My goal is to append the results to one new column or another depending on certain conditions. The code, as it stands looks something like this:

df = pd.DataFrame({'A': [list with classifier ids],  # Only 3 ids, One word strings
                   'B': [List of text to be classified],  # Millions of unique rows, lines of text around 5-25 words long
                   'C': [List of the old classes]}  # Hundreds of possible classes, four digit integers stored as strings

df.sort_values('A', inplace=True)

new_col1, new_col2 = [], []
for name, group in df.groupby('A', sort=False):
    classifier = classy_dict[name]
    vectors = vectorize(group.B.values)

    preds = classifier.predict(vectors)
    scores = classifier.decision_function(vectors)

    for tup in zip(preds, scores, group.C.values):
        if tup[2] == tup[0]:
            new_col1.append(np.nan)
            new_col2.append(tup[2])

        else:
            new_col1.append(str(classifier.classes_[tup[1].argsort()[-5:]]))
            new_col2.append(np.nan)

df['D'] = new_col1
df['E'] = new_col2

The Issue

I am concerned that groupby will not iterate in a top-down, order-of-appearance manner as I expect. Iteration order when sort=False is not covered in the docs

My Expectations

All I'm looking for here is some affirmation that groupby('col', sort=False) does iterate in the top-down order-of-appearance way that I expect. If there is a better way to make all of this work, suggestions are appreciated.

Here is the code I used to test my theory on sort=False iteration order:

from numpy.random import randint
import pandas as pd
from string import ascii_lowercase as lowers

df = pd.DataFrame({'A': [lowers[randint(3)] for _ in range(100)],
                   'B': randint(10, size=100)})

print(df.A.unique())  # unique values in order of appearance per the docs

for name, group in df.groupby('A', sort=False):
    print(name)

Edit: The above code makes it appear as though it acts in the manner that I expect, but I would like some more undeniable proof, if it is available.

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