The numpy function std() computes the sample standard deviation of a list or an array. Consider...

 

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The numpy function std() computes the sample standard deviation of a list or an array. Consider the following list: [4, 11, 5, 12, 12, 5, 3, 6, 10] Compute the standard deviation with the numpy function std(). Then round your answer to three decimals using np.round(x, 3), where x is the result of your computation, and report the output. The numpy function cumprod() computes the cumulative product of elements along a given axis. If you have a one-dimensional list or array, it is simply the cumulative product until each item. For example, np. cumprod([1, 2, 3, 4]) would yield an answer of array([1, 2, 6, 24]). You are given the following two-dimensional array: two_dim = np.array([[1, 3, 5], [-1, 1, -1]]) Compute the cumprod() function along the axis-0. Report the last element in the second row. The numpy function std() computes the sample standard deviation of a list or an array. Consider the following list: [4, 11, 5, 12, 12, 5, 3, 6, 10] Compute the standard deviation with the numpy function std(). Then round your answer to three decimals using np.round(x, 3), where x is the result of your computation, and report the output. The numpy function cumprod() computes the cumulative product of elements along a given axis. If you have a one-dimensional list or array, it is simply the cumulative product until each item. For example, np. cumprod([1, 2, 3, 4]) would yield an answer of array([1, 2, 6, 24]). You are given the following two-dimensional array: two_dim = np.array([[1, 3, 5], [-1, 1, -1]]) Compute the cumprod() function along the axis-0. Report the last element in the second row.