For df, I wish to produce an interactive box plot showing the distribution of all numeric attributes, represented as seperate subplots.
In [2]: import pandas as pd
...: import numpy as np
...: import holoviews as hv
...: import hvplot.pandas # noqa
...: import feather
...: hv.extension("bokeh","plotly")
In [4]: df.head()
Out[4]:
area error compactness error concave points error concavity error fractal dimension error mean area mean compactness ... worst concavity worst fractal dimension worst perimeter worst radius worst smoothness worst symmetry worst texture
0 153.40 0.04904 0.01587 0.05373 0.006193 1001.0 0.27760 ... 0.7119 0.11890 184.60 25.38 0.1622 0.4601 17.33
1 74.08 0.01308 0.01340 0.01860 0.003532 1326.0 0.07864 ... 0.2416 0.08902 158.80 24.99 0.1238 0.2750 23.41
2 94.03 0.04006 0.02058 0.03832 0.004571 1203.0 0.15990 ... 0.4504 0.08758 152.50 23.57 0.1444 0.3613 25.53
3 27.23 0.07458 0.01867 0.05661 0.009208 386.1 0.28390 ... 0.6869 0.17300 98.87 14.91 0.2098 0.6638 26.50
4 94.44 0.02461 0.01885 0.05688 0.005115 1297.0 0.13280 ... 0.4000 0.07678 152.20 22.54 0.1374 0.2364 16.67
[5 rows x 32 columns]
In [8]: df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 569 entries, 0 to 568
Data columns (total 32 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 area error 569 non-null float64
1 compactness error 569 non-null float64
2 concave points error 569 non-null float64
3 concavity error 569 non-null float64
4 fractal dimension error 569 non-null float64
5 mean area 569 non-null float64
6 mean compactness 569 non-null float64
7 mean concave points 569 non-null float64
8 mean concavity 569 non-null float64
9 mean fractal dimension 569 non-null float64
10 mean perimeter 569 non-null float64
11 mean radius 569 non-null float64
12 mean smoothness 569 non-null float64
13 mean symmetry 569 non-null float64
14 mean texture 569 non-null float64
15 perimeter error 569 non-null float64
16 radius error 569 non-null float64
17 smoothness error 569 non-null float64
18 symmetry error 569 non-null float64
19 target 569 non-null int64
20 target_name 569 non-null category
21 texture error 569 non-null float64
22 worst area 569 non-null float64
23 worst compactness 569 non-null float64
24 worst concave points 569 non-null float64
25 worst concavity 569 non-null float64
26 worst fractal dimension 569 non-null float64
27 worst perimeter 569 non-null float64
28 worst radius 569 non-null float64
29 worst smoothness 569 non-null float64
30 worst symmetry 569 non-null float64
31 worst texture 569 non-null float64
dtypes: category(1), float64(30), int64(1)
memory usage: 138.6 KB
If I use the Bokeh backend then the plot successfully produces, although it is not interactive:
In [5]: hv.extension("bokeh")
...: df.hvplot.box(subplots=True,
...: shared_axes=False,
...: height=500,width=900)
Also, subplots=True does not appear to have an effect on the plot. I have also experienced this issue for hvplot.kde(), but I have successfully used this attribute with hvplot.hist(). (I have raised a seperate issue about this.)
If I switch to the Plotly backend then my plot produces, however, the attribute names have been truncated to a single letter, many of which are not unique, hence my box plots end up stacked on top of each other:
In [6]: hv.extension("plotly")
...: df.hvplot.box(subplots=True,
...: shared_axes=False,
...: height=500,width=900)
I was successfully able to rename some attributes based on index position, however it seems like the non unique letter version of attribute names still causes an issue:
In [7]: hv.extension("plotly")
...: plot = df.hvplot.box(subplots=True,
...: shared_axes=False,
...: height=500,width=900)
...:
...: plot.opts(xticks=[(0,'area error'), (1,'compactness error'), (2,'concave points error'),
...: (3,'concavity error'), (4,'fractal dimension error'), (5,'mean area'),
...: (6,'mean compactness'), (7,'mean concave points'), (8,'mean concavity'),
...: (9,'mean fractal dimension'), (10,'mean perimeter'), (11,'mean radius'),
...: (12,'mean smoothness'), (13,'mean symmetry'), (14,'mean texture'), (15,'perimeter error'),
...: (16,'radius error'), (17,'smoothness error'), (18,'symmetry error'), (19,'target'),
...: (20,'target_name'), (21,'texture error'), (22,'worst area'), (23,'worst compactness'),
...: (24,'worst concave points'), (25,'worst concavity'), (26,'worst fractal dimension'),
...: (27,'worst perimeter'), (28,'worst radius'), (29,'worst smoothness'), (30,'worst symmetry'),
...: (31,'worst texture')])
My ultimate aim would be to produce a box plot for all numeric attributes and have each box plot in its own subplot.
Any advice how to solve this issue would be greatly appreciated.
Thanks.
Software Versions:
pandas 1.0.3 py37h6c726b0_0
numpy 1.18.1 py37h7241aed_0
holoviews 1.13.1 py_0 pyviz
hvplot 0.5.2 py_0 pyviz
feather-format 0.4.0 py_1003 conda-forge
bokeh 1.4.0 py37_0
plotly 4.5.4 py_0 plotly