Analyzing Student Performance

Note

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Description: You are a data analyst at a school, and you need to analyze the performance of students in a mathematics exam. The school has provided you with a dataset containing the scores of 50 students in three subjects: Algebra, Geometry, and Calculus.

Your task is to:

• Load the dataset into a Pandas DataFrame
• Calculate the average score for each subject
• Identify the top 5 students with the highest overall score (average of all three subjects)
• Create a new column to indicate whether each student passed or failed the exam (passing score is 80 or higher)

# import libraries
import pandas as pd
import numpy as np
import sys
 
print('Python version ' + sys.version)
print('Pandas version ' + pd.__version__)
print('Numpy version ' + np.__version__)

Python version 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]
Pandas version 2.2.1
Numpy version 1.26.4

The Data §

The dataset contains information about the performance of 50 students in a mathematics exam, covering three subjects: Algebra, Geometry, and Calculus.

Columns: §

• Student_ID: A unique identifier for each student (integer).
• Algebra: The student’s score in Algebra (integer).
• Geometry: The student’s score in Geometry (integer).
• Calculus: The student’s score in Calculus (integer).

Student_ID	Algebra	Geometry	Calculus
1	85	90	78
2	92	88	95
3	78	75	82
4	95	92	89
5	88	85	92
6	76	78	85
7	89	91	96
8	83	80	88
9	90	95	92
10	81	83	86
11	86	89	93
12	93	96	91
13	84	82	89
14	91	94	97
15	79	81	87
16	87	86	90
17	94	97	95
18	82	84	91
19	96	98	99
20	80	79	83
21	98	99	100
22	75	77	81
23	92	93	98
24	89	92	95
25	77	76	80
26	95	96	98
27	81	82	87
28	90	91	94
29	78	80	84
30	97	99	100
31	84	86	90
32	91	94	97
33	76	78	82
34	88	90	93
35	82	84	89
36	96	98	100
37	85	87	92
38	79	81	86
39	93	95	98
40	80	82	85
41	87	89	94
42	94	96	99
43	83	85	91
44	92	94	97
45	77	79	83
46	90	92	95
47	86	88	93
48	98	100	100
49	81	83	88
50	95	97	99

df = pd.read_clipboard()
df

	Student_ID	Algebra	Geometry	Calculus
0	1	85	90	78
1	2	92	88	95
2	3	78	75	82
3	4	95	92	89
4	5	88	85	92
5	6	76	78	85
6	7	89	91	96
7	8	83	80	88
8	9	90	95	92
9	10	81	83	86
10	11	86	89	93
11	12	93	96	91
12	13	84	82	89
13	14	91	94	97
14	15	79	81	87
15	16	87	86	90
16	17	94	97	95
17	18	82	84	91
18	19	96	98	99
19	20	80	79	83
20	21	98	99	100
21	22	75	77	81
22	23	92	93	98
23	24	89	92	95
24	25	77	76	80
25	26	95	96	98
26	27	81	82	87
27	28	90	91	94
28	29	78	80	84
29	30	97	99	100
30	31	84	86	90
31	32	91	94	97
32	33	76	78	82
33	34	88	90	93
34	35	82	84	89
35	36	96	98	100
36	37	85	87	92
37	38	79	81	86
38	39	93	95	98
39	40	80	82	85
40	41	87	89	94
41	42	94	96	99
42	43	83	85	91
43	44	92	94	97
44	45	77	79	83
45	46	90	92	95
46	47	86	88	93
47	48	98	100	100
48	49	81	83	88
49	50	95	97	99

# make sure data types look good
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 50 entries, 0 to 49
Data columns (total 4 columns):
 #   Column      Non-Null Count  Dtype
---  ------      --------------  -----
 0   Student_ID  50 non-null     int64
 1   Algebra     50 non-null     int64
 2   Geometry    50 non-null     int64
 3   Calculus    50 non-null     int64
dtypes: int64(4)
memory usage: 1.7 KB

# calculate the average score for each subject
df['median'] = df[['Algebra', 'Geometry', 'Calculus']].median(axis=1) # I usually choose the median over the average
 
# identify the top 5 students with the highest overall score (average of all three subjects)
df.sort_values(by='median', ascending=False).head()

	Student_ID	Algebra	Geometry	Calculus	median
47	48	98	100	100	100.0
29	30	97	99	100	99.0
20	21	98	99	100	99.0
18	19	96	98	99	98.0
35	36	96	98	100	98.0

# create a new column to indicate whether each student passed or failed the exam (passing score is 80 or higher)
df['passFail'] = df['median'].apply(lambda x: "pass" if x >= 80 else "fail")
df

	Student_ID	Algebra	Geometry	Calculus	median	passFail
0	1	85	90	78	85.0	pass
1	2	92	88	95	92.0	pass
2	3	78	75	82	78.0	fail
3	4	95	92	89	92.0	pass
4	5	88	85	92	88.0	pass
5	6	76	78	85	78.0	fail
6	7	89	91	96	91.0	pass
7	8	83	80	88	83.0	pass
8	9	90	95	92	92.0	pass
9	10	81	83	86	83.0	pass
10	11	86	89	93	89.0	pass
11	12	93	96	91	93.0	pass
12	13	84	82	89	84.0	pass
13	14	91	94	97	94.0	pass
14	15	79	81	87	81.0	pass
15	16	87	86	90	87.0	pass
16	17	94	97	95	95.0	pass
17	18	82	84	91	84.0	pass
18	19	96	98	99	98.0	pass
19	20	80	79	83	80.0	pass
20	21	98	99	100	99.0	pass
21	22	75	77	81	77.0	fail
22	23	92	93	98	93.0	pass
23	24	89	92	95	92.0	pass
24	25	77	76	80	77.0	fail
25	26	95	96	98	96.0	pass
26	27	81	82	87	82.0	pass
27	28	90	91	94	91.0	pass
28	29	78	80	84	80.0	pass
29	30	97	99	100	99.0	pass
30	31	84	86	90	86.0	pass
31	32	91	94	97	94.0	pass
32	33	76	78	82	78.0	fail
33	34	88	90	93	90.0	pass
34	35	82	84	89	84.0	pass
35	36	96	98	100	98.0	pass
36	37	85	87	92	87.0	pass
37	38	79	81	86	81.0	pass
38	39	93	95	98	95.0	pass
39	40	80	82	85	82.0	pass
40	41	87	89	94	89.0	pass
41	42	94	96	99	96.0	pass
42	43	83	85	91	85.0	pass
43	44	92	94	97	94.0	pass
44	45	77	79	83	79.0	fail
45	46	90	92	95	92.0	pass
46	47	86	88	93	88.0	pass
47	48	98	100	100	100.0	pass
48	49	81	83	88	83.0	pass
49	50	95	97	99	97.0	pass

BONUS §

def pass_fail(score):
    if score >= 80:
        return "pass"
    else:
        return "fail"
 
# calculate pass fail on all 3 subjects
label = df[['Algebra', 'Geometry', 'Calculus']].map(pass_fail)
label.head()

	Algebra	Geometry	Calculus
0	pass	pass	fail
1	pass	pass	pass
2	fail	fail	pass
3	pass	pass	pass
4	pass	pass	pass

# merge the two dataframes
df.merge(right=label,left_index=True, right_index=True)

	Algebra	Geometry	Calculus
0	pass	pass	fail
1	pass	pass	pass
2	fail	fail	pass
3	pass	pass	pass
4	pass	pass	pass

Summary: §

The tutorial demonstrated how to analyze a dataset of student performance in a mathematics exam using Pandas. It covered importing libraries, loading data, checking data types, calculating average scores, identifying top performers, and creating new columns to indicate pass/fail status.

Key Takeaways: §

• Loading data from clipboard into a Pandas DataFrame
• Checking data types and info using df.info() and df.head()
• Calculating median/average scores using df.median() or df.mean()
• Creating new columns using df['new_column']
• Sorting data using df.sort_values() and selecting top rows using df.head()
• Applying conditional logic using apply() and lambda functions (e.g., pass/fail status)
• Merging DataFrames using df.merge()
• Using map function to apply pass/fail logic to individual subjects