I am using pandas and numpy libraries, to calculate the pearson correlation of two simple lists. The output of the below code is the matrix of correlation:
import numpy as np
import pandas as pd
x = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
y = np.array([2, 1, 4, 5, 8, 12, 18, 25, 96, 48])
z = np.array([5, 3, 2, 1, 0, -2, -8, -11, -15, -16])
x, y, z = pd.Series(x), pd.Series(y), pd.Series(z)
xyz = pd.DataFrame({'dist-values': x, 'uptime-values': y, 'speed-values': z})
matrix = xyz.corr(method="pearson")
After using the .unstack(), and .to_dict() functions on the output we can have a dictionary in the below format, and base on the answer on this post, we can convert the output to a list of dictionaries:
result = (matrix.unstack().rename_axis(['f1', 'f2'])
.reset_index(name='value').to_dict('records')
)
# the output format after printing
[{'f1': 'dist-values', 'f2': 'dist-values', 'value': 1.0},
{'f1': 'dist-values', 'f2': 'uptime-values', 'value': 0.7586402890911869},
{'f1': 'dist-values', 'f2': 'speed-values', 'value': -0.9680724198337364},
{'f1': 'uptime-values', 'f2': 'dist-values', 'value': 0.7586402890911869},
{'f1': 'uptime-values', 'f2': 'uptime-values', 'value': 1.0},
{'f1': 'uptime-values', 'f2': 'speed-values', 'value': -0.8340792243486527},
{'f1': 'speed-values', 'f2': 'dist-values', 'value': -0.9680724198337364},
{'f1': 'speed-values', 'f2': 'uptime-values', 'value': -0.8340792243486527},
{'f1': 'speed-values', 'f2': 'speed-values', 'value': 1.0}]
But I need a more complicated format, and the output should be like this:
[
{'name': 'dist-values', 'data': [{'x': 'dist-values', 'y': 1.0}, {'x': 'uptime-values', 'y': 0.7586402890911869}, {'x': 'speed-values', 'y': -0.9680724198337364}]},
{'name': 'uptime-values', 'data': [{'x': 'dist-values', 'y': 0.7586402890911869}, {'x': 'uptime-values', 'y': 1.0}, {'x': 'speed-values', 'y': -0.8340792243486527}]},
{'name': 'speed-values', 'data': [{'x': 'dist-values', 'y': -0.9680724198337364}, {'x': 'uptime-values', 'y': -0.8340792243486527}, {'x': 'speed-values', 'y': 1.0}]},
]
There are only three features in this code, and the correlation matrix has only 9 elements, but in a bigger matrix, how we can implement this conversion? Is there an efficient way to do it? Thanks.
You can try list-comprehension to obtain your output:
out = [
{"name": i, "data": [{"x": c, "y": row[c]} for c in row.index]}
for i, row in matrix.iterrows()
]
print(out)
Prints:
[
{
"name": "dist-values",
"data": [
{"x": "dist-values", "y": 1.0},
{"x": "uptime-values", "y": 0.7586402890911869},
{"x": "speed-values", "y": -0.9680724198337364},
],
},
{
"name": "uptime-values",
"data": [
{"x": "dist-values", "y": 0.7586402890911869},
{"x": "uptime-values", "y": 1.0},
{"x": "speed-values", "y": -0.8340792243486527},
],
},
{
"name": "speed-values",
"data": [
{"x": "dist-values", "y": -0.9680724198337364},
{"x": "uptime-values", "y": -0.8340792243486527},
{"x": "speed-values", "y": 1.0},
],
},
]
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