{"id":53021,"date":"2024-04-15T23:47:56","date_gmt":"2024-04-15T23:47:56","guid":{"rendered":"https:\/\/exam.pscnotes.com\/mcq\/?p=53021"},"modified":"2024-04-15T23:47:56","modified_gmt":"2024-04-15T23:47:56","slug":"cov-and-corr-supports-the-optional-min_periods-keyword","status":"publish","type":"post","link":"https:\/\/exam.pscnotes.com\/mcq\/cov-and-corr-supports-the-optional-min_periods-keyword\/","title":{"rendered":"cov and corr supports the optional min_periods keyword."},"content":{"rendered":"

[amp_mcq option1=”TRUE” option2=”nan” option3=”nan” option4=”nan” correct=”option2″]<\/p>\n

The correct answer is False<\/strong>.<\/p>\n

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

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

“`
\nimport pandas as pd<\/p>\n

x = pd.Series([1, 2, 3, 4, 5])
\ny = pd.Series([6, 7, 8, 9, 10])<\/p>\n

print(x.cov(y))<\/p>\n

\n
\n

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