Compute three measures of a nonempty one-dimensional numeric dataset. The mean is:
xˉ=n1i=1∑nxiThe median is the middle sorted value, or the average of the two middle values when n is even. The mode is the most frequent value. If several values share the highest frequency, choose the smallest. Return mean, median, and mode in a dictionary of Python floats.
Input: x = [1, 2, 3, 4, 5]
Output: {"mean": 3.0, "median": 3.0, "mode": 1.0}
Explanation: Every value appears once, so the smallest value wins the mode tie.
Input: x = [1, 2, 2, 3, 4]
Output: {"mean": 2.4, "median": 2.0, "mode": 2.0}
Input: x = [1, 2, 3, 4]
Output: {"mean": 2.5, "median": 2.5, "mode": 1.0}
Use np.mean and np.median for the first two values.
Count values with Counter, find the highest frequency, then take the smallest matching key.
Accepts: array
Compute three measures of a nonempty one-dimensional numeric dataset. The mean is:
xˉ=n1i=1∑nxiThe median is the middle sorted value, or the average of the two middle values when n is even. The mode is the most frequent value. If several values share the highest frequency, choose the smallest. Return mean, median, and mode in a dictionary of Python floats.
Input: x = [1, 2, 3, 4, 5]
Output: {"mean": 3.0, "median": 3.0, "mode": 1.0}
Explanation: Every value appears once, so the smallest value wins the mode tie.
Input: x = [1, 2, 2, 3, 4]
Output: {"mean": 2.4, "median": 2.0, "mode": 2.0}
Input: x = [1, 2, 3, 4]
Output: {"mean": 2.5, "median": 2.5, "mode": 1.0}
Use np.mean and np.median for the first two values.
Count values with Counter, find the highest frequency, then take the smallest matching key.
Accepts: array