correlation matrix python
import seaborn as sns
df = sns.load_dataset('iris')
corr_matrix = df.corr()
corr_matrix.style.background_gradient(cmap='coolwarm')
# 'RdBu_r', 'BrBG_r', & PuOr_r are other good diverging colormapscorrelation matrix python
import seaborn as sns
df = sns.load_dataset('iris')
corr_matrix = df.corr()
corr_matrix.style.background_gradient(cmap='coolwarm')
# 'RdBu_r', 'BrBG_r', & PuOr_r are other good diverging colormapspython calculate correlation
# For a measure the strength of association between two variables;
import numpy as np
from scipy.stats import spearmanr, pearsonr, kendalltau
serie1 = np.array([-2, -1, 0, 1, 4])
serie2 = np.array([-2.5, -.75, 1, 2, 6])
# linear ==> Pearson
pearsonr(serie1, serie2)
pearsonrResult(correlation=0.9964803994464919, pvalue=0.0002505212149642603)
# Non-linear ==> Spearman or kendall
spearmanr(serie1, serie2)
SpearmanrResult(correlation=0.9999999999999999, pvalue=1.4042654220543672e-24)
kendalltau(serie1, serie2)
KendalltauResult(correlation=0.9999999999999999, pvalue=0.016666666666666666)correlation python
import numpy as np
import scipy.stats
x = np.arange(15, 20)
y = np.arange(5, 10)
stat, p = scipy.stats.pearsonr(x, y)Copyright © 2021 Codeinu
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