CBSE Class 12 Informatics Practices Practical File, Python and SQL with Output

The CBSE Class 12 Informatics Practices (065) practical is 30 marks, and the report file needs both pandas programs and SQL queries. Every one below was executed before publishing — the pandas output is what pandas printed, and the SQL output is what the database returned.

import pandas as pd
marks = pd.Series([78, 91, 84, 66], index=["Aarav", "Diya", "Ishaan", "Meera"])
print(marks)
print("Highest:", marks.max(), "scored by", marks.idxmax())

Aarav     78
Diya      91
Ishaan    84
Meera     66
dtype: int64
Highest: 91 scored by Diya

import pandas as pd
data = {"Name": ["Aarav", "Diya", "Ishaan", "Meera"],
        "CS": [78, 91, 84, 66],
        "IP": [82, 88, 79, 71]}
df = pd.DataFrame(data)
print(df)
print("Shape:", df.shape)

     Name  CS  IP
0   Aarav  78  82
1    Diya  91  88
2  Ishaan  84  79
3   Meera  66  71
Shape: (4, 3)

import pandas as pd
df = pd.DataFrame({"Name": ["Aarav", "Diya"], "CS": [78, 91]})
df["Result"] = ["Pass" if m >= 33 else "Fail" for m in df["CS"]]
df.loc[len(df)] = ["Ishaan", 84, "Pass"]
print(df)

     Name  CS Result
0   Aarav  78   Pass
1    Diya  91   Pass
2  Ishaan  84   Pass

import pandas as pd
df = pd.DataFrame({"Marks": [72, 85, 72, 90, 66]})
print("Mean  :", df["Marks"].mean())
print("Median:", df["Marks"].median())
print("Mode  :", df["Marks"].mode()[0])

Mean  : 77.0
Median: 72.0
Mode  : 72

import pandas as pd
df = pd.DataFrame({"Name": ["Aarav", "Diya", "Ishaan", "Meera"],
                   "Marks": [78, 91, 45, 66]})
print("Scored 70 or more:")
print(df[df["Marks"] >= 70])

Scored 70 or more:
    Name  Marks
0  Aarav     78
1   Diya     91

import pandas as pd
df = pd.DataFrame({"Name": ["Aarav", "Diya"], "Marks": [78, 91]})
df.to_csv("result.csv", index=False)
back = pd.read_csv("result.csv")
print(back)

    Name  Marks
0  Aarav     78
1   Diya     91

import pandas as pd
df = pd.DataFrame({"Name": ["Aarav", "Diya", "Ishaan"], "Marks": [78, 91, 45]})
s = df.sort_values("Marks", ascending=False).reset_index(drop=True)
print(s)

     Name  Marks
0    Diya     91
1   Aarav     78
2  Ishaan     45

import pandas as pd
df = pd.DataFrame({"Section": ["A", "B", "A", "B"],
                   "Marks": [78, 91, 66, 85]})
print(df.groupby("Section")["Marks"].mean())

Section
A    72.0
B    88.0
Name: Marks, dtype: float64

CREATE TABLE student(rollno INTEGER, name TEXT, marks INTEGER);
INSERT INTO student VALUES (1,'Aarav',78),(2,'Diya',91),(3,'Ishaan',45);
SELECT * FROM student;

rollno   name   marks
------  ------  -----
     1  Aarav      78
     2  Diya       91
     3  Ishaan     45

CREATE TABLE student(rollno INTEGER, name TEXT, marks INTEGER);
INSERT INTO student VALUES (1,'Aarav',78),(2,'Diya',91),(3,'Ishaan',45);
SELECT COUNT(*) AS total, MAX(marks) AS highest,
       MIN(marks) AS lowest, ROUND(AVG(marks),2) AS average FROM student;

total  highest  lowest  average
-----  -------  ------  -------
    3       91      45    71.33

CREATE TABLE emp(name TEXT, dept TEXT, salary INTEGER);
INSERT INTO emp VALUES ('Aarav','Sales',30000),('Diya','Sales',42000),
                       ('Ishaan','IT',55000),('Meera','IT',48000);
SELECT dept, COUNT(*) AS staff, AVG(salary) AS avg_salary
FROM emp GROUP BY dept HAVING COUNT(*) > 1;

dept   staff  avg_salary
-----  -----  ----------
IT         2     51500.0
Sales      2     36000.0

CREATE TABLE student(rollno INTEGER, name TEXT, marks INTEGER);
INSERT INTO student VALUES (1,'Aarav',78),(2,'Diya',91),(3,'Aditi',66);
SELECT name, marks FROM student WHERE name LIKE 'A%' ORDER BY marks DESC;

name   marks
-----  -----
Aarav     78
Aditi     66

Related: Class 12 IP syllabus · IP 065 weightage · IP sample papers

Every program on this page was executed and its output captured from the run. Curated by Kajal Ma'am (MCA), teaching computer subjects since 2004.