Quiz-Pandas-I-13-May

When you store a NaN value in a series object , Pandas requires the data type to be of__.

• Integer type

• Object type

• Floating point

• String type

Q1. Which of the following statement will import pandas?

(a) import panda as py

(b) import pandas as pd

(c) import pand as py as pd

(d) import pandas as py.pd

• a

• b

• c

• d

What is the output for the following code?

obj2=pd.Series( range(5))

print(obj2)

• 0 1

  1 2

  2 3

  3 4

  4 5

• 0 0

  1 1

  2 2

  3 3

  4 4

• 1 0

  2 1

  3 2

  4 3

  5 4

• None of the above

Predict the following output:

medals=pd.Series(15,index=range(1,6,2))

print(medals)

• 1 15

  2 15

  6 15

• 1 1

  3 1

  5 1

• 1 15

  2 15

  3 15

• 1 15

  3 15

  5 15

Which values cannot be compared using comparison operator?

• Positive

• Negative

• NaN

• float

The output of :

obj=pd.Series([7,np.NaN,2])

print(obj[2])

• 7.0

• 2.0

• NaN

• 6.0

Predict the output:

obj2=pd.Series([3.5,5.,6.5,8.])

print(obj2.size,obj2.hasnans)

• 4 False

• 4 True

• 4 F

• 3 True

What will be the output of following code?

stu={'A':44,'B':44,'C':45,"D":47}

s8=pd.Series(stu)

print(s8[ :2]*100)

• A 4400

  B 4000

  dtype: int64

• A 4300

  B 4000

  dtype: int64

• A 4300

  B 3000

  dtype: int64

• A 3300

  B 4000

  dtype: int64

If a series object s1 is created with following values:

a=np.arange(9,13)

o1=pd.Series(index=a,data=a*2)

then the output of the following query will be:

o1[: :-7]

• 12 22

  dtype: int32

• 11 24

  dtype: object

• 12 24

  dtype: int32

• 11 21

  dtype: object

n1=np.arange(9,33)

obj=pd.Series(index=n1,data=an1*2)

o7.head()

will return the output as_.

• last 5 rows

• first 5 rows including label

• first 5 rows

• None of the above