Data Handling using Pandas Class 12 NCERT Solution

Data Handling using Pandas Class 12

Data Handling using Pandas Class 12

SUMMARY :

NumPy, Pandas and Matplotlib are Python libraries for scientific and analytical use. 

pip install pandas is the command to install Pandas library.

A data structure is a collection of data values and the operations that can be applied to that 
data. It enables efficient storage, retrieval and modification to the data.
 
Two main data structures in Pandas library are Series and DataFrame. To use these 
data structures, we first need to import the Pandas library.

A Series is a one-dimensional array containing a sequence of values. Each value has a data label 
associated with it also called its index. 

The two common ways of accessing the elements of a series are Indexing and Slicing.

There are two types of indexes: positional index and labelled index. Positional index takes an 
integer value that corresponds to its position in the series starting from 0, whereas labelled index 
takes any user-defined label as index.

When positional indices are used for slicing, the value at end index position is excluded, i.e., only 
(end - start) number of data values of the series are extracted. However with labelled indexes the
value at the end index label is also included in the output. 

All basic mathematical operations can be performed on Series either by using the 
operator or by using appropriate methods of the Series object. 

While performing mathematical operations index matching is implemented and if no matching 
indexes are found during alignment, Pandas returns NaN so that the operation does not fail.

A DataFrame is a two-dimensional labeled data structure like a spreadsheet. It contains rows 
and columns and therefore has both a row and column index.

When using a dictionary to create a DataFrame, keys of the Dictionary become the column labels 
of the DataFrame. A DataFrame can be thought of as a dictionary of lists/ Series (all Series/columns 
sharing the same index label for a row).

Data can be loaded in a DataFrame from a file on the disk by using Pandas read_csv function.

Data in a DataFrame can be written to a text file on disk by using the pandas.DataFrame.to_
csv() function.

DataFrame.T gives the transpose of a DataFrame. 

Pandas haves a number of methods that support label based indexing but every label asked for 
must be in the index, or a KeyError will be raised. 

DataFrame.loc[ ] is used for label based indexing of rows in DataFrames. 

Pandas.DataFrame.append() method is used to merge two DataFrames. 

Pandas supports non-unique index values. Only 
if a particular operation that does not support 
duplicate index values is attempted, an exception is raised at that time.

The basic difference between Pandas Series and NumPy ndarray is that operations between Series 
automatically align the data based on labels. Thus, we can write computations without considering 
whether all Series involved have the same label or not whereas in case of ndarrays it raises an error.

Data Handling using Pandas Class 12

Data Handling using Pandas Class 12

Q1. What is a Series and how is it different from a 1-D array, a list and a dictionary?

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Q2. What is a DataFrame and how is it different from a 2-D array?

Q3. How are DataFrames related to Series?

Q4. What do you understand by the size of (i) a Series, (ii) a DataFrame?

Data Handling using Pandas Class 12 NCERT Solutions

Q5. Create the following Series and do the specified operations:

a) EngAlph, having 26 elements with the alphabets as values and default index values.

b) Vowels, having 5 elements with index labels ‘a’, ‘e’, ‘i’, ‘o’ and ‘u’ and all the five values set to zero. Check if it is an empty series.

c) Friends, from a dictionary having roll numbers of five of your friends as data and their first name as keys.

d) MTseries, an empty Series. Check if it is an empty series.

e) MonthDays, from a numpy array having the number of days in the 12 months of a year. The labels should be the month numbers from 1 to 12.

Show Answer


Ans. 

a)

import pandas as pd

EngAlph=pd.Series(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z'])
print(EngAlph)

OR

import pandas as pd
ch =[chr(i) for i in range(97, 123)]
EngAlph = pd.Series(ch)
print(EngAlph)

OR

import pandas as pd

EngAlph=pd.Series(['A','B','C','D','E','F','G','H','I','J','K','L','M', 'N','O','P','Q','R','S','T','U','V','W','X','Y','Z'])
print(EngAlph)

OR

import pandas as pd
ch =[chr(i) for i in range(65,91)]
EngAlph = pd.Series(ch)
print(EngAlph)

b)

import pandas as pd
Vowels=pd.Series([2, 4, 6, 8, 10],index=['a', 'e', 'i', 'o', 'u']) # v has five elements
Vowels[:] = 0  # setting all elements of Series 'v' to 0
print(Vowels)

OUTPUT : 
a    0
e    0
i     0
o    0
u    0
dtype: int64


c)

import pandas as pd
Friends=pd.Series({"Amit" : 1, "Renu" : 21, "Suman" : 5, "Ashu" : 3, "Rohit" : 8})
print(Friends)

d) 

import pandas as pd
MTseries = pd.Series(dtype=int) #we specify a dtype explicitly to silence warning.
print(MTseries.empty)

e)

import pandas as pd
import numpy as np
days = np.array([31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31])
MonthDays  = pd.Series(days,index=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
print(MonthDays)

Data Handling using Pandas Class 12 NCERT Solutions

Q6. Using the Series created in Question 5, write commands for the following:

a) Set all the values of Vowels to 10 and display the Series.

Show Answer


Ans. 


import pandas as pd
Vowels=pd.Series(0,index=['a', 'e', 'i', 'o', 'u'])
Vowels[:] = 10 # setting all values to 10
print(Vowels)

OR



import pandas as pd
Vowels=pd.Series(0,index=['a', 'e', 'i', 'o', 'u'])
Vowels.loc['a' : 'u'] = 10 # setting all values to 10
print(Vowels)

OR



import pandas as pd
Vowels=pd.Series(0,index=['a', 'e', 'i', 'o', 'u'])
Vowels.iloc[0 : 4] = 10 # setting all values to 10
print(Vowels)

Data Handling using Pandas Class 12

b) Divide all values of Vowels by 2 and display the Series.

Show Answer


Ans. 

import pandas as pd
Vowels=pd.Series(0,index=['a', 'e', 'i', 'o', 'u'])
Vowels.iloc[0 : 4] = 10
print(Vowels/2) #divide all values by 2 

c) Create another series Vowels1 having 5 elements with index labels ‘a’, ‘e’, ‘i’, ‘o’ and ‘u’ having values [2,5,6,3,8] respectively.

Show Answer


Ans.

import pandas as pd
Vowels1 = pd.Series([2, 5, 6, 3, 8],index=['a', 'e', 'i', 'o', 'u'])
print(Vowels1)

d) Add Vowels and Vowels1 and assign the result to Vowels3.

Show Answer


Ans. 

import pandas as pd
Vowels=pd.Series([2, 4, 6, 8, 10],index=['a', 'e', 'i', 'o', 'u']) # v has five elements
Vowels[:] = 0  # setting all elements of Series 'v' to 0
Vowels.iloc[0 : 4] = 10
Vowels = Vowels/2
Vowels1 = pd.Series([2, 5, 6, 3, 8],index=['a', 'e', 'i', 'o', 'u'])
Vowels3 = Vowels + Vowels1
print(Vowels3)

e) Subtract, Multiply and Divide Vowels by Vowels1.

Show Answer



Ans.

import pandas as pd
Vowels=pd.Series([2, 4, 6, 8, 10],index=['a', 'e', 'i', 'o', 'u']) # v has five elements
Vowels[:] = 0  # setting all elements of Series 'v' to 0
Vowels.iloc[0 : 4] = 10
Vowels = Vowels/2
Vowels1 = pd.Series([2, 5, 6, 3, 8],index=['a', 'e', 'i', 'o', 'u'])
Vowels3 = Vowels - Vowels1 #Subtraction
print(Vowels3)
Vowels3 = Vowels * Vowels1 #Multiplication
print(Vowels3)
Vowels3 = Vowels / Vowels1 #Division
print(Vowels3)

f) Alter the labels of Vowels1 to [‘A’, ‘E’, ‘I’, ‘O’, ‘U’].

Show Answer


Ans. 

import pandas as pd
Vowels1 = pd.Series([2, 5, 6, 3, 8],index=['a', 'e', 'i', 'o', 'u'])
Vowels1.index = ['A', 'E', 'I', 'O', 'U']
print(Vowels1)

Data Handling using Pandas Class 12 NCERT Solutions

Q7. Using the Series created in Question 5, write commands for the following :

a) Find the dimensions, size and values of the Series EngAlph, Vowels, Friends, MTseries, MonthDays.

Show Answer


Ans.

import pandas as pd
EngAlph=pd.Series(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z'])
print("Dimesnsion of Series is : ", EngAlph.ndim)
print("Size of Series is : ", EngAlph.size)
print("Values of Series are : ", EngAlph.values)


print("---------------------------------------------------------------------------------------")

Vowels=pd.Series([2, 4, 6, 8, 10],index=['a', 'e', 'i', 'o', 'u'])
print("Dimesnsion of Series is : ", Vowels.ndim)
print("Size of Series is : ", Vowels.size)
print("Values of Series are : ", Vowels.values)

print("---------------------------------------------------------------------------------------")

Friends=pd.Series({"Amit" : 1, "Renu" : 21, "Suman" : 5, "Ashu" : 3, "Rohit" : 8})
print("Dimesnsion of Series is : ", Friends.ndim)
print("Size of Series is : ", Friends.size)
print("Values of Series are : ", Friends.values)

print("---------------------------------------------------------------------------------------")

MTseries = pd.Series(dtype=int)
print("Dimesnsion of Series is : ", MTseries.ndim)
print("Size of Series is : ", MTseries.size)
print("Values of Series are : ", MTseries.values)

print("---------------------------------------------------------------------------------------")

MonthDays  = pd.Series([31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31],index=[1,2,3,4,5,6,7,8,9,10,11,12])
print("Dimesnsion of Series is : ", MonthDays.ndim)
print("Size of Series is : ", MonthDays.size)
print("Values of Series are : ", MonthDays.values)

Data Handling using Pandas Class 12

b) Rename the Series MTseries as SeriesEmpty.

Show Answer


Ans. 

import pandas as pd
MTseries = pd.Series(dtype=int)
print(MTseries)
MTseries=MTseries.rename("SeriesEmpty")
print(MTseries)

c) Name the index of the Series MonthDays as monthno and that of Series Friends as Fname.

Show Answer


Ans

import numpy as np
import pandas as pd


days = np.array([31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31])
MonthDays  = pd.Series(days,index=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
print(MonthDays)
MonthDays.index.name="MonthNo" #Actual code to change the index name
print(MonthDays.index)


Friends=pd.Series({"Amit" : 1, "Renu" : 21, "Suman" : 5, "Ashu" : 3, "Rohit" : 8})
print(Friends)
Friends.index.name="Fname"
print(Friends.index)

d) Display the 3rd and 2nd value of the Series Friends, in that order.

Show Answer


Ans.

import pandas as pd
Friends=pd.Series({"Amit" : 1, "Renu" : 21, "Suman" : 5, "Ashu" : 3, "Rohit" : 8})
print(Friends[2])
print(Friends[1])

e) Display the alphabets ‘e’ to ‘p’ from the Series EngAlph.

Show Answer


Ans. 

import pandas as pd
EngAlph=pd.Series(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z'])

print(EngAlph[4 : 16]) 

OR

print(EngAlph.loc[4 : 15])

OR

print(EngAlph.iloc[4 : 16])

f) Display the first 10 values in the Series EngAlph.

Show Answer


Ans. 

import pandas as pd
EngAlph=pd.Series(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z'])
print(EngAlph.head(10)) 

h) Display the MTseries.

Show Answer


Ans. 

import pandas as pd
MTseries = pd.Series(dtype=int)
print(MTseries)

Data Handling using Pandas Class 12 NCERT Solutions

8. Using the Series created in Question 5, write commands for the following:

a) Display the names of the months 3 through 7 from the Series MonthDays.

Show Answer


Ans.

import numpy as np
import pandas as pd

days = np.array([31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31])
MonthDays  = pd.Series(days,index=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
print(MonthDays[2 : 7])

b) Display the Series MonthDays in reverse order.

Show Answer


Ans. 

import numpy as np
import pandas as pd

days = np.array([31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31])
MonthDays  = pd.Series(days,index=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
print(MonthDays[: : -1])

Data Handling using Pandas Class 12 NCERT Solutions

Q9. 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.

	2014	2015	2016	2017
Madhu	100.5	12000	20000	50000
Kusum	150.8	18000	50000	60000
Kinshuk	200.9	22000	70000	70000
Ankit	30000	30000	10000	80000
Shruti	40000	45000	125000	90000

Show Answer


Ans.

import pandas as pd
d = {2014:[100.5,150.8,200.9,30000,4000],
     2015:[12000,18000,22000,30000,45000],
     2016:[20000,50000,70000,10000,125000],
     2017:[50000,60000,70000,80000,90000]}
I = ['Madhu',"Kusum","Kinshuk","Ankit","Shruti"]
Sales=pd.DataFrame(d, index = I)
print(Sales)

Q10. Use the DataFrame created in Question 9 above to do the following:

a) Display the row labels of Sales.

Show Answer


Ans. 

print(Sales.index)

b) Display the column labels of Sales.

Show Answer


Ans.

print(Sales.columns)

c) Display the data types of each column of Sales.

Show Answer


Ans.

print(Sales.dtypes)

d) Display the dimensions, shape, size and values of Sales.

Show Answer


Ans.

print(Sales.ndim) #for dimension
print(Sales.shape) #for shape
print(Sales.size) #for size
print(Sales.values) #for values

e) Display the last two rows of Sales.

Show Answer


Ans.

print(Sales.tail(2))

f) Display the first two columns of Sales.

Show Answer


Ans.

print(Sales[[2014, 2015]])

OR

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

g) Create a dictionary using the following data. Use this dictionary to create a DataFrame Sales2.

	2018
Madhu	160000
Kusum	110000
Kinshuk	500000
Ankit	340000
Shruti	900000

Show Answer


Ans.

import pandas as pd
d1={2018 : [160000,110000,500000,340000,900000]}

L = ["Madhu","Kusum","Kinshuk","Ankit","Shruti"]
Sales2=pd.DataFrame(d1, index= L)
print(Sales2)

h) Check if Sales2 is empty or it contains data.

Show Answer


Ans.

print(Sales2.empty)

Data Handling using Pandas Class 12 NCERT Solutions

Q11. Use the DataFrame created in Question 9 above to do the following:

a) Append the DataFrame Sales2 to the DataFrame Sales.

Show Answer


Ans. 

Sales = Sales.append(Sales2)
print(Sales)

b) Change the DataFrame Sales such that it becomes its transpose.

Show Answer


Ans. 

print(Sales.T)

c) Display the sales made by all sales persons in the year 2017.

Show Answer


Ans. 

print(Sales[2017])

OR

print(Sales.loc[ : ,2017])

d) Display the sales made by Madhu and Ankit in the year 2017 and 2018.

Show Answer


Ans. 

print(Sales.loc[['Madhu' , 'Ankit'], [2017 , 2018]])

e) Display the sales made by Shruti 2016.

Show Answer


Ans.

print(Sales.loc[['Shruti'], [2016]])

OR

print(Sales.loc[Sales.index=='Shruti',2016])

Data Handling using Pandas Class 12 NCERT Solutions

f) Add data to Sales for salesman Sumeet where the sales made are [196.2, 37800, 52000, 78438, 38852] in the years [2014, 2015, 2016, 2017, 2018] respectively

Show Answer


Ans. 

Sales.loc["Sumeet"] = [196.2, 37800, 52000, 78438, 38852]

g) Delete the data for the year 2014 from the DataFrame Sales.

Show Answer


Ans.

Sales.drop(columns=2014) 

h) Delete the data for sales man Kinshuk from the DataFrame Sales.

 
Show Answer


Ans. 

Sales.drop("Kinshuk", axis=0)

i) Change the name of the salesperson Ankit to Vivaan and Madhu to Shailesh.

Show Answer


Ans. 

Sales=Sales.rename({"Ankit" : "Vivaan", "Madhu" : "Shailesh"}, axis="index")

j) Update the sale made by Shailesh in 2018 to 100000.

Show Answer


Ans. 

Sales.loc[Sales.index==”Shailesh”, 2018]=100000

k) Write the values of DataFrame Sales to a comma separated file SalesFigures.csv on the disk. Do not write the row labels and column labels

Show Answer


Ans. 

Sales.to_csv("E:\salesFigures.csv", index=False, header=False)

l) Read the data in the file SalesFigures.csv into a DataFrame SalesRetrieved and Display it. Now update the row labels and column labels of SalesRetrieved to be the same as that of Sales.

Show Answer


Ans. 

SalesRetrieved=pd.read_csv("E:\salesFigures.csv",names=['2014', '2015', '2016', '2017', '2018'])
SalesRetrieved.index=['Madhu', 'Kusum', 'Kinshuk', 'Ankit', 'Shruti', 'Sumeet']

Data Handling using Pandas Class 12 NCERT Solutions

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Data Handling using Pandas Class 12 NCERT Solutions

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Data Handling using Pandas Class 12 NCERT Solutions

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Data Handling using Pandas Class 12 NCERT Solutions

Data Handling using Pandas Class 12 NCERT Solutions

Data Handling using Pandas Class 12 NCERT Solutions