MCQ DataFrame-II

Q1.	In Pandas _______________ is used to store data in multiple columns. a. Series b. DataFrame c. Both of the above d. None of the above Ans. b. DataFrame
Q2.	A _______________ is a two-dimensional labelled data structure . a. DataFrame b. Series c. List d. None of the above Ans. a. DataFrame
Q3.	. _____________ data Structure has both a row and column index. a. List b. Series c. DataFrame d. None of the above Ans. c. DataFrame
Q4.	Which library is to be imported for creating DataFrame? a. Python b. DataFrame c. Pandas d. Random Ans. c. Pandas
Q5.	Which of the following function is used to create DataFrame? a. DataFrame( ) b. NewFrame( ) c. CreateDataFrame( ) d. None of the Above Ans. a. DataFrame( )
Q6.	The following code create a dataframe named ‘D1’ with _______________ columns. import pandas as pd D1 = pd.DataFrame([1,2,3] ) a. 1 b. 2 c. 3 d. 4 Ans. a. 1
Q7.	We can create DataFrame from _____ a. Numpy arrays b. List of Dictionaries c. Dictionary of Lists d. All of the above Ans. d. All of the above
Q8.	Which of the following is used to give user defined column index in DataFrame? a. index b. column c. columns d. colindex Ans. c. columns
Q9.	The following code create a dataframe named ‘D1’ with ___________ columns. import pandas as pd LoD = [{‘a’:10, ‘b’:20}, {‘a’:5, ‘b’:10, ‘c’:20}] D1 = pd.DataFrame(LoD) a. 1 b. 2 c. 3 d. 4 Ans. c. 3
Q10.	The following code create a dataframe named ‘D1’ with ______ rows. import pandas as pd LoD = [{'a':10, 'b':20}, {'a':5, 'b':10, 'c':20}] D1 = pd.DataFrame(LoD) a. 0 b. 1 c. 2 d. 3 Ans. c. 2
Q11.	When we create DataFrame from List of Dictionaries, then dictionary keys will become ____________ a. Column labels b. Row labels c. Both of the above d. None of the above Ans. a. Column labels
Q12.	When we create DataFrame from List of Dictionaries, then number of columns in DataFrame is equal to the _______ a. maximum number of keys in first dictionary of the list b. maximum number of different keys in all dictionaries of the list c. maximum number of dictionaries in the list d. None of the above Ans. b. maximum number of different keys in all dictionaries of the list
Q13.	When we create DataFrame from List of Dictionaries, then number of rows in DataFrame is equal to the ____________ a. maximum number of keys in first dictionary of the list b. maximum number of keys in any dictionary of the list c. number of dictionaries in the list d. None of the above Ans. c. number of dictionaries in the list
Q14.	In given code dataframe ‘D1’ has ________ rows and _______ columns. import pandas as pd LoD = [{‘a’:10, ‘b’:20}, {‘a’:5, ‘b’:10, ‘c’:20},{‘a’:7, ‘d’:10, ‘e’:20}] D1 = pd.DataFrame(LoD) a. 3, 3 b. 3, 4 c. 3, 5 d. None of the above Ans. c. 3, 5
Q15.	When we create DataFrame from Dictionary of List then Keys becomes the _____________ a. Row Labels b. Column Labels c. Both of the above d. None of the above Ans. b. Column Labels
Q16.	When we create DataFrame from Dictionary of List then List becomes the ________________ a. Row Labels b. Column Labels c. Values of rows d. None of the above Ans. c. Values of rows
Q17.	In given code dataframe ‘D1’ has _____ rows and ______ columns. import pandas as pd LoD = {“Name” : [“Amit”, “Anil”,”Ravi”], “RollNo” : [1,2,3]} D1 = pd.DataFrame(LoD) a. 3, 3 b. 3, 2 c. 2, 3 d. None of the above Ans. b. 3, 2
Q18.	We can create a DataFrame using a single series. (T/F) a. True b. False Ans. a. True
Q19.	DataFrame created from single Series has ____ column. a. 1 b. 2 c. n (Where n is the number of elements in the Series) d. None of the above Ans. a. 1
Q20.	In given code dataframe ‘D1’ has _____ rows and _____ columns. import pandas as pd S1 = pd.Series([1, 2, 3, 4], index = ['a', 'b','c','d']) S2 = pd.Series([11, 22, 33, 44], index = ['a', 'bb','c','dd']) D1 = pd.DataFrame([S1,S2]) a. 2, 4 b. 4, 6 c. 4, 4 d. 2, 6 Ans. d. 2, 6
Q21.	In DataFrame, by default new column added as the _____________ column a. First (Left Side) b. Second c. Last (Right Side) d. Random Ans. c. Last (Right Side)
Q22.	We can add a new row to a DataFrame using the _____________ method a. rloc[ ] b. iloc[ ] c. loc[ ] d. None of the above Ans. c. loc[ ]
Q23.	D1[ : ] = 77 , will set __________ values of a Data Frame ‘D1’ to 77. a. Only First Row b. Only First Column c. All d. None of the above Ans. c. All
Q24.	In the following statement, if column ‘Rollno’ already exists in the DataFrame ‘D1’ then the assignment statement will _____________ D1['Rollno'] = [1,2,3] #There are only three rows in DataFrame D1' a. Return error b. Replace the already existing values. c. Add new column d. None of the above Ans. b. Replace the already existing values.
Q25.	In the following statement, if column ‘Rollno’ already exists in the DataFrame ‘D1’ then the assignment statement will __________ D1['Rollno'] = [1, 2] #There are only three rows in DataFrame D1' a. Return error b. Replace the already existing values. c. Add new column d. None of the above Ans. a. Return error
Q26.	In the following statement, if column ‘Rollno’ already exists in the DataFrame ‘D1’ then the assignment statement will __________ D1['Rollno'] = 11 a. Return error b. Change all values of column Roll numbers to 11 c. Add new column d. None of the above Ans. b. Change all values of column Roll numbers to 11
Q27.	DF1.loc[ ] method is used to ______ # DF1 is a DataFrame a. Add new row in a DataFrame ‘DF1’ b. To change the data values of a row to a particular value c. Both of the above d. None of the above Ans. c. Both of the above
Q28.	Which method is used to delete row or column in DataFrame? a. delete( ) b. del( ) c. drop( ) d. None of the above Ans. c. drop( )
Q29.	To delete a row, the parameter axis of function drop( ) is assigned the value ______________ a. 0 b. 1 c. 2 d. 3 Ans. a. 0
Q30.	To delete a column, the parameter axis of function drop( ) is assigned the value _____________ a. 0 b. 1 c. 2 d. 3 Ans. b. 1
Q31.	The following statement will _________ df = df.drop(['Name', 'Class', 'Rollno'], axis = 1) #df is a DataFrame object a. delete three columns having labels ‘Name’, ‘Class’ and ‘Rollno’ b. delete three rows having labels ‘Name’, ‘Class’ and ‘Rollno’ c. delete any three columns d. return error Ans. a. delete three columns having labels ‘Name’, ‘Class’ and ‘Rollno’
Q32.	If the DataFrame has more than one row with the same label, then DataFrame.drop( ) method will delete _____ a. first matching row from it. b. all the matching rows from it c. last matching row from it. d. Return Error Ans. b. all the matching rows from it
Q33.	Write the code to remove duplicate row labelled as ‘R1’ from DataFrame ‘DF1’ a. DF1 = DF1.drop(‘R1’, axis = 0) b. DF1 = DF1.drop(‘R1’, axis = 1) c. DF1 = DF1.del(‘R1’, axis = 0) d. DF1 = DF1.del(‘R1’, axis = 1) Ans. a. DF1 = DF1.drop(‘R1’, axis = 0)
Q34.	Which method is used to change the labels of rows and columns in DataFrame? a. change( ) b. rename( ) c. replace( ) d. None of the above Ans. b. rename( )
Q35.	The parameter axis=’index’ of rename( ) function is used to specify that the ________ a. row and column label is to be changed b. column label is to be changed c. row label is to be changed d. None of the above Ans. c. row label is to be changed
Q36.	What will happen if in the rename( ) function we pass only a value for a row label that does not exist? a. it returns an error. b. matching row label will not change . c. the existing row label will left as it is. d. None of the above Ans. c. the existing row label will left as it is.
Q37.	What value should be given to axis parameter of rename function to alter column name? a. column b. columns c. index d. None of the above Ans. b. columns
Q38.	The following statement is __________ >>> DF=DF.rename({‘Maths’:’Sub1′,‘Science’:’Sub2′}, axis=’index’) #DF is a DataFrame a. altering the row labels b. altering the column labels c. altering the row and column labels (both) d. Error Ans. a. altering the row labels
Q39.	Write a statement to delete column labelled as ‘R1’ of DataFrame ‘DF’.. a. DF= DF.drop(‘R1’, axis=0) b. DF= DF.del(‘R1’, axis=0) c. DF= DF.drop(‘R1’, axis=0, row = ‘duplicate’) d. None of the above Ans. d. None of the above
Q40.	Which of the following parameter is used to specify row or column in rename function of DataFrame? a. rowindex b. colindex c. Both of the above d. index Ans. d. index
Q41.	Which of the following are ways of indexing to access Data elements in a DataFrame? a. Label based indexing b. Boolean Indexing c. All of the above d. None of the above Ans. c. All of the above
Q42.	DataFrame.loc[ ] is an important method that is used for ____________ with DataFrames a. Label based indexing b. Boolean based indexing c. Both of the above d. None of the above Ans. a. Label based indexing
Q43.	The following statement will return the column as a _______ >>> DF.loc[: , 'Name'] #DF is a DataFrame object a. DataFrame b. Series c. List d. Tuple Ans. b. Series
Q44.	The following two statement will return _______________ >>> DF.loc[:,'Name'] #DF is a DataFrame object >>> DF['Name'] #DF is a DataFrame object a. Same Output b. Name column of DataFrame DF c. Both of the above d. Different Output Ans. c. Both of the above
Q45.	The following statement will display ________ rows of DataFrame ‘DF’ print(df.loc[[True, False,True]]) a. 1 b. 2 c. 3 d. 4 Ans. b. 2
Q46.	We can use the ______ method to merge two DataFrames a. merge( ) b. join( ) c. append( ) d. drop( ) Ans. c. append( )
Q47.	We can merge/join only those DataFrames which have same number of columns.(T/F) a. True b. False Ans. b. False
Q48.	What we are doing in the following statement? dF1=dF1.append(dF2) #dF1 and dF2 are DataFrame object a. We are appending dF1 in dF2 b. We are appending dF2 in dF1 c. We are creating Series from DataFrame d. None of the above Ans. b. We are appending dF2 in dF1
Q49.	. ______________ parameter is used in append( ) function of DataFrame to get the column labels in sorted order. a. sorted b. sorter c. sort d. None of the above Ans. c. sort
Q50.	. ________ parameter of append( ) method may be set to True when we want to raise an error if the row labels are duplicate. a. verify_integrity b. verifyintegrity c. verify.integrity d. None of the above Ans. a. verify_integrity
Q51.	The ________________parameter of append() method in DataFrame may be set to True, when we do not want to use row index labels. a. ignore_index_val b. ignore_index_value c. ignore_index d. None of the above Ans. c. ignore_index
Q52.	The append() method of DataFrame can also be used to append ____________to a DataFrame a. Series b. Dictionary c. Both of the above d. None of the above Ans. c. Both of the above
Q53.	Which of the following attribute of DataFrame is used to display row labels? a. columns b. index c. dtypes d. values Ans. b. index
Q54.	Which of the following attribute of DataFrame is used to display column labels? a. columns b. index c. dtypes d. values Ans. a. columns
Q55.	Which of the following attribute of DataFrame is used to display data type of each column in DataFrame? a. Dtypes b. DTypes c. dtypes d. datatypes Ans. c. dtypes
Q56.	Which of the following attribute of DataFrame display all the values from DataFrame? a. values b. Values c. val d. Val Ans. a. values
Q57.	Which of the following attribute of DataFrame display the dimension of DataFrame a. shape b. size c. dimension d. values Ans. a. shape
Q58.	If the following statement return (5, 3) it means _____ >>> DF.shape #DF is a DataFrame object a. DataFrame DF has 3 rows 5 columns b. DataFrame DF has 5 rows 3 columns c. DataFrame DF has 3 rows 5 rowlabels d. None of the above Ans. b. DataFrame DF has 5 rows 3 columns
Q59.	Transpose the DataFrame means _____________ a. Row indices and column labels of the DataFrame replace each other’s position b. Doubling the number of rows in DataFrame c. Both of the above d. None of the above Ans. a. Row indices and column labels of the DataFrame replace each other’s position
Q60.	Which of the following is used to display first 2 rows of DataFrame ‘DF’? a. DF.head( ) b. DF.header(2) c. DF.head(2) d. None of the above Ans. c. DF.head(2)
Q61.	Which of the following statement is Transposing the DataFrame ‘DF1’? a. DF1.transpose b. DF1.T c. DF1.Trans d. DF1.t Ans. b. DF1.T
Q62.	Following statement will display ___________ rows from DataFrame ‘DF1’. >>> DF1.head() a. All b. 2 c. 3 d. 5 Ans. d. 5
Q63.	Which of the following function display the last ‘n’ rows from the DataFrame? a. head( ) b. tail( ) c. Tail( ) d. None of the above Ans. b. tail( )
Q64.	Which property of dataframe is used to check that dataframe is empty or not? a. isempty b. IsEmpty c. empty d. Empty Ans. c. empty
Q65.	Write the output of the statement >>>df.shape , if df has the following structure.       Name     Class      Rollno 0    Amit       6                  1 1    Anil         7                  2 2    Ravi        8                  3 a. (3, 4) b. (4, 3) c. (3, 3) d. None of the above Ans. c. (3, 3)
Q66.	Write the output of the statement >>>df.size , if df has the following structure:       Name     Class      Rollno 0    Amit       6                  1 1    Anil         7                  2 2    Ravi        8                  3 a. 9 b. 12 c. 6 d. None of the above Ans. a. 9
Q67.	Write the output of the statement >>>df.empty , if df has the following structure:       Name     Class      Rollno 0    Amit       6                  1 1    Anil         7                  2 2    Ravi        8                  3 a. True b. False c. Yes d. None of the above Ans. b. False
Q68.	Parameters of read_csv( ) function is _____ a. sep b. header c. Both of the above d. None of the above Ans. c. Both of the above
Q69.	Which of the following function is used to load the data from the CSV file into a DataFrame? a. read.csv( ) b. readcsv( ) c. read_csv( ) d. Read_csv( ) Ans. c. read_csv( )
Q70.	The default value for sep parameter is _________ a. comma b. semicolon c. space d. None of the above Ans. c. space
Q71.	Write statement to display the row labels of ‘DF’. a. DF.Index b. DF.index( ) c. DF.index d. DF.row_index Ans. c. DF.index
Q72.	Write statement to display the column labels of DataFrame ‘DF’ a. DF.Column b. DF.column c. DF.columns d. DF.Columns Ans. c. DF.columns
Q73.	Display first row of dataframe ‘DF’ a. print(DF.head(1)) b. print(DF[0 : 1]) c. print(DF.iloc[0 : 1]) d. All of the above Ans. d. All of the above
Q74.	Display last two rows from dataframe ‘DF’ a. print(DF[-2 : -1]) b. print(DF.iloc[-2 : -1]) c. print(DF.tail(2)) d. All of the above Ans. c. print(DF.tail(2))
Q75.	Write statement to display the data types of each column of dataframe ‘DF’. a. DF.types( ) b. DF.dtypes c. DF.dtypes( ) d. None of the above Ans. b. DF.dtypes
Q76.	Write statement to display the dimension of dataframe ‘DF’. a. DF.dim b. DF.ndim c. DF.dim( ) d. None of the above Ans. b. DF.ndim
Q77.	Write statement to transpose dataframe DF. a. DF.T b. DF.transpose c. DF.t d. DF.T( ) Ans. a. DF.T
Q78.	Write statement to display first two columns of dataframe ‘DF’. a. DF[DF.columns[ 0 : 2 ] ] b. DF.columns[ 0 : 2 ] c. Both of the above d. None of the above Ans. a. DF[DF.columns[ 0 : 2 ] ]
Q79.	Write statement to display the shape of dataframe ‘DF’. a. DF.Shape b. DF.shape c. DF.shapes d. DF.Shapes Ans. b. DF.shape
Q80.	Write a statement to Check if DF is empty or it contains data. a. DF.Empty b. DF.empty( ) c. DF.empty d. None of the above Ans. c. DF.empty
Consider the DataFrame ‘DF’ given below and answer the questions from Q81 to Q90. Following DataFrame ‘DF’ containing year wise sales figures for five sales persons 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 Python DataFrame ‘DF’
Q81.	Write a statement to append the DataFrame ‘DF2’ to the DataFrame ‘DF’ a. DF.append(DF2) b. DF2.append(DF) c. DF2.update(DF) d. None of the above Ans. a. DF.append(DF2)
Q82.	Write a statement to display the sales made by all sales persons in the year 2017. a. print(DF.loc[: , 2017]) b. print(DF[2017]) c. Both of the above d. None of the above Ans. print(DF.loc[2017])
Q83.	Write a statement to add new column for another year ‘2018’ with values 55000, 65000, 75000, 85000, 95000 a. DF[2018] = 55000, 65000, 75000, 85000, 95000 b. DF[2018] = [55000, 65000, 75000, 85000, 95000] c. DF[2018] = (55000, 65000, 75000, 85000, 95000) d. All of the above Ans. d. All of the above
Q84.	Write a statement to add new row for ‘Raman’ with values 55000, 66000, 77000, 88000 a. DF.loc[‘Raman’] = 55000, 66000, 77000, 88000 b. DF.loc[‘Raman’] = [55000, 66000, 77000, 88000] c. Both of the above d. None of the above Ans. c. Both of the above
Q85.	Raman was caught in the case of cheating so his Boss decided to set his sales of all years to 0(Zero). Help him to write the code for same. a. DF.loc[‘Raman’] = {0} b. DF.loc[‘Raman’] = [0] c. DF.loc[‘Raman’] = 0 d. All of the above Ans. c. DF.loc[‘Raman’] = 0
Q86.	Write a statement to delete the record of ‘Shruti’ a. print(DF.drop(‘Shruti’,axis=0)) b. print(DF.drop(‘Shruti’)) c. both of the above d. none of the above Ans. c. both of the above
Q87.	Write a statement to delete a column having column label as 2017. a. print(DF.drop(2017,axis=0)) b. print(DF.drop(2017,axis=1)) c. print(DF.drop(‘2017’,axis=1)) d. All of the above Ans. b. print(DF.drop(2017,axis=1))
Q88.	Write a statement to delete two columns having column label as 2017 and 2016 a. print(DF.drop([2017, 2016], axis=1)) b. print(DF.drop((2017, 2016), axis=1)) c. Both of the above d. print(DF.drop([2017,2016],axis=0)) Ans. a. print(DF.drop([2017, 2016], axis=1))
Q89.	Replace the row label ‘Ankit’ with ‘Ankita’ in dataframe ‘DF’ a. DF.Rename({‘Ankit’ : ‘Ankita’}) b. DF.rename({‘Ankit’ : ‘Ankita’}) c. DF.repalce({‘Ankit’:’Ankita’}) d. None of the above Ans. b. DF.rename({‘Ankit’ : ‘Ankita’})
Q90.	Replace the column label from 2016 to 2020. a. DF.rename({2016 : 2020}, axis = ‘columns’) b. DF.rename({2016 : 2020}, axis = ‘index’) c. DF.rename({2016 : 2020}, axis = ‘column’) d. DF.rename({2016 : 2020}, axis = columns) Ans. a. DF.rename({2016 : 2020},axis = ‘columns’)
Consider the DataFrame ‘DF’ given below and answer the questions from Q91 to Q100. Following DataFrame ‘DF’ containing marks of five students in three subjects. Harry Kiran Anuj Karan Rounaq Science 85 82 65 60 90 Maths 90 95 85 80 75 English 80 85 75 70 60 Python DataFrame
Q91.	Display the marks of Harry in Maths Subject. a. print(DF.loc[‘Maths’, ‘Harry’]) b. print(DF.Loc[‘Maths’, ‘Harry’]) c. print(DF.loc(‘Maths’, ‘Harry’)) d. None of the above Ans. a. DF.loc[‘Maths’, ‘Harry’]
Q92.	Display the marks of Karan in all Subjects a. print(DF.loc[‘Science’ : ‘English’, ‘Karan’]) b. print(DF[‘Karan’]) c. Both of the above d. None of the above Ans. c. Both of the above
Q93.	Display marks of Karan and Rounaq in Maths and Science a. print(DF.loc[‘Science’ : ‘Maths’, ‘Karan’ : ‘Rounaq’]) b. print(DF.loc[‘Science’ : ‘Maths’, [‘Karan’ : ‘Rounaq’]]) c. Both of the above d. None of the above Ans. a. print(DF.loc[‘Science’ : ‘Maths’, ‘Karan’ : ‘Rounaq’])
Q94.	Display marks of all students in Maths and Science. a. print(DF.loc[‘Maths’ : ‘Science’]) b. print(DF.loc[‘Science’ : ‘Maths’]) c. Both of the above d. None of the above Ans. b. print(DF.loc[‘Science’ : ‘Maths’])
Q95.	Write a statement to check that in which subject kiran scored more than 90. a. DF.loc[ : , ‘Kiran’] >= 90 b. DF.loc[:, ‘Kiran’] < 90 c. DF.loc[: , ‘Kiran’] > 90 d. None of the above Ans. c. DF.loc[: , ‘Kiran’] > 90
Q96.	Write a statement to rename the subject ‘Maths’ to ‘Mathematics’ a. DF.ren({“Maths” : “Mathematics”}) b. DF.Rename({“Maths”:”Mathematics”}) c. DF.rename({“Maths”:”Mathematics”}) d. DF.replace({“Maths”:”Mathematics”}) Ans. c. DF.rename({“Maths”:”Mathematics”})
Q97.	Write a statement to remove column labelled as ‘Harry’ a. print(DF.drop(‘Harry’, axis = 0)) b. print(DF.drop(‘Harry’, axis = 1)) c. Both of the above d. None of the above Ans. b. print(DF.drop(‘Harry’, axis = 1))
Q98.	Write a statement to increase five marks of all students in all subjects. a. DF[ : ] = DF[ : ]+5 b. DF[ : ] = DF[ : ]+[5] c. Both of the above d. None of the above Ans. a. DF[ : ] = DF[ : ]+5
Q99.	Write a statement to add new column labelled ‘Ruby’ with values 85, 75, 79. a. DF[Ruby]=[85, 75, 79] b. DF[‘Ruby’] = [85, 75, 79] c. DF[‘Ruby’=[85,75,79]] d. None of the above Ans. b. DF[‘Ruby’] = [85, 75, 79]
Q100.	Write a statement to increase marks of ‘Anuj’ in ‘Maths’ Subject by 10. a. DF.loc[‘Maths’, ‘Anuj’]=DF.loc[‘Maths’, ‘Anuj’]+10 b. DF.loc[‘Anuj’, ‘Maths’]=DF.loc[‘Maths’, ‘Anuj’]+10 c. DF.loc[‘Anuj’, ‘Maths’]=DF.loc[‘Anuj’, ‘Maths’]+10 d. None of the above Ans. a. DF.loc[‘Maths’, ‘Anuj’]=DF.loc[‘Maths’, ‘Anuj’]+10