#40 Pandas: Visualization - 2 in Python - 26 | Tutorial
The video discusses Pandas how to create scatter matrix plot, Andrews curves, parallel coordinates, lag plots, RadViz, bootstrap plot and autocorrelation plots in Python.
Timeline & Data
(Python 3.7)
00:00 - Welcome
00:12 - Outline of video
00:44 - Open Jupyter notebook
00:55 - Data
01:28 - Scatter matrix: import scatter_matrix
01:58 - Scatter matrix: create plot: scatter_matrix()
02:41 - Scatter matrix: interpret plot
04:17 - Create kde plot
05:58 - Andrews curves: import andrews_curves
06:09 - Andrews curves: create plot -1: andrews_curves()
06:58 - Andrews curves: interpret plot
08:00 - Andrews curves: create plot -2: andrews_curves()
08:20 - Andrews curves: interpret plot
10:10 - Parallel coordinates: import parallel_coordinates
10:39 - Parallel coordinates: create plot -1: parallel_coordinates()
11:24 - Parallel coordinates: interpret plot
12:06 - Parallel coordinates: create plot -2: parallel_coordinates()
12:14 - Parallel coordinates: interpret plot
13:08 - Lag plot: import lag_plot
13:19 - Lag plot: create plot - 1: lag_plot()
13:41 - Lag plot: interpret plot
14:20 - Lag plot: create plot - 2: lag_plot()
14:44 - Lag plot: interpret plot
15:05 - RadViz plot: import radviz
15:31 - RadViz plot: create plot - 1: radviz()
15:57 - RadViz plot: interpret plot
17:56 - RadViz plot: create plot - 2: radviz()
18:21 - RadViz plot: interpret plot
19:47 - Bootstrap plot: import bootstrap_plot
19:58 - Bootstrap plot: create plot: bootstrap_plot()
20:30 - Bootstrap plot: interpret plot
21:46 - Autocorrelation plot: import autocorrelation_plot
21:59 - Autocorrelation plot: create plot - 1: autocorrelation_plot()
22:27 - Autocorrelation plot: interpret plot
22:54 - Autocorrelation plot: create plot - 2: autocorrelation_plot()
23:12 - Autocorrelation plot: interpret plot
23:45 - Ending notes
#################
Data
#################
flowers = pd.DataFrame({
'species':np.random.choice(a=['Alyssum','Amaranthus','Aster','Rose', 'Lily', 'Jonquil'], size=1000),
'shades':np.random.choice(a=['red','blue'], size=1000),
'prod_summer': 260+np.random.randn(1000)*np.sin(np.pi/4),
'prod_winter': 150+np.random.normal(100, 30, 1000),
'prod_fall': 355+np.random.normal(300, 130, 1000),
'prod_spring': 440+np.random.normal(450, 70, 1000),
'num_orders': np.random.randn(1000)*2
}
,index=pd.date_range('2019-01-01', periods=1000)
)
a = pd.DataFrame({
'A':['m','m','m','n','n','n','o','o','o'],
'a1':[1,2,3, 1,2,-3, 300,324,323],
'a2':[90,91,95, 150,151,-152, 343,313,311],
'a3':[10,12,13, 22,21,-23, 333,290,299],
'a4':[90,91,95, 150,151,-152, 311,314,317]
})
b = pd.DataFrame({
'x': [1,2,3,4,5,6,6,5,4,3,2,1],
'y': [-1,-2,-3,-4,-5,-6,-6,-5,-4,-3,-2,-1]
})
g = pd.DataFrame({
'A':['m','m','m','n','n','n','o','o','o'],
'a1':[1,2,3, 1,2,3, 300,324,323],
'a2':[90,91,95, 150,151,152, 343,313,311],
'a3':[10,10,30, 15,21,27, 333,290,299],
'a4':[1e3,1e3,1e3, 15,13,12, 304,314,317]
})
###################
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