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Accessing a value in a 2D arrayAccessing columns of a 2D arrayAccessing rows of a 2D arrayCalculating the determinant of a matrixChecking allowed values for a NumPy data typeChecking if a NumPy array is a view or copyChecking the version of NumPyChecking whether a NumPy array contains a given rowComputing Euclidean distance using NumpyConcatenating 1D arraysConverting array to lowercaseConverting type of NumPy array to stringCreating a copy of an arrayDifference between Python List and Numpy arrayDifference between the methods array_equal and array_equivDifference between the methods mod and fmodDifference between the methods power and float_powerFinding the closest value in an arrayFinding the Index of Largest Value in a Numpy ArrayFinding the Index of Smallest Value in a Numpy ArrayFinding the most frequent value in a NumPy arrayFlattening Numpy arraysGetting constant PiGetting elements from a two dimensional array using two dimensional array of indicesGetting indices of N maximum valuesGetting indices of N minimum valuesGetting the number of columns of a 2D arrayGetting the number of non-zero elements in a NumPy arrayGetting the number of rows of a 2D arrayInitializing an array of onesInitializing an array of zerosInitializing an identity matrixLimiting array values to a certain rangePerforming linear regressionPrinting full or truncated NumPy arrayPrinting large Numpy arrays without truncationRemoving rows containing NaN in a NumPy arrayReversing a NumPy arraySaving NumPy array to a fileShape of Numpy ArraysSorting value of one array according to anotherSuppressing scientific notation
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Accessing a value in a 2D arrayAccessing columns of a 2D arrayAccessing rows of a 2D arrayCalculating the determinant of a matrixChecking allowed values for a NumPy data typeChecking if a NumPy array is a view or copyChecking the version of NumPyChecking whether a NumPy array contains a given rowComputing Euclidean distance using NumpyConcatenating 1D arraysConverting array to lowercaseConverting type of NumPy array to stringCreating a copy of an arrayDifference between Python List and Numpy arrayDifference between the methods array_equal and array_equivDifference between the methods mod and fmodDifference between the methods power and float_powerFinding the closest value in an arrayFinding the Index of Largest Value in a Numpy ArrayFinding the Index of Smallest Value in a Numpy ArrayFinding the most frequent value in a NumPy arrayFlattening Numpy arraysGetting constant PiGetting elements from a two dimensional array using two dimensional array of indicesGetting indices of N maximum valuesGetting indices of N minimum valuesGetting the number of columns of a 2D arrayGetting the number of non-zero elements in a NumPy arrayGetting the number of rows of a 2D arrayInitializing an array of onesInitializing an array of zerosInitializing an identity matrixLimiting array values to a certain rangePerforming linear regressionPrinting full or truncated NumPy arrayPrinting large Numpy arrays without truncationRemoving rows containing NaN in a NumPy arrayReversing a NumPy arraySaving NumPy array to a fileShape of Numpy ArraysSorting value of one array according to anotherSuppressing scientific notation
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Performing linear regression in NumPy

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schedule Mar 10, 2022
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Linear regression, in essence, is about computing the line of best fit given some data points. We can use NumPy's polyfit(~) method to find this line of best fit easily.

Here's some toy dataset, which we will visualize using matplotlib:

import matplotlib.pyplot as plt

x = [1,2,4,5]
y = [1,4,5,6]
plt.scatter(x, y)
plt.show()

This produces the following:

Our goal is to fit a linear line through the data points. We do this by using Numpy's polyfit(~) method:

fitted_coeff = np.polyfit(x, y, deg=1)
print(fitted_coeff)
array([1.1, 0.7])

Here, the deg=1 just means that we want to fit a degree 1 polynomial, that is, the line y=mx+b. The returned values are the coefficients of the line of best fit, and the first value is the coefficient of the largest degree, that is, m=1.1 and b=0.7.

Let's graph the line of best fit to see how good it is:

x = [1,2,4,5]
y = [1,4,5,6]
plt.scatter(x, y)

line_x = np.linspace(1, 5, 100)
plt.plot(line_x, line_x * fitted_coeff[0] + fitted_coeff[1])

plt.show()

This produces the following:

This looks like a solid fit.

WARNING

Numpy's polyfit(~) method is just for computing the line of best fit

Numpy's polyfit(~) method does not compute any statistical measures like residuals and p-values. This method is only used when you just need the coefficients - nothing more, nothing less.

To perform linear regression at a more comprehensive level, use scipy.stats.linregress.

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Published by Isshin Inada
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