Question 1

Question 1Assertion (A). To use the Pandas library in a Python program, one must import it.Reasoning (R). The only alias name that can be used with the Pandas library is pd.Both A and R are true and R is the correct explanation of A.Both A and R are true but R is not the correct explanation of A.A is true but R is false.A is false but R is true.

Accepted Answer

A is true but R is false.ExplanationIn order to work with Pandas in Python, we need to import the Pandas library into our Python environment using the statement import pandas as pd. While pd is a common alias used with the Pandas library, it's not the only alias that can be used. We can import Pandas using other alias names as well.

Question 2

Question 2Assertion. A series is a 1D data structure which is value-mutable but size-immutable.Reason. Every time you change the size of a series object, change does not take place in the existing series object, rather a new series object is created with the new size.Both A and R are true and R is the correct explanation of A.Both A and R are true but R is not the correct explanation of A.A is true but R is false.A is false but R is true.

Accepted Answer

Both A and R are true and R is the correct explanation of A.ExplanationA series is a one-dimensional data structure that is value-mutable but size-immutable. This means that we can modify the values within a series, but we cannot change its size once it's created. Every time we attempt to change the size of a series object by adding or dropping an element, internally a new series object is created with the new size.

Question 3

Question 3Assertion. A dataframe is a 2D data structure which is value mutable and size mutable.Reason. Every change in a dataframe internally creates a new dataframe object.Both A and R are true and R is the correct explanation of A.Both A and R are true but R is not the correct explanation of A.A is true but R is false.A is false but R is true.

Accepted Answer

A is true but R is false.ExplanationA DataFrame is a two-dimensional data structure that is both value-mutable and size-mutable. This means that we can modify the values within a DataFrame, change its size once it's created, and add or drop elements in an existing DataFrame object without creating a new DataFrame internally.

Question 4

Question 4Assertion. A dataframe is value mutable and size-mutable.Reason. All changes occur in-place in a dataframe.Both A and R are true and R is the correct explanation of A.Both A and R are true but R is not the correct explanation of A.A is true but R is false.A is false but R is true.

Accepted Answer

Both A and R are true and R is the correct explanation of A.ExplanationA DataFrame is a two-dimensional data structure that is both value-mutable and size-mutable. This means that we can modify the values within a DataFrame, change its size in place once it's created, and add or drop elements in an existing DataFrame object without creating a new DataFrame internally.

Question 5

Question 5Assertion. A series object stores values of homogeneous types.Reason. Even if values appear to be of different types, internally they are stored in a common datatype.Both A and R are true and R is the correct explanation of A.Both A and R are true but R is not the correct explanation of A.A is true but R is false.A is false but R is true.

Accepted Answer

Both A and R are true and R is the correct explanation of A.ExplanationA Series object in Pandas stores values of homogeneous types, meaning all values are of the same data type. Even if values appear to be of different types, internally they are stored in a common datatype.

Question 6

Question 6Assertion. Arithmetic operations on two series objects take place on matching indexes.Reason. Non-matching indexes are removed from the result of arithmetic operation on series objects.Both A and R are true and R is the correct explanation of A.Both A and R are true but R is not the correct explanation of A.A is true but R is false.A is false but R is true.

Accepted Answer

A is true but R is false.ExplanationArithmetic operations on two Series objects take place on matching indexes. When performing operations on objects with non-matching indexes, Pandas aligns the indexes and adds values for matching indexes, resulting in NaN (Not a Number) for non-matching indexes in both objects.

Question 7

Question 7Assertion. Arithmetic operations on two series objects take place on matching indexes.Reason. For non-matching indexes of series objects in an arithmetic operation, NaN is returned.Both A and R are true and R is the correct explanation of A.Both A and R are true but R is not the correct explanation of A.A is true but R is false.A is false but R is true.

Accepted Answer

Both A and R are true and R is the correct explanation of A.ExplanationArithmetic operations on two Series objects take place on matching indexes. When performing operations on objects with non-matching indexes, Pandas aligns the indexes and adds values for matching indexes, resulting in NaN (Not a Number) for non-matching indexes in both objects.

Question 8

Question 8Assertion. While changing the values of a column in a dataframe, if the column does not exist, an error occurs.Reason. If values are provided for a non-existing column in a dataframe, a new column is added with those values.Both A and R are true and R is the correct explanation of A.Both A and R are true but R is not the correct explanation of A.A is true but R is false.A is false but R is true.

Accepted Answer

A is false but R is true.ExplanationWhile changing the values of a column in a dataframe where the column does not exist does not cause an error. Instead, a new column with those values is added to the dataframe. If values are provided for a non-existing column in a dataframe, a new column is added with those values.

Question 9

Question 9Assertion. .loc() is a label based data selecting method to select a specific row(s) or column(s) which we want to select.Reason. .iloc() can not be used with default indices if customized indices are provided.Both A and R are true and R is the correct explanation of A.Both A and R are true but R is not the correct explanation of A.A is true but R is false.A is false but R is true.

Accepted Answer

A is true but R is false.ExplanationThe .loc() is a label-based method in Pandas used for selecting specific rows or columns based on their labels (indices). While .iloc() can be used with default indices (0-based integer indices) even if customized indices are provided. .iloc[] is primarily used for integer-location based indexing.

Question 10

Question 10Assertion. DataFrame has both a row and column index.Reason. A DataFrame is a two-dimensional labelled data structure like a table of MySQL.Both A and R are true and R is the correct explanation of A.Both A and R are true but R is not the correct explanation of A.A is true but R is false.A is false but R is true.

Accepted Answer

Both A and R are true and R is the correct explanation of A.ExplanationA DataFrame in Pandas has both a row index and a column index. It is a two-dimensional labeled data structure, similar to a table in MySQL, each value is identifiable with the combination of row and column indices.

Question 11

Question 1What is the use of Python Pandas library ?

Accepted Answer

Pandas is the most popular library for data analysis. It offers data I/O, computations across rows/columns, subset selection, dataset merging, handling missing data, group-wise operations, data reshaping, time-series analysis, and integrates with visualization tools.

Question 12

Question 2Name the Pandas object that can store one dimensional array like object and can have numeric or labelled indexes.

Accepted Answer

The Pandas object that can store one-dimensional array like objects with numeric or labeled indexes is called a "Series Object".

Question 13

Question 3Can you have duplicate indexes in a series object ?

Accepted Answer

Yes, series object can have duplicate indexes.

Question 14

Question 4What do these attributes of series signify ?(i) size(ii) itemsize(iii) nbytes

Accepted Answer

(i) size — It returns the number of elements in the underlying data.(ii) itemsize — It returns the size of the dtype of the item of the underlying data.(iii) nbytes — It returns the number of bytes in the underlying data.

Question 15

Question 5If S1 is a series object then how will len(S1) and S1.count() behave ?

Accepted Answer

len(S1) returns the total number of elements in the Series S1 object, including NaNs, while S1.count() returns the count of non-NaN values in the S1 Series object.

Question 16

Question 6What are NaNs ? How do you store them in a data structures ?

Accepted Answer

NaN stands for 'Not a Number'. In Python libraries like NumPy and Pandas, NaN is the legal empty value used to represent missing or undefined values, and we can use np.NaN (imported NumPy as np) to specify a missing value.

Question 17

Question 7True/False. Series objects always have indexes 0 to n -1.

Accepted Answer

FalseReason — Series objects allow labeled indexes and can take any user-defined labels. Hence, their indexes are not necessarily from 0 to n-1.

Question 18

Question 8What is the use of del statement ?

Accepted Answer

The del statement in pandas is used to delete a column from a dataframe.

Question 19

Question 9What does drop() function do ?

Accepted Answer

The drop() function in pandas is used to delete rows from a dataframe.

Question 20

Question 10What is the role of inplace argument in rename() function.

Accepted Answer

The inplace argument in the rename() function in pandas specifies whether to modify the dataframe in place or return a new dataframe with the changes. When inplace = True is set, the dataframe is modified directly, and the changes are applied to the existing dataframe. If inplace = False or not specified (default), a new dataframe with the changes is returned, leaving the original dataframe unchanged.

Python Pandas — I

Assertions And Reasons

Checkpoint 11

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Multiple Choice Questions

Truefalse Questions

Type A Very Short Answer Questions

Type B Short Answer Questionsconceptual Questions

Type C Long Answer Questions