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Python Pandas MCQs
Pandas are a Python package that is used to manipulate large data sets. Analysis, cleaning, investigating, and modifying data are some of the features available in this program. Python module pandas provide quick, versatile, and expressive data structures that are designed to enable working with "relational" or "labeled" data both easy and intuitive to use. It aspires to serve as the essential highlevel building block for performing realistic, realworld data analysis in Python at the highest level of abstraction. Additionally, it aspires to be the most powerful and flexible opensource data analysis/manipulation tool available in any language, with the ability to run in any environment. Pandas make it possible to evaluate large amounts of data and provide conclusions based on statistical theory. Pandas are capable of cleaning up jumbled data sets and making them readable and relevant. In data science, it is critical to have data that is relevant.
Python Pandas MCQs: This section contains multiplechoice questions and answers on Python Pandas. These MCQs are written for beginners as well as advanced, practice these MCQs to enhance and test the knowledge of Python Pandas.
List of Python Pandas MCQs
1. What will be the output of following code?
import pandas as pd
series1 = pd.Series([10,20,30,40,50])
print (series1)

0 10
1 20
2 30
3 40
4 50
dtype: int64

1 10
2 20
3 30
4 40
5 50
dtype: int64

0 10
1 20
2 30
3 40
4 50
dtype: float32
 None of the above mentioned
Answer: A)
0 10
1 20
2 30
3 40
4 50
dtype: int64
Explanation:
Pandas Series is a onedimensional labeled array that may carry data of any type. It is used to store text, numbers, and other data like integers, strings, float, python objects, etc. The index labels are used to refer to the labels on the axes as a whole. Pandas Series can be considered as a column in an Excel file.
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2. Observe the following code and identify what will be the output?
import pandas as pd
Series1 = pd.Series([10,20,30,40,50])
Series2 = Series1*2
print(Series1)
print(Series2)

1 10
2 20
3 30
4 40
5 50
dtype: int64
1 20
2 40
3 60
3 80
5 100
dtype: int64

0 10
1 20
2 30
3 40
4 50
dtype: int64
0 20
1 40
2 60
3 80
4 100
dtype: int64

0. 10
1. 20
2. 30
3. 40
4. 50
dtype: int64
0. 20
1. 40
2. 60
3. 80
4. 100
dtype: int64

10
20
30
40
50
dtype: int64
20
40
60
80
100
dtype: int64
Answer: B)
0 10
1 20
2 30
3 40
4 50
dtype: int64
0 20
1 40
2 60
3 80
4 100
dtype: int64
Explanation:
in the above code, Series1 is having a list of numbers from 10 to 50. Series2 = Series1*2, which means every element of Series1 will be multiplied by 2 and then will print.
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3. What will be the output of following code?
import pandas as pnd
pnd.Series([1,2], index= ['a','b','c'])
 Syntax Error
 Index Error
 Value Error
 None of the above mentioned
Answer: C) Value Error
Explanation:
In the above code, value error will be.
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4. A series object is size mutable.
 True
 False
Answer: B) False
Explanation:
A series object is size mutable. Series Objects are variable in terms of their values, but they are immutable in terms of their sizes. Vector operation refers to the fact that when we apply a function or expression to an object, it is applied to each individual item in the object.
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5. A Dataframe object is value mutable.
 True
 False
Answer: A) True
Explanation:
Sequence Objects are mutable in terms of their values, but they are not mutable in terms of their sizes. When we apply a function or expression to an object, it is applied to each individual item in the object, which is known as vector operation.
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6. What will be the minimum number of arguments require to pass in pandas series?
 2
 3
 4
 None of the above mentioned
Answer: D) None of the above mentioned
Explanation:
There will be 1 number of arguments requires to pass in pandas series.
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7. Amongst which of the following is / are used to analyze the data in pandas.
 Dataframe
 Series
 Both A and B
 None of the mentioned above
Answer: C) Both A and B
Explanation:
We can use series and dataframe to analyze the data in Pandas. Series is one onedimensional labeled array that can store any data type like integers, strings, floatingpoint numbers, Python objects, etc. A DataFrame is a 2dimensional labeled data structure with columns that can be of a variety of different kinds. We can think of it as a spreadsheet, a SQL table, or a dict of Series objects. It is one of the most widely used Pandas objects.
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8. During the execution of following code, what will be the response, we get 
import pandas as pd
s =pd.Series([1,2,3,4,5],index= ['a','b','c','d','e'])
print(s['f'])
 KeyError
 IndexError
 ValueError
 None of the above mentioned
Answer: A) KeyError
Explanation:
We will get "KeyError" during the execution of above mentioned code.
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9. Amongst which of the following is a correct syntax for panda's dataframe?
 Pandas.DataFrame(data, index, dtype, copy)
 pandas.DataFrame( data, index, columns, dtype, copy)
 pandas.DataFrame(data, index, dtype, copy)
 pandas.DataFrame( data, index, rows, dtype, copy)
Answer: A) pandas.DataFrame( data, index, columns, dtype, copy)
Explanation:
A syntax of pandas.DataFrame( data, index, columns, dtype, copy).
 data  data can be represented in a variety of ways, including ndarray, series, map, lists, dict, constants, and another DataFrame.
 index  for the row labels, the index to be used for the resulting frame is optional default index. If no index is provided, np.arange(n) is used.
 columns  the optional default syntax for column labels is np.arange, which stands for numeric range (n). When there is no index passed, the following is true.
 dtype  dtype identifies the data type of each column.
 copy  this command is used for data copying if the default value is False, else it is not used.
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10. Amongst which of the following can be used to create various inputs using pandas DataFrame.
 Lists, dict
 Series
 Numpy ndarrays and Another DataFrame
 All of the above mentioned
Answer: D) All of the above mentioned
Explanation:
A pandas DataFrame can be created using various inputs like Lists, dict, Series, Numpy ndarrays, Another DataFrame.
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11. Observe the following code and identify what will be the output when we run following code 
Import pandas as pd
Import numpy as np
df = pd.DataFrame(np.array([[4,6,9],[5,1,3]]))
print(df.shape)
 SyntaxError: invalid syntax
 KeyError
 IndexError
 None of the mentioned above
Answer: A) SyntaxError: invalid syntax
Explanation:
When we run the code, invalid syntax error will be reflected.
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12. Amongst which of the following is / are false statement / statements 
 iteritems() returns each column's value in form of series object.
 tail() returns any number of bottom rows by specifying values of number's argument.
 A and B both
 None of the mentioned above
Answer: D) None of the mentioned above
Explanation:
The iteritems() returns each column's value in form of series object and tail() returns any number of bottom rows by specifying values of number's argument. Hence, nothing is a false statement mentioned above.
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13. Observe the following code and identify what will be the output when we run following code 
import pandas as pd
df = pd.DataFrame()
print (df)

Empty DataFrame
Columns: []
Index: []

Empty Series
Columns: [5]
Index: [0]

Empty DataFrame
Columns: [2]
Index: [3]
 None of the mentioned above
Answer: A)
Empty DataFrame
Columns: []
Index: []
Explanation:
In the above code, we dint pass any argument in pd.DataFrame() so it will give Empty DataFrame and null values in Columns: [] and Index: [].
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14. Amongst which of the following is / are not correct to access individual item from dataframe 'df'.
 df.iat[2,2]
 df.loc[2,2]
 df.at[2,2]
 df[0,0]
Answer: D) df[0,0]
Explanation:
df[0,0] is incorrect.
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15. Amongst which of the following is / are not an iterative function for dataframe?
 iterrows()
 itercolumns()
 iteritems()
 All of the mentioned above
Answer: B) itercolumns()
Explanation:
The itercolumns() is not an iterative function for dataframe.
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16. What will be output of following code –?
import pandas as pd
data = [['Anuj',21],['Rama',25],['Kapil',22]]
df = pd.DataFrame(data,columns=['Name','Age'])
print (df)

Name Age
0 Anuj 21
1 Rama 25
2 Kapil 22

Name Age
0 Anuj 21
1 Kapil 22
2 Rama 25

Name Age
0 Kapil 22
1 Rama 25
2 Anuj 21

Name Age
0 Rama 25
1 Anuj 21
2 Kapil 22
Answer: A)
Name Age
0 Anuj 21
1 Rama 25
2 Kapil 22
Explanation:
In the above code, the data is including name and age of the candidates like [['Anuj',21],['Rama',25],['Kapil',22]]. And in the next line of the code df = pd.DataFrame(data,columns=['Name','Age']), name and age is strong in df and when we print(df), this will print the name and age of the candidates on screen.
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17. What will be output of following code –?
import pandas as pd
S1=pd.Series([100,200,300,400,500],index=['A','B','C','D','E'])
S2=pd.Series([1,2,3,4,5],index=['A','B','C','D','E'])
print(S1*S2)

A 10
B 40
C 90
D 160
E 250
dtype: int64

A 100
B 400
C 900
D 1600
E 2500
dtype: int64

A 1000
B 4000
C 9000
D 16000
E 25000
dtype: int64

A 100
B 4000
C 900
D 16000
E 2500
dtype: int64
Answer: B)
A 100
B 400
C 900
D 1600
E 2500
dtype: int64
Explanation:
In the above code, we have taken a series of numbers of 100,200,300,400,500 and index value like 'A','B','C','D','E'. In the next line of code, Series of numbers is 1,2,3,4,5 and index value is 'A','B','C','D','E'. In the third line of code, we are multiplying series 1and series 2 so the outcome of the program will be multiplication of series numbers.
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18. What will be output of following code?
import numpy as np
array1=np.array([100,200,300,400,500,600,700])
print(array1[1:5:2])
 [200 300]
 [200 700]
 [200 400]
 [200 400]
Answer: C) [200 400]
Explanation:
When we will run the code, get [200 400] as output.
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19. Indexing in Series is similar to that for NumPy arrays.
 True
 False
Answer: A) True
Explanation:
Indexing in Series is analogous to indexing in NumPy arrays, and it is used to retrieve entries inside a series of elements. There are two sorts of indexes: positional indexes and labelled indexes. Positional indexes accept an integer value that corresponds to their position in the series starting from zero, whereas labelled indexes take any userdefined label as the index for the positional index.
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20. DataFrame accepts many different kinds of input.
 True
 False
Answer: A) True
Explanation:
A DataFrame is a 2dimensional labelled data structure with columns that can be of a variety of different kinds. You can think of it as a spreadsheet or a SQL table, or as a dict of Series objects arranged in a hierarchy. It is, by far, the most often encountered Pandas object. DataFrame, like Series, allows a wide variety of different types of input:
 1D ndarrays, lists, and dicts, as well as a series of 2D numpy.ndarrays
 ndarray is a structured or record ndarray
 A Sequence of Events
 Another DataFrame has been added.
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