I don't get how the line: results[i, sequence] = 1 works in the following.

I am following along in the debugger with some sample code in a Manning book: "Deep Learning with Python" (Example 3.5-classifying-movie-reviews.ipynb from the book) and while I understand what the code does, I don't get the syntax or how it works. I'm trying to learn Python and Deep learning together and want to understand what the code is doing.

def vectorize_sequences(sequences, dimension=10000):# Create an all-zero matrix of shape (len(sequences), dimension)results = np.zeros((len(sequences), dimension))for i, sequence in enumerate(sequences):results[i, sequence] = 1.  # <--- How does this work?return results

• This creates results, a 25,000 x 10,000 array of zeros.
• sequences is a list-of-tuples, for example (3, 0, 5). It then walks sequences and for each non-zero value of each sequence, set the corresponding index in results[i] to 1.0. They call it one-hot encoding.
• I don't understand how the line: results[i, sequence] = 1 accomplishes this in numpy.
• I get the for i, sequence in enumerate(sequences) part: just enumerating the sequences list and keeping track of i.
• I'm guessing there is some numpy magic that is somehow setting values in results[i] based on examining sequence[n] element by element and inserting a 1.0 whenever sequence[n] is non-zero(?) Just want to understand the syntax.

Assuming sequence is a list of integers,

results[i,sequence] = 1

is equivalent to

for j in sequence:results[i][j] = 1