Class 12 Informatics Practices Practical file 2024-2025

Class 12 Informatics Practices Practical file 2024-2025

1.Create a Series with following lists:

section=[‘A’,’B’,’C’,’D’,’E’]  #INDEXES
strength=[40,38,33,35,30]  #DATA

import pandas as pd
import numpy as np
section=['A','B','C','D','E']
strength=[40,38,33,35,30]
s=pd.Series(index=section,data=strength)
print(s)

Output

A   40
B   38
C   33
D   35
E   30
dtype: int64

2. Create a Series by using following dictionary:

Department={‘Computer’:30,’Finance’:20,’HR’:5}
strength=[40,38,33,35,30]  #DATA

import pandas as pd
import numpy as np
Department={'Computer':30,'Finance':20,'HR':5}
s=pd.Series(Department)
print(s)

Output

Computer 30
Finance 20
HR 5
dtype: int64

3. Display those values from series which are more than 80.

import pandas as pd
import numpy as np
Data=[89,34,45,90,33,45,67,91]
s=pd.Series(Data)
print(s[s>80])

Output

0   89
3   90
7   91
dtype: int64

4. Create a DataFrame by using following dictionary:

Items={ ‘Havels’:{‘Fan’:100,’LED’:20,’Sockets’:50,’Switches’:45},
‘Goldmetal’:{‘Fan’:150,’LED’:30,’Sockets’:40,’Switches’:50},
‘Sunrise’:{‘Fan’:80,’LED’:25,’Sockets’:35,’Switches’:55} }

import pandas as pd
import numpy as np
Items={
'Havels':{'Fan':100,'LED':20,'Sockets':50,'Switches':45},
'Goldmetal':{'Fan':150,'LED':30,'Sockets':40,'Switches':50},
'Sunrise':{'Fan':80,'LED':25,'Sockets':35,'Switches':55}
}
d=pd.DataFrame(Items)
print('Details of Items')
print(d)

Output

Details of Items
Havels Goldmetal Sunrise
Fan 100 150 80
LED 20 30 25
Sockets 50 40 35
Switches 45 50 55

5.  Create a dataframe using given dictionary and find number of records column wise as well as row wise.

Doctors={ ‘Fever’:{‘Amit’:20,’Rahul’:30,’Radhika’:15,’Naina’:50},
‘Cold’:{‘Amit’:10,’Rahul’:13,’Radhika’:5,’Naina’:52},
‘Typhoid’:{‘Amit’:12,’Rahul’:10,’Radhika’:35,’Naina’:np.NAN}}

import pandas as pd 
import numpy as np 
Doctors={ 'Fever':{'Amit':20,'Rahul':30,'Radhika':15,'Naina':50},\
'Cold':{'Amit':10,'Rahul':13,'Radhika':5,'Naina':52},\
'Typhoid':{'Amit':12,'Rahul':10,'Radhika':35,'Naina':np.NAN}} 
d=pd.DataFrame(Doctors)
print(d)
print('Number of records (columns wise)') 
print(d.count())
print('Number of records (row wise)') 
print(d.count(axis=1))

Output

          Fever Cold Typhoid
Amit      20     10    12.0
Rahul     30     13    10.0
Radhika   15      5    35.0
Naina     50      52   NaN
Number of records (columns wise)
Fever 4
Cold 4
Typhoid 3
dtype: int64
Number of records (row wise)
Amit 3
Rahul 3
Radhika 3
Naina 2
dtype: int64

6. Create dataframe by using given dictionary and find sum of quantity row wise as well as column wise. 

Items={ ‘Havels’:{‘Fan’:100,’LED’:20,’Sockets’:50,’Switches’:45},
‘Goldmetal’:{‘Fan’:150,’LED’:30,’Sockets’:40,’Switches’:50},
‘Sunrise’:{‘Fan’:80,’LED’:25,’Sockets’:35,’Switches’:55} }

import pandas as pd
import numpy as np
Items={
'Havels':{'Fan':100,'LED':20,'Sockets':50,'Switches':45},
'Goldmetal':{'Fan':150,'LED':30,'Sockets':40,'Switches':50},
'Sunrise':{'Fan':80,'LED':25,'Sockets':35,'Switches':55}
}
print("=====================")
print('Sum of quantity Brand wise')
print(d.sum())

print("=====================")
print('Sum of quantity - Item wise')
print(d.sum(axis=1))

Output

Sum of quantity Brand wise
Havels 215
Goldmetal 270
Sunrise 195
dtype: int64
=====================
Sum of quantity - Item wise
Fan 330
LED 75
Sockets 125
Switches 150
dtype: int64

7. Create a DataFrame by using following dictionary and display marks of ‘Sunita’ only:

Students={
‘Ramesh’:{‘Eng’:80,’Maths’:66,’Hindi’:78,’AI’:90},
‘Sunita’:{‘Eng’:74,’Maths’:86,’Hindi’:82,’AI’:92},
‘Naresh’:{‘Eng’:56,’Maths’:93,’Hindi’:88,’AI’:99},
}

import pandas as pd
import numpy as np
Students={
'Ramesh':{'Eng':80,'Maths':66,'Hindi':78,'AI':90},
'Sunita':{'Eng':74,'Maths':86,'Hindi':82,'AI':92},
'Naresh':{'Eng':56,'Maths':93,'Hindi':88,'AI':99},
}
d=pd.DataFrame(Students)

print("Marks of Sunita")
for i,row in d.iterrows():
  print(i,row['Sunita'])

Output

Marks of Sunita
Eng 74
Maths 86
Hindi 82
AI 92

8. Create a DataFrame by using following dictionary and display average marks in all subjects:

Students={
‘Ramesh’:{‘Eng’:80,’Maths’:66,’Hindi’:78,’AI’:90},
‘Sunita’:{‘Eng’:74,’Maths’:86,’Hindi’:82,’AI’:92},
‘Naresh’:{‘Eng’:56,’Maths’:93,’Hindi’:88,’AI’:99},
}

import pandas as pd
import numpy as np
Students={
'Ramesh':{'Eng':80,'Maths':66,'Hindi':78,'AI':90},
'Sunita':{'Eng':74,'Maths':86,'Hindi':82,'AI':92},
'Naresh':{'Eng':56,'Maths':93,'Hindi':88,'AI':99},
}
d=pd.DataFrame(Students)
print("Subject wise average marks")
print(d.mean(axis=1))

Output

Eng 70.000000
Maths 81.666667
Hindi 82.666667
AI 93.666667
dtype: float64

9. Create a DataFrame by using following dictionary and display degree wise maximum marks:

Data={
‘name’:[‘Ram’,’Sham’,’Seeta’,’Harish’,’Anoop’,’Sahil’],
‘degree’:[‘BCA’,’BCA’,’BCA’,’MCA’,’MBA’,’BCA’],
‘marks’:[87,56,90,89,57,88]
}

import pandas as pd
import numpy as np
Data={
'name':['Ram','Sham','Seeta','Harish','Anoop','Sahil'],
'degree':['BCA','BCA','BCA','MCA','MBA','BCA'],
'marks':[87,56,90,89,57,88]
}
d=pd.DataFrame(Data)
print(d.groupby('degree')['marks'].max())

Output

BCA 90
MBA 57
MCA 89
Name: marks, dtype: int64

10. Create a DataFrame by using following dictionary and display number of students who have opted ‘BCA’:

Data={
‘name’:[‘Ram’,’Sham’,’Seeta’,’Harish’,’Anoop’,’Sahil’],
‘degree’:[‘BCA’,’BCA’,’BCA’,’MCA’,’MBA’,’BCA’],
‘marks’:[87,56,90,89,57,88]
}

import pandas as pd
import numpy as np
Data={
'name':['Ram','Sham','Seeta','Harish','Anoop','Sahil'],
'degree':['BCA','BCA','BCA','MCA','MBA','BCA'],
'marks':[87,56,90,89,57,88]
}
print('Number of students in BCA:')
print(d.groupby('degree')['marks'].count()['BCA'])

Output

Number of students in BCA:
4

11. Replace all negative values in dataframe with -1.

import pandas as pd
s1={
'Data1':[2,3,-5,4,-9],
'Data2':[-4,7,-2,3,5]
}
df=pd.DataFrame(s1)
print(df)

print('After replacing negative values with -1')
df[df<0]=-1
print(df)

Output

Data1    Data2
0     2       -4
1     3        7
2    -5       -2
3     4        3
4    -9        5
After replacing negative values with -1
     Data1   Data2
0     2       -1
1     3        7
2    -1       -1
3     4        3
4    -1        5

12. Replace all missing values in dataframe with 0.

import pandas as pd
s1={
'Data1':[2,3,np.NAN,4,np.NAN],
'Data2':[-4,7,-2,3,np.NAN]
}
df=pd.DataFrame(s1)
print(df)

print('After replacing missing values with 0')
df=df.fillna(0)
print(df)

Output

    Data1    Data2
0    2.0     -4.0
1    3.0      7.0
2    NaN     -2.0
3    4.0      3.0
4    NaN      NaN
After replacing missing values with 0
    Data1    Data2
0    2.0     -4.0
1    3.0      7.0
2    0.0     -2.0
3    4.0      3.0
4    0.0      0.0

13. Save dataframe data into a csv file named hospital.csv

import pandas as pd 
import numpy as np 
Doctors={ 'Fever':{'Amit':20,'Rahul':30,'Radhika':15,'Naina':50},\
'Cold':{'Amit':10,'Rahul':13,'Radhika':5,'Naina':52},\
'Typhoid':{'Amit':12,'Rahul':10,'Radhika':35,'Naina':np.NAN}} 
df=pd.DataFrame(Doctors)
df.to_csv("hospital.csv")
print("File created")

Output

File Created

14. Reading data from csv file named hospital.csv into dataframe and display it.

import pandas as pd 
df=pd.read_csv("hospital.csv")
print('Content of file:')
print(df)

Output

File Created

15. Python program to save data from a datagram to MYSQL table marks.

import pandas as pd
import mysql.connector as sqltor
mycon=sqltor.connect(host="localhost",user="root",passwd="root",database="data")
cur=mycon.cursor()

Marks={
'rollno':[1,2,3,4,5,6],
'name':['Aman','Suman','Raman','Ayush','Ramesh','Rajan'],
'Percentage':[67,74.7,98.2,56.6,98.1,89]
}

d=pd.DataFrame(Marks)
for (r,rowdata) in d.iterrows():
r=rowdata[0]
n=rowdata[1]
m=rowdata[2]
cur.execute("insert into marks values(%d,%s,%f);"%(r,n,m,))
mycon.commit()
mycon.close()
print("Record Saved")

Output

Record Saved

16. Python program to read and display records from MYSQL table student.

import pandas as pd
import mysql.connector as sqltor
mycon=sqltor.connect(host="localhost",user="root",passwd="root",database="data")
if mycon.is_connected():
df=pd.read_sql("Select * from student",mycon)
   print(df)
else:
   print("Connection not established")
mycon.close()

Output

 rollno    name    marks
0    1        aman    98.0
1    2        Raman   85.0
2    3        Suman   58.0
3    4        Manu    95.0
4    5        Raju    94.0

17. Python program to read and display records from MYSQL table student where marks are more than 90.

import pandas as pd
import mysql.connector as sqltor
mycon=sqltor.connect(host="localhost",user="root",passwd="root",database="data")
if mycon.is_connected():
df=pd.read_sql("Select * from student where marks>90",mycon)
   print(df)
else:
   print("Connection not established")
mycon.close()

Output

    rollno    name    marks
0    1        aman    98.0
1    4        Manu    95.0
2    5        Raju    94.0

18. Python program to display bar graph on the basis of two lists Months=[‘Jan’,’Feb’,’Mar’,’Apr’]
and Attendance=[87,68,90,78]

import matplotlib.pyplot as plt
Months=['Jan','Feb','Mar','Apr']
Attendance=[87,68,90,78]
plt.bar(Months,Attendance,align="center",color="green")
plt.show()

Output

Class 12 Informatics Practices Practical file 2024-2025

19. Python program to display pie chart on the basis of two lists:

State=[‘Punjab’,’Jharkhand’,’Rajasthan’,’UP’]
Density=[72,62,45,78]

import matplotlib.pyplot as plt
State=['Punjab','Jharkhand','Rajasthan','UP']
Density=[72,62,45,78]
plt.pie(Density,labels=State)
plt.show()

Output

Class 12 Informatics Practices Practical file 2024-2025

20. Write SQL statement to create a table named student with following structure.

Field Name                Data Type                             Size
rollno                   int                                     3     
Name                     Varchar                                 20
Marks                    Float
Class                    Varchar                                 10

Answer

Create Table Student(rollno int(3), Name Varchar(20), Marks Float, Class Varchar(10));

21. Write SQL statements to insert following records in the above table.

1    Amandeep      5000        XII
2    Raman         4500        XII 
3    Joel          3500        X
4    Radhika       4800        XI
5    Suman         4200        XI
Answer
Insert into Student values(1, 'Amandeep',5000,'XII');
Insert into Student values(2, 'Raman',4500,'XII');
Insert into Student values(3, 'Joel',3000,'X');
Insert into Student values(4, 'Radhika',4800,'XI');
Insert into Student values(5, 'Suman',4200,'XI');

22. Write SQL statements to display all records from the above table.

Select * from student;

23. Write SQL statements to increase marks of all students by 5.

Update student set marks=marks+5;

24. Write SQL statements to delete record of student ‘Raman’.

Delete from student where name='Raman';

24. Write SQL statements to find number of students in each class.

Select class, count(*) from student group by class;

25. Write SQL statements to list name and marks of all students in descending order of their marks.

Select name, marks from student order by marks desc;

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