﻿[Script Info] Title: [Events] Format: Layer, Start, End, Style, Name, MarginL, MarginR, MarginV, Effect, Text Dialogue: 0,0:00:00.98,0:00:02.60,Default,,0000,0000,0000,,All right, why don't we walk through the solution? Dialogue: 0,0:00:02.60,0:00:07.19,Default,,0000,0000,0000,,Here we've created the olympic_medal_counts_df data frame as we Dialogue: 0,0:00:07.19,0:00:08.42,Default,,0000,0000,0000,,did in the previous exercise. Dialogue: 0,0:00:09.79,0:00:13.08,Default,,0000,0000,0000,,Now we're going to define this variable called bronze_at_least_one_gold. Dialogue: 0,0:00:14.28,0:00:16.92,Default,,0000,0000,0000,,What we're doing here is we're picking out the bronze column of Dialogue: 0,0:00:16.92,0:00:18.94,Default,,0000,0000,0000,,the olympics_medal_counts_df data frame. Dialogue: 0,0:00:20.24,0:00:23.17,Default,,0000,0000,0000,,Then we're picking out only the subset of that column. Dialogue: 0,0:00:23.17,0:00:27.37,Default,,0000,0000,0000,,With the indices matching the indices where olympic_metal_counts_df's gold Dialogue: 0,0:00:27.37,0:00:30.83,Default,,0000,0000,0000,,column is greater than or equal to one. Dialogue: 0,0:00:30.83,0:00:35.60,Default,,0000,0000,0000,,Then, we're using numpy.mean to take the average of Dialogue: 0,0:00:35.60,0:00:41.77,Default,,0000,0000,0000,,those values in assigning them to this variable, avg_bronze_at_least_one_gold.