MCQ Series

 1.   Series is ……………….. data structure of Python Pandas.

(a)                1-Dimensional

(b)                2-Dimensional

(c)          Multi Dimensional

(d)                None of these

Ans : (a)

2.   Which of the following is true?

(a)  If data is an ndarray, index must be the same length as data

(b)  Series is a one-dimensional labeled array capable of holding any data type.

(c) Both A and B

(d)  None of the above

Ans : (c)

Explanation: Both option A and B are true.

3.   What will be output for the following code?

import pandas as pd

s = pd.Series([1,2,3,4,5],index = ['a','b','c','d','e'])

print s['a']

(a)                1

(b)                2

(c)          3

(d)                4

Ans : (a)

Explanation: Retrieve a single element using index label value.

 

4.   What will be correct syntax for pandas series?

(a)                pandas_Series( data, index, dtype, copy)

(b)                pandas.Series( data, index, dtype)

(c)          pandas.Series( data, index, dtype, copy)

(d)                pandas_Series( data, index, dtype)

Ans : (c)

Explanation: A pandas Series can be created using the following constructor : pandas.Series( data, index, dtype, copy)

 

5.   Why ndim is used?

(a)                Returns the number of elements in the underlying data.

(b)                Returns the Series as ndarray.

(c)         Returns the number of dimensions of the underlying data, by definition 1.

(d)                Returns a list of the axis labels

Ans : (c)

Explanation: ndim : Returns the number of dimensions of the underlying data, by definition 1

 

6.   What is the purpose of itemsize attributes in Series.

(a)                Return the type of data values stored in Series object.

(b)                Returns the size in byte for each item

(c)         Tells the type of the object passed to it.

(d)                None of these

Ans B,

Explanation: Returns the size in byte for each item.

 

7.   What is the meaning of NaN

(a)                Not any Number

(b)                Not any Numeric

(c)         Not available number

(d)                None of these

Ans: A, missing or empty values(NaN).

 

8.   What is the purpose of the following code:

>>>obj1=pd.Series()

>>>obj1.empty

(a)  Not any number

(b)  Empty series

(c)  Both (a) and (b)

(d)  Delete series

Ans. b, to check series empty or not.

 

9.   Which is not series attribute:

(a)  index

(b)  dtype

(c)  shape

(d)  all of these

Ans : (d)

 

10.        What will return Series.hasnAns :

(a)  It returns a tuple of shape of the data.

(b)  It returns the size of the data.

(c)  It returns True if there are any NaN values, otherwise returns false.

(d)  It returns the number of bytes in the data.

Ans: (c)

 

11.        When a Series created, are the data values mutable or immutable.

(a)        Yes, data can be modified and are mutable.

(b)        No, data can be modified and are mutable.

(c) Yes, data can not be modified and are immutable.

(d)        No, data can not be modified and are immutable.

Ans: (c)

12.        Consider the following snippet:

import pandas as pd

import numpy as np

ser=pd.Series(np.arange(10,20,1))

print(ser.head())

(a)        0    11

1    12

2    13

3    14

4    15

dtype: int32

(b)        0    10

1    11

2    12

3    13

4    14

dtype: int32

(c) 1    11

2    12

3    13

4    14

5    15

dtype: int32

(d)        Both (a) and (c)

Ans: (b)

 

13.        What is the output of the following snippet:

import pandas as pd

import numpy as np

ser=pd.Series([10,20,np.nan,40,50])

print(ser.count())

(a)        5

(b)        4

(c)     3

(d)        6

Ans : (b), It is not 5 count method returns the number of  non-NaN values is series.

 

14.        What is the output of the following snippet:

import pandas as pd1

import numpy as np1

data={'P':0,'R':1,'E':2,'M':2}

s=pd1.Series(data,index=['P','c','M','a'])

print(s)

(a)                P    0.0

c    NaN

M    2.0

a    NaN

dtype: float64

(b)                P    0.0

c    1.0

M    2.0

a    NaN

dtype: float64

(c) P    0.0

c    1.0

M   2.0

a    2.0

dtype: float64

(d)                P    NaN

c    0.0

M   2.0

a    NaN

dtype: float64

Ans: (a)

15.        What in output of the following code :

import pandas as pd

seriesCapCntry=pd.Series(['NewDelhi','WashingtonDC','London','Paris'],\

                         index=['India','USA','UK','France'])

print(seriesCapCntry['USA':'France'])

(a)        USA   WashingtonDC

France   Paris

dtype: object

(b)        UK            London

France   Paris

dtype: object

(c) USA       WashingtonDC

UK              London

France           Paris

dtype: object

(d)        None of these

Ans: (c)

 

16.        What in output of the following code :

import pandas as pd

seriesCapCntry=pd.Series(['NewDelhi','WashingtonDC','London','Paris'],\

                         index=['India','USA','UK','France'])

print(seriesCapCntry[1:3])

(a)        USA    WashingtonDC

UK           London

dtype: object

(b)        UK            London

France   Paris

dtype: object

(c) USA       WashingtonDC

UK              London

France           Paris

dtype: object

(d)        None of these

Ans: (a), it excludes the value at index position 3.

 

17.        Given a Pandas series called s1, the command which will display the las 3 rows is___________

(a)        print(s1.tail(3))

(b)        print(s1.Tail(3))

(c) print(s1.tails(3))

(d)        print(s1.Tails(3))

Ans: (a)

18.        What is the output of the following snippet?

import pandas as pd

s1=pd.Series(‘Hello’,[1,2,3,4])

print(s1)

(a)        1 Hello

2 Hello

3 Hello

4 Hello

dtype: Object

(b)        0  Hello

1  1

2  2

3   3

4   4

(c) 1  Hello

2  1

3  2

4   3

5   4

(d)        None of these

Ans : (a)

19.        What is the output of the following snippet?

import pandas as pd

import numpy as np

s1=pd.Series([10,11,20,np.NaN,44,60])

print(s1[:2])

(a)        1      11

2     20

(b)        0      10

1     11

(c) 0  11

1     20

(d)        2      20

2     NaN

Ans : (b)

 

20.        What is the output of the following snippet?

import pandas as pd

import numpy as np

s1=pd.Series([10,11,20,np.NaN,44,60])

print(s1[::2])

(a)        0    10.0

2    20.0

3     44.0

(b)        0    20.0

2    44.0

4     Nan

(c) Both (a) and (b)

(d)        None of these

Ans : (a)

 

21.        What is the output of the following snippet?

import pandas as pd

import numpy as np

s1=pd.Series([10,11,20,np.NaN,44,60])

print(s1[::-2])

(a)        5    60.0

3     NaN

1    11.0

dtype: float64

(b)        5    20.0

3     NaN

1    11.0

dtype: float64

(c) 5    440.0

3     NaN

1    11.0

dtype: float64

(d)        All of these

Ans : (a)

 

22.        Which source code is correct for following output?

a    6700

b    5600

c    5000

d    5200

dtype: int64

(a)        import pandas as pd

idx=['a','b','c','d']

s2=pd.Series([6700,5600,5000,5200])

print(s2)

(b)        import pandas as pd

idx=['a','b','c','d']

s2=pd.Series([6700,5600,5000,5200] index=idx)

print(s2)

(c) import pandas as pd

s2=pd.Series([6700,5600,5000,5200])

print(s2)

(d)        import pandas as pd

idx=['a','b','c','d']

s2=pd.Series([6700,5600,5000,5200], index=idx)

print(s2)

Ans: (d)

 

23.        What is the output of the following snippet?

import pandas as pd

info=pd.Series(data=[111,222,333])

print(info>112)

(a)                0    True

1     True

2     True

dtype: bool

(b)                0    False

1     True

2     False

dtype: bool

(c) 0    False

1     True

2     True

dtype: bool

(d)                0    False

1     True

2     True

dtype: bool

Ans: (d)

 

24.        Which source code is correct for following output?

1    222

2    333

dtype: int64

(a)                import pandas as pd

info=pd.Series(data=[111,222,333])

print(info[info>112])

(b)                import pandas as pd

info=pd.Series(data=[111,222,333])

print(info[info>=112])

(c) import pandas as pd

info=pd.Series(data=[111,222,333])

print(info[info==112])

(d)                Both (a) and (b)

Ans (d)

 

25.        Fill in the blanks:

import pandas as ________

s2=pds.Series([6700,5600,4000,5200], index=['a','b','c','d'])

print(s2.sort_values(___________))

(a)        pd, ascending=True

(b)        Pds, ascending=False

(c) pds, ascending=True

(d)        None of these

Ans: (d)