{"id":37388,"date":"2022-02-17T18:07:09","date_gmt":"2022-02-17T12:37:09","guid":{"rendered":"https:\/\/mcq-questions.com\/?p=37388"},"modified":"2022-02-19T10:20:59","modified_gmt":"2022-02-19T04:50:59","slug":"mcq-questions-for-class-12-informatics-practices-python-pandas-part-1","status":"publish","type":"post","link":"https:\/\/mcq-questions.com\/mcq-questions-for-class-12-informatics-practices-python-pandas-part-1\/","title":{"rendered":"MCQ Questions for Class 12 Informatics Practices – Python Pandas Part 1"},"content":{"rendered":"
Question . 1.<\/p>\n
(a) p(d)Series(empty)
\n(b) p(d)Series(np.NaN)
\n(c) p(d)Series( )
\n(d) All of these
\nAnswer:
\n(c) p(d)Series( )<\/p>\n
<\/p>\n
Question 2.<\/p>\n
(a) p(d)Series(data = array, dtype = int16)
\n(b) p(d)Series(data = array, dtype = numpy.int 16)
\n(c) p(d)Series(data= array.dtype = pandas.int 16)
\n(d) All of the above
\nAnswer:
\n(b) p(d)Series(data = array, dtype = numpy.int16)<\/p>\n
Question 3.<\/p>\n
(a) index
\n(b) size
\n(c) itemsize
\n(d) ndim
\nAnswer:
\n(d) ndim<\/p>\n
Question 4.<\/p>\n
(a) index
\n(b) size
\n(c) itemsize
\n(d) ndim
\nAnswer:
\n(c) itemsize<\/p>\n
Question 5.<\/p>\n
(a) index
\n(b) size
\n(c) itemsize
\n(d) ndim
\nAnswer:
\n(b) size<\/p>\n
Question 6.<\/p>\n
(a) hasnans
\n(b) nbytes
\n(c) ndim
\n(d) dtype
\nAnswer:
\n(b) nbytes<\/p>\n
Question 7.<\/p>\n
(a) hasnans
\n(b) n bytes
\n(c) n dim
\n(d) dtype
\nAnswer:
\n(a) hasnans<\/p>\n
Question 8.<\/p>\n
To display third element of a series object S, you will write<\/p>\n
(a) S(:3)
\n(b) S[2]
\n(c) S[3]
\n(d) S[:2]
\nAnswer:
\n(b) 5(2]<\/p>\n
Question 9.<\/p>\n
(a) S(:3]
\n(b) 5(3]
\n(c) 5 (3rd]
\n(d) All of these
\nAnswer:
\n(a) S(:3]<\/p>\n
Question 10.<\/p>\n
(a) head()
\n(b) head(5)
\n(c) tail()
\n(d) tail(5)
\nAnswer:
\n(c) tail(), (d) tail(5)<\/p>\n
Question 11.<\/p>\n
(a) Null
\n(b) None
\n(c) Missing
\n(d) NaN
\nAnswer:
\n(d) NaN<\/p>\n
<\/p>\n
Question 12.<\/p>\n
(a) print(SeQuestion uences.head(4))
\n(b) print (SeQuestion uences. Head(4))
\n(c) print(SeQuestion uences.heads(4)
\n(d) print(SeQuestion uences.Heads(4))
\nAnswer:
\n(a) print(SeQuestion uences.head(4))<\/p>\n
Question 13.<\/p>\n
(a) dictionary\u2019s values
\n(b) inner dictionary’s keys
\n(c) outer dictionary\u2019s keys
\n(d) None of these
\nAnswer:
\n(b) inner dictionary’s keys<\/p>\n
Question 14.<\/p>\n
(a) dictionary\u2019s values
\n(b) inner dictionary’s keys
\n(c) outer dictionary’s keys
\n(d) None of these
\nAnswer:
\n(c) outer dictionary\u2019s keys<\/p>\n
Question 15.<\/p>\n
(a) rows
\n(b) columns
\n(c) values
\n(d) datatype
\nAnswer:
\n(a) rows<\/p>\n
Question 16.<\/p>\n
(a) rows
\n(b) columns
\n(c) values
\n(d) datatype
\nAnswer:
\n(b) columns<\/p>\n
Question 17.<\/p>\n
(a) size
\n(b) shape
\n(c) values
\n(d) ndim
\nAnswer:
\n(a) size<\/p>\n
Question 18.<\/p>\n
(a) size
\n(b) shape
\n(c) values
\n(d) ndim
\nAnswer:
\n(c) values<\/p>\n
Question 19.<\/p>\n
(a) size
\n(b) shape
\n(c) values
\n(d) ndim
\nAnswer:
\n(d) ndim<\/p>\n
Question 20.<\/p>\n
(a) D1.T
\n(b) D1. Transpose
\n(c) D1.Swap
\n(d) All of these
\nAnswer:
\n(a) D1.T<\/p>\n
Question 21.<\/p>\n
(a) row( )
\n(b) column( )
\n(c) loc( )
\n(d) All of these
\nAnswer:
\n(c) loc( )<\/p>\n
<\/p>\n
Question 22.<\/p>\n
(a) DF.loc[6:9, 3:5]
\n(b) DF.loc[6:10, 3:6]
\n(c) DF.iloc(6:10, 3:6]
\n(d) DF.iloc[6:9, 3:5]
\nAnswer:
\n(c) DF.iloc[6:10,3:6]<\/p>\n
Question 23.<\/p>\n
(a) DF(4, 6] = 35
\n(b) DF(3, 5] = 35
\n(c) DF.iat(4, 6] = 35
\n(d) DF.iat(3, 5] = 35 .
\nAnswer:
\n(d) DF.iat(3, 5] = 35<\/p>\n
Question 24.<\/p>\n
(a) A scalar value
\n(b) An ndarray
\n(c) A python diet
\n(d) All of these
\nAnswer:
\n(d) All of these<\/p>\n
Question 25.<\/p>\n
(a) The standard marker for missing data in Pandas is NaN
\n(b) Series act in a way similar to that of an array
\n(c) Both (a) and (b)
\n(d) None of the above
\nAnswer:
\n(c) Both (a) and (b)<\/p>\n
Question 26.<\/p>\n
(a) remove
\n(b) del
\n(c) drop
\n(d) cancel
\nAnswer:
\n(b) del<\/p>\n
Question 27.<\/p>\n
(a) remove
\n(b) del
\n(c) drop
\n(d) cancel
\nAnswer:
\n(c) drop<\/p>\n
Question 28<\/p>\n
(a) numpy
\n(b) pandas
\n(c) Both (a) and (b)
\n(d) None of these
\nAnswer:
\n(b) pandas<\/p>\n
Question 29.<\/p>\n
(a) dataframe
\n(b) vector
\n(c) series
\n(d) All of these
\nAnswer:
\n(c) series<\/p>\n
Question 30.<\/p>\n
(a) dataframe
\n(b) vector
\n(c) series
\n(d) All of these
\nAnswer:
\n(a) dataframe<\/p>\n
Question 31.<\/p>\n
(a) none
\n(b) no
\n(c) NaN
\n(d) None of these
\nAnswer:
\n(c) NaN<\/p>\n
<\/p>\n
Question 32.<\/p>\n
(a) ctype
\n(b) atype
\n(c) ntype
\n(d) dtype
\nAnswer:
\n(d) dtype<\/p>\n
Question 33.<\/p>\n
(a) gen( )
\n(b) len( )
\n(c) gen( )
\n(d) cen( )
\nAnswer:
\n(b) len()<\/p>\n
Question 34.<\/p>\n
(a) acount( )
\n(b) allcount( )
\n(c) count( )
\n(d) dcount( )
\nAnswer:
\n(c) count( )<\/p>\n
Question 35.<\/p>\n
(a) non- value
\n(b) text
\n(c) numeric
\n(d) value
\nAnswer:
\n(d) value<\/p>\n
Question 36.<\/p>\n
(a) size
\n(b) variable
\n(c) shape
\n(d) value
\nAnswer:
\n(a) size<\/p>\n
Question 37.<\/p>\n
(a) value, size
\n(b) size, value
\n(c) size, size
\n(d) value, value
\nAnswer:
\n(b) size, value<\/p>\n
Question 38.<\/p>\n
(a) row
\n(b) record
\n(c) column
\n(d) None of these
\nAnswer:
\n(c) column<\/p>\n
Question 39.<\/p>\n
(a) loc
\n(b) voc
\n(c) doc
\n(d) toe
\nAnswer:
\n(a) loc<\/p>\n
Question 40.<\/p>\n
(a) in
\n(b) or
\n(c) more
\n(d) at
\nAnswer:
\n(d) at<\/p>\n
Question 41.<\/p>\n
(a) lot
\n(b) doc
\n(c) toe
\n(d) iat
\nAnswer:
\n(d) iat<\/p>\n
<\/p>\n
Question 42.<\/p>\n
(a) outplace()
\n(b) atplace
\n(c) inplace
\n(d) ofplace
\nAnswer:
\n(c) inplace<\/p>\n
Question 43.<\/p>\n
(a) NumPy
\n(b) Pandas
\n(c) Matplotlib
\n(d) All of these
\nAnswer:
\n(d) All of these<\/p>\n
Question 44.<\/p>\n
(a) Number Python
\n(b) Numerical Python
\n(c) Numbers in Python
\n(d) None of these
\nAnswer:
\n(b) Numerical Python<\/p>\n
Question 45.<\/p>\n
(a) Pandas
\n(b) NumPy
\n(c) Matplotlib
\n(d) All of these
\nAnswer:
\n(d) All of these<\/p>\n
Question 46.<\/p>\n
(a) Panel Data Analysis
\n(b) Panel Data Analyst
\n(c) Panel Data
\n(d) Panel Dashboard
\nAnswer:
\n(c) Panel Data<\/p>\n
Question 47.<\/p>\n
(a) Math
\n(b) Random
\n(c) Pandas
\n(d) None of these
\nAnswer:
\n(c) Pandas<\/p>\n
Question 48.<\/p>\n
(a) series
\n(b) dataframe
\n(c) Both of the above
\n(d) None of these
\nAnswer:
\n(c) Both of the above<\/p>\n
Question 49.<\/p>\n
(a) Pandas
\n(b) NumPy
\n(c) Matplotlib
\n(d) None of these
\nAnswer:
\n(c) Matplotlib<\/p>\n
Question 50.<\/p>\n
(a) float
\n(b) integer
\n(c) string
\n(d) All of these
\nAnswer:
\n(d) All of these<\/p>\n
<\/p>\n
Question 51<\/p>\n
(a) NumPy
\n(b) Pandas
\n(c) Matplotlib
\n(d) All of these
\nAnswer:
\n(b) Pandas<\/p>\n
Question 52.<\/p>\n
(a) pip install pandas
\n(b) install pandas
\n(c) pip pandas
\n(d) None of these
\nAnswer:
\n(a) pip install pandas<\/p>\n
Question 53.<\/p>\n
(a) data structure
\n(b) dataframe
\n(c) table
\n(d) None of these
\nAnswer:
\n(a) data structure<\/p>\n
Question 54.<\/p>\n
(a) dataframe
\n(b) series .
\n(c) Both of the above
\n(d) None of these
\nAnswer:
\n(b) series<\/p>\n
Question 55.<\/p>\n
(a) We can create Series from Dictionary in Python,
\n(b) Keys of dictionary become index of the series.
\n(c) Order of indexes created from Keys may not be in the same order as typed in dictionary.
\n(d) All are correct
\nAnswer:
\n(d) All are correct<\/p>\n
Question 56.<\/p>\n
(a) 3
\n(b) 2
\n(c) 1
\n(d) 0
\nAnswer:
\n(d) 0<\/p>\n
Question 57.<\/p>\n
(a) Data value
\n(b) Index
\n(c) Value
\n(d) None of these
\nAnswer:
\n(b) Index<\/p>\n
Question 58.<\/p>\n
(a) NumPy
\n(b) Pandas
\n(c) Matplotlib
\n(d) None of these
\nAnswer:
\n(b) Pandas<\/p>\n
<\/p>\n
Question 59.<\/p>\n
(a) series( )
\n(b) 5eries( )
\n(c) createSeries( )
\n(d) None of these
\nAnswer:
\n(b) Series( )<\/p>\n
Question 60.<\/p>\n
(a) Output:
\n0 10
\n1 20
\n2 30
\ndtype: int64<\/p>\n
(b) Output:
\n10
\n20
\n30
\ndtype: int64<\/p>\n
(c) Output:
\n0
\n1
\n2
\ndtype: int64
\n(d) None of these<\/p>\n
Answer:
\n(a) Output:
\n0 10
\n1 20
\n2 30
\ndtype: int64<\/p>\n
Question 61.<\/p>\n
(a) index
\n(b) data
\n(c) value
\n(d) None of these
\nAnswer:
\n(a) index<\/p>\n
Question 62.<\/p>\n
(a) 1
\n(b) 2
\n(c) 3
\n(d) 4
\nAnswer:
\n(d) 4<\/p>\n
Question 63.<\/p>\n
(a) 51 = p(d)Series(None)
\n(b) 51 = p(d)5eries()
\n(c) Both of the above
\n(d) None of these
\nAnswer:
\n(c) Both of the above<\/p>\n
Question 64.<\/p>\n
(a) 5
\n(b) 4
\n(c) 6
\n(d) None of these
\nAnswer:
\n(a) 5<\/p>\n
Question 65.<\/p>\n
(a) index of the series
\n(b) value of the series
\n(c) caption of the series
\n(d) None of these
\nAnswer:
\n(a) index of the series<\/p>\n
Question 66.<\/p>\n
(a) a 14
\nb 14
\nc 14
\ndtype: int64<\/p>\n
(b) a 14
\ndtype: int64<\/p>\n
(c) Error<\/p>\n
(d) None of these<\/p>\n
Answer:
\n(a) a 14
\nb 14
\nc 14
\ndtype: int64<\/p>\n
<\/p>\n
Question 67.<\/p>\n
(a) a 14
\nb 7
\nc 7<\/p>\n
(b) a 14
\nb 7
\ndtype: int64<\/p>\n
(c) Error<\/p>\n
(d) None of these
\nAnswer:
\n(c) Error<\/p>\n
Question 68.<\/p>\n
(a) 14 1
\n7 4
\n9 7
\ndtype: int64<\/p>\n
(b) 1 14
\n47
\n7 9
\ndtype: int64<\/p>\n
(c) Error<\/p>\n
(d) None of these
\nAnswer:
\n(b) 1 14
\n47
\n79
\ndtype: int64<\/p>\n
Question 69.<\/p>\n
(a) import pandas as pd
\n51 = p(d)Series(data = [31,28,31], index=[“Jan”,”Feb”,,,Mar”]) print(SI)<\/p>\n
(b) import pandas as pd
\nS1 = p(d)Series((31,28,31], index=[“Jan”,”Feb”,”Mar”]) print(SI)<\/p>\n
(c) Both of the above<\/p>\n
(d) None of the above<\/p>\n
Answer:
\n(c) Both of the above<\/p>\n
Question 70.<\/p>\n
(a) a 31
\ne 25
\ni 19
\no 13
\nu 7
\ndtype: int64<\/p>\n
(b) a 31
\ne 25
\ni 19
\ndtype: int64<\/p>\n
(c) Error<\/p>\n
(d) None of these
\nAnswer:
\n(a) a 31
\ne 25
\ni 19
\no 13
\nu 7
\ndtype: int64<\/p>\n
Question 71.<\/p>\n
(a) SyntaxError
\n(b) IndexError
\n(c) ValueError
\n(d) None of these
\nAnswer:
\n(c) ValueError<\/p>\n
Question 72.<\/p>\n
(a) 0 31
\n12
\n2-6
\ndtype: int64<\/p>\n
(b) 0 31
\n12
\n2-6
\n3 31
\n4 2
\ndtype: int64<\/p>\n
(c) 0 31
\n12
\n2-6
\n3 31
\n4 2
\n5 -6
\ndtype: int64<\/p>\n
(d) 0 31
\n12
\n2-6
\n3 31
\ndtype: int64<\/p>\n
Answer:
\n(d) 0 31
\n12
\n2-6
\n3 31
\ndtype: int64<\/p>\n
Question 73.<\/p>\n
(a) column
\n(b) cell
\n(c) table
\n(d) None of these
\nAnswer:
\n(a) column<\/p>\n
Question 74.<\/p>\n
(a) True
\n(b) False
\n(c) Error
\n(d) None of these
\nAnswer:
\n(a) True<\/p>\n
<\/p>\n
Question 75.<\/p>\n
(a) 3 False
\n5 False
\n1 False
\ndtype: bool<\/p>\n
(b) 3 False
\n5 False
\n1 False
\ndtype: bool<\/p>\n
(c) 3 True
\n5 True
\n1 True
\ndtype: bool<\/p>\n
(d) None of these<\/p>\n
Answer:
\n(b) 3 False
\n5 False
\n1 False
\ndtype: bool<\/p>\n
Python Pandas Class 12 MCQ Questions Part 1 Question . 1. To create an empty series object, you can use: (a) p(d)Series(empty) (b) p(d)Series(np.NaN) (c) p(d)Series( ) (d) All of these Answer: (c) p(d)Series( ) Question 2. To specify datatype int 16 for a series object,you can write: (a) p(d)Series(data = array, dtype = int16) …<\/p>\n