The correct answer is False.

The cov and corr functions in Pandas do not support the optional min_periods keyword. This keyword is used to specify the minimum number of observations required to calculate the covariance or correlation. If the min_periods keyword is not specified, the covariance or correlation will be calculated using all available observations.

The following code shows an example of how to calculate the covariance and correlation of two series, x and y, using the cov and corr functions:

“`
import pandas as pd

x = pd.Series([1, 2, 3, 4, 5])
y = pd.Series([6, 7, 8, 9, 10])

print(x.cov(y))

14.0

print(x.corr(y))

0.8660254037844386

“`

The cov function returns the covariance of x and y, which is 14.0. The corr function returns the correlation of x and y, which is 0.8660254037844386.

If we try to specify the min_periods keyword, we will get an error:

“`
print(x.cov(y, min_periods=2))

ValueError: min_periods must be >= 2

“`

This is because the cov and corr functions require at least two observations to calculate the covariance or correlation.