Centred on Zero

10 pts · 10 coins

Machine Learning in CodeMedium

A running club has a table of members with columns on very different scales. You want to see one member's numbers after standard scaling.

Task: write standardise_row(table, row).

table is a dict mapping column names to lists of numbers.
Scale all of the table's columns with StandardScaler. Each column is centred on 0 and divided by its own spread.
Return the scaled values of row number row (counting from 0), as a list in column order, each rounded to 2 decimal places.

table = {"weight": [110, 130, 150, 160, 170], "sweet": [2, 3, 6, 7, 8]}

row = 0

returns [-1.58, -1.38]

table = {"km_per_week": [5, 20, 35, 50], "age": [25, 31, 40, 52], "races": [0, 2, 1, 5]}

row = 3

returns [1.34, 1.47, 1.6]

solution.py

1import pandas as pd
2from sklearn.preprocessing import StandardScaler
3
4
5def standardise_row(table, row):
6    pass
7