An asterisk between a list and an integer extends the list multiple times, to give a new list containing multiple copies of the original list's elements. Each element may be any kind of object (including another list). mylist = [e1, ... en] newlist = mylist * 3 print(newlist) prints: [e1, ... en, e1, ... en, e1, ... en] This is the same for other list-like data containers, such as strings and tuples. # Strings "abc" * 3 # Returns "abcabcabc" # Tuples (1, 2, 3) * 3 # Returns (1, 2, 3, 1, 2, 3, 1, 2, 3) # List of lists [list1, list2, list3] * 3 # Returns [list1, list2, list3, list1, list2, list3, list1, list2, list3] More on reddit.com

student_scores is a list of strings, not integers. You need to use int to parse the string to an integer before trying to multiply it. You currently have int wrapping the entire multiplication, which will have the result of essentially flooring the product. More on reddit.com

I'm not quite sure I follow your example, but several of the operations you mention can be vectorised. It looks like you merge your data so you're working within a single dataframe. Here's an example illustrating the concepts you mention. import pandas df = pandas.DataFrame({ "a": [1, 2, 3, 4], "b": [5, 6, 7, 8], "c": [9, 10, 11, 12], "r": [0, 1, 2, 3] }) # dummy mask for illustration purposes; select which rows are "true" for the # purposes of the updates you want to make conditions = pandas.Series([True, False, False, True]) # columns to update sel_cols = ["a", "b", "c"] # renaming the columns new_cols = ["mod_" + x for x in sel_cols] # grab the ratio column for the rows in scope, then expand it into a table # so you can do element-wise matrix multiplication ratio_frame = df.loc[conditions, ["r"] * len(sel_cols)] ratio_frame.columns = sel_cols source_data = df.loc[conditions, sel_cols] result = (ratio_frame * source_data).rename(columns = dict(zip(sel_cols, new_cols))) # output df.loc[conditions, new_cols] = result Gives: In [2]: df Out[2]: a b c r mod_a mod_b mod_c 0 1 5 9 0 0.0 0.0 0.0 1 2 6 10 1 NaN NaN NaN 2 3 7 11 2 NaN NaN NaN 3 4 8 12 3 12.0 24.0 36.0 More on reddit.com

Note that this has nothing to do with b being rectangular or not, but about the size of its last dimension. Broadcasting will match dimensions from the back, and so multiplying a 2D with a 1D array would try to multiply each column of the 2D array with the corresponding scalar from the 1D array. If you want to multiply each row instead, you will need to expand a to shape (3, 1): a[:, np.newaxis] * b More on reddit.com