Download Term 1 Practical File IP Class 12

Hello friends in this section we provide Term 1 Practical File IP Class 12. Which include CBSE pattern important practical.  A comprehensive guide will provide you with practical file solutions. As per the term wise syllabus of the session 2021-22, the practical for Informatics Practices class 12 will be divided into two terms with 15 + 15 marks distribution. So in this article, you will get complete information and practical file for the same. Download this file and your this code in your system or python editor. Learn each and every section of this practical before presenting this file in front of the Your school teacher. 

Term 1 Practical File IP Class 12 As per the term wise syllabus of the session 2021-22, the distribution of practical examinations will be something like the following where Term 1 Practical File IP Class 12 is one of the components of the practical exam. 

Topic	Marks
Pandas program (pen and paper or Collab or any online idle or pyroid screen for mobile)	8
Practical File 15 Pandas Programs	3
Project synopsis	2
Viva	2
Total	15

Practical questions for Term 1 Practical File IP Class 12

We start with some basic programs and then go with some advance program that will helps you in this practical file. In the first part of practical questions for Term 1 Practical File IP Class 12, I will cover the data handling using pandas I topic. First, we will see the list of programs suggested by CBSE.

Suggested practical list by CBSE for data handling

1. Create a panda’s series from a dictionary of values and a ndarray.
2. Given a Series, print all the elements that are above the 75th percentile.
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.
4. Create a data frame for examination result and display row labels, column labels data types of
   each column and the dimensions
5. Filter out rows based on different criteria such as duplicate rows.
6. Importing and exporting data between pandas and CSV file.

Suggested practical list by CBSE for data visualization

1. Given the school result data, analyses the performance of the students on different parameters,
   e.g subject wise or class wise.
2. For the Data frames created above, analyze, and plot appropriate charts with title and legend.
3. 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.

[1] Write a program to generate a series of float numbers from 41.0 to 60.0 with an increment of 2.5 each.

Solution:

import pandas as pd
import numpy as np
def fl_ser():
    n = np.arange(41,60,2.5)
    s = pd.Series(n)
    print(s)
fl_ser()

The Output is:

Output Program Term 1 Practical File IP Class 12

[2] Write a program to generate a series of 10 numbers with a scalar value of 44.

Solution:

import pandas as pd
def fl_scv():
    print(pd.Series(44,range(1,11)))
fl_scv()

The output of program 2 term 1 practical file IP class 12

[3] Create a panda’s series from a dictionary of values and a ndarray.

Solution:

import pandas as pd
import numpy as np
def pro3():
    #Creating series from a dictionary
    d={'Jan':31,'Feb':28,'Mar':31,'Apr':30}
    s=pd.Series(d)
    print("Series from dictionary")
    print("~~~~~~~~~~~~~~~~~~~~~~~")
    print(s)
    #Creating series from an ndarray
    ar=np.array([2,3,4,5,6])
    print("\nSeries from ndarray")
    print("~~~~~~~~~~~~~~~~~~~~~~~")
    s1=pd.Series(ar)
    print(s1)
pro3()

series from dictionary and ndarray

[4] Given a Series, print all the elements that are above the 75th percentile.

Solution:

import pandas as pd
def Ser_stumarks():
    std_marks = []
    for i in range(1,6):
        m = int(input("Enter the Percentile:"))
        std_marks.append(m)
    s = pd.Series(index=range(1201,1206),data=std_marks)
    print("Data fetched from the series are:")
    print(s[s>=75])
Ser_stumarks()

Output of Program4 Term 1 practical file ip class 12

[5] Create a data frame for examination results and display row labels, column labels data types of each column and the dimensions.

Solution:

import pandas as pd
def df_std_res():
    res={'Amit':[76,78,75,66,68],
        'Shialesh':[78,56,77,49,55],
        'Rani':[90,91,93,97,99],
        'Madan':[55,48,59,60,66],
        'Radhika':[78,79,85,88,86]}
    df=pd.DataFrame(res)
    print("Prinitng row labels in a list:")
    print("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
    idx=df.index
    l=list(idx)
    print(l)
    print("Prinitng row labels in a list:")
    print("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
    print("[",end=" ")
    for col in df.columns:
        print(col,end=" ")
    print("]")
    print("Printing Data Types of each column")
    print("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
    print(df.dtypes)
    print("Printing dimensions of Data Frame")
    print("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
    print(df.ndim)
df_std_res()

[6] Create a dataframe and iterate them over rows.

Solution:

import pandas as pd 

data = [["Virat",55,66,31],["Rohit",88,66,43],[
         "Hardik",99,101,68]]

players = pd.DataFrame(data,
          columns = ["Name","Match-1","Match-2","Match-3"])

print("Iterating by rows:")
print("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
for index, row in players.iterrows():
    print(index, row.values)


print("Iterating by columns:")
print("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
for index, row in players.iterrows():
    print(index, row["Name"],row["Match-1"],
          row["Match-2"],row["Match-3"])

iterrows in dtaframe class 12

[7] Create a dataframe and print it along with their index using iteritems().

import pandas as pd
def df_operations():
    sc_4yrs={2016:{'Virat Kohli':2595,'Rohit Sharma':2406,'Shikhar Dhawan':2378},
         2017:{'Virat Kohli':2818,'Rohit Sharma':2613,'Shikhar Dhawan':2295},
         2018:{'Virat Kohli':2735,'Rohit Sharma':2406,'Shikhar Dhawan':2378},
         2019:{'Virat Kohli':2455,'Rohit Sharma':2310,'Shikhar Dhawan':1844}}
    df=pd.DataFrame(sc_4yrs)
    print(df)
    print("------------------------------------------------------------------------")
    for (year,runs) in df.iteritems():
        print("Year:",year)
        print(runs)
df_operations()

iteritems in dataframe class 12 ip

[8] Create the following DataFrame Sales containing year wise sales figures for five salespersons in INR. Use the years as column labels, and salesperson names as row labels.

	2018	2019	2020	2021
Kapil	110	205	177	189
Kamini	130	165	175	190
Shikhar	115	206	157	179
Mohini	118	198	183	169

1. Create the DataFrame.
2. Display the row labels of Sales.
3. Display the column labels of Sales.
4. Display the data types of each column of Sales.
5. Display the dimensions, shape, size and values of Sales.

import pandas as pd
#Creating DataFrame
d = {2018:[110,130,115,118],
     2019:[205,165,175,190],
     2020:[115,206,157,179],
     2021:[118,198,183,169]}
sales=pd.DataFrame(d,index=['Kapil','Kamini','Shikhar','Mohini'])
#Display row lables
print("Row Lables:\n",sales.index)
print("~~~~~~~~~~~~~~~~~~~~~~~~~~\n")
#Display column lables
print("Column Lables:\n",sales.columns)
print("~~~~~~~~~~~~~~~~~~~~~~~~~~\n")
#Display data type
print("\nDisplay column data types")
print("~~~~~~~~~~~~~~~~~~~~~~~~~~")
print(sales.dtypes)
print("\nDisplay the dimensions, shape, size and values of Sales")
print("~~~~~~~~~~~~~~~~~~~~~~~~~~")
print("Dimensions:",sales.ndim)
print("Shape:",sales.shape)
print("Size:",sales.size)
print("Values:",sales.values)

[9] Consider above dataframe and write code to do the following:

1. Display the last two rows of Sales.
2. Display the first two columns of Sales.

import pandas as pd
#Creating DataFrame
d = {2018:[110,130,115,118],
     2019:[205,165,175,190],
     2020:[115,206,157,179],
     2021:[118,198,183,169]}
sales=pd.DataFrame(d,index=['Kapil','Kamini','Shikhar','Mohini'])
print("Display last two rows of DataFrame:")
print("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")

#Method 1
print("Using tail function:")
print(Sales.tail(2))

#Method 2
print("Using iloc")
print(Sales.iloc[-2:])

#With Specific Columns, I have prnted two columns
print("Sepcific Columns")
print(Sales.iloc[-2:,-2:])


print("Display first two columns of Dataframe:")
print("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
#Method 1
print(Sales[[2014,2015]])

#Method 2
print(Sales[Sales.columns[0:2]])

#Method 3
print(Sales.iloc[:, 0:2] )

[10] Use above dataframe and do the following:

1. Change the DataFrame Sales such that it becomes its transpose.
2. Display the sales made by all sales persons in the year 2018.
3. Display the sales made by Kapil and Mohini in the year 2019 and 2020.
4. Add data to Sales for salesman Nirali where the sales made are
   • [221, 178, 165, 177, 210] in the years [2018, 2019, 2020, 2021] respectively
5. Delete the data for the year 2018 from the DataFrame Sales.
6. Delete the data for sales man Shikhar from the DataFrame Sales.
7. Change the name of the salesperson Kamini to Rani and Kapil to Anil.
8. Update the sale made by Mohini in 118 to 150 in 2018.

Solution:

import pandas as pd
#Creating DataFrame
d = {2018:[110,130,115,118],
     2019:[205,165,175,190],
     2020:[115,206,157,179],
     2021:[118,198,183,169]}
sales=pd.DataFrame(d,index=['Kapil','Kamini','Shikhar','Mohini'])
print("Transpose:")
print("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
print(sales.T)
print("\nSales made by each salesman in 2018")

#Method 1
print(sales[2018])

#Method 2
print(sales.loc[:,2018])

print("Sales made by Kapil and Mohini:")
#Method 1
print(sales.loc[['Kapil','Mohini'], [2019,2020]])

#Method 2
print(sales.loc[sales.index.isin(["Kapil","Mohini"]),[2019,2020]])

print("Add Data:")
sales.loc["Nirali"]=[221, 178, 165, 177]
print(sales)

print("Delete Data for 2018:")
sales=sales.drop(columns=2018)
print(sales)


Sales.drop(columns=2018,inplace=True)
print(Sales)

sales=sales.drop("Shikhar",axis=0)
#sales.drop("kinshuk")
print(sales)

sales=sales.rename({"Kamini":"Rani","Kapil":"Anil"},axis="index")
print(sales)

sales.loc[sales.index=="Mohini",2018]=150
print(sales)

If you are looking for more practice questions on data frame follow the below-given link that will definitely provide you support in making Term 1 Practical File IP Class 12:

Month	January	February	March	April	May
Sales	510	350	475	580	600

Weekly Sales Report

1. Write a title for the chart “The Monthly Sales Report“
2. Write the appropriate titles of both the axes
3. Write code to Display legends
4. Display blue color for the line
5. Use the line style – dashed
6. Display diamond style markers on data points

import matplotlib.pyplot as pp
mon =['January','February','March','April','May']
sales = [510,350,475,580,600]
pp.plot(mon,sales,label='Sales',color='b',linestyle='dashed',marker='D')
pp.title("The Monthly Sales Report")
pp.xlabel("Months")
pp.ylabel("Sales")
pp.legend()
pp.show()

Ouptut:

practical file questions matplotlib pyplot for class 12 IP

[12] Pratyush Garments has recorded the following data into their register for their income from cotton clothes and jeans. Plot them on the line chart.

Day	Monday	Tuesday	Wednesday	Thursday	Friday
Cotton	450	560	400	605	580
Jeans	490	600	425	610	625

Apply following customization to the line chart.

1. Write a title for the chart “The Weekly Garment Orders”.
2. Write the appropriate titles of both the axes.
3. Write code to Display legends.
4. Display your choice of colors for both the lines cotton and jeans.
5. Use the line style – dotted for cotton and dashdot for jeans.
6. Display plus markers on cotton and x markers of jeans.

import matplotlib.pyplot as pp
day =['Monday','Tuesday','Wednesday','Thursday','Friday']
ct = [450,560,400,605,580]
js = [490,600,425,610,625]
pp.plot(day,ct,label='Cotton',color='g',linestyle='dotted',marker='+')
pp.plot(day,js,label='Food',color='m',linestyle='dashdot',marker='x')
pp.title("The Weekly Garment Orders")
pp.xlabel("Days")
pp.ylabel("Orders")
pp.legend()
pp.show()

Output: