Practical File Class 12 IP

 

Data Handling

Program 1: Create a panda’s series from a dictionary of values and a ndarray

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'''Python program to create a panda’s series
from a dictionary of values and a ndarray'''
import pandas as pd
import numpy as np
s=pd.Series(np.array([2,4,5,7,9,8,9]))
print(s)
'''Python program to create a panda’s series from a dictionary of values and a ndarray''' import pandas as pd import numpy as np s=pd.Series(np.array([2,4,5,7,9,8,9])) print(s)
'''Python program to create a panda’s series
from a dictionary of values and a ndarray'''

import pandas as pd

import numpy as np

s=pd.Series(np.array([2,4,5,7,9,8,9]))

print(s)

Output:

0 2
1 4
2 5
3 7
4 9
5 8
6 9
dtype: int32
>>>
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# import panda lib as pd
import pandas as pd
# creating a dictionary
dictionary={'A':10, 'B':20, 'C':30}
#creating a series
series=pd.Series(dictionary)
print(series)
# import panda lib as pd import pandas as pd # creating a dictionary dictionary={'A':10, 'B':20, 'C':30} #creating a series series=pd.Series(dictionary) print(series)
# import panda lib as pd
import pandas as pd

# creating a dictionary

dictionary={'A':10, 'B':20, 'C':30}

#creating a series

series=pd.Series(dictionary)

print(series)

Output:

A 10
B 20
C 30
dtype: int64
>>>

Program 2:Given a Series, print all the elements that are above the 75th percentile.

 

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# import panda lib as pd
import pandas as pd
import numpy as np
series=pd.Series(np.array([1,4,6,7,8,9,11]))
print (series)
result=series.quantile(q=0.75)
print()
print ("75 percentile of the series is : ")
print(result)
print()
print ("The Elements above 75 percentile is : ")
print(series[series>result])
# import panda lib as pd import pandas as pd import numpy as np series=pd.Series(np.array([1,4,6,7,8,9,11])) print (series) result=series.quantile(q=0.75) print() print ("75 percentile of the series is : ") print(result) print() print ("The Elements above 75 percentile is : ") print(series[series>result])
# import panda lib as pd
import pandas as pd
import numpy as np

series=pd.Series(np.array([1,4,6,7,8,9,11]))

print (series)

result=series.quantile(q=0.75)

print()

print ("75 percentile of the series is : ")

print(result)

print()

print ("The Elements above 75 percentile is : ")

print(series[series>result])

 

Output:

0 1
1 4
2 6
3 7
4 8
5 9
6 11
dtype: int32

75 percentile of the series is : 
8.5

The Elements above 75 percentile is : 
5 9
6 11
dtype: int32
>>>

 

Program 3: Create a Data Frame quarterly sales where each row contains the item category, item name, and expenditure. Group the rows by the category and print the total expenditure per category.

 

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# import panda lib as pd
import pandas as pd
dictionary={'item_cat':['Tata', 'Hyundai', 'Maruti', 'Renault'],
'item_name':['Harrier', 'KIA', 'Swift', 'Duster'],
'expenditure':[2000000, 700000, 900000, 1400000]}
quarter_sales=pd.DataFrame(dictionary)
print (quarter_sales)
q_sale=quarter_sales.groupby('item_cat')
print("Result after Filtering DataFrame")
print (q_sale['item_cat','expenditure'].sum())
# import panda lib as pd import pandas as pd dictionary={'item_cat':['Tata', 'Hyundai', 'Maruti', 'Renault'], 'item_name':['Harrier', 'KIA', 'Swift', 'Duster'], 'expenditure':[2000000, 700000, 900000, 1400000]} quarter_sales=pd.DataFrame(dictionary) print (quarter_sales) q_sale=quarter_sales.groupby('item_cat') print("Result after Filtering DataFrame") print (q_sale['item_cat','expenditure'].sum())
# import panda lib as pd
import pandas as pd

dictionary={'item_cat':['Tata', 'Hyundai', 'Maruti', 'Renault'],
            'item_name':['Harrier', 'KIA', 'Swift', 'Duster'],
            'expenditure':[2000000, 700000, 900000, 1400000]}

quarter_sales=pd.DataFrame(dictionary)


print (quarter_sales)

q_sale=quarter_sales.groupby('item_cat')

print("Result after Filtering DataFrame")

print (q_sale['item_cat','expenditure'].sum())

Output:

 item_cat item_name expenditure
0 Tata Harrier 2000000
1 Hyundai KIA 700000
2 Maruti Swift 900000
3 Renault Duster 1400000
Result after Filtering DataFrame

           expenditure
item_cat 
Hyundai 700000
Maruti 900000
Renault 1400000
Tata 2000000
>>>

Program 4:Create a data frame for examination result and display row labels, column labels data types of each column and the dimensions

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import pandas as pd
dictionary={'class':['I', 'II','III','IV','V','VI','VII','VIII','IX','X','XI','XII', ],
'pass_percentage':[100,100,100,100,100,100,100,100,96,99.10,98,97.7]}
result=pd.DataFrame(dictionary)
print (result)
print (result.dtypes)
print("shape of the dataframe is : ")
print (result.shape)
import pandas as pd dictionary={'class':['I', 'II','III','IV','V','VI','VII','VIII','IX','X','XI','XII', ], 'pass_percentage':[100,100,100,100,100,100,100,100,96,99.10,98,97.7]} result=pd.DataFrame(dictionary) print (result) print (result.dtypes) print("shape of the dataframe is : ") print (result.shape)
import pandas as pd

dictionary={'class':['I', 'II','III','IV','V','VI','VII','VIII','IX','X','XI','XII', ],
            'pass_percentage':[100,100,100,100,100,100,100,100,96,99.10,98,97.7]}

result=pd.DataFrame(dictionary)

print (result)

print (result.dtypes)

print("shape of the dataframe is : ")

print (result.shape)

Output:

class pass_percentage
0 I 100.0
1 II 100.0
2 III 100.0
3 IV 100.0
4 V 100.0
5 VI 100.0
6 VII 100.0
7 VIII 100.0
8 IX 96.0
9 X 99.1
10 XI 98.0
11 XII 97.7
class object
pass_percentage float64
dtype: object
shape of the dataframe is : 
(12, 2)
>>>

 

Program 5:Filter out rows based on different criteria such as duplicate rows.

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'''Python program to Filter out rows based
on different criteria such as duplicate rows'''
import pandas as pd
dic={'Name':['Ajay','Banti','Chandrkant','Deepak','Geeta','Harshit'],
'CS_Marks':[80,78,80,92,78,99]}
marks=pd.DataFrame(dic)
print(marks)
#Finding duplicate rows
print("Duplicate Rows : ")
duplicateRow = marks[marks.duplicated('CS_Marks',keep=False)]
print(duplicateRow)
'''Python program to Filter out rows based on different criteria such as duplicate rows''' import pandas as pd dic={'Name':['Ajay','Banti','Chandrkant','Deepak','Geeta','Harshit'], 'CS_Marks':[80,78,80,92,78,99]} marks=pd.DataFrame(dic) print(marks) #Finding duplicate rows print("Duplicate Rows : ") duplicateRow = marks[marks.duplicated('CS_Marks',keep=False)] print(duplicateRow)
'''Python program to Filter out rows based
on different criteria such as duplicate rows'''

import pandas as pd

dic={'Name':['Ajay','Banti','Chandrkant','Deepak','Geeta','Harshit'],
            'CS_Marks':[80,78,80,92,78,99]}

marks=pd.DataFrame(dic)

print(marks)

#Finding duplicate rows

print("Duplicate Rows : ")

duplicateRow = marks[marks.duplicated('CS_Marks',keep=False)]

print(duplicateRow)

Output:

CS_Marks Name
0 80 Ajay
1 78 Banti
2 80 Chandrkant
3 92 Deepak
4 78 Geeta
5 99 Harshit
Duplicate Rows : 
CS_Marks Name
0 80 Ajay
1 78 Banti
2 80 Chandrkant
4 78 Geeta
>>>

 

Program 6:Importing and exporting data between pandas and CSV file

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'''Importing data between pandas and CSV file'''
import pandas as pd
#make data frame
df=pd.read_csv("d:\std.csv")
print(df)
'''Importing data between pandas and CSV file''' import pandas as pd #make data frame df=pd.read_csv("d:\std.csv") print(df)
'''Importing data between pandas and CSV file'''
import pandas as pd

#make data frame

df=pd.read_csv("d:\std.csv")

print(df)

Output:

ROLL No Student Name Class Marks
0 1 Arnav III 100
1 2 Bikram V 98
2 3 Charu VII 99
3 4 Dhruv VIII 91
4 5 Harshit X 80
>>>

 

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'''Exporting data between pandas and CSV file'''
import pandas as pd
dic=[{'Name': 'Amit', 'Class': 12},
{'Name': 'Abhay', 'Class': 10},
{'Name': 'Harshit', 'Class': 9},
{'Name': 'Arnav', 'Class': 11}]
#make data frame
df_1=pd.DataFrame(dic)
# saving the data frame
df_1.to_csv('d:\Dataframe1.csv')
'''Exporting data between pandas and CSV file''' import pandas as pd dic=[{'Name': 'Amit', 'Class': 12}, {'Name': 'Abhay', 'Class': 10}, {'Name': 'Harshit', 'Class': 9}, {'Name': 'Arnav', 'Class': 11}] #make data frame df_1=pd.DataFrame(dic) # saving the data frame df_1.to_csv('d:\Dataframe1.csv')
'''Exporting data between pandas and CSV file'''

import pandas as pd

dic=[{'Name': 'Amit', 'Class': 12},
     {'Name': 'Abhay', 'Class': 10},
     {'Name': 'Harshit', 'Class': 9},
     {'Name': 'Arnav', 'Class': 11}]

#make data frame

df_1=pd.DataFrame(dic)

# saving the data frame

df_1.to_csv('d:\Dataframe1.csv')

 

5.2 Visualization

Program 7:Given the school result data, analyses the performance of the students on different parameters, e.g subject wise or class wise.

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import matplotlib.pyplot as plt
subject=['English Core','Mathematics', 'Physics', 'Chemistry', 'I.P.']
percentage=[83,95,70, 89, 100]
plt.bar(subject,percentage, align='center', color='green')
plt.xlabel('Subject Name')
plt.ylabel('Student Name')
plt.title('Result Analysis Bar Graph ')
plt.show()
import matplotlib.pyplot as plt subject=['English Core','Mathematics', 'Physics', 'Chemistry', 'I.P.'] percentage=[83,95,70, 89, 100] plt.bar(subject,percentage, align='center', color='green') plt.xlabel('Subject Name') plt.ylabel('Student Name') plt.title('Result Analysis Bar Graph ') plt.show()
import matplotlib.pyplot as plt

subject=['English Core','Mathematics', 'Physics', 'Chemistry', 'I.P.']

percentage=[83,95,70, 89, 100]

plt.bar(subject,percentage, align='center', color='green')

plt.xlabel('Subject Name')

plt.ylabel('Student Name')

plt.title('Result Analysis Bar Graph  ')

plt.show()

 

Output:

IP9

Program 8:For the Data frames created above, analyze, and plot appropriate charts with title and legend.

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import matplotlib.pyplot as plt
import numpy as np
place=['1st','2nd','3rd']
pcm_percentage=[80,90,100]
pcb_percenatge=[90,100,80]
comm_percentage=[100,90,80]
x=np.arange(len(place))
plt.bar(x,pcm_percentage, label='PCM', width=0.25, color='red')
plt.bar(x+.25,pcb_percenatge, label='PCB', width=0.25, color='green')
plt.bar(x+.50,comm_percentage, label='Commerce', width=0.25, color='yellow')
plt.xticks(x,place)
plt.xlabel('Position')
plt.ylabel('Percentage')
plt.title('Result Analysis Bar Graph')
plt.legend()
plt.show()
import matplotlib.pyplot as plt import numpy as np place=['1st','2nd','3rd'] pcm_percentage=[80,90,100] pcb_percenatge=[90,100,80] comm_percentage=[100,90,80] x=np.arange(len(place)) plt.bar(x,pcm_percentage, label='PCM', width=0.25, color='red') plt.bar(x+.25,pcb_percenatge, label='PCB', width=0.25, color='green') plt.bar(x+.50,comm_percentage, label='Commerce', width=0.25, color='yellow') plt.xticks(x,place) plt.xlabel('Position') plt.ylabel('Percentage') plt.title('Result Analysis Bar Graph') plt.legend() plt.show()
import matplotlib.pyplot as plt

import numpy as np

place=['1st','2nd','3rd']

pcm_percentage=[80,90,100]

pcb_percenatge=[90,100,80]

comm_percentage=[100,90,80]

x=np.arange(len(place))

plt.bar(x,pcm_percentage, label='PCM', width=0.25, color='red')

plt.bar(x+.25,pcb_percenatge, label='PCB', width=0.25, color='green')

plt.bar(x+.50,comm_percentage, label='Commerce', width=0.25, color='yellow')

plt.xticks(x,place)

plt.xlabel('Position')

plt.ylabel('Percentage')

plt.title('Result Analysis Bar Graph')

plt.legend()

plt.show()

Output:

p10

 

Program 9:Take data of your interest from an open source (e.g. data.gov.in), aggregate and summarize it. Then plot it using different plotting functions of the Matplotlib library.

 

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import pandas as pd
import matplotlib.pyplot as plt
dframe=pd.read_csv("D:\Covid_Vaccine.csv")
print(dframe)
import pandas as pd import matplotlib.pyplot as plt dframe=pd.read_csv("D:\Covid_Vaccine.csv") print(dframe)
import pandas as pd

import matplotlib.pyplot as plt

dframe=pd.read_csv("D:\Covid_Vaccine.csv")

print(dframe)

 

Output:

Program12

 

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import pandas as pd
import matplotlib.pyplot as plt
dframe=pd.read_csv("D:\Covid_Vaccine.csv")
slices=(dframe['TOTAL DOSES ADMINISTERED'].head(6))
states=(dframe['STATE/UTS'].head(6))
colours=['c','b','y','m','r','gold']
exp=[0,0,0,0,0,0.1]
plt.pie(slices, labels=states, colors=colours, startangle=90, explode=exp, shadow=True, autopct='%.1f%%')
plt.title('Vaccine Adminstrated ')
plt.legend()
plt.show()
import pandas as pd import matplotlib.pyplot as plt dframe=pd.read_csv("D:\Covid_Vaccine.csv") slices=(dframe['TOTAL DOSES ADMINISTERED'].head(6)) states=(dframe['STATE/UTS'].head(6)) colours=['c','b','y','m','r','gold'] exp=[0,0,0,0,0,0.1] plt.pie(slices, labels=states, colors=colours, startangle=90, explode=exp, shadow=True, autopct='%.1f%%') plt.title('Vaccine Adminstrated ') plt.legend() plt.show()
import pandas as pd

import matplotlib.pyplot as plt

dframe=pd.read_csv("D:\Covid_Vaccine.csv")

slices=(dframe['TOTAL DOSES ADMINISTERED'].head(6))

states=(dframe['STATE/UTS'].head(6))

colours=['c','b','y','m','r','gold']

exp=[0,0,0,0,0,0.1]

plt.pie(slices, labels=states, colors=colours, startangle=90, explode=exp, shadow=True, autopct='%.1f%%')

plt.title('Vaccine Adminstrated ')

plt.legend()

plt.show()

 

Output:

P9

 

Data Management

1. Create a student table with the student id, name, and marks as attributes where the student id is the primary key.

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CREATE TABLE Student
(
Student_ID int Primary Key,
Student_Name varchar(25),
Marks int
);
CREATE TABLE Student ( Student_ID int Primary Key, Student_Name varchar(25), Marks int );
CREATE TABLE Student  
(  
Student_ID int Primary Key,  
Student_Name  varchar(25),  
Marks int  
); 

 

IP Practical File 12

 

2. Insert the details of a new student in the above table.

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insert into student values(101, Amit, 87);
insert into student values(101, Amit, 87);
insert into student values(101, Amit, 87);

IP practical file class 12

3. Delete the details of a student in the above table.

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delete from student where Student_ID=101;
delete from student where Student_ID=101;
delete from student where Student_ID=101;

 

4. Use the select command to get the details of the students with marks more than 80.

 

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select * from student where marks>80;
select * from student where marks>80;
select * from student where marks>80;

 

5. Find the min, max, sum, and average of the marks in a student marks table.

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select max(marks), min(marks), sum(marks) , avg(marks) from student;
select max(marks), min(marks), sum(marks) , avg(marks) from student;
select max(marks), min(marks), sum(marks) , avg(marks) from student;

 

6. Write a SQL query to order the (student ID, marks) table in descending order of the marks.

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select * from student order by marks desc;
select * from student order by marks desc;
select * from student order by marks desc;

 

 

 

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