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NumPy | corrcoef method
schedule Aug 12, 2023
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Numpy's corrcoef(~)
method computes the Pearson's correlation coefficient given two arrays:
$$r_{xy}=\frac{\sum^n_{i=1}(x_i-\bar{x})(y_i-\bar{y})}{\sqrt{\sum^n_{i=1}(x_i-\bar{x})^2\sum^n_{i=1}(y_i-\bar{y})^2}}=\frac{\mathrm{cov}(x,y)}{\sqrt{\mathrm{var}(x)\cdot\mathrm{var}(y)}}$$
Parameters
1. x
| array-like
Each row represents a separate column data (i.e. a variable). See the example below for clarification.
2. y
| array-like
| optional
Instead of specifying your dataset as a new row in x, you can just specify it as y
.
3. rowvar
| boolean
| optional
If True, then each row in x represents a variable. If False, then each column in x represents a variable. By default, rowvar=True
.
Return value
A Numpy array that contains the correlation coefficients of the given dataset.
Examples
Basic usage
np.corrcoef([2,3,5,6],[3,5,8,12])
array([[1. , 0.97913005], [0.97913005, 1. ]])
This is exactly the same as specifying a 2D array:
np.corrcoef([[2,3,5,6],[3,5,8,12]])
array([[1. , 0.97913005], [0.97913005, 1. ]])
Published by Isshin Inada
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