Question 1

Question 37Identify the correct option to select first four rows and second to fourth columns from a DataFrame 'Data':display(Data.iloc[1 : 4, 2 : 4])display(Data.iloc[1 : 5, 2 ; 5])print(Data.iloc[0 : 4, 1 : 4])print(Data.iloc[1 : 4, 2 : 4])

Accepted Answer

print(Data.iloc[0 : 4, 1 : 4])Reason — To display subset from dataframe using row and column numeric index/position, iloc is used with syntax .iloc[:, :]. Therefore, according to this syntax, print(Data.iloc[0 : 4, 1 : 4]) is correct statement to display first four rows and second to fourth columns from a DataFrame Data.

Question 2

Question 38To delete a column from a DataFrame, you may use ............... statement.removedeldropcancel

Accepted Answer

delReason — The del statement is used to delete a column from a DataFrame.

Source: This question is from Python Pandas — I, Informatics Practices &mdash; Class 12, CBSE Board.
Key Concepts CoveredThis question tests your understanding of the following concepts from the chapter Python Pandas — I: Question, Delete, Column, Dataframe, Use, Statement. These are fundamental topics in Informatics Practices that students are expected to master as part of the CBSE Class 12 curriculum.A thorough understanding of these concepts will help you answer similar questions confidently in your CBSE examinations. These topics are frequently tested in both objective and subjective sections of Informatics Practices papers. We recommend revising the relevant section of your textbook alongside practising these solved examples to build a strong foundation.
How to Approach MCQsMultiple choice questions test your ability to quickly recall facts and apply concepts. Read all options carefully before selecting your answer. Use the process of elimination to rule out clearly incorrect options first. If two options seem similar, look for the subtle difference that makes one more accurate. For numerical MCQs, verify your answer by substituting it back into the problem.
Key Points to RememberRead the question carefully and identify all parts before answering.
Use the terminology specific to this subject and chapter.
Review the textbook content for this chapter before attempting questions.
Practice writing concise, well-structured answers within time limits.
Related Questions from Python Pandas — IQ1: Question 1 Consider following Series object namely S : 0 0.430271 1 0.617328 2 -0.265421 3...
Q10: Question 7 Find the Error : data = np . array ([ &#x27;a&#x27; , &#x27;b&#x27; , &#x27;c&#x27; , &#x27;d&#x27; , &#x27;e&#x27; , &#x27;f&#x27; ]) s = pd ....
Q11: Question 7 Write a program that stores the sales of 5 fast moving items of a store for each month...
Q12: Question 8 Three Series objects store the marks of 10 students in three terms. Roll numbers of...
Q13: Question 10 If Ser is a Series type object having 30 values, then how are statements (a), (b) and...
Practice more questions from Python Pandas — I &mdash; Informatics Practices, Class 12 CBSE

Question 3

Question 39To delete a row from a DataFrame, you may use ............... statement.removedeldropcancel

Accepted Answer

dropReason — The drop statement is used to delete a row from a DataFrame.

Source: This question is from Python Pandas — I, Informatics Practices &mdash; Class 12, CBSE Board.
Key Concepts CoveredThis question tests your understanding of the following concepts from the chapter Python Pandas — I: Question, Delete, Row, Dataframe, Use, Statement. These are fundamental topics in Informatics Practices that students are expected to master as part of the CBSE Class 12 curriculum.A thorough understanding of these concepts will help you answer similar questions confidently in your CBSE examinations. These topics are frequently tested in both objective and subjective sections of Informatics Practices papers. We recommend revising the relevant section of your textbook alongside practising these solved examples to build a strong foundation.
How to Approach MCQsMultiple choice questions test your ability to quickly recall facts and apply concepts. Read all options carefully before selecting your answer. Use the process of elimination to rule out clearly incorrect options first. If two options seem similar, look for the subtle difference that makes one more accurate. For numerical MCQs, verify your answer by substituting it back into the problem.
Key Points to RememberRead the question carefully and identify all parts before answering.
Use the terminology specific to this subject and chapter.
Review the textbook content for this chapter before attempting questions.
Practice writing concise, well-structured answers within time limits.
Related Questions from Python Pandas — IQ1: Question 1 Consider following Series object namely S : 0 0.430271 1 0.617328 2 -0.265421 3...
Q10: Question 7 Find the Error : data = np . array ([ &#x27;a&#x27; , &#x27;b&#x27; , &#x27;c&#x27; , &#x27;d&#x27; , &#x27;e&#x27; , &#x27;f&#x27; ]) s = pd ....
Q11: Question 7 Write a program that stores the sales of 5 fast moving items of a store for each month...
Q12: Question 8 Three Series objects store the marks of 10 students in three terms. Roll numbers of...
Q13: Question 10 If Ser is a Series type object having 30 values, then how are statements (a), (b) and...
Practice more questions from Python Pandas — I &mdash; Informatics Practices, Class 12 CBSE

Question 4

Question 40Sudhanshu has written the following code to create a DataFrame with boolean index :import numpy as np
import pandas as pd
    df = pd.DataFrame(data = [[5, 6, 7]], index = [true, false, true]) 
    print(df)While executing the code, she is getting an error, help her to rectify the code :df = pd.DataFrame([True, False, True], data = [5, 6, 7])df = pd.DataFrame(data = [5, 6, 7], index = [True, False, True])df = pd.DataFrame([true, false, true], data = [5, 6, 7])df = pd.DataFrame(index = [true, false, true], data = [[5, 6, 7]])

Accepted Answer

df = pd.DataFrame(data = [5, 6, 7], index = [True, False, True])Reason — The index values 'true' and 'false' should have the first letter capitalized to match Python's boolean values. Also, the 'data' parameter should contain the list of values to be included in the DataFrame. Hence, df = pd.DataFrame(data = [5, 6, 7], index = [True, False, True]) is correct.

Question 5

Question 7Consider two objects x and y. x is a list whereas y is a Series. Both have values 20, 40, 90, 110.What will be the output of the following two statements considering that the above objects have been created already ?(a) print (x*2)(b) print (y*2)Justify your answer.

Accepted Answer

(a)Output[20, 40, 90, 110, 20, 40, 90, 110]
In the first statement, x represents a list. When a list is multiplied by 2 (x*2), it replicates each element of the list twice.(b)Output0     40
1     80
2    180
3    220
dtype: int64
In the second statement, y represents a Series. When a Series is multiplied by a value, each element of the Series is multiplied by 2, as Series supports vectorized operations.

Question 6

Question 8Given a dataframe df as shown below : ABD015171911618202202122What will be the result of following code statements ?(a) df['C'] = np.NaN(b) df['C'] = [2, 5](c) df['C'] = [12, 15, 27]

Accepted Answer

(a) df['C'] = np.NaN — This statement will add a new column 'C' to the dataframe and assign np.NaN (Not a Number) to all rows in this new column.The updated dataframe will look like this:Output    A   B     D    C
0   15  17   19  NaN
1   16  18   20  NaN
2   20  21   22  NaN
(b) df['C'] = [2, 5] — This statement will result in error because the length of the list [2, 5] does not match the number of rows in the DataFrame df.(c) df['C'] = [12, 15, 27] — This statement will add a new column 'C' to the dataframe and assign the values from the list [12, 15, 27] to the new column. This time, all rows in the new column will be assigned a value.The updated dataframe will look like this:Output    A   B   D   C
0  15  17  19  12
1  16  18  20  15
2  20  21  22  27

Question 7

Question 9Write code statements to list the following, from a dataframe namely sales:(a) List only columns 'Item' and 'Revenue'.(b) List rows from 3 to 7.(c) List the value of cell in 5th row, 'Item' column.

Accepted Answer

(a)>>> sales[['Item', 'Revenue']](b)>>> sales.iloc[2:7](c)>>> sales.Item[4]

Question 8

Question 1Consider following Series object namely S :0      0.430271                          
1      0.617328                          
2     -0.265421                          
3     -0.836113                              
dtype:float64                                
What will be returned by following statements ?(a) S * 100(b) S > 0(c) S1 = pd.Series(S)(d) S2 = pd.Series(S1) + 3What will be the values of Series objects S1 and S2 created above ?

Accepted Answer

(a) S * 100Output0    43.0271
1    61.7328
2   -26.5421
3   -83.6113
dtype: float64
(b) S > 0Output0     True
1     True
2    False
3    False
dtype: bool
(c) S1 = pd.Series(S)Output0    0.430271
1    0.617328
2   -0.265421
3   -0.836113
dtype: float64
(d) S2 = pd.Series(S1) + 3Output0    3.430271
1    3.617328
2    2.734579
3    2.163887
dtype: float64
The values of Series object S1 created above is as follows:0    0.430271
1    0.617328
2   -0.265421
3   -0.836113
dtype: float64
The values of Series object S2 created above is as follows:0    3.430271
1    3.617328
2    2.734579
3    2.163887
dtype: float64

Question 9

Question 2Consider the same Series object, S, given in the previous question. What output will be produced by following code fragment ?S.index = ['AMZN', 'AAPL', 'MSFT', 'GOOG']
print(S) 
print(S['AMZN'])
S['AMZN'] = 1.5
print(S['AMZN'])
print(S)

Accepted Answer

OutputAMZN    0.430271
AAPL    0.617328
MSFT   -0.265421
GOOG   -0.836113
dtype: float64
0.430271
1.5
AMZN    1.500000
AAPL    0.617328
MSFT   -0.265421
GOOG   -0.836113
dtype: float64
ExplanationThe provided code fragment first changes the index labels of the Series S to ['AMZN', 'AAPL', 'MSFT', 'GOOG'], prints the modified Series S, and then proceeds to print and modify the value corresponding to the 'AMZN' index. Specifically, it prints the value at the 'AMZN' index before and after assigning a new value of 1.5 to that index. Finally, it prints the Series S again, showing the updated value at the 'AMZN' index.

Question 10

Question 4What will be the output produced by following code, considering the Series object S given above ?(a) print(S[1:1])(b) print(S[0:1])(c) print(S[0:2])(d)S[0:2] = 12   
print(S)  
(e)print(S.index)    
print(S.values)

Accepted Answer

(a)OutputSeries([], dtype: int64)
ExplanationThe slice S[1:1] starts at index 1 and ends at index 1, but because the end index is exclusive, it does not include any elements, resulting in an empty Series.(b)Outputpencils    20
dtype: int64
ExplanationThe slice S[0:1] starts at index 0 and ends at index 1, but because the end index is exclusive, it includes only one element i.e., the element at index 0.(c)Outputpencils      20
notebooks    33
dtype: int64
ExplanationThe slice S[0:2] starts at index 0 and ends at index 1, hence, it includes two elements i.e., elements from index 0 and 1.(d)Outputpencils      12
notebooks    12
scales       52
erasers      10
dtype: int64
ExplanationThe slice S[0:2] = 12 assigns the value 12 to indices 0 and 1 in Series S, directly modifying those elements. The updated Series is then printed.(e)OutputIndex(['pencils', 'notebooks', 'scales', 'erasers'], dtype = 'object')
[20 33 52 10]
ExplanationThe code print(S.index) displays the indices of Series S, while print(S.values) displays the values of Series.

Question 11

Question 6(a)Find the error in following code fragment :S2 = pd.Series([101, 102, 102, 104])   
print(S2.index)  
S2.index = [0, 1, 2, 3, 4, 5]  
S2[5] = 220  
print(S2)

Accepted Answer

S2 = pd.Series([101, 102, 102, 104])  
print(S2.index)  
S2.index = [0, 1, 2, 3, 4, 5]  #Error 1
S2[5] = 220  
print(S2)  Error 1 — The Series S2 initially has four elements, so assigning a new index list of six elements ([0, 1, 2, 3, 4, 5]) to S2.index will raise a ValueError because the new index list length does not match the length of the Series.The corrected code is:S2 = pd.Series([101, 102, 102, 104])
print(S2.index)
S2.index = [0, 1, 2, 3]
S2[5] = 220
print(S2)

Question 12

Question 7Find the Error :data = np.array(['a', 'b', 'c', 'd', 'e', 'f'])  
s = pd.Series(data, index = [100, 101, 102, 103, 104, 105])   
print(s[102, 103, 104] )  Can you correct the error ?

Accepted Answer

The error in the above code is in the line print(s[102, 103, 104]). When accessing elements in a pandas Series using square brackets, we should use a list of index values, not multiple separate index values separated by commas.The corrected code is:data = np.array(['a', 'b', 'c', 'd', 'e', 'f'])  
s = pd.Series(data, index = [100, 101, 102, 103, 104, 105])  
print(s[[102, 103, 104]])

Question 13

Question 10If Ser is a Series type object having 30 values, then how are statements (a), (b) and (c), (d) similar and different ?(a) print(Ser.head())(b) print(Ser.head(8))(c) print(Ser.tail())(d) print(Ser.tail(11))

Accepted Answer

The statements (a), (b), (c) and (d) are all used to view the values from a pandas Series object Ser. However, they differ in the number of values they display.(a) print(Ser.head()): This statement will display the first 5 values from the Series Ser.(b) print(Ser.head(8)): This statement will display the first 8 values from the Series Ser.(c) print(Ser.tail()): This statement will display the last 5 values from the Series Ser.(d) print(Ser.tail(11)): This statement will display the last 11 values from the Series Ser.

Question 14

Question 11What advantages does dataframe offer over series data structure ? If you have similar data stored in multiple series and a single dataframe, which one would you prefer and why ?

Accepted Answer

The advantages of using a DataFrame over a Series are as follows:A DataFrame can have multiple columns, whereas a Series can only have one.A DataFrame can store data of different types in different columns, whereas a Series can only store data of a single type.A DataFrame allows to perform operations on entire columns, whereas a Series only allows to perform operations on individual elements.A DataFrame allows to index data using both row and column labels, whereas a Series only allows to index data using a single label.If there is similar data stored in multiple Series and a single DataFrame, I would prefer to use the DataFrame. This is because a DataFrame allows us to store and manipulate data in a more organized and structured way, and it allows us to perform operations on entire columns. Additionally, a DataFrame allows us to index data using both row and column labels, which makes it easier to access and manipulate data.

Question 15

Question 14Given :import pandas as pd
d = {'one' : pd.Series([1., 2., 3.], index = ['a', 'b', 'c']), 'two' : pd.Series([1., 2., 3., 4.], index = ['a', 'b', 'c', 'd'])} 
df = pd.DataFrame(d)
df1 = pd.DataFrame(d, index = ['d', 'b', 'a'])
df2 = pd.DataFrame(d, index = ['d', 'a'], columns = ['two', 'three'])
print(df) 
print(df1) 
print(df2)What will Python show the result as if you execute above code ?

Accepted Answer

Output  one  two
a  1.0  1.0
b  2.0  2.0
c  3.0  3.0
d  NaN  4.0
   one  two
d  NaN  4.0
b  2.0  2.0
a  1.0  1.0
   two three
d  4.0   NaN
a  1.0   NaN
ExplanationThe given code creates three pandas DataFrames df, df1, and df2 using the same dictionary d with different index and column labels. The first DataFrame df is created using the dictionary d with index labels taken from the index of the Series objects in the dictionary. The resulting DataFrame has two columns 'one' and 'two' with index labels 'a', 'b', 'c', and 'd'. The values in the DataFrame are filled in accordance to the index and column labels. The second DataFrame df1 is created with the same dictionary d but with a custom index ['d', 'b', 'a']. The third DataFrame df2 is created with a custom index ['d', 'a'] and a custom column label ['two', 'three']. Since the dictionary d does not have a column label three, all its values are NaN (Not a Number), indicating missing data.

Question 16

Question 15(a)From the DataFrames created in previous question, write code to display only row 'a' from DataFrames df, df1, and df2.Solutionimport pandas as pd
d = {'one' : pd.Series([1., 2., 3.], index = ['a', 'b', 'c']), 'two' : pd.Series([1., 2., 3., 4.], index = ['a', 'b', 'c', 'd'])} 
df = pd.DataFrame(d)
df1 = pd.DataFrame(d, index = ['d', 'b', 'a'])
df2 = pd.DataFrame(d, index = ['d', 'a'], columns = ['two', 'three'])
print(df.loc['a',:])
print(df1.loc['a',:])
print(df2.loc['a',:])Outputone    1.0
two    1.0
Name: a, dtype: float64
one    1.0
two    1.0
Name: a, dtype: float64
two      1.0
three    NaN
Name: a, dtype: object

Accepted Answer

importpandasaspdd={'one':pd.Series([1.,2.,3.],index=['a','b','c']),'two':pd.Series([1.,2.,3.,4.],index=['a','b','c','d'])}df=pd.DataFrame(d)df1=pd.DataFrame(d,index=['d','b','a'])df2=pd.DataFrame(d,index=['d','a'],columns=['two','three'])print(df.loc['a',:])print(df1.loc['a',:])print(df2.loc['a',:])Outputone    1.0two    1.0Name: a, dtype: float64one    1.0two    1.0Name: a, dtype: float64two      1.0three    NaNName: a, dtype: object

Question 17

Question 15(b)From the DataFrames created in previous question, write code to display only rows 0 and 1 from DataFrames df, df1, and df2.Solutionimport pandas as pd
d = {'one' : pd.Series([1., 2., 3.], index = ['a', 'b', 'c']), 'two' : pd.Series([1., 2., 3., 4.], index = ['a', 'b', 'c', 'd'])} 
df = pd.DataFrame(d)
df1 = pd.DataFrame(d, index = ['d', 'b', 'a'])
df2 = pd.DataFrame(d, index = ['d', 'a'], columns = ['two', 'three'])
print(df.iloc[0:2])
print(df1.iloc[0:2])
print(df2.iloc[0:2])Output   one  two
a  1.0  1.0
b  2.0  2.0
   one  two
d  NaN  4.0
b  2.0  2.0
   two three
d  4.0   NaN
a  1.0   NaN

Accepted Answer

importpandasaspdd={'one':pd.Series([1.,2.,3.],index=['a','b','c']),'two':pd.Series([1.,2.,3.,4.],index=['a','b','c','d'])}df=pd.DataFrame(d)df1=pd.DataFrame(d,index=['d','b','a'])df2=pd.DataFrame(d,index=['d','a'],columns=['two','three'])print(df.iloc[0:2])print(df1.iloc[0:2])print(df2.iloc[0:2])Outputone  twoa  1.0  1.0b  2.0  2.0   one  twod  NaN  4.0b  2.0  2.0   two threed  4.0   NaNa  1.0   NaN

Question 18

Question 15(c)From the DataFrames created in previous question, write code to display only rows 'a' and 'b' for columns 1 and 2 from DataFrames df, df1 and df2.Solutionimport pandas as pd
d = {'one' : pd.Series([1., 2., 3.], index = ['a', 'b', 'c']), 'two' : pd.Series([1., 2., 3., 4.], index = ['a', 'b', 'c', 'd'])} 
df = pd.DataFrame(d)
df1 = pd.DataFrame(d, index = ['d', 'b', 'a'])
df2 = pd.DataFrame(d, index = ['d', 'a'], columns = ['two', 'three'])
print(df.loc['a' : 'b', :])
print(df1.loc['b' : 'a', :])
print(df2.loc['d' : 'a', :])Output   one  two
a  1.0  1.0
b  2.0  2.0
   one  two
b  2.0  2.0
a  1.0  1.0
   two three
d  4.0   NaN
a  1.0   NaN

Accepted Answer

importpandasaspdd={'one':pd.Series([1.,2.,3.],index=['a','b','c']),'two':pd.Series([1.,2.,3.,4.],index=['a','b','c','d'])}df=pd.DataFrame(d)df1=pd.DataFrame(d,index=['d','b','a'])df2=pd.DataFrame(d,index=['d','a'],columns=['two','three'])print(df.loc['a':'b', :])print(df1.loc['b':'a', :])print(df2.loc['d':'a', :])Outputone  twoa  1.0  1.0b  2.0  2.0   one  twob  2.0  2.0a  1.0  1.0   two threed  4.0   NaNa  1.0   NaN

Question 19

Question 15(d)From the DataFrames created in previous question, write code to add an empty column 'x' to all DataFrames.Solutionimport pandas as pd
d = {'one' : pd.Series([1., 2., 3.], index = ['a', 'b', 'c']), 'two' : pd.Series([1., 2., 3., 4.], index = ['a', 'b', 'c', 'd'])} 
df = pd.DataFrame(d)
df1 = pd.DataFrame(d, index = ['d', 'b', 'a'])
df2 = pd.DataFrame(d, index = ['d', 'a'], columns = ['two', 'three'])
df['x'] = None
df1['x'] = None
df2['x'] = None
print(df)
print(df1)
print(df2)Output   one  two     x
a  1.0  1.0  None
b  2.0  2.0  None
c  3.0  3.0  None
d  NaN  4.0  None
   one  two     x
d  NaN  4.0  None
b  2.0  2.0  None
a  1.0  1.0  None
   two three     x
d  4.0   NaN  None
a  1.0   NaN  None

Accepted Answer

importpandasaspdd={'one':pd.Series([1.,2.,3.],index=['a','b','c']),'two':pd.Series([1.,2.,3.,4.],index=['a','b','c','d'])}df=pd.DataFrame(d)df1=pd.DataFrame(d,index=['d','b','a'])df2=pd.DataFrame(d,index=['d','a'],columns=['two','three'])df['x']=Nonedf1['x']=Nonedf2['x']=Noneprint(df)print(df1)print(df2)Outputone  two     xa  1.0  1.0  Noneb  2.0  2.0  Nonec  3.0  3.0  Noned  NaN  4.0  None   one  two     xd  NaN  4.0  Noneb  2.0  2.0  Nonea  1.0  1.0  None   two three     xd  4.0   NaN  Nonea  1.0   NaN  None

Question 20

Question 16What will be the output of the following program ?import pandas as pd
dic = {'Name' : ['Sapna', 'Anmol', 'Rishul', 'Sameep'], 'Agg' : [56, 67, 75, 76], 'Age' : [16, 18, 16, 19]}
df = pd.DataFrame(dic, columns = ['Name', 'Age'])
print(df)(a)    Name   Agg Age
101 Sapna  56  16
102 Anmol  67  18
103 Rishul 75  16
104 Sameep 76  19
(b)    Name   Agg   Age
0   Sapna  56    16
1   Anmol  67    18
2   Rishul  75   16
3   Sameep  76   19
(c)    Name 
0   Sapna 
1   Anmol 
2   Rishul
3   Sameep 
(d)    Name   Age
0   Sapna  16
1   Anmol  18
2   Rishul  16
3   Sameep  19

Accepted Answer

(d)Output     Name  Age
0   Sapna   16
1    Amol   18
2  Rishul   16
3  Sameep   19
ExplanationThe code creates a DataFrame df with columns 'Name' and 'Age' using a dictionary. It contains data about individual's names and ages. The DataFrame is then printed, displaying the specified columns.

Python Pandas — I

Multiple Choice Questions

Type A Very Short Answer Questions

Type B Short Answer Questionsconceptual Questions

Type C Long Answer Questions