#51 Pandas (Part 28) Percent change, Covariance, Correlation in Python | Tutorial

The video discusses percent change, covariance and correlation in Pandas Python.

Timeline
(Python 3.7)

00:00 - Welcome
00:10 - Outline of video
01:00 - Open Jupyter notebook
01:12 - Data : None
02:08 - Percent change:Series: .pct_change()
04:00 - Percent change:Series: using 'periods'
05:57 - Percent change:DataFrame: .pct_change()
06:44 - Percent change: DataFrame: using 'periods'
08:46 - Covariance:Series: .cov()
09:00 - Covariance:DataFrame: .cov()
12:07 - Covariance:DataFrame: with NaN
12:50 - Covariance:DataFrame: using 'min_periods'
13:44 - Correlation:Series: Pearson (default): .corr()
15:09 - Visualize correlation using a scatter plot
15:58 - Correlation:Series: Spearman: .corr()
16:15 - Correlation:Series: Kendall: .corr()
16:41 - Correlation:DataFrame
17:03 - Visualize correlation using a scatter plot
18:13 - Correlation:DataFrame: using 'min_periods'
20:00 - Custom Function with .corr(): create custom function
21:22 - Custom Function with .corr(): use custom function
23:19 - Correlation: .corrwith()
25:08 - Ending notes