我正在嘗試用 scipy 計算散點圖的相關系數，問題是，我在 ndarray 中有一種復雜的資料集，基本語法對我不起作用......
這是我的完整代碼：
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.patches as mpatches
from matplotlib.pyplot import figure
figure(figsize=(12, 10), dpi=200)
import scipy.stats
from scipy.stats import t
plt.close('all')
data = np.array([
[22.8, 14.4],
[19.6, 3.6],
[0.3, 16.6],
[8.9, 7],
[13.7, 13.4],
[14.7, 1.5],
[1.9, 0.4],
[-1.8, 0.3],
[-3, -15.3],
[-5.9, -6.3],
[-13.4, -15],
[-5.7, -34.8],
[-6.8, -12.9],
])
custom_annotations = ["K464E", "K472E", "R470E", "K464A", "M155E", "K472A", "M155A", "Q539A", "M155R", "D244A", "E247A", "E247R", "D244K"]
class_colours = ["r", "r", "r", "r", "r", "r", "g", "g", "b", "b", "b", "b", "b"]
for i, point in enumerate(data):
plt.scatter(point[0], point[1], marker='o', label=custom_annotations[i], c=class_colours[i], edgecolors='black', linewidths=1, alpha=0.75)
plt.annotate(custom_annotations[i], (data[i,0], data[i,1]))
plt.xlabel(r'$\Delta V_{0.5}$ Apo wild-type mHCN2 (mV)', fontsize=10)
plt.ylabel(r'$\Delta \psi$ cAMP-bound wild-type mHCN2 (mV)', fontsize=10)
plt.title('$\Delta \psi$ cAMP-bound wild-type mHCN2 (HHU) vs Change in relative current (Jena)', fontsize=10)
plt.axvline(0, c=(.5, .5, .5), ls= '--')
plt.axhline(0, c=(.5, .5, .5), ls= '--')
scipy.stats.pearsonr(data[i,0], data[i,1])
plt.legend(ncol=3, loc=(1.04,0))
plt.show()
uj5u.com熱心網友回復：
pearsonr在您的資料上運行良好
scipy.stats.pearsonr(data[:,0], data[:,1]) #change i to : to get the whole col.
# this returns (r_coeff, p_value)
i正如錯誤所說，您傳遞了兩個浮點數（即 row 的值），但是corr需要兩個陣列，在您的情況下是兩列。
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標籤：Python 麻木的 matplotlib scipy
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