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#54 Pandas (Part 31): Intuition and code to calculate rolling mean and sum in Python | Tutorial
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The video discusses the intuition behind the weights for a rolling window. And then, walks through coding examples to calculate rolling mean and rolling sum using window types such as boxcar, gaussian and exponential.
Timeline
(Python 3.7)
00:00 - Welcome
00:09 - Outline of video
00:46 - 'win_type' in Pandas
01:41 - Intuition for rolling using win_type: .mean()
07:35 - Open Jupyter notebook
07:43 - Data
07:58 - Using a custom function with .apply() with .rolling()
10:16 - Rolling mean using win_type: Boxcar
11:40 - Rolling mean
12:04 - Rolling mean using win_type: Gaussian
13:01 - Rolling mean using win_type: Exponential
14:08 - Rolling sum using win_type: Gaussian, Exponential
15:25 - Visualize rolling mean
17:38 - Ending notes
Timeline
(Python 3.7)
00:00 - Welcome
00:09 - Outline of video
00:46 - 'win_type' in Pandas
01:41 - Intuition for rolling using win_type: .mean()
07:35 - Open Jupyter notebook
07:43 - Data
07:58 - Using a custom function with .apply() with .rolling()
10:16 - Rolling mean using win_type: Boxcar
11:40 - Rolling mean
12:04 - Rolling mean using win_type: Gaussian
13:01 - Rolling mean using win_type: Exponential
14:08 - Rolling sum using win_type: Gaussian, Exponential
15:25 - Visualize rolling mean
17:38 - Ending notes
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