Programming With Python MCQ Quiz - Objective Question with Answer for Programming With Python - Download Free PDF

The correct answer is Option 1.

Key Points

• The head() and tail() functions are used in the Pandas library to access specific portions of a DataFrame.
  head() returns the first five rows of the DataFrame by default. You can specify the number of rows to return by passing an integer parameter.
  tail() returns the last five rows of the DataFrame by default. Similar to head(), you can specify a different number of rows by passing an integer parameter.
  These functions are useful for quickly inspecting the beginning or end of a DataFrame.
  For example, df.head(3) would return the first three rows, while df.tail(3) would return the last three rows.
  Additional Information These functions are particularly helpful in large datasets where you want to verify the structure and contents without loading the entire dataset into memory.
  They are often used in data exploration and data cleaning stages of data analysis.
  Here is a source code example demonstrating the use of head() and tail() functions:

  import pandas as pd

  # Creating a sample DataFrame
  data = {
      'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eva'],
      'Age': [24, 27, 22, 32, 29],
      'City': ['New York', 'Los Angeles', 'Chicago', 'Houston', 'Phoenix']
  }

  df = pd.DataFrame(data)

  # Displaying the first five rows
  print("First five rows using head():")
  print(df.head())

  # Displaying the last five rows
  print("Last five rows using tail():")
  print(df.tail())

  # Displaying the first three rows using head()
  print("First three rows using head(3):")
  print(df.head(3))

  # Displaying the last three rows using tail(3)
  print("Last three rows using tail(3):")
  print(df.tail(3))

The correct answer is Option 1.

Key Points

• The drop() method in Pandas is used to remove rows or columns from a DataFrame.
• By default, the drop() method does not modify the original DataFrame. Instead, it returns a new DataFrame with the specified rows or columns removed.
• To permanently remove rows or columns from the original DataFrame, you need to either use the inplace=True parameter or explicitly assign the returned DataFrame back to the original DataFrame.
• Example of using drop() method:

         import pandas as pd

         # Create a sample DataFrame
         df = pd.DataFrame({
                'A': [1, 2, 3, 4],
                'B': [5, 6, 7, 8],
                'C': [9, 10, 11, 12]
               })

          # Dropping column 'B' and assigning it back to the original DataFrame
         df = df.drop('B', axis=1)
          print(df)

Additional Information

•  The rename() method can rename both column labels and row labels (index).
• The append() method returns a new DataFrame and does not modify the original DataFrame.
• The fillna() method is used to fill NaN values with a specified value, and it does not delete NaN values.

The correct answer is A Pandas Series allows labeled indexing, whereas a NumPy ndarray only supports integer-based indexing..

Key Points

• A Pandas Series is a one-dimensional array-like object capable of holding any data type (integers, strings, floating point numbers, etc.). It has a labeled index, which means that each element in the Series can be accessed via labels as well as integer positions.
  • The labeled indexing feature allows for more intuitive and flexible data manipulation, especially when working with time series data or other data that naturally carries labels.
  • For example, a Pandas Series can be indexed by dates, names, or any other custom labels defined by the user.
  • Here is a code example demonstrating labeled indexing in a Pandas Series:

    
    import pandas as pd

    # Creating a Pandas Series with labeled indexing
    data = pd.Series([1, 2, 3], index=['a', 'b', 'c'])

    # Accessing elements via labels
    print(data['a'])  # Output: 1

    # Accessing elements via integer positions
    print(data[0])  # Output: 1
    
    

• In contrast, a NumPy ndarray is a multi-dimensional, homogeneous array of fixed-size items. It supports integer-based indexing but does not have a built-in mechanism for labeled indexing.
  • NumPy arrays are efficient and fast for numerical computations but lack the flexibility of labeled indexing provided by Pandas Series.
  • Here is a code example demonstrating integer-based indexing in a NumPy ndarray:

    
    import numpy as np

    # Creating a NumPy ndarray
    data = np.array([1, 2, 3])

    # Accessing elements via integer positions
    print(data[0])  # Output: 1
    
    

Additional Information

• Pandas Series is built on top of NumPy, so it inherits many of NumPy's functionalities and optimizations.
• While NumPy arrays are ideal for numerical operations, Pandas Series is more suited for data analysis tasks where labeled data is beneficial.
• Both Pandas and NumPy are essential tools in the data science ecosystem, and understanding their differences can help in choosing the right tool for the task at hand.

The correct answer is Option 2.

Key Points

• When performing an arithmetic operation between two Pandas Series with different indices, the operation will align the Series based on their indices.
• If an index is present in one Series but not the other, the result will have a NaN (Not a Number) value for that index.
• This behavior ensures that the Series are combined correctly, even if they do not have matching indices.
• Example:

          Consider two Series: Series A with indices [1, 2, 3] and Series B with indices [2, 3, 4].
          Performing an addition operation will result in a new Series with indices [1, 2, 3, 4].
          The values for index 1 and 4 will be NaN since they are not present in both Series.

Additional Information

•  This alignment feature in Pandas helps in handling data that may come from different sources and might not be perfectly aligned.
• NaN values can be handled or filled using various Pandas methods like fillna() or dropna() depending on the use case.
• It is important to understand this behavior to avoid unexpected results in data analysis.

The correct answer is NumPy is primarily used for handling structured tabular data..

Key Points

• NumPy is primarily used for numerical computing and handling arrays and matrices, not specifically for structured tabular data.
  • NumPy provides support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.
  • It is optimized for performance and can handle large datasets efficiently.
• Pandas is the library that is primarily used for handling structured tabular data.
  • Pandas DataFrame is designed to handle heterogeneous data, making it ideal for data manipulation and analysis.
  • It provides tools for reading and writing data, handling missing data, and merging or joining datasets.
• NumPy is a fundamental package for scientific computing in Python, and Pandas is built on top of NumPy to provide additional functionalities for data analysis.

Additional Information

• While NumPy requires data to be homogeneous, Pandas can handle heterogeneous data, making it more flexible for various data types.
• Pandas also provides built-in support for data visualization, whereas NumPy does not have such capabilities.
• NumPy and Pandas together form a powerful toolkit for data science and analytical workflows in Python.
• Understanding the strengths of each library can help in selecting the right tool for specific data processing tasks.

object oriented programming: object-oriented programming is an approach to designing modular reusable software systems. The object-oriented approach is an evolution of good design practices that go back to the very beginning of computer programming. object-orientation is simply the logical extension of older techniques such as structured programming and abstract data types.

oops objects

Classes are regarded as types for instance. (option 4 is correct)

Instances can not change their type at runtime.

The list of classes is fully known at compile-time and cannot change after that.

Compilers are used at build-time. Compile-time errors indicate problems.

Classes encode much of their meaning and behavior through imperative functions and methods.

Instances are anonymous insofar that they cannot easily be addressed from outside of an executing program.

Closed world: If there is not enough information to prove a statement true, then it is assumed to be false.

The correct answer is option 1.

Options are described as:

Option 1: xyzp = 5000 6000 7000 8000

False initialization, Spaces are not allowed in variable names or values. Commas are required between values. The correct declaration is: xyzp = 5000,6000,7000,8000.

Option 2: x_y_z_p = 5,000,000

 It is a valid declaration and x_y_z_p prints the (5, 0, 0).

Option 3: x, y, z, p = 5000, 6000, 7000, 8000

It is a valid declaration and x store and prints the 5000, And y tore and prints the 6000, Z Store and prints the7000 and p store and prints the 8000.

Option 4:xyzp = 5,000,000

 It is a valid declaration and xyzp prints the (5, 0, 0).

Hence the correct answer is xyzp = 5000 6000 7000 8000.

The correct answer is:

option 2) zara

Explanation:

The max() function in Python returns the largest item in an iterable or the largest of two or more arguments. When applied to a list of strings, it returns the string that would come last if the list were sorted alphabetically.

In this case, 'xyz', 'zara', and 'Wscubetech' are the elements of the list. 'zara' is considered the largest because the alphabetical comparison is case-sensitive and uppercase letters are considered smaller than lowercase letters in ASCII comparison. So 'Wscubetech' is not the largest even though 'W' comes after 'z' in the alphabet. Hence, 'zara' is the largest item in the list.

The correct answer is 4.

Key Points

• The Python function len() is used to determine the number of items in an object.
• In this case, the object is a list: ["hello", 2, 4, 6].
• The list contains four items: a string ("hello"), and three integers (2, 4, 6).
• Therefore, len(["hello", 2, 4, 6]) returns 4.

The correct answer is Class.

Key Points

• In Python, core data types are built-in types that are used to store and manipulate data.
• Classes are not considered a core data type in Python. Instead, they are a blueprint for creating user-defined objects.

Additional Information

• Tuples: Tuples are immutable sequences in Python, meaning their elements cannot be changed once assigned. They are used to store collections of items.
• Lists: Lists are mutable sequences, meaning their elements can be changed. They are used to store collections of items and are very versatile.
• Dictionaries: Dictionaries are mutable mappings in Python. They store key-value pairs and provide a way to quickly retrieve values based on their keys.

The correct answer is option 1.

Concept:

Statement 1: There can try block without catch block but vice versa is not possible.

True, there may or may not be a catch block in the try block. But without a try block, a catch block cannot exist in a program. Similar to if-block, else-block can only be written if and only if it is present in the program.

Statement 2:  There is no limit on the number of catches, or blocks corresponding to a try block.

True, there is no limit on the number of catch blocks corresponding to a try block. This is because the error can be of any type and for each type, a new catch block can be defined. This is to make sure all types of exceptions can be handled.

Statement 3: The object must be created of a specific class of which the error has occurred otherwise runtime error will occur.

False, We can also create an object of a class and access it in another class. This is often used for better organization of classes.

Statement 4: To execute a code with each and every run of the program, the code is written in the finally block.

True, Whether an exception is handled or not, the finally block is always run. Since the exception may or may not occur, it comprises all the required statements that must be printed. After the try-catch block comes the finally block.

Hence the correct answer is i and ii, iv.

The correct answer is option 4.

Concept:

Object-oriented programming:

Object-oriented programming (OOP) is a programming approach that organizes software design around data, rather than functions and logic. An object is a data field that has distinct features and behavior.

Advantages of object-oriented programming:

• Oops supports code reusability. Inheritance supports the concept of “reusability”.
• Oops, can create more than one instance of a class without interference. It can always create multiple instances of a class. Each object will hold its own individual inner variables.
• OOP does not have platform independence. Although C++ supports OOP, it is not a platform-independent language. The programming language determines platform independence.

Hence the correct answer is All options are correct.

Mistake Points option 3 is not a feature for all oops supported languages.

The correct answer is Division.

Key Points

• The slash (/) symbol is commonly used in mathematics and programming to represent the division operation.
• Division is a basic arithmetic operation where one number (the dividend) is divided by another number (the divisor) to get a quotient.
• For example, in the expression 10 / 2, 10 is divided by 2, resulting in a quotient of 5.

Additional Information

• Exponentiation (Option 1): This is represented by the caret (^) symbol or sometimes by double asterisks (**). It is used to raise a number to the power of another number. For example, 2^3 or 2**3 represents 2 raised to the power of 3, which equals 8.
• Multiplication (Option 3): This is represented by the asterisk (*) symbol. It is used to multiply two numbers. For example, 4 * 3 equals 12.
• Subtraction (Option 4): This is represented by the minus (-) symbol. It is used to subtract one number from another. For example, 7 - 5 equals 2.

The correct answer is option 1.

Concept:

Object-oriented language:

Object-oriented language (OOL) is a high-level computer programming language used to develop software applications by implementing objects and their related processes within the programming context.

Explanation:

• Object-Oriented programming languages such as C++, C#, Java, and others adopt the bottom-up method.
• Bottom-up methods begin with specifics and progress to higher conceptual levels, decreasing complexity and making the notion more understandable.
• May has redundancy since we divide the problem into smaller chunks and then develop each component independently. If data encapsulation and data concealing are utilised, this strategy has less redundancy.

Hence the correct answer is Bottom up approach.

The correct answer is all options are correct.

Key Points

• The SQL server is an example of a relational database management system.
• The index (example, CREATE_INDEX) statement of the SQL server is used to create the index in the database table which performs fast searches.
• The index of the SQL server provides an index to a record.
• It also enhances the performance of the query.

Thus the correct answer is all options are correct.

Additional Information

• An index builds a key which is created in a combination of two or more columns in the record.
• These keys are stored in a structured manner so that, whenever the SQL server calls for a record, this key helps the server to find the record very fast and efficiently.
• Syntax of creating an index is given below,