XII Informatics Practices Practical List with Solution 2019-20 (Python - New Course)

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XII Informatics Practices Practical List with Solution 2019-20 (Python - New Course)
S. No.
Practical Details
Date of Practical
Data handling using Python libraries
1.       
Task: Write a python code to create a dataframe with suitable headings from the list given below :
[['K1', 'Mohanbari', 2019], ['K2', 'Imphal',2013], ['K3', 'Tawang', 2018]]
Python Code:
import pandas as pd
# initialize list of lists
data = [['K1', 'KV Mohanbari', 2003], ['K2', 'KV Imphal',1996], ['K3', 'Tawang', 2001]]
# Create the pandas DataFrame
df = pd.DataFrame(data, columns = ['ID', 'KV_Name', 'Established'])
# print DataFrame.
print(df )

Output:
   ID                  KV_Name        Established
0  K1    KV Mohanbari   2003
1  K2                KV Imphal                    1996
2  K3      Tawang         2001

2.       
Task: Consider a Data Frame, where each row contains the item category, item name, and expenditure.
·         Group the rows by the category, and print the total expenditure per category.
Python Code:
import pandas as pd
itemname=['Ball Point Pen','Permanent Marker Pen','Whiteboard Marker Pen', 'A3 Paper Sheet','A4 Paper Sheet','Diary Large','Diary Small',           'Notebook Large- Ruled','Notebook Medium- Ruled']
itemCategory=['Pen','Marker','Marker','Paper','Paper','Diary','Diary',               'Notebook','Notebook']
price=[20.00,40.00,50.00,320.00,300.00,189.00,90.50,180.00,120.00]
data = {'Item Category': itemCategory, 'Item Name': itemname, 'Expenditure': price}
df_Company = pd.DataFrame(data)
grpBy1 = df_Company.groupby(by='Item Category')
print(grpBy1.sum())

Output:
                        Expenditure
Item Category            
Diary                           279.5
Marker                        90.0
Notebook         300.0
Paper                           620.0
Pen                              20.0

3.       
Task: Given a Series, print all the elements that are above the 75th percentile.
Python Code:
import pandas as pd
stdPercent = [1,2,3,4,5,6,7,8,9,10]
s=pd.Series(stdPercent)
per_75=s.quantile(.75)
print("75th Percentile:",per_75)
print("List of elements of Series that are above the 75th percentile: ")
for i in range(len(s)):
    if s[i]>=per_75:
        print(s[i])

Output:
75th Percentile: 7.75
List of elements of Series that are above the 75th percentile:
8
9
10

4.       
Task: Given few details of students data, aggregate it. Print the highest, lowest, and mean of marks obtained by students.
Python Code:
import pandas as pd
dct = {"AdmNo": [1,2,3,4],"Name":['Aman','Rahul', 'Saurab','Zoya'], "Class":[12,9,11,12], "Gender":['B','B','B','G'], "Marks": [13, 13, 50, 85] }
df= pd.DataFrame(dct, index=['A', 'B', 'C', 'D'])
print("Maximum marks: ", df['Marks'].max())
print("Minimum marks: ", df['Marks'].min())
print("Mean/Average marks: ", df['Marks'].mean())

Output:
Maximum marks:  85
Minimum marks:  13
Mean/Average marks:  40.25

5.       
Task: Find the Category wise sum and count of all the items using pivot_table() of pandas library:
itemname=['Ball Point Pen','Permanent Marker Pen','Whiteboard Marker Pen', 'A3 Paper Sheet','A4 Paper Sheet','Diary Large','Diary Small',           'Notebook Large- Ruled','Notebook Medium- Ruled']
itemCategory=['Pen','Marker','Marker','Paper','Paper','Diary','Diary',               'Notebook','Notebook']
price=[20.00,40.00,50.00,320.00,300.00,189.00,90.50,180.00,120.00]
data = {'Item Category': itemCategory, 'Item Name': itemname, 'Expenditure': price}

Python Code:
import pandas as pp
itemname=['Ball Point Pen','Permanent Marker Pen','Whiteboard Marker Pen', 'A3 Paper Sheet','A4 Paper Sheet','Diary Large','Diary Small',           'Notebook Large- Ruled','Notebook Medium- Ruled']
itemCategory=['Pen','Marker','Marker','Paper','Paper','Diary','Diary',               'Notebook','Notebook']
price=[20.00,40.00,50.00,320.00,300.00,189.00,90.50,180.00,120.00]
data = {'Item Category': itemCategory, 'Item Name': itemname, 'Expenditure': price}
df=pp.DataFrame(data)
print(df)
pvt = pp.pivot_table(df, index=['Item Category'], values=['Expenditure'],
aggfunc=['sum' ,'count'])
print(pvt)

Output:
  Item Category            Item Name                             Expenditure
0           Pen                 Ball Point Pen                          20.0
1        Marker                          Permanent Marker Pen         40.0
2        Marker                          Whiteboard Marker Pen         50.0
3         Paper                A3 Paper Sheet                       320.0
4         Paper                A4 Paper Sheet                       300.0
5         Diary                Diary Large                             189.0
6         Diary                Diary Small                             90.5
7      Notebook             Notebook Large- Ruled           180.0
8      Notebook             Notebook Medium- Ruled     120.0

                         sum                count
                        Expenditure     Expenditure
Item Category                       
Diary               279.5               2
Marker             90.0                2
Notebook        300.0                2
Paper                           620.0               2
Pen                              20.0                 1

6.       
Task: Write a Pandas program to select the name of persons whose height is between 5 to 5.5 (both values inclusive)
'name': ['Asha', 'Radha', 'Kamal', 'Divy', 'Anjali'],
'height': [ 5.5, 5, np.nan, 5.9, np.nan],
'age': [11, 23, 22, 33, 22]
Python Code:
import pandas as pd
import numpy as np
pers_data = {'name': ['Asha', 'Radha', 'Kamal', 'Divy',
'Anjali'],
'height': [ 5.5, 5, np.nan, 5.9, np.nan],
'age': [11, 23, 22, 33, 22]}
labels = ['a', 'b', 'c', 'd', 'e']
df = pd.DataFrame(pers_data , index=labels)
print("Persons whose height is between 5 and 5.5")
print(df[(df['height']>= 5 )& (df['height']<= 5.5)])

Output:
Persons whose height is between 5 and 5.5
    name  height  age
a   Asha     5.5   11
b  Radha     5.0   23

7.       
Task: Write a Python program to find the total marks obtained and percentage of marks of each student.
Python Code:
import pandas as pd
dct = {"SID":[1,5,7,8], "SNAME":["A", "B", "C","D"],"MARKS1":[80,90,20,50],
       "MARKS2":[80,90,20,50], "MARKS3":[80,90,20,50]}
df=pd.DataFrame(dct)

for i in df.index:
    m1 = df.iloc[i]["MARKS1"]
    m2 = df.iloc[i]["MARKS2"]
    m3 = df.iloc[i]["MARKS3"]
    total = m1+m2+m3
    per = total/3
    print("Total marks obtained by ", df.iloc[i]["SNAME"],": ", total)
    print("Percentage of ", df.iloc[i]["SNAME"],": ", per)
    print("------------------------------------------------")
   
Output:
Total marks obtained by  A :  240
Percentage of  A :  80.0
------------------------------------------------
Total marks obtained by  B :  270
Percentage of  B :  90.0
------------------------------------------------
Total marks obtained by  C :  60
Percentage of  C :  20.0
------------------------------------------------
Total marks obtained by  D :  150
Percentage of  D :  50.0
------------------------------------------------

8.       
Task: Write Python code to perform the join, slice & subset operations on numpy arrays.
Python Code:
import numpy as np
ar1 = np.array([1,2,3,4,5,6,7,8])
ar2 = np.array([7,6,5,4,3,2,1,0])
print("Slice operation: ")
print(ar1[0:len(ar1):2])
print(ar2[3:5])
print("Subset operation: ")
ar3, ar4= np.split(ar1, 2)
ar5, ar6 = np.split(ar2, 2)
print(ar3)
print(ar4)
print("Join operation: ")
print(np.hstack((ar3, ar4)))
print(np.vstack((ar3, ar5)))

Output:
Slice operation:
[1 3 5 7]
[4 3]
Subset operation:
[1 2 3 4]
[5 6 7 8]
Join operation:
[1 2 3 4 5 6 7 8]
[[1 2 3 4]
 [7 6 5 4]]

9.       
Task: Write a NumPy program to append values to the end of an array. Expected Output:
Original array:
[10, 20, 30]
After append values to the end of the array:
[10 20 30 40 50 60 70 80 90]
Python Code:
import numpy as np
x = [10, 20, 30]
print("Original array:")
print(x)
x = np.append(x, [40, 50, 60,70, 80, 90])
print("After append values to the end of the array:")
print(x)

Output:
Original array:
[10, 20, 30]
After append values to the end of the array:
[10 20 30 40 50 60 70 80 90]

10.   
Task: Write Python program to perform the basic arithmetic operations on ndarrays (numpy arrays).
Python Code:
import numpy as np
arr1 = np.array([[1,2,3], [1,2,3], [7,2,3]])
arr2 = np.array([[1,2,3], [4,5,6], [7,8,9]])
print("Array Addition: ")
print(np.add(arr1,arr2))
print("Array Difference: ")
print(np.subtract(arr1,arr2))
print("Array Division: ")
print(np.divide(arr1,2))
print("Array Multiplication: ")
print(np.multiply(arr1,arr2))
Output:
Array Addition:
[[ 2  4  6]
 [ 5  7  9]
 [14 10 12]]
Array Difference:
[[ 0  0  0]
 [-3 -3 -3]
 [ 0 -6 -6]]
Array Division:
[[0.5 1.  1.5]
 [0.5 1.  1.5]
 [3.5 1.  1.5]]
Array Multiplication:
[[ 1  4  9]
 [ 4 10 18]
 [49 16 27]]

11.   
Task: Write Python code to find the Covariance and Correlation of given data.
Python Code:
import numpy as np
data=np.array([12,13,14,15,16])
print("Covariance: ")
print(np.cov(data,ddof=0))
ar1=np.array([1,2,3,4,5,6])
ar2=np.array([11,12,3,4,5,6])
print("Correlation: ")
print(np.corrcoef(ar1,ar2))

Output:
Covariance:
2.0
Correlation:
[[ 1.         -0.63906444]
 [-0.63906444  1.        ]]

12.   
Task: Write Python code to plot bar graph of the following set of data:
prog_lanugages = ('Python', 'C/C++', 'Java', 'Javascript', 'C#', 'PHP')
y_pos = np.arange(len(prog_lanugages))
Python Code:
import matplotlib.pyplot as plt;
import numpy as np
prog_lanugages = ('Python', 'C/C++', 'Java', 'Javascript', 'C#', 'PHP')
y_pos = np.arange(len(prog_lanugages))
use_percent = [30,6,19,8,7,6]
plt.bar(y_pos, use_percent, align='center', alpha=0.5)
plt.xticks(y_pos, prog_lanugages)
plt.ylabel('Usage %')
plt.xlabel('Programming language')
plt.title('Programming language usage percentage (2019)')
plt.show()

Output:

13.   
Objective: To plot Histogram
Task: Write Python code to plot a Histogram of the following data (No of Students vs. Percentage obtained ):
Roll: [1,2,3,4,5,6,7,8,9,10,11,12]
Percent: [12,95,80,55,67,89,90,39,60,94,77,46]
Python Code:
import pandas as pd
import matplotlib.pyplot as plt
dict1 = {"Roll":[1,2,3,4,5,6,7,8,9,10,11,12], "Percent":[12,95,80,55,67,89,90,39,60,94,77,46]}
df = pd.DataFrame(dict1)
df1 = df["Percent"].sort_values()
plt.hist(df1, bins=[0,10,20,30,40,50,65,70,80,90,100],edgecolor='red')
plt.yticks(range(0,6))
plt.xlabel("Percentage Range")
plt.ylabel("No of Students")
plt.title('Result Analysis- XII KVC')
plt.show()

Output:

14.   
Task: Write a NumPy program to create a 3x3 identity matrix, i.e. diagonal elements are 1, the rest are 0. Replace all 0 to random number from 10 to
20.
Python Code:
import numpy as np
array1=np.identity(3)
print("Identitry Matrix: ")
print(array1)
x=np.where(array1==0)
for i in x:
 array1[x]=np.random.randint(low=10,high=20)
print("Matrix after replacement: ")
print(array1)

Output:
Identitry Matrix:
[[1. 0. 0.]
 [0. 1. 0.]
 [0. 0. 1.]]
Matrix after replacement:
[[ 1. 15. 15.]
 [15.  1. 15.]
 [15. 15.  1.]]

15.   
Task: Write Python code to draw a Box plot of the following data:
IP_Marks = [79, 99,75,35,89,76,100,59,14]
Python Code:
import matplotlib.pyplot as plt
IP_Marks = [79, 99,75,35,89,76,100,59,14]
plt.boxplot(IP_Marks)
plt.ylabel("Marks Obtained")
plt.title("IP MARKS - Box Plot")
plt.show()

Output:

16.   
Task: Write Python code to draw a Scatter plot of the following data:
x_Ball = [1,2,3,4,5,6]
y_Run = [3,1,0,4,6,4]
Python Code:
import matplotlib.pyplot as plt
x_Ball = [1,2,3,4,5,6]
y_Run = [3,1,0,4,6,4]
plt.scatter(x_Ball, y_Run)
plt.xlabel("Ball No.")
plt.ylabel("Runs Scored")
plt.title("Over Details")
plt.show()

Output:

Data Management: SQL + web-server
17.   
Objective: Understanding of SQL Commands & SQL Aggregate function.
Task A: Write SQL query to Create a table Student with following details:
Field
Data Type (Size)
StudentId
int (11)
Name
varchar(30)
Gender
char (1)
Marks
decimal(4,1)
SQL Query:  CREATE TABLE Student (Studentid INT(11), Name VARCHAR(30), Gender CHAR(1), Marks DECIMAL(4,1));

Table: Student
StudentId
Name
Gender
Marks
1
Subhash Goyal
M
91
2
Nidhi Sharma
F
92
3
Manab Das
M
99
4
Jyoti Baruah
F
97
5
George Jacob
M
87
6
Sunit Saha
M
76
6
Aman Kumar
M
74
9
Ankita Phukan
F
89
10
Abhinash Lepcha
M
94

Task B: Write a SQL query to insert the first record in the Student table.
SQL Query: INSERT INTO Student VALUES (1, ‘Subhash Goyal’, ‘M’,91);

Task C: Write a SQL query to display the names and marks of all those students who have secured marks above 80.
SQL Query: SELECT Name, Marks FROM Student WHERE Marks > 80;

Task D: Write a SQL query to display roll numbers and marks of students who have secured marks in the range 70 to 80 (including 70 and 80).
SQL Query: SELECT Rollno, Name, Marks FROM Student WHERE Marks
BETWEEN 70 AND 80;

Task E: Write a SQL query to display rows from the table Student with names starting with 'A'.
SQL Query: SELECT * FROM Student WHERE Name LIKE 'A%';

Task F: Write a SQL query to order the (student ID, marks) table in descending order of the marks.
SQL Query: SELECT StudentID, Marks from Student ORDER BY Marks DESC;

Task G: Write a SQL query to change the marks of ‘Manab Das’ from 99 to 100.
SQL Query: UPDATE Student SET Marks = 100 WHERE Name =’Manab Das’;

Task H: Write a SQL query to find the highest marks obtained by any student.
SQL Query: SELECT MAX(Marks) FROM Student;

Task I: Write a SQL query to find the lowest marks obtained by a male student.
SQL Query: SELECT MIN(Marks) FROM Student WHERE Gender = ‘M’;

Task J: Write a SQL query to find the average marks obtained by female students.
SQL Query: SELECT AVG(Marks) FROM Student WHERE Gender = ‘F’;

Task K: Write a SQL query to find the sum of marks obtained by students.
SQL Query: SELECT SUM(Marks) FROM Student;

Task L: Write a SQL query to find the total number of male and female students in Student table.
SQL Query: SELECT COUNT(*) FROM Student GROUP BY Gender;

18.   
Task: Write a Python program to create a database “KVS” to show the integration of SQL with Python by importing MYSQL DB. Also list all the databases using Python code.
Python Code:
import mysql.connector as sql
con = sql.connect(host="localhost", user="root", passwd="tiger")
curs = con.cursor()
query ="CREATE DATABASE KVS;"
print("Database created.")
print("List of Databases: ")
query1 = "SHOW DATABASES;"
curs.execute(query1)
for i in curs:
    print(i)

Output:
Database created.
List of Databases:
('information_schema',)
('kvs',)
('mysql',)
('test',)

19.   
Task: Write a Python program to create a Table “Student” to show the integration of SQL with Python by importing MYSQL DB. Details of columns of tables:
Field/ Column
Data Type (Size)
Roll
int
Name
varchar(30)
Gender
char (1)
Class
int

Python Code:
import mysql.connector as sql
con = sql.connect(host="localhost", user="root", passwd="tiger", database="kvs")
curs = con.cursor()
query ="CREATE TABLE Student (Roll int, Name varchar(30), Gender char(1), Class int);"
print("Table created.")
curs.execute(query)
print("List of Tables: ")
query1 = "SHOW TABLES;"
curs.execute(query1)
for i in curs:
    print(i)

Output:
Table created.
List of Tables:
('student',)

20.   
Task: Write a Python program to insert following records in table “Student” to show the integration of SQL with Python by importing MYSQL DB:
Roll
Name
Gender
Class
1
AMAN YADAV
M
12
2
SIDDHI BARUAH
F
12

Python code:
import mysql.connector as sql
con = sql.connect(host="localhost", user="root", passwd="tiger", database="kvs")
curs = con.cursor()
query ="INSERT INTO Student VALUES(1, 'AMAN YADAV', 'M', 12);"
curs.execute(query)
query1 ="INSERT INTO Student VALUES(2, 'SIDDHI BARUAH', 'F', 12);"
curs.execute(query1)
con.commit()
print("Record inserted.")

Output:
Record inserted.

21.   
Task: Write a Python program to display all records of table “Student” to show the integration of SQL with Python by importing MYSQL DB.
Python Code:
import mysql.connector as sql
con = sql.connect(host="localhost", user="root", passwd="tiger", database="kvs")
curs = con.cursor()
query ="SELECT * FROM Student;"
curs.execute(query)
records = curs.fetchall()
for i in records:
    print (i)

Output:
(1, 'AMAN YADAV', 'M', 12)
(2, 'SIDDHI BARUAH', 'F', 12)

22.   
Task: Write a Django based web server to parse a user request (POST), and write it to a CSV file.
Python Code:

Output:

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