Excel vs. Sci Kit Learn Linear Regression or scipy.stats Provide Different Slopes, Intercepts, R2 Values

I cannot figure out why I get different values for slope, intercept, and r2 values from excel vs. scikit learn (or scipy.stats!). This is a very simple linear regression, literally six "x" values and six "y" values. I use Excel all the time for regression, but can’t figure out why the numbers don’t match.

import numpy as np
from scipy import stats as stats

my_ys = [0, 0.700287879, 1.475575758, 2.266727273, 2.973575758, 3.749106061]
my_xs = [0, 2.5, 5, 7.5, 10, 12.5]


array_y = np.array(my_ys)
array_x = np.array(my_xs)
slope, intercept, rvalue, pvalue, stderr = stats.linregress(array_x, array_y)

my_r2 = rvalue**2

print("r2 = ", my_r2)
print("intercept is: ", intercept)
print("slope is: ", slope)

Produces:

r2 =  0.9997292041335445
intercept is:  -0.021731601619047636
slope is:  0.3012176623657143

Similarly:

from sklearn.linear_model import LinearRegression
import numpy as np 

my_ys = [0, 0.700287879, 1.475575758, 2.266727273, 2.973575758, 3.749106061]
my_xs = [0, 2.5, 5, 7.5, 10, 12.5]

array_y = np.array(my_ys)
array_x = np.array(my_xs).reshape((-1,1))

#create the model
my_model = LinearRegression().fit(array_x, array_y)

#find the fit
r2_of_standard_regression = my_model.score(array_x, array_y)

#give the intercept or b value of the regression
b_intercept_of_standard_regression = my_model.intercept_

#give the slope or m of the y=mx+b
m_slope_of_standard_regression = my_model.coef_


print("r2 = ", r2_of_standard_regression)
print("intercept is: ", b_intercept_of_standard_regression)
print("slope is: ", m_slope_of_standard_regression)

Produces:

r2 =  0.999729204133545
intercept is:  -0.021731601619047636
slope is:  [0.30121766]

However, when I use Excel (either Linest, or just a fit to a scatter plot), I get different values:

Output from Excel

If the image doesn’t show:

M = 0.309692595
B = -0.048802217
r2 = 0.998402196

Why is it different? And, how can I get Python to match my Excel numbers?