Class 12 - Computer Science Pre Board Oct 2021 Result

Max Marks : 35

S.No.	Name	Marks
1	Sameer Saha	12
2	Ajit Kumar	30
3	Kundan Kumar Rana	31
4	Abhishek	23
5	Imran Khan	27
6	Manish Singh	28
7	Atul	29
8	Abhay Pratap Dubey	29
9	Prince	27
10	Akash Verma	21
11	Vikas	28
12	Pratham	28
13	Sagar Upadhyay	28
14	Shailove Singh	25
15	Yugal Kishor Tiwari	24
16	Devesh Singh	30
17	Mayank Saraswat	27
18	Abhishek Jha	25
19	Yash Raj	29
20	Siddharth	21
21	Mahesh Kumar	15
22	Ujjval Jha	26
23	Sujeet Kumar	20
24	Lucky Bisht	20
25	Deepak Kumar Yadav	22
26	Anurag	25
27	Mohd Saad	15
28	Shubham Chaurasia	24
29	Nikhil	21
30	Nirdev	23
31	Manish Singh	22
32	Sumit	20
33	Vansh Tyagi	22
34	Gaurav	30
35	Aksh Gupta	30
36	Jwala Gupta	30

 

 

General Questions of Computer - 2

1. Name of 5 Mobile Payment App

Popular online payment apps or payment apps or e - wallet list in India

·        Paytm.

·        Google Pay.

·        PhonePe.

·        BHIM.

·        Amazon Pay.

·        JIO Money.

·        Freecharge.

·        Yono SBI.

·        Airtel Money.

·        Payzapp.

2. Full form of:

BHIM - Bharat Interface for Money

YONO - You Only Need One

UPI - Unified Payments Interface

IFSC - Indian Financial System Code

NEFT - National Electronic Funds Transfer

RTGS - Real-Time Gross Settlement

MICR - Magnetic Ink Character Recognition

RAM - Random Access Memory

ROM - Read Only Memory

ASCII - American Standard Code For Information Interchange

UTF - Unicode Transformation Format

ISCII - Indian Script Code for Information Interchange

TFT - Thin Film Transistor

CRT – Cathode Ray Tube.

LCD - Liquid Crystal Display

LED – Light Emitting Diode

CPU - Central Processing Unit

CU – Control Unit

ALU - Arithmetic Logic Unit

OMR - Optical Mark Recognition

PS/2 - Personal System/2

XP - Experience

3. Name of 5 E-Commerce Website.

·        Amazon

·        Flipkart

·        Snapdeal

·        Myntra

·        PayTm

·        Shopclues

 

4. Name of 5 Educational Website.

·        W3 School

·        Byju’s

·        Unacademy

·        S P SHARMA CLASSES

·        Geeksforgeeks

·        Tutorialspoint

·        JavaTpoint

    ·        TgtPgtCS

    ·        GBSSS

5. Name of 5 Application Software.

Types of Application Software

 

Application Software Type	Examples
Word processing software	MS Word, WordPad and Notepad
Database software	MySql, Oracle, MS Access etc
Spreadsheet software	Apple Numbers, Microsoft Excel
Multimedia software	Real Player, VLC Media Player
Presentation Software	Microsoft PowerPoint, Keynotes

 

6. Name of 5 E-Mail Website

·        Gmail,

·        Yahoo

·        Outlook

·        AOL

·        Zoho

·        Mail.Com

·        ProtonMail.

 

7. Name of 5 Video Editing Apps

·        Magisto.

·        Hyperlapse.

·        Wondershare Filmora.

·        InShot.

·        WeVideo.

·        Splice.

·        Adobe Premiere Rush.

·        Blender.

 

8. Name of 5 Shorts Video Social Apps

·        Instagram

·        Josh

·        Moj

·        MX TakaTak

·        Chingari

·        Mitron

 

9. Name of 5 Computer Games

·        League of Legends

·        Counter-Strike: Global Offensive

·        GTA V (Grand Theft Auto V)

·        PUBG

·        Game of Thrones: Beyond the Wall

10. Name of 5 IT Companies of India

·        Tata Consultancy Services (TCS)

·        Infosys

·        Wipro

·        HCL Technologies

·        Tech Mahindra

Python Test

 

CLICK HERE TO START TEST

DataFrame iloc, loc, del, drop, pop in Python - 7

# Using loc and iloc 

 import pandas as pd

D={"Name":["Ram","Raj","Sam","John","Amit"],"Roll No.":[10,20,30,40,50], \

   "IP":[25,30,32,21,22]}

df=pd.DataFrame(D)

print(df)

print(df.iloc[1:3])

print(df.loc[1:3])

Output:

Name  Roll No.  IP

0   Ram        10  25

1   Raj        20  30

2   Sam        30  32

3  John        40  21

4  Amit        50  22

  Name  Roll No.  IP

1  Raj        20  30

2  Sam        30  32

   Name  Roll No.  IP

1   Raj        20  30

2   Sam        30  32

3  John        40  21

#Using of pop in DataFrame

import pandas as pd

D={"Name":["Ram","Raj","Sam","John","Amit"],"Roll No.":[10,20,30,40,50], \

   "IP":[25,30,32,21,22]}

df=pd.DataFrame(D)

print(df)

print (df.pop('IP'))

Output:

Name  Roll No.  IP

0   Ram        10  25

1   Raj        20  30

2   Sam        30  32

3  John        40  21

4  Amit        50  22

0    25

1    30

2    32

3    21

4    22

Name: IP, dtype: int64

#Using drop in DataFrame

import pandas as pd

D={"Name":["Ram","Raj","Sam","John","Amit"],"Roll No.":[10,20,30,40,50], \

   "IP":[25,30,32,21,22]}

df=pd.DataFrame(D)

print(df)

print (df.drop('Name',axis=1))

Output:

Name  Roll No.  IP

0   Ram        10  25

1   Raj        20  30

2   Sam        30  32

3  John        40  21

4  Amit        50  22

Roll No.  IP

0        10  25

1        20  30

2        30  32

3        40  21

4        50  22

#Using del in DataFrame

import pandas as pd

D={"Name":["Ram","Raj","Sam","John","Amit"],"Roll No.":[10,20,30,40,50], \

   "IP":[25,30,32,21,22]}

df=pd.DataFrame(D)

print(df)

del df['Name']

print(df)

Output:

Name  Roll No.  IP

0   Ram        10  25

1   Raj        20  30

2   Sam        30  32

3  John        40  21

4  Amit        50  22

Roll No.  IP

0        10  25

1        20  30

2        30  32

3        40  21

4        50  22

DataFrame in Python - 6

1.

 #Add New Column in DataFrame using df['columnname']

import pandas as pd

D={"Name":["Ram","Raj","Sam","John","Amit"],"Roll No.":[10,20,30,40,50]}

df=pd.DataFrame(D)

print(df)

df["IP"]=[25,30,32,21,22]

df["Maths"]=[22,20,22,31,32]

df["Total"]=df["IP"]+df["Maths"]

df["Pecentage"]=df["Total"]*100/70

print(df)

Output:

   Name  Roll No.

0   Ram        10

1   Raj        20

2   Sam        30

3  John        40

4  Amit        50

   Name  Roll No.  IP  Maths  Total  Pecentage

0   Ram        10  25     22     47  67.142857

1   Raj        20  30     20     50  71.428571

2   Sam        30  32     22     54  77.142857

3  John        40  21     31     52  74.285714

4  Amit        50  22     32     54  77.142857

2.

#Add New Column in DataFrame using insert() function

import pandas as pd

D={"Name":["Ram","Raj","Sam","John","Amit"],"Roll No.":[10,20,30,40,50]}

df=pd.DataFrame(D)

print(df)

df.insert(2,"IP",[25,30,32,21,22])

df.insert(3,"Maths",[22,20,22,31,32])

df["Total"]=df["IP"]+df["Maths"]

df["Pecentage"]=df["Total"]*100/70

print(df)

Output:

   Name  Roll No.

0   Ram        10

1   Raj        20

2   Sam        30

3  John        40

4  Amit        50

   Name  Roll No.  IP  Maths  Total  Pecentage

0   Ram        10  25     22     47  67.142857

1   Raj        20  30     20     50  71.428571

2   Sam        30  32     22     54  77.142857

3  John        40  21     31     52  74.285714

4  Amit        50  22     32     54  77.142857

3.

Access a column from DataFrame using df['columnname'] and df.columnname

import pandas as pd

D={"Name":["Ram","Raj","Sam","John","Amit"],"Roll No.":[10,20,30,40,50], \

   "IP":[25,30,32,21,22]}

df=pd.DataFrame(D)

print(df)

print(df['IP'])

print(df.IP)

Output:

   Name  Roll No.  IP

0   Ram        10  25

1   Raj        20  30

2   Sam        30  32

3  John        40  21

4  Amit        50  22

0    25

1    30

2    32

3    21

4    22

Name: IP, dtype: int64

0    25

1    30

2    32

3    21

4    22

Name: IP, dtype: int64

4.

#selecting a column from a DataFrame using iloc

import pandas as pd

D={"Name":["Ram","Raj","Sam","John","Amit"],"Roll No.":[10,20,30,40,50], \

   "IP":[25,30,32,21,22]}

df=pd.DataFrame(D)

print(df)

print(df['IP'])

print(df['Roll No.'])

print (df.iloc[:,[1,2]])

Output

 ==========

   Name  Roll No.  IP

0   Ram        10  25

1   Raj        20  30

2   Sam        30  32

3  John        40  21

4  Amit        50  22

0    25

1    30

2    32

3    21

4    22

Name: IP, dtype: int64

0    10

1    20

2    30

3    40

4    50

Name: Roll No., dtype: int64

   Roll No.  IP

0        10  25

1        20  30

2        30  32

3        40  21

4        50  22

DataFrame in Python - 5

1. 

Add New Row in DataFrame 

import pandas as pd

D={'Name':['ritu','ajay','pankaj','aditya'],'English':[67,78,75,88],'Bio':[78,67,89,90],'Physics':[34,45,67,78],'Chem':[23,32,67,87]}

df=pd.DataFrame(D)

print(df)

#Add New Row in DataFrame

df.loc['4']={'Name':'udai','English':55 ,'Bio':98,'Physics':89 ,'Chem':56}

print(df)

Output:

========== RESTART: C:\Users\acer\AppData\Local\Programs\Python\Python39\DF4.py ==========

     Name  English  Bio  Physics  Chem

0    ritu       67   78       34    23

1    ajay       78   67       45    32

2  pankaj       75   89       67    67

3  aditya       88   90       78    87

     Name  English  Bio  Physics  Chem

0    ritu       67   78       34    23

1    ajay       78   67       45    32

2  pankaj       75   89       67    67

3  aditya       88   90       78    87

4    udai       55   98       89    56

2. 

Modify the row in DataFrame

import pandas as pd

D={'Name':['ritu','ajay','pankaj','aditya'],'English':[67,78,75,88],'Bio':[78,67,89,90],'Physics':[34,45,67,78],'Chem':[23,32,67,87]}

df=pd.DataFrame(D)

print(df)

#modify the row

df.loc[1]=["ajay",61,62,63,64]

print(df)

Output:

 ==========

     Name  English  Bio  Physics  Chem

0    ritu       67   78       34    23

1    ajay       78   67       45    32

2  pankaj       75   89       67    67

3  aditya       88   90       78    87

     Name  English  Bio  Physics  Chem

0    ritu       67   78       34    23

1    ajay       61   62       63    64

2  pankaj       75   89       67    67

3  aditya       88   90       78    87

>>> 

3.

Rename the column in DataFrame

import pandas as pd

D=[10,20,30,40,50]

df=pd.DataFrame(D)

print(df)

df.columns=["Roll No."]

print(df)

Output:

    0

0  10

1  20

2  30

3  40

4  50

   Roll No.

0        10

1        20

2        30

3        40

4        50

4. 

Rename Multiple columns in DataFrame

import pandas as pd

D={"Name":["Ram","Raj","Sam","John","Amit"],"Roll No.":[10,20,30,40,50]}

df=pd.DataFrame(D)

print(df)

df.columns=["Std_Name","Std_id"]

print(df)

Output:

   Name  Roll No.

0   Ram        10

1   Raj        20

2   Sam        30

3  John        40

4  Amit        50

  Std_Name  Std_id

0      Ram      10

1      Raj      20

2      Sam      30

3     John      40

4     Amit      50

import pandas as pd

D={"Name":["Raju","Raj","Ram"],"IP":[30,32,28],"Bio":[25,28,32]}

df=pd.DataFrame(D)

print(df)

df.rename(columns={"Bio":"Biology","IP":"Informatics Practices"},inplace=True)

print(df)

   Name  IP  Bio

0  Raju  30   25

1   Raj  32   28

2   Ram  28   32

   Name  Informatics Practices  Biology

0  Raju                     30       25

1   Raj                     32       28

2   Ram                     28       32

WAP in Python to create a calculator

#WAP in Python to create a calculator

a=int(input("Enter First Number: "))

b=int(input("Enter Second Number: "))

sum=a+b

sub=a-b

pro=a*b

div=a/b

fdiv=a//b

exp=a**b

rem=a%b

print("Sum=",sum)

print("Difference=",sub)

print("Product=",pro)

print("Division=",div)

print("Floor Division=",fdiv)

print("Exponational=",exp)

print("Modulus=",rem)

Output

Enter First Number: 22

Enter Second Number: 5

Sum= 27

Difference= 17

Product= 110

Division= 4.4

Floor Division= 4

Exponational= 5153632

Modulus= 2

2.

#WAP in Python to calculate simple interest

P=float(input("Enter the principal amount: "))

R=float(input("Enter rate of interest Yearly: "))

T=int(input("Enter the number of year: "))

SI=P*R*T/100

print("Simple Interest=",SI)

Output:

Enter the principal amount: 5000

Enter rate of interest Yearly: 24

Enter the number of year: 5

Simple Interest= 6000.0