A small table of measurements is stored row by row: header names the columns and each row in rows holds one value per column. Write column_means(header, rows) that returns a dictionary mapping each column name to the mean of that column.
Examples
Input: header = ["height", "weight"]
rows = [[170, 65], [180, 80], [160, 59]]
Output: {"height": 170.0, "weight": 68.0}
Input: header = ["t"], rows = [[1.5], [2.0]]
Output: {"t": 1.75}
Floats are compared with a tolerance of 1e-6.
Constraints
1 <= len(header) <= 100,1 <= len(rows) <= 10**4- Every row has exactly
len(header)numbers; column names are distinct.
Goals
- Turn rows into columns with `zip(*rows)`
- Pair names with values using `dict(zip(names, values))`
- Compute a mean per column in one comprehension