scikit-posthocs.ipynb

Kernel: Python 3 (Ubuntu Linux)

scikit-posthocs

Pairwise Multiple Comparisons Post-hoc Tests

in Python 3 (Ubuntu Linux)

https://github.com/maximtrp/scikit-posthocs

import scikit_posthocs as sp
x = [[1,2,3,5,1], [12,31,54], [10,12,6,74,11]]
sp.posthoc_conover(x, p_adjust = 'holm')

array([[-1.        ,  0.00119517,  0.00278329],
       [ 0.00119517, -1.        ,  0.18672227],
       [ 0.00278329,  0.18672227, -1.        ]])

import scikit_posthocs as sp
import pandas as pd
x = pd.DataFrame({"a": [1,2,3,5,1], "b": [12,31,54,62,12], "c": [10,12,6,74,11]})
x = x.melt(var_name='groups', value_name='values')
x

	groups	values
0	a	1
1	a	2
2	a	3
3	a	5
4	a	1
5	b	12
6	b	31
7	b	54
8	b	62
9	b	12
10	c	10
11	c	12
12	c	6
13	c	74
14	c	11

sp.posthoc_conover(x, val_col='values', group_col='groups')

	a	b	c
a	-1.000000	0.000109	0.001853
b	0.000109	-1.000000	0.121659
c	0.001853	0.121659	-1.000000

pc = sp.posthoc_conover(x, val_col='values', group_col='groups')
heatmap_args = {'linewidths': 0.25, 'linecolor': '0.5', 'clip_on': False, 'square': True, 'cbar_ax_bbox': [0.80, 0.35, 0.04, 0.3]}
sp.sign_plot(pc, **heatmap_args)

(<matplotlib.axes._subplots.AxesSubplot at 0x7f67b595ffd0>,
 <matplotlib.colorbar.ColorbarBase at 0x7f67b160c128>)

pc = sp.posthoc_conover(x, val_col='values', group_col='groups')
# Format: diagonal, non-significant, p<0.001, p<0.01, p<0.05
cmap = ['1', '#fb6a4a',  '#08306b',  '#4292c6', '#c6dbef']
heatmap_args = {'cmap': cmap, 'linewidths': 0.25, 'linecolor': '0.5', 'clip_on': False, 'square': True, 'cbar_ax_bbox': [0.80, 0.35, 0.04, 0.3]}
sp.sign_plot(pc, **heatmap_args)

(<matplotlib.axes._subplots.AxesSubplot at 0x7f67b1636e10>,
 <matplotlib.colorbar.ColorbarBase at 0x7f67b15efd68>)