Скачать или смотреть #41 Pandas: Visualization - 3 in Python - 27 | Tutorial

The video discusses Pandas methods to format plots in Python.

Timeline & Data
(Python 3.7)

00:00 - Welcome
00:10 - Outline of video
00:54 - Open Jupyter notebook
01:03 - Data
01:30 - Create plot: Series: Style
04:04 - Create plot: DataFrame: Style: (with legend)
04:26 - Create plot: DataFrame: Style: (remove legend)
04:49 - Create plot: Log y-axis
05:49 - Create plot: Secondary y-axis
09:30 - Format x-axis: single plot
10:39 - Format x-axis: multiple plots
12:36 - Create subplots: DataFrame: default
13:12 - Create subplots: DataFrame: layout()
13:53 - Create subplots: Grid: DataFrame: create positions for plot grid
15:24 - Create subplots: Grid: DataFrame: plot with positions
16:43 - Create plots with Error bars: create data
17:56 - Create plots with Error bars: bar plot with error bars
18:58 - Create plot with Table-1
21:00 - Create plot with Table-2
24:42 - Create plot with Table-3
26:36 - Using colormaps: without cm
27:28 - Using colormaps: with cm
28:19 - 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)
)
#################