Python Statistics Standard Score

09 Sep 2019

Standard Scores

import pandas as pd

standard_score = pd.DataFrame({"subject": ["A", "B", "C", "D", "E", "F", "G", "H", "I", "J",
                                           "K", "L", "M", "N", "O", "P", "Q", "R", "S", "T"],
                               "raw score": [10, 9, 3, 10, 9, 2, 2, 10, 5, 5,
                                             1, 6, 8, 6, 6, 1, 3, 6, 10, 8]})

standard_score
subject raw score
0 A 10
1 B 9
2 C 3
3 D 10
4 E 9
5 F 2
6 G 2
7 H 10
8 I 5
9 J 5
10 K 1
11 L 6
12 M 8
13 N 6
14 O 6
15 P 1
16 Q 3
17 R 6
18 S 10
19 T 8 
mean = 6

sd = 3.18


computing standard score

def score_calculate(raw_score):
    return round(((raw_score - mean) / sd), 2)

standard_score["standard score"] = standard_score["raw score"].apply(score_calculate)

standard_score
subject raw score standard score
0 A 10 1.26
1 B 9 0.94
2 C 3 -0.94
3 D 10 1.26
4 E 9 0.94
5 F 2 -1.26
6 G 2 -1.26
7 H 10 1.26
8 I 5 -0.31
9 J 5 -0.31
10 K 1 -1.57
11 L 6 0.00
12 M 8 0.63
13 N 6 0.00
14 O 6 0.00
15 P 1 -1.57
16 Q 3 -0.94
17 R 6 0.00
18 S 10 1.26
19 T 8 0.63


mean of standard score

round(standard_score["standard score"].mean(), 2)

0.0


standard deviation of standard score

round(standard_score["standard score"].std(), 2)

1.0
 python  statistics

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