May 19

Python/ML Correlation Coefficients

df_s_transpose_pearson = df_s_transpose.corr(method = ‘pearson’, numeric_only = True)
df_s_transpose_pearson

# Pearson Correlation Coefficient

df_s_transpose_pearson = df_s_transpose.corr(method = ‘pearson’, numeric_only = True)
df_s_transpose_pearson

Pearson Correlation Coefficient based Adjacency Graph Matrix

df_s_transpose_pearson[df_s_transpose_pearson >= 0.5] = 1
df_s_transpose_pearson[df_s_transpose_pearson < 0.5] = 0
df_s_transpose_pearson

Create a Graph

import networkx as nx
Graph_pearson = nx.Graph(df_s_transpose_pearson)

before the above step do: make the diagonal element to be zero. No self loop/edge

import numpy as np
np.fill_diagonal(df_s_transpose_pearson.values, 0)

Draw the graph

nx.draw_networkx(Graph_pearson, pos = nx.circular_layout( Graph_pearson ), node_color = ‘r’, edge_color = ‘b’)