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NumPy MCQ : Set 3

NumPy MCQ

1). What is the purpose of the np.random.rand() function?

a) Generates a random integer
b) Returns a random permutation of an array
c) Generates an array of random floats in the range [0, 1)
d) Calculates the cumulative sum of an array

Correct answer is: c) Generates an array of random floats in the range [0, 1)
Explanation: The np.random.rand() function generates an array of random floats in the half-open interval [0, 1). It takes the desired shape of the array as arguments.

2). How can you calculate the element-wise arccosine of a NumPy array?

a) np.arccos(arr)
b) np.arcsin(arr)
c) np.arctan(arr)
d) np.arccosh(arr)

Correct answer is: a) np.arccos(arr)
Explanation: The np.arccos() function in NumPy calculates the element-wise arccosine (inverse cosine) of a given array. It takes the array as an argument and returns a new array with the arccosine values.

3). What does the np.delete() function do?

a) Deletes an element from an array
b) Deletes a row or column from a 2-dimensional array
c) Deletes multiple elements from an array
d) Deletes a subarray from an array

Correct answer is: b) Deletes a row or column from a 2-dimensional array
Explanation: The np.delete() function in NumPy deletes a specified row or column from a 2-dimensional array. It takes the array and the index of the row or column to delete as arguments.

4). Which NumPy function is used to calculate the element-wise absolute difference between two arrays?

a) np.absdiff()
b) np.subtract()
c) np.diff()
d) np.absolute_diff()

Correct answer is: a) np.absdiff()
Explanation: The np.absdiff() function in NumPy calculates the element-wise absolute difference between two arrays. It takes the two arrays as arguments and returns a new array with the absolute differences.

5). What does the np.stack() function do?

a) Combines multiple arrays into a single array
b) Splits an array into multiple subarrays
c) Joins arrays along a new axis
d) Reverses the order of elements in an array

Correct answer is: c) Joins arrays along a new axis
Explanation: The np.stack() function in NumPy joins arrays along a new axis. It takes the arrays to stack as arguments and returns a new array with the arrays stacked along the specified axis.

6). How can you calculate the element-wise tangent of a NumPy array?

a) np.tan(arr)
b) np.arctan(arr)
c) np.sin(arr)
d) np.cos(arr)

Correct answer is: a) np.tan(arr)
Explanation: The np.tan() function in NumPy calculates the element-wise tangent of a given array. It takes the array as an argument and returns a new array with the tangent values.

7). What is the purpose of the np.full() function?

a) Generates an array filled with ones
b) Generates an array filled with zeros
c) Generates an array with a specified shape and fill value
d) Generates an array with random values

Correct answer is: c) Generates an array with a specified shape and fill value
Explanation: The np.full() function in NumPy generates an array with a specified shape and fill value. It takes the desired shape and the fill value as arguments and returns an array with the specified shape, filled with the fill value.

8). Which NumPy function is used to calculate the element-wise hyperbolic sine of an array?

a) np.sinh()
b) np.cosh()
c) np.tanh()
d) np.arcsinh()

Correct answer is: a) np.sinh()
Explanation: The np.sinh() function in NumPy calculates the element-wise hyperbolic sine of a given array. It takes the array as an argument and returns a new array with the hyperbolic sine values.

9). What does the np.argsort() function return?

a) The sorted array
b) The indices that would sort the array
c) The indices that sort the array in descending order
d) The indices of the maximum values in the array

Correct answer is: b) The indices that would sort the array
Explanation: The np.argsort() function returns the indices that would sort an array in ascending order. The initial array is unaltered.

10). How can you calculate the element-wise bitwise AND of two NumPy arrays?

a) np.bitwise_and(arr1, arr2)
b) np.bitwise_or(arr1, arr2)
c) np.bitwise_xor(arr1, arr2)
d) np.bitwise_not(arr1)

Correct answer is: a) np.bitwise_and(arr1, arr2)
Explanation: The np.bitwise_and() function in NumPy calculates the element-wise bitwise AND of two arrays. It takes the two arrays as arguments and returns a new array with the bitwise AND result.

11). What does the np.isinf() function do?

a) Checks if an array contains infinite values
b) Computes the element-wise exponential of an array
c) Calculates the cumulative sum of an array
d) Calculates the element-wise reciprocal of an array

Correct answer is: a) Checks if an array contains infinite values
Explanation: The np.isinf() function in NumPy checks if an array contains infinite values. It returns a Boolean array indicating which elements are infinite.

12). Which NumPy function is used to calculate the element-wise square root of an array?

a) np.square_root()
b) np.power(arr, 0.5)
c) np.sqrt(arr)
d) np.abs_sqrt(arr)

Correct answer is: c) np.sqrt(arr)
Explanation: The np.sqrt() function in NumPy calculates the element-wise square root of a given array. It takes the array as an argument and returns a new array with the square root values.

13). What is the purpose of the np.rot90() function?

a) It rotates an array by 90 degrees counter-clockwise
b) It rotates an array by 90 degrees clockwise
c) It reshapes an array to a specified shape
d) It transposes the rows and columns of an array

Correct answer is: a) It rotates an array by 90 degrees counter-clockwise
Explanation: The np.rot90() function in NumPy rotates an array by 90 degrees counter-clockwise. It takes the array and the number of rotations as arguments and returns a new array with the specified rotation.

14). Which NumPy function is used to calculate the element-wise exponential of an array?

a) np.exp()
b) np.power()
c) np.log()
d) np.square()

Correct answer is: a) np.exp()
Explanation: The np.exp() function in NumPy calculates the element-wise exponential of a given array. It takes the array as an argument and returns a new array with the exponential values.

15). What does the np.median() function return?

a) The mean of the array
b) The maximum value in the array
c) The minimum value in the array
d) The median of the array

Correct answer is: d) The median of the array
Explanation: The np.median() function in NumPy calculates the median value of an array. It takes the array as an argument and returns the median value.

16). How can you calculate the element-wise absolute difference between two NumPy arrays?

a) np.absdiff(arr1, arr2)
b) np.subtract(arr1, arr2)
c) np.add(arr1, arr2)
d) np.multiply(arr1, arr2)

Correct answer is: a) np.absdiff(arr1, arr2)
Explanation: The np.absdiff() function in NumPy calculates the element-wise absolute difference between two arrays. It takes the two arrays as arguments and returns a new array with the absolute differences.

17). Which NumPy function is used to calculate the element-wise arc tangent of two arrays?

a) np.arctan2()
b) np.arcsin()
c) np.arccos()
d) np.tan()

Correct answer is: a) np.arctan2()
Explanation: The np.arctan2() function in NumPy calculates the element-wise arc tangent of the quotient of two arrays. It takes the numerator array and the denominator array as arguments and returns a new array with the arc tangent values.

18). What is the purpose of the np.fliplr() function?

a) It flips the array vertically
b) It flips the array horizontally
c) It changes the shape of the array
d) It transposes the rows and columns of the array

Correct answer is: b) It flips the array horizontally
Explanation: The np.fliplr() function in NumPy flips the array horizontally. It takes the array as an argument and returns a new array with the columns reversed.

19). Which NumPy function is used to calculate the element-wise hyperbolic tangent of an array?

a) np.tanh()
b) np.sinh()
c) np.cosh()
d) np.arctanh()

Correct answer is: a) np.tanh()
Explanation: The np.tanh() function in NumPy calculates the element-wise hyperbolic tangent of a given array. It takes the array as an argument and returns a new array with the hyperbolic tangent values.

20). What does the np.vstack() function do?

a) Vertically stacks multiple arrays
b) Horizontally stacks multiple arrays
c) Splits an array into multiple subarrays
d) Calculates the dot product of two arrays

Correct answer is: a) Vertically stacks multiple arrays
Explanation: The np.vstack() function in NumPy vertically stacks multiple arrays. It takes the arrays to stack as arguments and returns a new array with the arrays stacked vertically.

21). What does the np.full_like() function do?

a) Creates a new array with the same shape and data type as a specified array, filled with a specified value
b) Creates a new array with the same shape as a specified array, filled with a specified value
c) Creates a new array with the same data type as a specified array, filled with a specified value
d) Creates a new array with the same shape and data type as a specified array, filled with ones

Correct answer is: a) Creates a new array with the same shape and data type as a specified array, filled with a specified value
Explanation: The np.full_like() function in NumPy creates a new array with the same shape and data type as a specified array. It takes the specified array and the fill value as arguments and returns a new array filled with the specified value.

22). How can you calculate the element-wise bitwise OR of two NumPy arrays?

a) np.bitwise_or(arr1, arr2)
b) np.bitwise_and(arr1, arr2)
c) np.bitwise_xor(arr1, arr2)
d) np.bitwise_not(arr1)

Correct answer is: a) np.bitwise_or(arr1, arr2)
Explanation: The np.bitwise_or() function in NumPy calculates the element-wise bitwise OR of two arrays. It takes the two arrays as arguments and returns a new array with the bitwise OR result.

23). What is the purpose of the np.linspace() function?

a) Generates a sequence of evenly spaced numbers over a specified range
b) Generates an array with random values
c) Generates an array filled with ones
d) Generates an array filled with zeros

Correct answer is: a) Generates a sequence of evenly spaced numbers over a specified range
Explanation: The np.linspace() function in NumPy generates a sequence of evenly spaced numbers over a specified range. It takes the start value, end value, and the number of elements as arguments.

24). What does the np.ptp() function calculate?

a) The sum of all elements in the array
b) The median of the array
c) The peak-to-peak (range) value of the array
d) The array’s total product of all of its elements

Correct answer is: c) The peak-to-peak (range) value of the array
Explanation: The np.ptp() function in NumPy calculates the peak-to-peak (range) value of an array. It takes the array as an argument and returns the difference between the maximum and minimum values.

25). Which NumPy function is used to calculate the element-wise sigmoid function of an array?

a) np.sinh()
b) np.cosh()
c) np.tanh()
d) np.expit()

Correct answer is: d) np.expit()
Explanation: The np.expit() function in NumPy calculates the element-wise sigmoid function of a given array. It takes the array as an argument and returns a new array with the sigmoid values.

NumPy MCQ : Set 2

NumPy MCQ

1). What does the `np.unique()` function do?

a) Removes duplicate elements from an array
b) Returns the element-wise square of an array
c) Computes the cumulative sum of an array
d) Performs element-wise division of two arrays

Correct answer is: a) Removes duplicate elements from an array
Explanation: The `np.unique()` function in NumPy returns the sorted, unique values in an array, removing any duplicate elements.

2). How can you calculate the element-wise sine of a NumPy array?

a) np.sin(arr)
b) np.cos(arr)
c) np.tan(arr)
d) All of the above

Correct answer is: a) np.sin(arr)
Explanation: The `np.sin()` function in NumPy calculates the element-wise sine of a given array. It takes the array as an argument and returns a new array with the sine values.

3). Which NumPy function is used to compute the covariance matrix of a set of variables?

a) np.cov()
b) np.correlate()
c) np.corrcoef()
d) np.corr()

Correct answer is: a) np.cov()
Explanation: The `np.cov()` function in NumPy is used to compute the covariance matrix of a set of variables. It takes the variables as arguments and returns the covariance matrix.

4). What does the `np.concatenate()` function do?

a) Combines multiple arrays into a single array
b) Splits an array into multiple subarrays
c) Reverses the order of elements in an array
d) Sorts the elements of an array in ascending order

Correct answer is: a) Combines multiple arrays into a single array
Explanation: The `np.concatenate()` function in NumPy is used to combine multiple arrays along a specified axis, resulting in a single array.

5). How can you calculate the element-wise logarithm of a NumPy array?

a) np.log(arr)
b) np.exp(arr)
c) np.power(arr, 2)
d) np.sqrt(arr)

Correct answer is: a) np.log(arr)
Explanation: The `np.log()` function in NumPy calculates the element-wise natural logarithm (base e) of a given array. It takes the array as an argument and returns a new array with the logarithmic values.

6). What is the purpose of the `np.where()` function?

a) It finds the maximum value in an array
b) It replaces elements in an array based on a condition
c) It calculates the element-wise sum of two arrays
d) It arranges array elements in decreasing order.

Correct answer is: b) It replaces elements in an array based on a condition
Explanation: The `np.where()` function in NumPy is used to replace elements in an array based on a specified condition. It takes the condition, the values to replace when the condition is True, and the values to replace when the condition is False.

7). What does the `np.pad()` function do?

a) Adds padding to an array
b) Removes padding from an array
c) Changes the shape of an array
d) Calculates the element-wise difference of two arrays

Correct answer is: a) Adds padding to an array
Explanation: The `np.pad()` function in NumPy is used to add padding to an array. It takes the array, the pad widths, and the padding mode as arguments, and returns a new array with the specified padding.

8). How can you calculate the element-wise product of two NumPy arrays?

a) np.multiply(arr1, arr2)
b) np.dot(arr1, arr2)
c) np.cross(arr1, arr2)
d) np.divide(arr1, arr2)

Correct answer is: a) np.multiply(arr1, arr2)
Explanation: The `np.multiply()` function in NumPy calculates the element-wise product of two arrays. It takes the two arrays as arguments and returns a new array with the product of corresponding elements.

9). What does the `np.nan` value represent in NumPy?

a) Not a number
b) Null or missing value
c) Infinite value
d) Negative infinity

Correct answer is: a) Not a number
Explanation: The `np.nan` value in NumPy represents a “Not a Number” value, typically used to denote missing or undefined data.

10). Which NumPy function is used to calculate the element-wise absolute difference between two arrays?

a) np.abs()
b) np.diff()
c) np.subtract()
d) np.absolute()

Correct answer is: d) np.absolute()
Explanation: The `np.absolute()` function in NumPy calculates the element-wise absolute difference between two arrays. It takes the two arrays as arguments and returns a new array with the absolute differences.

11). What is the purpose of the `np.clip()` function?

a) It rounds the elements of an array to the nearest integer
b) It limits the values of an array to a specified range
c) It calculates the cumulative product of an array
d) It computes the element-wise exponential of an array

Correct answer is: b) It limits the values of an array to a specified range
Explanation: The `np.clip()` function in NumPy limits the values of an array to a specified range. It takes the array, the minimum value, and the maximum value as arguments, and returns a new array with the values clipped to the specified range.

12). How can you calculate the element-wise division of two NumPy arrays?

a) np.divide(arr1, arr2)
b) np.multiply(arr1, arr2)
c) np.power(arr1, arr2)
d) np.dot(arr1, arr2)

Correct answer is: a) np.divide(arr1, arr2)
Explanation: The `np.divide()` function in NumPy calculates the element-wise division of two arrays. It takes the two arrays as arguments and returns a new array with the quotient of corresponding elements.

13). What does the `np.around()` function do?

a) The array’s elements are rounded to the nearest integer.
b) Rounds the elements of an array to a specified number of decimals
c) Returns the integer part of the elements in an array
d) Calculates the element-wise absolute value of an array

Correct answer is: b) Rounds the elements of an array to a specified number of decimals
Explanation: The `np.around()` function in NumPy rounds the elements of an array to a specified number of decimals. It takes the array and the number of decimals as arguments and returns a new array with the rounded values.

14). Which NumPy function is used to calculate the element-wise arctangent of an array?

a) np.arctan()
b) np.arcsin()
c) np.arccos()
d) np.arctan2()

Correct answer is: a) np.arctan()
Explanation: The `np.arctan()` function in NumPy calculates the element-wise arctangent (inverse tangent) of a given array. It takes the array as an argument and returns a new array with the arctangent values.

15). What is the purpose of the `np.isclose()` function?

a) It checks if two arrays are element-wise equal
b) It calculates the element-wise sum of two arrays
c) It computes the element-wise exponential of an array
d) It rounds the elements of an array to the nearest integer

Correct answer is: a) It checks if two arrays are element-wise equal
Explanation: The `np.isclose()` function in NumPy checks if two arrays are element-wise equal within a specified tolerance. It returns a Boolean array indicating the element-wise equality.

16). How can you calculate the element-wise power of a NumPy array?

a) np.power(arr, exponent)
b) np.sqrt(arr)
c) np.abs(arr)
d) np.log(arr)

Correct answer is: a) np.power(arr, exponent)
Explanation: The `np.power()` function in NumPy calculates the element-wise power of a given array with a specified exponent. It takes the array and the exponent as arguments and returns a new array with the powered values.

17). What does the `np.mean()` function return if the array contains NaN values?

a) NaN
b) 0
c) The mean of the non-NaN values
d) An error is raised

Correct answer is: c) The mean of the non-NaN values
Explanation: The `np.mean()` function ignores NaN values when calculating the mean of an array. It returns the mean of the non-NaN values in the array.

18). Which NumPy function is used to calculate the element-wise cosine of an array?

a) np.sin()
b) np.cos()
c) np.tan()
d) np.arccos()

Correct answer is: b) np.cos()
Explanation: The `np.cos()` function in NumPy calculates the element-wise cosine of a given array. It takes the array as an argument and returns a new array with the cosine values.

19). What is the purpose of the `np.zeros()` function?

a) It generates an array filled with zeros
b) It generates an array filled with ones
c) It generates an array filled with random values
d) It generates an array with a specified shape and values

Correct answer is: a) It generates an array filled with zeros
Explanation: The `np.zeros()` function in NumPy generates an array filled with zeros. It takes the desired shape of the array as an argument and returns an array with the specified shape and filled with zeros.

20). Which of the following functions is used to find the variance of an array?

a) var()
b) variance()
c) numpy.var()
d) numpy.variance()

Correct answer is: c) numpy.var()
Explanation: The var() function is a NumPy function that returns the variance of an array. The variance() function is a Python function that returns the variance of a sequence. The numpy.var() and numpy.variance() functions are equivalent.

21). How can you calculate the element-wise floor division of two NumPy arrays?

a) np.floor_divide(arr1, arr2)
b) np.divide(arr1, arr2)
c) np.multiply(arr1, arr2)
d) np.subtract(arr1, arr2)

Correct answer is: a) np.floor_divide(arr1, arr2)
Explanation: The `np.floor_divide()` function in NumPy calculates the element-wise floor division of two arrays. It takes the two arrays as arguments and returns a new array with the quotient of corresponding elements, rounded down to the nearest integer.

22). Which NumPy function is used to calculate the element-wise logarithm base 10 of an array?

a) np.log10()
b) np.log2()
c) np.log()
d) np.log1p()

Correct answer is: a) np.log10()
Explanation: The np.log10() function in NumPy calculates the element-wise logarithm base 10 of a given array. It takes the array as an argument and returns a new array with the logarithm values.

23). What does the np.ravel() function do?

a) Reshapes an array to a specified shape
b) Returns a sorted copy of an array
c) Flattens an array into a 1-dimensional array
d) Rounds the elements of an array to the nearest integer

Correct answer is: c) Flattens an array into a 1-dimensional array
Explanation: The np.ravel() function in NumPy flattens an array into a 1-dimensional array by concatenating all elements. It returns a new array with the flattened structure.

24). How can you calculate the element-wise square of a NumPy array?

a) np.square(arr)
b) np.power(arr, 2)
c) np.sqrt(arr)
d) np.abs(arr)

Correct answer is: a) np.square(arr)
Explanation: The np.square() function in NumPy calculates the element-wise square of a given array. It takes the array as an argument and returns a new array with the squared values.

25). Which NumPy function is used to calculate the element-wise inverse of an array?

a) np.inverse()
b) np.reciprocal()
c) np.divide(1, arr)
d) np.divide(arr, 1)

Correct answer is: b) np.reciprocal()
Explanation: The np.reciprocal() function in NumPy calculates the element-wise reciprocal (inverse) of a given array. It takes the array as an argument and returns a new array with the reciprocal values.

NumPy MCQ : Set 1

NumPy MCQ

1). What is NumPy?

a) A programming language
b) A numerical computing library for Python
c) A data visualization library
d) A machine learning framework

Correct answer is: b) A numerical computing library for Python
Explanation: NumPy is a powerful library for numerical computing in Python. It offers support for sizable, multidimensional arrays and matrices, as well as a range of mathematical operations for effectively using these arrays.

2). Which of the following is not a key feature of NumPy?

a) N-dimensional array object
b) Broadcasting functions
c) Linear algebra routines
d) Data visualization tools

Correct answer is: d) Data visualization tools
Explanation: While NumPy provides efficient numerical computing capabilities, it does not include built-in data visualization tools. For data visualization, libraries like Matplotlib or Seaborn are commonly used in conjunction with NumPy.

3). What is the main benefit of using NumPy arrays over Python lists?

a) NumPy arrays can store elements of different data types
b) NumPy arrays consume less memory
c) NumPy arrays can have variable lengths
d) NumPy arrays support dynamic resizing

Correct answer is: b) NumPy arrays consume less memory
Explanation: NumPy arrays are more memory-efficient compared to Python lists because they store elements of the same data type in a contiguous block of memory. This allows for better performance and reduced memory consumption.

4). Which of the following statements is true about NumPy arrays?

a) NumPy arrays are immutable
b) NumPy arrays can have varying lengths
c) NumPy arrays can only store numeric data types
d) NumPy arrays are resizable

Correct answer is: c) NumPy arrays can only store numeric data types
Explanation: NumPy arrays are designed to work with homogeneous data, meaning they can only store elements of a single data type, such as integers, floats, or complex numbers.

5). How can you create a NumPy array from a Python list?

a) np.array(list_name)
b) numpy.array(list_name)
c) np.ndarray(list_name)
d) numpy.ndarray(list_name)

Correct answer is: a) np.array(list_name)
Explanation: The `np.array()` function in NumPy can be used to create an array from a Python list. It accepts the argument list and returns a NumPy array.

6). Which NumPy function is used to calculate the mean of an array?

a) np.mean()
b) np.average()
c) np.median()
d) np.sum()

Correct answer is: a) np.mean()
Explanation: The `np.mean()` function in NumPy is used to calculate the arithmetic mean of an array. It takes the array as an argument and returns the mean value.

7). Which NumPy function is used to find the maximum value in an array?

a) np.max()
b) np.maximum()
c) np.argmax()
d) np.amax()

Correct answer is: a) np.max()
Explanation: The `np.max()` function is used to find the maximum value in a NumPy array. It takes the array as an argument and returns the maximum value.

8). How can you reshape a 1-dimensional array into a 2-dimensional array with two rows?

a) arr.reshape((2, 2))
b) arr.reshape((2, -1))
c) arr.reshape((1, 2))
d) arr.reshape((2,))

Correct answer is: b) arr.reshape((2, -1))
Explanation: The `reshape()` function in NumPy can be used to change the shape of an array. By specifying the target shape as `(2, -1)`, NumPy automatically infers the number of columns based on the size of the array.

9). What does the `np.linspace()` function do?

a) Generates a sequence of equally spaced values
b) Returns a random permutation of an array
c) Calculates the logarithm of each element in an array
d) Performs element-wise multiplication of two arrays

Correct answer is: a) Generates a sequence of equally spaced values
Explanation: The `np.linspace()` function generates a sequence of equally spaced values over a specified interval. It takes the start, end, and the number of values to generate as arguments.

10). How can you concatenate two NumPy arrays vertically?

a) np.concatenate((arr1, arr2), axis=0)
b) np.vstack((arr1, arr2))
c) np.append(arr1, arr2, axis=0)
d) All of the above

Correct answer is: d) All of the above
Explanation: All the provided options can be used to concatenate two NumPy arrays vertically, resulting in an array that has the rows from both arrays stacked on top of each other.

11). What does the `np.random.randint()` function do?

a) Generates a random integer
b) Returns a random permutation of an array
c) Generates an array of random integers
d) Calculates the cumulative sum of an array

Correct answer is: c) Generates an array of random integers
Explanation: The `np.random.randint()` function generates an array of random integers from a specified low (inclusive) to high (exclusive) range. The function takes the low, high, and the size of the array as arguments.

12). Which NumPy function is used to compute the dot product of two arrays?

a) np.dot()
b) np.cross()
c) np.inner()
d) np.outer()

Correct answer is: a) np.dot()
Explanation: The `np.dot()` function in NumPy is used to compute the dot product of two arrays. It performs matrix multiplication if the inputs are 2-dimensional arrays.

13). What is the purpose of broadcasting in NumPy?

a) To perform element-wise operations on arrays of different shapes
b) To adjust the shape of an array to match another array
c) To randomly shuffle the elements of an array
d) To compute the cumulative sum of an array

Correct answer is: a) To perform element-wise operations on arrays of different shapes
Explanation: Broadcasting in NumPy allows for performing element-wise operations on arrays of different shapes by automatically adjusting their shapes to be compatible.

14). How can you save a NumPy array to a file?

a) np.save(file_name, arr)
b) np.savez(file_name, arr)
c) np.savetxt(file_name, arr)
d) All of the above

Correct answer is: d) All of the above
Explanation: All the provided options can be used to save a NumPy array to a file. `np.save()` saves the array in binary format, `np.savez()` saves multiple arrays in an uncompressed archive, and `np.savetxt()` saves the array as a text file.

15). How can you load a NumPy array from a file?

a) np.load(file_name)
b) np.loadtxt(file_name)
c) np.fromfile(file_name)
d) All of the above

Correct answer is: a) np.load(file_name)
Explanation: The `np.load()` function in NumPy is used to load a saved NumPy array from a file. It takes the file name as an argument and returns the loaded array.

16). Which NumPy function is used to calculate the standard deviation of an array?

a) np.std()
b) np.var()
c) np.mean()
d) np.median()

Correct answer is: a) np.std()
Explanation: The `np.std()` function in NumPy calculates the standard deviation of an array, which measures the spread of values around the mean. It takes the array as an argument and returns the standard deviation.

17). What does the `np.eye()` function do?

a) Generates a diagonal array
b) Returns the element-wise exponential of an array
c) Calculates the inverse of a matrix
d) Performs element-wise division of two arrays

Correct answer is: a) Generates a diagonal array
Explanation: A two-dimensional array with ones on the diagonal and zeros elsewhere is produced using the ‘np.eye()’ function. It takes the number of rows as an argument, and by default, it creates a square array.

18). Which NumPy function is used to find the indices of the sorted elements in an array?

a) np.sort()
b) np.argsort()
c) np.sort_indices()
d) np.searchsorted()

Correct answer is: b) np.argsort()
Explanation: The `np.argsort()` function in NumPy returns the indices that would sort an array in ascending order. It takes the array as an argument and returns an array of indices.

19). What is the purpose of the `axis` parameter in NumPy functions?

a) It specifies the data type of the array
b) It controls the dimensionality of the array
c) It determines the axis along which the operation is applied
d) It defines the shape of the resulting array

Correct answer is: c) It determines the axis along which the operation is applied
Explanation: The `axis` parameter in NumPy functions is used to specify the axis along which the operation is applied. It can be used to perform operations across rows, columns, or other dimensions of the array.

20). How can you calculate the element-wise square root of a NumPy array?

a) np.sqrt(arr)
b) np.square(arr)
c) np.power(arr, 0.5)
d) All of the above

Correct answer is: a) np.sqrt(arr)
Explanation: The `np.sqrt()` function in NumPy calculates the element-wise square root of a given array. It takes the array as an argument and returns a new array with the square root of each element.

21). What does the `np.transpose()` function do?

a) Flips the array vertically
b) Flips the array horizontally
c) Changes the shape of the array
d) Interchanges the rows and columns of the array

Correct answer is: d) Interchanges the rows and columns of the array
Explanation: The `np.transpose()` function in NumPy returns a view of the array with the rows and columns interchanged. The initial array is unaltered.

22). What is the purpose of the `np.meshgrid()` function?

a) It generates a grid of coordinates based on input arrays
b) It computes the element-wise product of two arrays
c) It calculates the dot product of two arrays
d) It reshapes an array into a specified shape

Correct answer is: a) It generates a grid of coordinates based on input arrays
Explanation: The `np.meshgrid()` function is used to generate a grid of coordinates based on the input arrays. It takes two or more arrays representing the coordinates and returns coordinate matrices for each pair of input arrays.

23). Which NumPy function is used to calculate the element-wise absolute value of an array?

a) np.abs()
b) np.absolute()
c) np.absolutes()
d) np.magnitude()

Correct answer is: a) np.abs()
Explanation: The `np.abs()` function in NumPy calculates the element-wise absolute value of a given array. It takes the array as an argument and returns a new array with the absolute values.

24). What does the `np.histogram()` function do?

a) Calculates a data set’s histogram
b) Generates a random array with a specified shape
c) Calculates the cumulative sum of an array
d) Performs element-wise multiplication of two arrays

Correct answer is: a) Calculates a data set’s histogram
Explanation: The `np.histogram()` function computes the histogram of a set of data. It takes the data and the number of bins as arguments and returns an array of bin counts and an array of bin edges.

25). How can you calculate the element-wise exponential of a NumPy array?

a) np.exp(arr)
b) np.power(arr, 2)
c) np.log(arr)
d) np.sin(arr)

Correct answer is: a) np.exp(arr)
Explanation: The `np.exp()` function in NumPy calculates the element-wise exponential of a given array. It takes the array as an argument and returns a new array with the exponential values.

Python Pandas MCQ : Set 6

Python Pandas MCQ

1). What is the output of the following code?

				
					import pandas as pd
import numpy as np
d = {'Sr.no.': [1, 2, 3, 4], 'Name': ['Alex', 'John', 'Peter', 'Klaus'], 'Age': [30, np.nan, 29, np.nan]}
df = pd.DataFrame(d)
print("Nan values in the dataframe: ", df.isnull().values.sum())

a) 0
b) 1
c) 2
d) 3

Correct answer is: c) 2
Explanation: The code creates a DataFrame `df` with four rows and three columns (‘Sr.no.’, ‘Name’, and ‘Age’). The ‘Age’ column contains two NaN (Not a Number) values. The `isnull().values` method is used to create a boolean array where True represents the presence of a NaN value, and False represents a non-NaN value. The `sum()` function is then applied to this boolean array to count the total number of True (i.e., NaN) values. In this case, the output will be `2`, indicating that there are two NaN value in the DataFrame.

2). What is the purpose of the following code?

				
					import pandas as pd
d = {'Sr.no.': [1, 2, 3, 4], 'Name': ['Alex', 'John', 'Peter', 'Klaus'], 'Age': [30, 27, 29, 33], 'Salary': [50000, 65000, 58000, 66000]}
df = pd.DataFrame(d)
df = df.sample(frac=1)
print("\nNew DataFrame:")
print(df)

a) To create a DataFrame with given data and shuffle its rows randomly.
b) To sort the DataFrame rows in ascending order based on a specified column.
c) To remove duplicate rows from the DataFrame.
d) To convert the DataFrame into a NumPy array.

Correct answer is: a) To create a DataFrame with given data and shuffle its rows randomly.
Explanation: The given code performs the following actions:
1. Import the Pandas library as `pd`.
2. Create a Python dictionary `d` containing four columns: ‘Sr.no.’, ‘Name’, ‘Age’, and ‘Salary’.
3. Use the dictionary to create a DataFrame `df`.
4. `df.sample(frac=1)` is used to shuffle the rows of the DataFrame randomly. The parameter `frac=1` specifies that the entire DataFrame should be sampled, effectively shuffling the entire DataFrame.
5. Finally, the shuffled DataFrame is printed with the message “New DataFrame:”.

3). What is the output of the following code?

				
					import pandas as pd
d = {'Rank':[1,2,3,4],'Name':['Alex','John','Peter','Klaus'],'Age':[30,27,29,33],'Salary':[50000,65000,58000,66000]}
df = pd.DataFrame(d)
df = df.rename(columns = {'Rank':'Sr.no.'})
print("New: \n",df)

a)
New:
Sr.no. Name Age Salary
0 1 Alex 30 50000
1 2 John 27 65000
2 3 Peter 29 58000
3 4 Klaus 33 66000

b)
New:
Rank Name Age Salary
0 1 Alex 30 50000
1 2 John 27 65000
2 3 Peter 29 58000
3 4 Klaus 33 66000

c)
New:
Sr.no. Name Age Salary
0 1 Alex 30 50000
1 2 John 27 65000
2 3 Peter 29 58000
3 4 Klaus 33 66000

d)
New:
Sr.no. Name Age Salary
1 1 Alex 30 50000
2 2 John 27 65000
3 3 Peter 29 58000
4 4 Klaus 33 66000

Correct answer is: b)
New:
Rank Name Age Salary
0 1 Alex 30 50000
1 2 John 27 65000
2 3 Peter 29 58000
3 4 Klaus 33 66000
Explanation: The code creates a DataFrame `df` using the provided dictionary `d`, which contains columns ‘Rank’, ‘Name’, ‘Age’, and ‘Salary’. The `rename()` function is then used to rename the ‘Rank’ column to ‘Sr.no.’. Finally, the modified DataFrame is printed using the `print()` function.

4). What is the output of the following code?

				
					import pandas as pd
d = {'Sr.no.': [1, 2, 3, 4], 'Name': ['Alex', 'John', 'Peter', 'Klaus'], 'Age': [30, 27, 29, 33], 'Salary': [50000, 65000, 58000, 66000]}
df = pd.DataFrame(d)
name_list = df['Name'].tolist()
print("List of names: ", name_list)

b) List of names: ‘Alex’, ‘John’, ‘Peter’, ‘Klaus’
c) List of names: ‘Alex’ ‘John’ ‘Peter’ ‘Klaus’
d) List of names: [‘Alex’, ‘John’, ‘Peter’]

Correct answer is: a) List of names: [‘Alex’, ‘John’, ‘Peter’, ‘Klaus’]
Explanation: The code creates a DataFrame named ‘df’ with columns ‘Sr.no.’, ‘Name’, ‘Age’, and ‘Salary’. Then, it extracts the ‘Name’ column using `df[‘Name’]`, converts it to a Python list using the `tolist()` method, and assigns it to the variable ‘name_list’. Finally, it prints the list of names. The correct output is “List of names: [‘Alex’, ‘John’, ‘Peter’, ‘Klaus’]”. Option a represents the correct list of names. Options b and c are incorrect because they use incorrect quotation marks and do not form a valid Python list. Option d is correct but contains unnecessary square brackets around the list, which are not present in the actual output.

5). What is the output of the following code?

				
					import pandas as pd
d = {'Sr.no.':[1,2,3,4],'Name':['Alex','John','Peter','Klaus'],'Age':[30,27,29,33],'Salary':[50000,65000,58000,66000]}
df = pd.DataFrame(d)
print("Row where Salary has maximum value:")
print(df['Salary'].argmax())

a) 0
b) 1
c) 2
d) 3

Correct answer is: d) 3
Explanation: The code creates a DataFrame `df` with four columns: ‘Sr.no.’, ‘Name’, ‘Age’, and ‘Salary’. It then prints the row where the ‘Salary’ column has the maximum value using the `argmax()` method) The `argmax()` method returns the index label (row number) of the first occurrence of the maximum value in the Series. In this case, the ‘Salary’ column has the maximum value of 66000 at index 3 (row 4, considering zero-based indexing), and hence the output of the code will be 3.

6). What is the output of the following code?

				
					import pandas as pd
d = {'Sr.no.':[1,2,3,4],'Name':['Alex','John','Peter','Klaus'],'Age':[30,27,29,33],'Salary':[50000,65000,58000,66000]}
df = pd.DataFrame(d)
print("Row where Salary has minimum value:")
print(df['Salary'].argmin())

a) 0
b) 1
c) 2
d) 3

Correct answer is: a) 0
Explanation: The code creates a DataFrame `df` with columns ‘Sr.no.’, ‘Name’, ‘Age’, and ‘Salary’. It then prints the row number where the ‘Salary’ column has the minimum value using the `argmin()` function. The `argmin()` function returns the index (row number) of the first occurrence of the minimum value in the Series. In this case, the ‘Salary’ column has the values [50000, 65000, 58000, 66000]. The minimum value in the ‘Salary’ column is 50000, which occurs in the first row (index 0) with the value ‘Alex’.

7). What is the output of the following code?

				
					import pandas as pd
import numpy as np
d = {'Sr.no.': [1, 2, 3, 4], 'Name': ['Alex', 'John', 'Peter', 'Klaus'], 'Age': [30, 27, 29, 33], 'Salary': [50000, 65000, 58000, 66000]}
df = pd.DataFrame(d)

for i in ["Company", 'Name']:
    if i in df.columns:
        print(f"{i} is present in DataFrame.")
    else:
        print(f"{i} is not present in DataFrame.")

a) Company is present in DataFrame.
Name is present in DataFrame.
b) Company is present in DataFrame.
Name is not present in DataFrame.
c) Company is not present in DataFrame.
Name is present in DataFrame.
d) Company is not present in DataFrame.
Name is not present in DataFrame.

Correct answer is: c) Company is not present in DataFrame.
Name is present in DataFrame.
Explanation: In the given code, a DataFrame `df` is created from the provided dictionary `d`. The DataFrame has columns: ‘Sr.no.’, ‘Name’, ‘Age’, and ‘Salary’. The code then iterates through the list `[“Company”, ‘Name’]` and checks if each item is present in the DataFrame’s columns using the `in` operator.

8). What is the output of the following code?

				
					import pandas as pd
d = {'Sr.no.':[1,2,3,4],'Name':['Alex','John','Peter','Klaus'],'Age':[30,27,29,33],'Salary':[50000,65000,58000,66000]}
df = pd.DataFrame(d)
print(df.dtypes)

a) Sr.no. int64
Name object
Age int64
Salary int64
dtype: object

b) Sr.no. int32
Name object
Age int32
Salary int32
dtype: object

c) Sr.no. int32
Name object
Age int64
Salary int32
dtype: object

d) Sr.no. int64
Name object
Age int32
Salary int64
dtype: object

Correct answer is: a) Sr.no. int64
Name object
Age int64
Salary int64
dtype: object
Explanation: The given code creates a DataFrame named `df` with four columns: ‘Sr.no.’, ‘Name’, ‘Age’, and ‘Salary’. The `print(df.dtypes)` statement will display the data types of each column in the DataFrame. In the DataFrame, the ‘Sr.no.’, ‘Age’, and ‘Salary’ columns contain integer values, so their data types are represented as `int64`, which is the standard 64-bit integer data type. The ‘Name’ column contains strings, so its data type is represented as `object`.

9). What is the output of the following code?

				
					import pandas as pd
l = [['Virat','cricket'],['Messi','football']]
df = pd.DataFrame(l)
print(df)

a)
0 1
0 Virat cricket
1 Messi football

b)
0 1
0 Virat
1 Messi
2 cricket
3 football

c)
0
0 Virat
1 Messi
2 cricket
3 football

d)
0 1
0 1 2
1 1 2

Correct answer is: a)
0 1
0 Virat cricket
1 Messi football
Explanation: The given code snippet imports the Pandas library as `pd` and creates a DataFrame `df` from a list of lists `l`. The DataFrame `df` contains two rows and two columns with data as follows:
0 1
0 Virat cricket
1 Messi football

10). What is the output of the following code?

				
					import pandas as pd
d = {'Sr.no.':[1,2,3,4],'Name':['Alex','John','Peter','Klaus'],'Age':[30,27,29,33],'Salary':[50000,65000,58000,66000]}
df = pd.DataFrame(d)
index = df.columns.get_loc('Age')
print("Index no of age column: ", index)

a) Index no of age column: 2
b) Index no of age column: 3
c) Index no of age column: 1
d) Index no of age column: 0

Correct answer is: a) Index no of age column: 2
Explanation: The code first imports the pandas library as pd. Then, it creates a DataFrame `df` with columns ‘Sr.no.’, ‘Name’, ‘Age’, and ‘Salary’. Next, it retrieves the index of the column ‘Age’ using the `get_loc()` method, and the index is assigned to the variable `index`. Finally, it prints the result as “Index no of age column: ” followed by the value of `index`. Since ‘Age’ is the third column in the DataFrame (index 2, as Python uses 0-based indexing), the correct output will be “Index no of age column: 2”. Thus, option a is the correct answer.

11). What is the purpose of the following code?

				
					import pandas as pd
d = {'Sr.no.': [1, 2, 3, 4], 'Name': ['Alex', 'John', 'Peter', 'Klaus'], 'Age': [30, 27, 29, 33], 'Salary': [50000, 65000, 58000, 66000]}
df = pd.DataFrame(d)
print("After removing first 2 rows of the DataFrame:")
df1 = df.iloc[2:]
print(df1)

a) It calculates the average age of all employees in the DataFrame.
b) It prints the original DataFrame without any modifications.
c) It creates a new DataFrame `df1` by removing the first 2 rows from the original DataFrame `df`.
d) It sorts the DataFrame in ascending order based on the ‘Age’ column.

Correct answer is: c) It creates a new DataFrame `df1` by removing the first 2 rows from the original DataFrame `df`.
Explanation: The code starts by importing the Pandas library and creating a DataFrame `df` with columns: ‘Sr.no.’, ‘Name’, ‘Age’, and ‘Salary’. The DataFrame contains information about four individuals, including their serial numbers, names, ages, and salaries. The next line of code prints a message indicating that the DataFrame will be modified by removing the first 2 rows. The subsequent line of code creates a new DataFrame `df1` by using the `iloc[2:]` attribute, which slices the original DataFrame `df` starting from the third row (index 2) to the end) This effectively removes the first two rows from `df`, and the resulting DataFrame is assigned to `df1`. Finally, the code prints the newly created DataFrame `df1`, which contains the data for the last two individuals in the original DataFrame `df`.

12). What is the purpose of the following code?

				
					import pandas as pd
d = {'Sr.no.':[1,2,3,4],'Name':['Alex','John','Peter','Klaus'],'Age':[30,27,29,33],'Salary':[50000,65000,58000,66000]}
df = pd.DataFrame(d)
print("Reverse row order:")
print(df.loc[::-1])

a) It creates a DataFrame `df` with columns ‘Sr.no.’, ‘Name’, ‘Age’, and ‘Salary’ and displays it in reverse order.
b) It reverses the order of the rows in the existing DataFrame `df` and displays the result.
c) It creates a new DataFrame by extracting rows from `df` in reverse order and displays it.
d) It sorts the DataFrame `df` in descending order based on the index labels and displays the result.

Correct answer is: b) It reverses the order of the rows in the existing DataFrame `df` and displays the result.
Explanation: The given code snippet uses the Pandas library to create a DataFrame `df` from a dictionary `d`, containing columns ‘Sr.no.’, ‘Name’, ‘Age’, and ‘Salary’. The DataFrame is then printed with a reverse row order using the `loc` attribute and the slicing notation `[::-1]`. Option b is the correct answer because the `df.loc[::-1]` syntax is used to reverse the order of rows in the DataFrame `df`. The result will display the DataFrame with rows in reverse order, meaning the last row becomes the first, the second-last row becomes the second, and so on.

13). What is the purpose of the following code?

				
					import pandas as pd
d = {'Sr.no.': [1, 2, 3, 4], 'Name': ['Alex', 'John', 'Peter', 'Klaus'], 'Age': [30, 27, 29, 33], 'Salary': [50000, 65000, 58000, 66000]}
df = pd.DataFrame(d)
print("Reverse column order:")
print(df.loc[:, ::-1])

a) It drops the ‘Sr.no.’ column from the DataFrame.
b) It sorts the DataFrame in reverse alphabetical order based on the column names.
c) It reverses the order of rows in the DataFrame.
d) It reverses the order of columns in the DataFrame.

Correct answer is: d) It reverses the order of columns in the DataFrame.
Explanation: The given code snippet imports the Pandas library as ‘pd’, creates a DataFrame ‘df’ with four columns (Sr.no., Name, Age, and Salary), and then prints the DataFrame in reverse column order using the `loc[:, ::-1]` slicing. `df.loc[:, ::-1]` is a pandas DataFrame indexing operation that selects all rows (`:`) and reverses the order of columns (`::-1`). As a result, the DataFrame ‘df’ will be printed with the columns in reverse order.

14). What is the purpose of the following code?

				
					import pandas as pd
d = {'Sr.no.':[1,2,3,4],'Name':['Alex','John','Peter','Klaus'],'Age':[30,27,29,33],'Salary':[50000,65000,58000,66000]}
df = pd.DataFrame(d)
print("Select string columns")
print(df.select_dtypes(include = "object"))

a) To create a DataFrame with columns for Sr.no., Name, Age, and Salary
b) To filter and display only the columns with string data type
c) To calculate the mean salary of the employees
d) To calculate the median age of the employees

Correct answer is: b) To filter and display only the columns with string data type
Explanation: The provided code performs the following tasks:
1. Import the Pandas library and assign the DataFrame with columns “Sr.no.”, “Name”, “Age”, and “Salary” to the variable `df`.
2. Prints the message “Select string columns”.
3. Uses the `select_dtypes()` function to filter and display only the columns with the data type “object” (i.e., columns with string values) from the DataFrame `df`.

15). What is the output of the following code?

				
					import pandas as pd
d = {'Name': ['Kate', 'Jason', 'ROBERT', 'MARK', 'Dwyane']}
df = pd.DataFrame(d)
df['Uppercase'] = list(map(lambda x: x.isupper(), df['Name']))
print(df)

a)
Name Uppercase
0 Kate False
1 Jason False
2 ROBERT True
3 MARK True
4 Dwyane False

b)
Name Uppercase
0 Kate False
1 Jason False
2 ROBERT False
3 MARK False
4 Dwyane False

c)
Name Uppercase
0 Kate True
1 Jason True
2 ROBERT True
3 MARK True
4 Dwyane True

d)
Name Uppercase
0 Kate False
1 Jason True
2 ROBERT False
3 MARK True
4 Dwyane False

Correct answer is: a)
Name Uppercase
0 Kate False
1 Jason False
2 ROBERT True
3 MARK True
4 Dwyane False
Explanation: The given code creates a DataFrame `df` with a ‘Name’ column containing five names. It then adds a new column ‘Uppercase’ to the DataFrame, which is populated with True or False based on whether each name in the ‘Name’ column contains all uppercase letters.

16). What is the purpose of the following code?

				
					import pandas as pd
d = {'Name':['kate','jason','ROBERT','MARK','dwyane']}
df = pd.DataFrame(d)
df['Lowercase'] = list(map(lambda x: x.islower(), df['Name']))
print(df)

Correct answer is: c) To add a new column ‘Lowercase’ indicating if each name is lowercase or not
Explanation: The purpose of this code is to add a new column named ‘Lowercase’ to the DataFrame ‘df’. The ‘Lowercase’ column is created using the `map()` function and a lambda function that checks if each name in the ‘Name’ column is lowercase or not. The resulting Boolean values (True or False) are added to the ‘Lowercase’ column. The code then prints the updated DataFrame, which includes the new ‘Lowercase’ column indicating whether each name is lowercase or not.

17). What is the purpose of the following code?

				
					import pandas as pd
d = {'Marks':['Pass','88','First Class','90','Distinction']}
df = pd.DataFrame(d)
df['Numeric'] = list(map(lambda x: x.isdigit(), df['Marks']))
print(df)

a) To convert the ‘Marks’ column into numeric values wherever possible.
b) To check whether each element in the ‘Marks’ column is a digit or not.
c) To remove rows from the DataFrame where the ‘Marks’ column contains non-numeric values.
d) To filter rows in the DataFrame where the ‘Marks’ column is numeric)

Correct answer is: b) To check whether each element in the ‘Marks’ column is a digit or not.
Explanation: The code creates a DataFrame `df` with a ‘Marks’ column containing a mixture of strings and numbers. The ‘Numeric’ column is then added to the DataFrame using the `map()` function and a lambda function. The `map()` function applies the lambda function to each element in the ‘Marks’ column, and the lambda function `lambda x: x.isdigit()` checks whether each element is a digit or not. The result of this check, i.e., True (if the element is a digit) or False (if it’s not), is stored in the ‘Numeric’ column.

18). What is the purpose of the following code?

				
					import pandas as pd
df = pd.DataFrame({'Sales': [55000, 75000, 330000, 10000]})
print("Original DataFrame:")
print("Length of sale_amount:")
df['Length'] = df['Sales'].map(str).apply(len)
print(df)

a) To calculate the total sales amount for each entry in the DataFrame.
b) To calculate the sum of all sales amounts in the DataFrame.
c) To convert the ‘Sales’ column values to strings and find the length of each value.
d) To sort the DataFrame in ascending order based on the ‘Sales’ column.

Correct answer is: c) To convert the ‘Sales’ column values to strings and find the length of each value.
Explanation: In the given code, a DataFrame `df` is created with a single column ‘Sales’ containing four entries representing sales amounts. The code then adds a new column ‘Length’ to the DataFrame by converting the values in the ‘Sales’ column to strings using `map(str)` and then applying the `len` function to calculate the length of each string value.

19). What is the purpose of the following code?

				
					import pandas as pd
import re

d = {'Company_mail': ['TCS tcs@yahoo.com', 'Apple apple@icloud)com', 'Google google@gmail.com']}
df = pd.DataFrame(d)
def find_email(text):
    email = re.findall(r'[\w\.-]+@[\w\.-]+', str(text))
    return ",".join(email)

df['email'] = df['Company_mail'].apply(lambda x: find_email(x))
print("Extracting email from dataframe columns:")
print(df)

a) To filter rows containing email addresses in the ‘Company_mail’ column.
b) To replace email addresses in the ‘Company_mail’ column with the word ’email’.
c) To split the ‘Company_mail’ column into two columns, one containing the company names and the other containing the email addresses.
d) To extract and store email addresses from the ‘Company_mail’ column into a new ’email’ column.

Correct answer is: d) To extract and store email addresses from the ‘Company_mail’ column into a new ’email’ column.
Explanation: The purpose of the given code is to extract and store email addresses from the ‘Company_mail’ column of the DataFrame into a new column called ’email’. The code uses a regular expression pattern (`r'[\w\.-]+@[\w\.-]+’`) to find email addresses in the text and the `find_email()` function is applied to each row of the ‘Company_mail’ column using the `apply()` method) The result is a DataFrame with an additional ’email’ column containing the extracted email addresses. The `print(df)` statement at the end displays the updated DataFrame with the extracted email addresses.

20). What is the purpose of the following code?

				
					import pandas as pd
df1 = pd.DataFrame({'ID': [1, 2, 3], 'Name': ['Yash', 'Gaurav', 'Sanket'], 'Age': [30, 27, 28]})
df2 = pd.DataFrame({'ID': [4, 3], 'Name': ['Tanmay', 'Athrva'], 'Age': [26, 22]})
result = pd.concat([df1, df2])
print("New dataframe")
print(result)

a) To merge two DataFrames df1 and df2 based on their common columns.
b) To concatenate two DataFrames df1 and df2 vertically, stacking one below the other.
c) To concatenate two DataFrames df1 and df2 horizontally, merging them side by side.
d) To create a new DataFrame by transposing the columns and rows of df1 and df2.

Correct answer is: b) To concatenate two DataFrames df1 and df2 vertically, stacking one below the other.
Explanation: The given code uses the `pd.concat()` function from the Pandas library to concatenate two DataFrames, df1 and df2, vertically. This results in a new DataFrame called ‘result’ where the rows of df2 are stacked below the rows of df1. The `pd.concat()` function is used to combine DataFrames, and the default behavior is to concatenate them vertically. This is the reason the new DataFrame ‘result’ contains all the rows from df1 followed by all the rows from df2.

21). What is the purpose of the following code?

				
					import pandas as pd
df1 = pd.DataFrame({'ID':[1,2,3],'Name':['Yash','Gaurav','Sanket'],
                   'Age':[30,27,28]})
df2 = pd.DataFrame({'ID':[4,3],'Name':['Tanmay','Athrva'],'Age':[26,22]})
result = pd.concat([df1,df2],axis=1)
print("New dataframe")
print(result)

a) Merging two DataFrames vertically based on the common column ‘ID’.
b) Merging two DataFrames horizontally based on the common column ‘Name’.
c) Concatenating two DataFrames horizontally, creating a new DataFrame.
d) Concatenating two DataFrames vertically, creating a new DataFrame.

Correct answer is: c) Concatenating two DataFrames horizontally, creating a new DataFrame.
Explanation: The purpose of the given code is to concatenate two DataFrames horizontally using the `pd.concat()` function with `axis=1`. Horizontal concatenation, or concatenation along columns, merges the two DataFrames side by side, based on their column names. The resulting DataFrame `result` will have all columns from both `df1` and `df2` in a single DataFrame. In this case, it will produce a new DataFrame with columns ‘ID’, ‘Name’, and ‘Age’ from both `df1` and `df2`.

22). What is the purpose of the following code?

				
					import pandas as pd
df1 = pd.DataFrame({'Id':['S1','S2','S3'],
                   'Name':['Ketan','Yash','Abhishek'],
                   'Marks':[90,87,77]})
df2 = pd.DataFrame({'Id':['S2','S4'],
                    'Name':['Yash','Gaurav'],
                   'Marks':[70,65]})
print('Dataframe 1: \n',df1)
print('Dataframe 2: \n',df2)
new = pd.merge(df1, df2, on='Id', how='inner')
print("Merged data:")
print(new)

a) To transpose the rows and columns of the merged DataFrame.
b) To calculate the mean of the ‘Marks’ column in each DataFrame.
c) To merge two DataFrames based on a common column ‘Id’ using an inner join.
d) To merge two DataFrames based on a common column ‘Name’ using an inner join.

Correct answer is: c) To merge two DataFrames based on a common column ‘Id’ using an inner join.
Explanation: The purpose of this code is to merge two DataFrames, `df1` and `df2`, based on a common column ‘Id’ using an inner join. The code uses the `pd.merge()` function, which is a powerful method in Pandas used to combine DataFrames based on shared columns. The ‘inner’ join ensures that only the rows with matching ‘Id’ values in both DataFrames are included in the resulting DataFrame ‘new’. The printed output displays the contents of `df1`, `df2`, and the merged DataFrame ‘new’, allowing us to observe the merging operation.

23). What is the output of the following code?

				
					import pandas as pd
import numpy as np

df = pd.DataFrame({'Sr.no.':[1,2,3,4],
                   'Name':['Alex','John','Peter','Klaus'],
                   'Age':[30,np.nan,29,np.nan]})

print("Original Dataframe: \n",df)
print(df.isna())

a) Original DataFrame:
Sr.no. Name Age
0 1 Alex 30.0
1 2 John NaN
2 3 Peter 29.0
3 4 Klaus NaN
dtype: float64

Output of `df.isna()`:
Sr.no. Name Age
0 False False False
1 False False True
2 False False False
3 False False True

b) Original DataFrame:
Sr.no. Name Age
0 1 Alex 30.0
1 2 John NaN
2 3 Peter 29.0
3 4 Klaus NaN
dtype: object

Output of `df.isna()`:
Sr.no. Name Age
0 False False False
1 False False True
2 False False False
3 True False True

c) Original DataFrame:
Sr.no. Name Age
0 1 Alex 30.0
1 2 John NaN
2 3 Peter 29.0
3 4 Klaus NaN
dtype: float64

Output of `df.isna()`:
Sr.no. Name Age
0 True False False
1 False False True
2 True False False
3 False False True

d) Original DataFrame:
Sr.no. Name Age
0 1 Alex 30.0
1 2 John NaN
2 3 Peter 29.0
3 4 Klaus NaN
dtype: object

Output of `df.isna()`:
Sr.no. Name Age
0 True False False
1 False False True
2 True False False
3 False False True

Correct answer is: a) Original DataFrame:
Sr.no. Name Age
0 1 Alex 30.0
1 2 John NaN
2 3 Peter 29.0
3 4 Klaus NaN
dtype: float64

Output of `df.isna()`:
Sr.no. Name Age
0 False False False
1 False False True
2 False False False
3 False False True
Explanation: The original DataFrame `df` contains columns ‘Sr.no.’, ‘Name’, and ‘Age’ with corresponding values. The `print(df.isna())` statement prints a DataFrame indicating whether each element in the original DataFrame is null (`NaN`) or not. The output shows `False` for non-null values and `True` for null values in the ‘Age’ column, as indicated by the `NaN` values.

24). What is the purpose of the following code?

				
					import pandas as pd
import numpy as np
df = pd.DataFrame({'Sr.no.':[1,2,3,4,5],
                   'Name':['Alex',np.nan,'Peter','Klaus','Stefan'],
                   'Age':[30,np.nan,29,22,22]})
print("Original Dataframe: \n",df)
result = df.fillna(df.mode().iloc[0])
print(result)

a) To remove duplicate rows from the DataFrame.
b) To calculate the median of each column in the DataFrame.
c) To fill missing values in the ‘Name’ and ‘Age’ columns with the most frequent value.
d) To calculate the cumulative sum of each column in the DataFrame.

Correct answer is: c) To fill missing values in the ‘Name’ and ‘Age’ columns with the most frequent value.
Explanation: The given code uses the Pandas library to manipulate the DataFrame `df`. The DataFrame contains three columns: ‘Sr.no.’, ‘Name’, and ‘Age’. It uses the `fillna()` method to fill missing values (NaN) in the ‘Name’ and ‘Age’ columns with the most frequent value present in each column. The most frequent value is calculated using the `mode()` function, and `iloc[0]` is used to access the first value in the resulting mode Series. This operation replaces the missing values with the most frequent non-missing value in their respective columns. The filled DataFrame is then stored in the variable `result`, and both the original and filled DataFrames are printed using the `print()` function.

25). What is the purpose of the following code?

				
					import pandas as pd
df = pd.read_excel("file name")
print(df)

a) To import the Pandas library and read an Excel file named “file name” into a DataFrame, then print the DataFrame.
b) To export an Excel file named “file name” into a Pandas DataFrame, then display the DataFrame.
c) To read a CSV file named “file name” into a Pandas DataFrame, then display the DataFrame.
d) To save a Pandas DataFrame into an Excel file named “file name”, then print the DataFrame.

Correct answer is: a) To import the Pandas library and read an Excel file named “file name” into a DataFrame, then print the DataFrame.
Explanation: The provided code performs the following actions:
1. `import pandas as pd`: This line imports the Pandas library and assigns it the alias `pd` to make it easier to refer to Pandas functions.
2. `df = pd.read_excel(“file name”)`: This line reads an Excel file named “file name” into a Pandas DataFrame `df`. The `read_excel()` function is used to read Excel files and create a DataFrame.
3. `print(df)`: This line prints the DataFrame `df`, which contains the data read from the Excel file.

Python Pandas MCQ : Set 5

Python Pandas MCQ

1). What is the output of the following code?

				
					import pandas as pd
df = pd.Series(['2 Feb 2020','5/11/2021','7-8-2022'])
print("Converting series of date strings to a timeseries:")
print(pd.to_datetime(df))

a) 0 2020-02-02
1 2021-05-11
dtype: datetime64[ns]

b) 0 Feb 02, 2020
1 May 11, 2021
2 Jul 08, 2022
dtype: datetime64[ns]

c) 0 2020-02-02
1 2021-11-05
2 2022-08-07
dtype: datetime64[ns]

d) ValueError: day is out of range for month

Correct answer is: c) 0 2020-02-02
1 2021-11-05
2 2022-08-07
dtype: datetime64[ns]
Explanation: The given code converts a pandas Series containing date strings into a time series using the `pd.to_datetime()` function. The function interprets various date formats and converts them into datetime64[ns] format. In the original Series, the date strings have different formats: ‘2 Feb 2020’, ‘5/11/2021’, and ‘7-8-2022’. The function `pd.to_datetime()` is capable of handling these formats and correctly converts them into the standard ‘YYYY-MM-DD’ format.

2). What is the output of the following code?

				
					import pandas as pd
df = pd.Series([54, 25, 38, 87, 67])
print("Index of the first smallest and largest value of the series:")
print(df.idxmin())
print(df.idxmax())

a) Index of the first smallest and largest value of the series:
1
3

b) Index of the first smallest and largest value of the series:
1
4

c) Index of the first smallest and largest value of the series:
2
3

d) Index of the first smallest and largest value of the series:
0
3

Correct answer is: a) Index of the first smallest and largest value of the series:
1
3
Explanation: The code defines a Pandas Series `df` containing five elements: [54, 25, 38, 87, 67]. The `idxmin()` function is then applied to find the index of the first smallest value in the Series, which is 1 (corresponding to the element 25). Similarly, the `idxmax()` function is used to find the index of the first largest value in the Series, which is 3 (corresponding to the element 87).

3). What is the output of the following code?

				
					import pandas as pd
dictionary = {'marks1':[34,20,32,30],'marks2':[36,22,10,44]}
df = pd.DataFrame(dictionary)
print(df)

a)
marks1 marks2
0 34 36
1 20 22
2 32 10
3 30 44

b)
marks1 marks2
0 34 36
1 20 22
2 32 10
3 30 44
4 22 33

c)
marks1 marks2
0 22 36
1 33 22
2 10 10
3 44 44

d)
marks1 marks2
0 34 22
1 20 30
2 32 32
3 30 10

Correct answer is: a)
marks1 marks2
0 34 36
1 20 22
2 32 10
3 30 44
Explanation: The given code imports the Pandas library as ‘pd’ and creates a DataFrame ‘df’ using a dictionary. The dictionary contains two keys ‘marks1’ and ‘marks2’, each representing a list of four integer values. The DataFrame ‘df’ is printed using the `print()` function. The resulting output displays the DataFrame ‘df’ with the ‘marks1’ and ‘marks2’ columns, and their respective integer values in each row. The values are displayed in the same order as in the dictionary.

4). What is the output of the following code?

				
					import pandas as pd
dictionary = {'marks1': [34, 20, 32, 30], 'marks2': [36, 22, 10, 44]}
df = pd.DataFrame(dictionary)
print("First n rows: \n", df.head(2))

a)
First n rows:
marks1 marks2
0 34 36
1 20 22

b)
First n rows:
marks1 marks2
0 34 36
1 20 22
2 32 10

c)
First n rows:
marks1 marks2
0 34 36

d)
First n rows:
marks1 marks2
1 20 22
2 32 10

Correct answer is: a)
First n rows:
marks1 marks2
0 34 36
1 20 22
Explanation: The `df.head(2)` function is used to access the first two rows of the DataFrame `df`. The DataFrame `df` is created from the provided dictionary, which has two columns ‘marks1’ and ‘marks2’ with four rows of data each. Therefore, the output will display the first two rows of the DataFrame, as shown in option (a). The head() function displays the specified number of rows from the top of the DataFrame, and since we passed 2 as an argument, it will display the first two rows of the DataFrame.

5). What is the output of the following code?

				
					import pandas as pd
d = {'Sr.no.': [1, 2, 3], 'Name': ['Alex', 'John', 'Peter'], 'Age': [30, 27, 29]}
df = pd.DataFrame(d)
print(df[['Name', 'Age']])

a)
Name Age
0 Alex 30
1 John 27
2 Peter 29

b)
Name Age
0 [‘Alex’, ‘John’, ‘Peter’] [30, 27, 29]

c)
Name Age
0 Alex 30
2 Peter 29

d)
Sr.no. Name Age
0 1 Alex 30
1 2 John 27

Correct answer is: a)
Name Age
0 Alex 30
1 John 27
2 Peter

Explanation: The provided code creates a DataFrame `df` with three columns: ‘Sr.no.’, ‘Name’, and ‘Age’. The `print(df[[‘Name’, ‘Age’]])` statement selects and prints only the ‘Name’ and ‘Age’ columns from the DataFrame.

6). What is the output of the following code?

				
					import pandas as pd
d = {'Sr.no.':[1,2,3],'Name':['Alex','John','Peter'],'Age':[30,27,29]}
df = pd.DataFrame(d)
print("Second row: \n", df.iloc[1,:])

a) Sr.no. 2
Name John
Age 27
Name: 1, dtype: object

b) Sr.no. 2
Name Alex
Age 26
Name: 1, dtype: object

c) Sr.no. 2
Name John
Age 29
Name: 1, dtype: object

d) Sr.no. 1
Name John
Age 28
Name: 2, dtype: object

Correct answer is: a) Sr.no. 2
Name John
Age 27
Name: 1, dtype: object
Explanation: The provided code creates a DataFrame ‘df’ with three columns: ‘Sr.no.’, ‘Name’, and ‘Age’, containing the given data) The `iloc` function is used to access rows and columns by their index position. In the `print()` statement, `df.iloc[1, :]` is used to select the second row of the DataFrame.

7). What is the output of the following code?

				
					import pandas as pd
d = {'Sr.no.':[1,2,3,4],'Name':['Alex','John','Peter','Klaus'],'Age':[30,27,29,33]}
df = pd.DataFrame(d)
print("Rows where age is greater than 29")
print(df[df['Age'] > 29])

a)
Sr.no. Name Age
0 1 Alex 30
3 4 Klaus 33

b)
Sr.no. Name Age
1 2 John 27
2 3 Peter 29

c)
Sr.no. Name Age
3 4 Klaus 33

d)
Sr.no. Name Age
0 1 Alex 30

Correct answer is: a)
Sr.no. Name Age
0 1 Alex 30
3 4 Klaus 33
Explanation: The given code creates a DataFrame ‘df’ with columns ‘Sr.no.’, ‘Name’, and ‘Age’. The `print(df[df[‘Age’] > 29])` statement filters the DataFrame ‘df’ to only include rows where the ‘Age’ column is greater than 29. The resulting output shows the rows where the condition is satisfied) In the original DataFrame ‘df’, there are two rows with ages greater than 29, which are Alex (30) and Klaus (33).

8). Which of the following is the output of the given code?

				
					import pandas as pd
d = {'Sr.no.': [1, 2, 3, 4], 'Name': ['Alex', 'John', 'Peter', 'Klaus'], 'Age': [30, 27, 29, 33]}
df = pd.DataFrame(d)
print("No. of rows: ", df.shape[0])
print("No. of columns: ", df.shape[1])

a) No. of rows: 3
No. of columns: 4

b) No. of rows: 4
No. of columns: 3

c) No. of rows: 4
No. of columns: 2

d) No. of rows: 2
No. of columns: 4

Correct answer is: b) No. of rows: 4
No. of columns: 3
Explanation: The code creates a DataFrame `df` from the dictionary `d` with 4 rows and 3 columns. The `shape` attribute of the DataFrame is a tuple that contains the number of rows and columns. The statement `df.shape[0]` prints the number of rows, which is 4, and `df.shape[1]` prints the number of columns, which is 3. Therefore, the correct output is “No. of rows: 4” and “No. of columns: 3”.

9). What is the output of the following code?

				
					import pandas as pd
d = {'Sr.no.':[1,2,3,4],'Name':['Alex','John','Peter','Klaus'],'Age':[30,np.nan,29,np.nan]}
df = pd.DataFrame(d)
print("Rows where age is missing:")
print(df[df['Age'].isnull()])

a)
Sr.no. Name Age
1 2 John NaN
3 4 Klaus NaN

b)
Sr.no. Name Age
0 1 Alex 30.0
2 3 Peter 29.0

c)
Sr.no. Name Age
1 2 John NaN
2 3 Peter NaN

d)
Sr.no. Name Age
1 2 John NaN
3 4 Klaus 29.0

Correct answer is: a)
Sr.no. Name Age
1 2 John NaN
3 4 Klaus NaN
Explanation: The given code creates a DataFrame `df` with four columns: ‘Sr.no.’, ‘Name’, and ‘Age’. Two of the ‘Age’ values are assigned as `np.nan`, which represents missing or not-a-number values in pandas. The code then prints the rows where the ‘Age’ column has missing values using `df[df[‘Age’].isnull()]`.

10). What is the output of the following code?

				
					import pandas as pd
d = {'Sr.no.': [1, 2, 3, 4], 'Name': ['Alex', 'John', 'Peter', 'Klaus'], 'Age': [30, 27, 29, 33]}
df = pd.DataFrame(d)
print("Rows where age is between 25 and 30 (inclusive):")
print(df[df['Age'].between(25, 30)])

a)
Sr.no. Name Age
1 2 John 27
2 3 Peter 29

b)
Sr.no. Name Age
0 1 Alex 30
1 2 John 27
2 3 Peter 29

c)
Sr.no. Name Age
2 3 Peter 29

d)
Sr.no. Name Age
0 1 Alex 30
1 2 John 27

Correct answer is: b)
Sr.no. Name Age
0 1 Alex 30
1 2 John 27
2 3 Peter 29
Explanation: The given code creates a DataFrame `df` with four columns: ‘Sr.no.’, ‘Name’, and ‘Age’. It then prints the rows where the ‘Age’ column falls between 25 and 30 (inclusive). The `between()` function in Pandas checks whether each element in the ‘Age’ column is between 25 and 30. When we apply this condition to the DataFrame using `df[‘Age’].between(25, 30)`, it returns a boolean Series with `True` for rows where the ‘Age’ is between 25 and 30 and `False` otherwise. The final output displays the rows where the condition is `True`, which corresponds to rows with ‘Age’ values of 27, 29, and 30 (inclusive).

11). What is the output of the following code?

				
					import pandas as pd
d = {'Sr.no.':[1,2,3,4],'Name':['Alex','John','Peter','Klaus'],'Age':[30,27,29,33]}
df = pd.DataFrame(d)
print(df)
print("Change the age of John to 24:")
df['Age'] = df['Age'].replace(27,24)
print(df)

a)
Sr.no. Name Age
0 1 Alex 30
1 2 John 24
2 3 Peter 29
3 4 Klaus 33

b)
Sr.no. Name Age
0 1 Alex 30
1 2 John 27
2 3 Peter 29
3 4 Klaus 33
Change the age of John to 24:
Sr.no. Name Age
0 1 Alex 30
1 2 John 24
2 3 Peter 29
3 4 Klaus 33

c)
Sr.no. Name Age
0 1 Alex 30
1 2 John 24
2 3 Peter 29
3 4 Klaus 33
Change the age of John to 27:
Sr.no. Name Age
0 1 Alex 30
1 2 John 27
2 3 Peter 29
3 4 Klaus 33

d)
Sr.no. Name Age
0 1 Alex 30
1 2 John 27
2 3 Peter 29
3 4 Klaus 33
Change the age of John to 24:
Sr.no. Name Age
0 1 Alex 30
1 2 John 24
2 3 Peter 29

Correct answer is: b)
Sr.no. Name Age
0 1 Alex 30
1 2 John 27
2 3 Peter 29
3 4 Klaus 33
Change the age of John to 24:
Sr.no. Name Age
0 1 Alex 30
1 2 John 24
2 3 Peter 29
3 4 Klaus 33
Explanation: The code creates a DataFrame named `df` with columns ‘Sr.no.’, ‘Name’, and ‘Age’. The initial DataFrame is as follows:
Sr.no. Name Age
0 1 Alex 30
1 2 John 27
2 3 Peter 29
3 4 Klaus 33
Then, it modifies the ‘Age’ of John by replacing the value 27 with 24.

12). What is the output of the following code?

				
					import pandas as pd
d = {'Sr.no.':[1,2,3,4],'Name':['Alex','John','Peter','Klaus'],'Age':[30,27,29,33]}
df = pd.DataFrame(d)
print("Sum of age columns: ",df['Age'].sum())

b) Sum of age columns: 119.0
c) Sum of age columns: 119.00
d) Sum of age columns: 120

Correct answer is: a) Sum of age columns: 119
Explanation: The given code creates a DataFrame `df` using the dictionary `d`, which contains three columns: ‘Sr.no.’, ‘Name’, and ‘Age’. Then, it calculates the sum of the ‘Age’ column using the `sum()` function and prints the result. The ‘Age’ column in the DataFrame contains the values [30, 27, 29, 33]. When you calculate the sum of these values, you get 30 + 27 + 29 + 33 = 119.

13). What is the output of the following code?

				
					import pandas as pd
d = {'Sr.no.':[1,2,3,4],'Name':['Alex','John','Peter','Klaus'],'Age':[30,27,29,33]}
df = pd.DataFrame(d)
df1 = {'Sr.no.':5,'Name':'Jason','Age':28}
df = df.append(df1, ignore_index=True)
print("New Series: ")
print(df)

a)
Sr.no. Name Age
0 1 Alex 30
1 2 John 27
2 3 Peter 29
3 4 Klaus 33
4 5 Jason 28

b)
Sr.no. Name Age
0 1 Alex 30
1 2 John 27
2 3 Peter 29
3 4 Klaus 33
4 Sr.no. Name Age
5 5 Jason 28

c)
Sr.no. Name Age
0 1 Alex 30
1 2 John 27
2 3 Peter 29
3 4 Klaus 33

d)
Sr.no. Name Age
0 1 Alex 30
1 2 John 27
2 3 Peter 29
3 4 Klaus 33
0 5 Jason 28

Correct answer is: a)
Sr.no. Name Age
0 1 Alex 30
1 2 John 27
2 3 Peter 29
3 4 Klaus 33
4 5 Jason 28
Explanation: The code creates a DataFrame `df` with columns ‘Sr.no.’, ‘Name’, and ‘Age’ using a dictionary `d`. Then, it appends a new row represented by the dictionary `df1` to the DataFrame using the `append()` method with `ignore_index=True`. The `ignore_index=True` ensures that the new row is appended with a new index.

14). What is the output of the following code?

				
					import pandas as pd
d = {'Sr.no.': [1, 2, 3, 4], 'Name': ['Alex', 'John', 'Peter', 'Klaus'], 'Age': [30, 27, 29, 33]}
df = pd.DataFrame(d)
new = df.sort_values(by=['Name'], ascending=[True])
print("After sorting:")
print(new)

a)
After sorting:
Sr.no. Name Age
0 1 Alex 30
1 2 John 27
3 4 Klaus 33
2 3 Peter 29

b)
After sorting:
Sr.no. Name Age
2 3 Peter 29
3 4 Klaus 33
0 1 Alex 30
1 2 John 27

c)
After sorting:
Sr.no. Name Age
3 4 Klaus 33
2 3 Peter 29
1 2 John 27
0 1 Alex 30

d)
After sorting:
Sr.no. Name Age
1 2 John 27
0 1 Alex 30
2 3 Peter 29
3 4 Klaus 33

Correct answer is: a)
After sorting:
Sr.no. Name Age
0 1 Alex 30
1 2 John 27
3 4 Klaus 33
2 3 Peter 29
Explanation: The given code snippet creates a DataFrame `df` with columns ‘Sr.no.’, ‘Name’, and ‘Age’. It then sorts the DataFrame `df` based on the ‘Name’ column in ascending order using the `sort_values()` method with `ascending=True`. The sorted DataFrame is stored in a new variable `new`.

15). What is the output of the following code?

				
					import pandas as pd
d = {'Sr.no.': [1, 2, 3, 4], 'Name': ['Alex', 'John', 'Peter', 'Klaus'], 'Age': [30, 27, 29, 33]}
df = pd.DataFrame(d)
print("Sum of age columns: ", df['Age'].mean())

a) 30.25
b) 29.75
c) 29.0
d) 32.25

Correct answer is: b) 29.75
Explanation: The given code creates a DataFrame `df` with columns ‘Sr.no.’, ‘Name’, and ‘Age’. It then calculates the mean (average) of the ‘Age’ column using the `mean()` function and prints the result. The ‘Age’ column contains values [30, 27, 29, 33]. To find the mean, you add all the values and divide the sum by the total number of values: Mean = (30 + 27 + 29 + 33) / 4 = 29.75

16). What is the output of the following code?

				
					import pandas as pd
d = {'Sr.no.': [1, 2, 3, 4], 'Name': ['Alex', 'John', 'Peter', 'Klaus'], 'Age': [30, 27, 29, 33]}
df = pd.DataFrame(d)
print("Change the name of John to Jim:")
df['Name'] = df['Name'].replace('John', 'Jim')
print(df)

a)
Sr.no. Name Age
0 1 Alex 30
2 3 Peter 29
3 4 Klaus 33

b)
Sr.no. Name Age
0 1 Alex 30
1 2 John 27
2 3 Peter 29
3 4 Klaus 33

c)
Sr.no. Name Age
0 1 Alex 30
1 2 Jim 27
2 3 Peter 29
3 4 Klaus 33

d)
Sr.no. Name Age
0 1 Alex 30
1 2 John 27
2 3 Peter 29
3 4 Klaus 33

Correct answer is: c)
Sr.no. Name Age
0 1 Alex 30
1 2 Jim 27
2 3 Peter 29
3 4 Klaus 33
Explanation: The given code creates a DataFrame `df` with columns ‘Sr.no.’, ‘Name’, and ‘Age’. Then, it replaces the value ‘John’ in the ‘Name’ column with ‘Jim’.

17). What is the output of the following code?

				
					import pandas as pd
d = {'Sr.no.':[1,2,3,4],'Name':['Alex','John','Peter','Klaus'],'Age':[30,27,29,33]}
df = pd.DataFrame(d)
df = df[df.Name != 'John']
print("New Series")
print(df)

a)
New Series
Sr.no. Name Age
0 1 Alex 30
2 3 Peter 29
3 4 Klaus 33

b)
New Series
Sr.no. Name Age
0 1 Alex 30
1 2 John 27
2 3 Peter 29
3 4 Klaus 33

c)
New Series
Sr.no. Name Age
1 2 John 27
2 3 Peter 29
3 4 Klaus 33

d)
New Series
Sr.no. Name Age
0 1 Alex 30
2 3 Peter 29
3 4 Klaus 33
1 2 John 27

Correct answer is: a)
New Series
Sr.no. Name Age
0 1 Alex 30
2 3 Peter 29
3 4 Klaus 33
Explanation: The code creates a DataFrame `df` with three columns: ‘Sr.no.’, ‘Name’, and ‘Age’. Then, it filters the DataFrame using the condition `df.Name != ‘John’`, which keeps all rows where the ‘Name’ column is not equal to ‘John’. The resulting DataFrame is printed as “New Series.”

18). What is the output of the following code?

				
					import pandas as pd
d = {'Sr.no.': [1, 2, 3, 4], 'Name': ['Alex', 'John', 'Peter', 'Klaus'], 'Age': [30, 27, 29, 33]}
df = pd.DataFrame(d)
Salary = [50000, 65000, 58000, 66000]
df['Salary'] = Salary
print("New Series: \n", df)

a)
Sr.no. Name Age Salary
0 1 Alex 30 50000
1 2 John 27 65000
2 3 Peter 29 58000
3 4 Klaus 33 66000

b)
Sr.no. Name Age
0 1 Alex 30
1 2 John 27
2 3 Peter 29
3 4 Klaus 33

c)
Sr.no. Name Age Salary
0 1 Alex 30 58000
1 2 John 27 65000
2 3 Peter 29 66000
3 4 Klaus 33 50000

d)
Sr.no. Name Age Salary
0 1 Alex 30 66000
1 2 John 27 65000
2 3 Peter 29 50000
3 4 Klaus 33 58000

Correct answer is: a)
Sr.no. Name Age Salary
0 1 Alex 30 50000
1 2 John 27 65000
2 3 Peter 29 58000
3 4 Klaus 33 66000
Explanation: The given code first creates a dictionary `d` containing data for the ‘Sr.no.’, ‘Name’, and ‘Age’ columns. It then creates a DataFrame `df` using this dictionary. Next, it defines a list `Salary` containing salary values and adds it to the DataFrame as a new column called ‘Salary’. The output of the `print()` function will display the updated DataFrame with the ‘Sr.no.’, ‘Name’, ‘Age’, and ‘Salary’ columns.

19). What is the output of the following code?

				
					import pandas as pd
d = [{'name':'Yash','percentage':78},{'name':'Rakesh','percentage':80},{'name':'Suresh','percentage':60}]
df = pd.DataFrame(d)
for index, row in df.iterrows():
    print(row['name'], row['percentage'])

a) Yash 78 Rakesh 80 Suresh 60
b) Yash Rakesh Suresh
c) 0 Yash 1 Rakesh 2 Suresh
d) Yash percentage Rakesh percentage Suresh percentage

Correct answer is: a) Yash 78 Rakesh 80 Suresh 60
Explanation: The code creates a DataFrame `df` using a list of dictionaries `d`. The DataFrame contains three rows, with ‘name’ and ‘percentage’ as columns. The `iterrows()` method is used to iterate over the rows of the DataFrame, and for each row, the ‘name’ and ‘percentage’ values are printed)

20). What is the output of the following code?

				
					import pandas as pd
d = {'name': ['Virat', 'Messi', 'Kobe'], 'sport': ['cricket', 'football', 'basketball']}
df = pd.DataFrame(d)
print("Names of columns: ")
print(list(df.columns.values))

a) `[‘name’, ‘sport’]`
b) `[‘Virat’, ‘Messi’, ‘Kobe’, ‘cricket’, ‘football’, ‘basketball’]`
c) `[‘name’, ‘Virat’, ‘Messi’, ‘Kobe’, ‘sport’, ‘cricket’, ‘football’, ‘basketball’]`
d) `[‘cricket’, ‘football’, ‘basketball’, ‘name’, ‘sport’]`

Correct answer is: a) `[‘name’, ‘sport’]`
Explanation: The given code first imports the pandas library and creates a DataFrame `df` from the dictionary `d`. The dictionary `d` consists of two key-value pairs: `’name’` with a list of names and `’sport’` with a list of corresponding sports.

21). What is the output of the following code?

				
					import pandas as pd
d = {'C1': [1, 3, 8], 'C2': [6, 8, 0], 'C3': [8, 2, 6]}
df = pd.DataFrame(d)
df = df.rename(columns={'C1': 'A', 'C2': 'B', 'C3': 'C'})
print("New DataFrame after renaming columns:")
print(df)

a)
New DataFrame after renaming columns:
A B C
0 1 6 8
1 3 8 2
2 8 0 6

b)
New DataFrame after renaming columns:
C1 C2 C3
0 1 6 8
1 3 8 2
2 8 0 6

c)
New DataFrame after renaming columns:
C1 C2 C3
0 1 3 8
1 6 8 0
2 8 2 6

d)
New DataFrame after renaming columns:
A B C
0 1 3 8
1 6 8 0
2 8 2 6

Correct answer is: a)
New DataFrame after renaming columns:
A B C
0 1 6 8
1 3 8 2
2 8 0 6
Explanation: The code creates a DataFrame `df` using the dictionary `d`, and then it renames the columns ‘C1’, ‘C2’, and ‘C3’ to ‘A’, ‘B’, and ‘C’ respectively. The `print(df)` statement displays the new DataFrame after renaming the columns. The output shows that the DataFrame `df` has been updated with the new column names ‘A’, ‘B’, and ‘C’, while maintaining the original data)

22). What is the output of the following code?

				
					import pandas as pd
d = {'Sr.no.':[1,2,3,4],'Name':['Alex','John','Peter','Klaus'],'Age':[30,27,29,33],'Salary':[50000,65000,58000,66000]}
df = pd.DataFrame(d)
df = df[['Sr.no.','Name','Salary','Age']]
print('After re-ordering columns: \n',df)

a) After re-ordering columns:
Sr.no. Name Salary Age
0 1 Alex 50000 30
1 2 John 65000 27
2 3 Peter 58000 29
3 4 Klaus 66000 33

b) After re-ordering columns:
Sr.no. Name Age Salary
0 1 Alex 30 50000
1 2 John 27 65000
2 3 Peter 29 58000
3 4 Klaus 33 66000

c) After re-ordering columns:
Sr.no. Salary Age Name
0 1 50000 30 Alex
1 2 65000 27 John
2 3 58000 29 Peter
3 4 66000 33 Klaus

d) The code will raise an error.

Correct answer is: a) After re-ordering columns:
Sr.no. Name Salary Age
0 1 Alex 50000 30
1 2 John 65000 27
2 3 Peter 58000 29
3 4 Klaus 66000 33
Explanation: The code first creates a DataFrame `df` using a dictionary `d`, which contains ‘Sr.no.’, ‘Name’, ‘Age’, and ‘Salary’ as keys, and their respective values. The DataFrame is then re-ordered to have ‘Sr.no.’, ‘Name’, ‘Salary’, and ‘Age’ as columns using the line `df = df[[‘Sr.no.’,’Name’,’Salary’,’Age’]]`. Finally, the output is printed using `print(‘After re-ordering columns: \n’, df)`.

23). What is the purpose of the following code?

				
					import pandas as pd
d = {'Sr.no.': [1, 2, 3, 4], 'Name': ['Alex', 'John', 'Peter', 'Klaus'], 'Age': [30, 27, 29, 33], 'Salary': [50000, 65000, 58000, 66000]}
df = pd.DataFrame(d)
df.to_csv('new_file.csv', sep='\t', index=False)
new = pd.read_csv('new_file.csv')
print(new)

a) To read a CSV file into a DataFrame and display its contents.
b) To create a new CSV file and write the DataFrame contents into it.
c) To convert the DataFrame into a NumPy array and print its values.
d) To sort the DataFrame based on the ‘Age’ column and print the result.

Correct answer is: b) To create a new CSV file and write the DataFrame contents into it.
Explanation: The given code performs the following operations:
1. A dictionary `d` is defined containing data related to ‘Sr.no.’, ‘Name’, ‘Age’, and ‘Salary’.
2. A DataFrame `df` is created using the data from the dictionary `d`.
3. The `to_csv()` method is used to export the DataFrame `df` to a CSV file named ‘new_file.csv’, using tab (`\t`) as the separator and excluding the index column.
4. The `pd.read_csv()` function is used to read the contents of the ‘new_file.csv’ CSV file back into a new DataFrame `new`.
5. Finally, the contents of the DataFrame `new` are printed, displaying the data from the ‘new_file.csv’ file.

24). What is the purpose of the following code?

				
					import pandas as pd
df = pd.read_csv("population_by_country_2020.csv")
df1 = df.drop(['Yearly Change','Net Change','Density (P/Km²)','Land Area (Km²)',
              'Migrants (net)','Fert. Rate','Med) Age','Urban Pop %','World Share'],axis=1)
print(df1.head(10))

a) To read a CSV file named “population_by_country_2020.csv” and display the first 10 rows of the DataFrame.
b) To drop specific columns from the DataFrame ‘df’ and display the first 10 rows of the modified DataFrame.
c) To filter the DataFrame ‘df’ and display the first 10 rows that meet a certain condition.
d) To rename specific columns in the DataFrame ‘df’ and display the first 10 rows of the modified DataFrame.

Correct answer is: b) To drop specific columns from the DataFrame ‘df’ and display the first 10 rows of the modified DataFrame.
Explanation: The given code imports the Pandas library as ‘pd’ and then reads a CSV file named “population_by_country_2020.csv” into a DataFrame ‘df’ using the `pd.read_csv()` function. After that, the code creates a new DataFrame ‘df1’ by dropping specific columns from ‘df’ using the `drop()` method) The columns [‘Yearly Change’, ‘Net Change’, ‘Density (P/Km²)’, ‘Land Area (Km²)’, ‘Migrants (net)’, ‘Fert. Rate’, ‘Med) Age’, ‘Urban Pop %’, ‘World Share’] are dropped from the DataFrame ‘df’. Finally, the code prints the first 10 rows of the modified DataFrame ‘df1’ using the `head(10)` method)

25). What is the purpose of the following code?

				
					import pandas as pd
d = {'Sr.no.':[1,2,3,4],'Name':['Alex','John','Peter','Klaus'],'Age':[30,np.nan,29,np.nan]}
df = pd.DataFrame(d)
print(df)
df.fillna(value = 25,inplace = True)
print("After filling nan values: \n",df)

a) To calculate the mean of the ‘Age’ column and fill the missing values with the mean.
b) To replace all the missing values in the ‘Age’ column with the value 25.
c) To drop the rows with missing values in the ‘Age’ column from the DataFrame.
d) To calculate the median of the ‘Age’ column and fill the missing values with the median.

Correct answer is: b) To replace all the missing values in the ‘Age’ column with the value 25.
Explanation: The given code is using the Pandas library to create a DataFrame `df` with columns ‘Sr.no.’, ‘Name’, and ‘Age’. The ‘Age’ column contains NaN (Not a Number) values. The `fillna()` method is then used to fill the missing (NaN) values in the ‘Age’ column with the value 25. The `inplace=True` argument is used to modify the DataFrame `df` in place, meaning the changes are applied directly to the original DataFrame.

Python Pandas MCQ : Set 4

Python Pandas MCQ

1). What is the output of the following code?

				
					import pandas as pd
df = pd.Series([15, 43, 88, 23])
print(df)

a) 15, 43, 88, 23
b) [15, 43, 88, 23]
c) 0 15
1 43
2 88
3 23
dtype: int64
d) None of the above

Correct answer is: c) 0 15
1 43
2 88
3 23
dtype: int64
Explanation: The code creates a Pandas Series named `df` with values [15, 43, 88, 23]. When the `print(df)` statement is executed, it prints the Series object `df` along with the index and the data type. In this case, the output displays the index values (0, 1, 2, 3) and the corresponding data values (15, 43, 88, 23) with a dtype of int64.

2). What is the output of the following code?

				
					import pandas as pd
df = pd.Series([15, 43, 88, 23])
print(df)
print(type(df))
print("Convert Pandas Series to Python list")
print(df.tolist())
print(type(df.tolist()))

a)
0 15
1 43
2 88
3 23
dtype: int64
<class ‘pandas.core.series.Series’>
Convert Pandas Series to Python list
[15, 43, 88, 23]
<class ‘list’>

b) 0 15
1 43
2 88
3 23
dtype: int32
<class ‘pandas.core.series.Series’>
Convert Pandas Series to Python list
[15, 43, 88, 23]
<class ‘list’>

c) 15 43 88 23
<class ‘pandas.core.series.Series’>
Convert Pandas Series to Python list
[15, 43, 88, 23]
<class ‘list’>

d) 15 43 88 23
<class ‘pandas.core.series.Series’>
Convert Pandas Series to Python list
15 43 88 23
<class ‘list’>

Correct answer is: a)
0 15
1 43
2 88
3 23
dtype: int64
<class ‘pandas.core.series.Series’>
Convert Pandas Series to Python list
[15, 43, 88, 23]
<class ‘list’>
Explanation: The code first creates a Pandas Series object named `df` with the values `[15, 43, 88, 23]`. When printing `df`, it displays the Series with the index and values. Next, the code prints the type of `df`, which is `<class ‘pandas.core.series.Series’>`.After that, it prints “Convert Pandas Series to Python list” as a message. Following that, `df.tolist()` converts the Pandas Series `df` into a Python list. When printing `df.tolist()`, it displays the list of values `[15, 43, 88, 23]`.

3). What is the output of the following code?

				
					import pandas as pd
df1 = pd.Series([1,6,9,5])
df2 = pd.Series([5,2,4,5])
print(df1+df2)

a) [6, 8, 13, 10]
b) [1, 6, 9, 5, 5, 2, 4, 5]
c) [6, 8, 13, 10, NaN, NaN, NaN, NaN]
d) [1, 6, 9, 5, 5, 2, 4, 5, NaN, NaN, NaN, NaN]

Correct answer is: a) [6, 8, 13, 10]
Explanation: The code creates two Series, `df1` and `df2`, with the values [1, 6, 9, 5] and [5, 2, 4, 5] respectively. When we perform the addition operation `df1+df2`, Pandas aligns the Series based on their indices and performs element-wise addition. In this case, the resulting Series will be [1+5, 6+2, 9+4, 5+5], which simplifies to [6, 8, 13, 10]. Therefore, the output of the code will be [6, 8, 13, 10].

4). What is the output of the following code?

				
					import pandas as pd
df1 = pd.Series([1, 6, 9, 5])
df2 = pd.Series([5, 2, 4, 5])
print(df1 - df2)

a) [4, 4, 5, 0]
b) [6, 4, 5, 0]
c) [1, 4, 5, 0]
d) [4, 2, 5, 0]

Correct answer is: a) [4, 4, 5, 0]
Explanation: When subtracting two Series in Pandas, the operation is performed element-wise. In this case, `df1 – df2` subtracts the corresponding elements of `df2` from `df1`. Thus, the resulting Series is [4, 4, 5, 0].

5). What is the output of the following code?

				
					import pandas as pd
df1 = pd.Series([1, 6, 9, 5])
df2 = pd.Series([5, 2, 4, 5])
print(df1 * df2)

a) 5, 12, 36, 25
b) 5, 12, 36, 30
c) 5, 12, 36, 45
d) 5, 6, 36, 25

Correct answer is: c) 5, 12, 36, 45
Explanation: The code creates two pandas Series objects, `df1` and `df2`, with corresponding values [1, 6, 9, 5] and [5, 2, 4, 5]. When the multiplication operator `*` is applied to these two Series, element-wise multiplication is performed) Therefore, the resulting Series will contain the products of the corresponding elements from `df1` and `df2`.

6). What is the output of the following code?

				
					import pandas as pd
df1 = pd.Series([1,6,9,5])
df2 = pd.Series([5,2,4,5])
print(df1/df2)

a) 0 0.2
1 3.0
2 2.25
3 1.0
dtype: float64

b) 0 0.2
1 3.0
2 2.25
3 5.0
dtype: float64

c) 0 5.0
1 3.0
2 2.25
3 5.0
dtype: float64

d) 0 0.2
1 6.0
2 2.25
3 1.0
dtype: float64

Correct answer is: a
Explanation: When dividing two Series (`df1` and `df2`), Pandas perform element-wise division. The resulting Series will have the same indices as the original Series.

7). What is the output of the following code?

				
					import pandas as pd
df1 = pd.Series([1, 6, 9, 5])
df2 = pd.Series([5, 2, 4, 5])
print("Equals:")
print(df1 == df2)

a) Equals:
0 False
1 False
2 False
3 True
dtype: bool

b) Equals:
0 False
1 True
2 False
3 True
dtype: bool

c) Equals:
0 True
1 False
2 False
3 True
dtype: bool

d) Equals:
0 False
1 False
2 True
3 False
dtype: bool

Correct answer is: a) Equals:
0 False
1 False
2 False
3 True
dtype: bool
Explanation: The code compares each element of `df1` with the corresponding element of `df2` and returns a boolean Series indicating whether each pair of elements is equal or not. In this case, the output shows that the first three pairs are not equal (`False`), while the last pair is equal (`True`).

8). What is the output of the following code?

				
					import pandas as pd
df1 = pd.Series([1, 6, 9, 5])
df2 = pd.Series([5, 2, 4, 5])
print("Greater than:")
print(df1 > df2)

a) Greater than:
0 False
1 True
2 True
3 False
dtype: bool

b) Greater than:
0 True
1 False
2 True
3 False
dtype: bool

c) Greater than:
0 False
1 False
2 True
3 False
dtype: bool

d) Greater than:
0 True
1 True
2 False
3 False
dtype: bool

Correct answer is: a) Greater than:
0 False
1 True
2 True
3 False
dtype: bool
Explanation: The code compares the elements of `df1` and `df2` element-wise using the `>` operator. In the output, each element represents the result of the comparison between the corresponding elements of `df1` and `df2`. If an element in `df1` is greater than the corresponding element in `df2`, the result will be `True`, otherwise `False`. In this case, the first element is not greater (False), the second and third elements are greater (True), and the fourth element is not greater (False).

9). What is the output of the following code?

				
					import pandas as pd
D = {'name':'Virat','sport':'cricket','age':32}
result = pd.Series(D)
print(result)

a)
name Virat
sport cricket
age 32
dtype: object

b)
0 Virat
1 cricket
2 32
dtype: object

c) {‘name’:’Virat’,’sport’:’cricket’,’age’:32}

d)
name cricket
sport Virat
age 32
dtype: object

Correct answer is: a)
name Virat
sport cricket
age 32
dtype: object
Explanation: The code creates a dictionary `D` with three key-value pairs representing a person’s name, sport, and age. It then converts the dictionary into a Pandas Series using `pd.Series(D)`. The resulting Series, `result`, will have the keys of the dictionary as the index labels and the corresponding values as the data)

10). What is the output of the following code?

				
					import numpy as np
import pandas as pd

array = np.array([5, 3, 8, 9, 0])
result = pd.Series(array)
print(result)

a) 5 3 8 9 0
b) 0 9 8 3 5
c) [5, 3, 8, 9, 0]
d) It will raise an error.

Correct answer is: c) [5, 3, 8, 9, 0]
Explanation: The code creates a NumPy array `array` with values [5, 3, 8, 9, 0]. Then, it creates a Pandas Series `result` from the array. Finally, it prints the Series `result`. The output of the code is a Pandas Series object represented as [5, 3, 8, 9, 0]. The values from the NumPy array are preserved in the Series.

11). What is the output of the following code?

				
					import pandas as pd
df1 = pd.Series([54, 27.90, 'sqa', 33.33, 'tools'])
print("Original Data Series:")
print(df1)
print("Change the said data type to numeric:")
df2 = pd.to_numeric(df1, errors='coerce')
print(df2)

a) Original Data Series:
0 54
1 27.9
2 sqa
3 33.33
4 tools
dtype: object
Change the said data type to numeric:
0 54.00
1 27.90
2 NaN
3 33.33
4 NaN
dtype: float64

b) Original Data Series:
0 54
1 27.9
2 sqa
3 33.33
4 tools
dtype: object
Change the said data type to numeric:
0 54
1 27.9
2 NaN
3 33.33
4 NaN
dtype: float64

c) Original Data Series:
0 54
1 27.9
2 sqa
3 33.33
4 tools
dtype: object
Change the said data type to numeric:
0 54.00
1 27.90
2 sqa
3 33.33
4 tools
dtype: object

d) Original Data Series:
0 54
1 27.9
2 sqa
3 33.33
4 tools
dtype: object
Change the said data type to numeric:
0 54
1 27.9
2 sqa
3 33.33
4 tools
dtype: object

Correct answer is: a) Original Data Series:
0 54
1 27.9
2 sqa
3 33.33
4 tools
dtype: object
Change the said data type to numeric:
0 54.00
1 27.90
2 NaN
3 33.33
4 NaN
dtype: float64
Explanation: The code first creates a Series `df1` with a mix of integer, float, and string values. Then it prints the original data series. After that, it uses the `pd.to_numeric()` function to convert the data type of `df1` to numeric) The `errors=’coerce’` parameter is used to convert non-numeric values to NaN (Not a Number) instead of raising an error. The resulting converted Series is stored in `df2`. Finally, it prints the converted Series `df2`.

12). What is the output of the following code?

				
					import numpy as np
import pandas as pd

df1 = pd.Series([54, 27.90, 'sqa', 33.33, 'tools'])
print("Original Data Series:")
print(df1)
print("Series to an array")
result = np.array(df1.values.tolist())
print(result)

a) [54, 27.9, ‘sqa’, 33.33, ‘tools’]
d) [54, 27.9, 33.33]
c) [’54’, ‘27.9’, ‘sqa’, ‘33.33’, ‘tools’]
d) Error: cannot convert ‘sqa’ and ‘tools’ to float

Correct answer is: a) [54, 27.9, ‘sqa’, 33.33, ‘tools’]
Explanation: The code creates a Pandas Series (`df1`) with values [54, 27.90, ‘sqa’, 33.33, ‘tools’]. It then converts the Series into a NumPy array using `np.array(df1.values.tolist())`. The resulting array contains all the values from the Series, including the numeric values and the string values. Therefore, the output of the code is [54, 27.9, ‘sqa’, 33.33, ‘tools’].

13). What is the output of the following code?

				
					import pandas as pd
a = [['Sqa', 'tool'], ['Learning', 'python'], ['Is', 'fun']]
result = pd.Series(a)
print(result)

a)
0 [Sqa, tool]
1 [Learning, python]
2 [Is, fun]
dtype: object

d)
0 Sqa tool
1 Learning python
2 Is fun
dtype: object

c)
0 Sqa
1 Learning
2 Is
dtype: object

0 tool
1 python
2 fun
dtype: object

d)
0 Sqa Learning Is
1 tool python fun
dtype: object

Correct answer is: a)
0 [Sqa, tool]
1 [Learning, python]
2 [Is, fun]
dtype: object
Explanation: The code creates a Pandas Series object `result` from the list `a`. Each element in the list becomes a separate entry in the Series. Since each element of `a` is a list itself, the output shows the list elements as individual entries in the Series, resulting in a Series with three rows and two columns. The output displays the Series with the index labels and the corresponding values.

14). What is the output of the following code?

				
					import pandas as pd
df = pd.Series([55, 23, 10, 87])
print("Original Data Series:")
print(df)
new = pd.Series(df).sort_values()
print(new)

a) Original Data Series:
0 55
1 23
2 10
3 87
dtype: int64
2 10
1 23
0 87
3 55
dtype: int64

b) Original Data Series:
55
23
10
87
dtype: int64
10
23
55
87
dtype: int64

c) Original Data Series:
0 55
1 23
2 10
3 87
dtype: int64
3 87
2 10
1 23
0 55
dtype: int64

d) Original Data Series:
55
23
10
87
dtype: int64
87
10
55
23
dtype: int64

Correct answer is: b) Original Data Series:
55
23
10
87
dtype: int64
10
23
55
87
dtype: int64
Explanation: The code first creates a Series `df` with values [55, 23, 10, 87]. It then prints the original data series. Next, it creates a new Series `new` by sorting the values of `df` in ascending order.

15). What is the output of the following code?

				
					import pandas as pd
df = pd.Series([54,27.9,'sqa',33.33,'tools'])
print("Original Data Series:")
print(df)
print("Data Series after adding some data:")
new = df.append(pd.Series(['python',100]))
print(new)

0 54
1 27.9
2 sqa
3 33.33
4 tools
dtype: object

Data Series after adding some data:
0 54
1 27.9
2 sqa
3 33.33
4 tools
5 python
6 100
dtype: object

b) Original Data Series:
0 54
1 27.9
2 sqa
3 33.33
4 tools
dtype: object

Data Series after adding some data:
0 54
1 27.9
2 sqa
3 33.33
4 tools
dtype: object

c) Original Data Series:
0 54
1 27.9
2 sqa
3 33.33
4 tools
dtype: object

Data Series after adding some data:
0 54
1 27.9
2 sqa
3 33.33
4 tools
5 python
dtype: int64

d) Original Data Series:
0 54
1 27.9
2 sqa
3 33.33
4 tools
dtype: object

Data Series after adding some data:
0 54
1 27.9
2 sqa
3 33.33
4 python
5 100
dtype: float64

Correct answer is: a) Original Data Series:
0 54
1 27.9
2 sqa
3 33.33
4 tools
dtype: object
Data Series after adding some data:
0 54
1 27.9
2 sqa
3 33.33
4 tools
5 python
6 100
dtype: object
Explanation: The code initializes a Pandas Series named `df` with values `[54, 27.9, ‘sqa’, 33.33, ‘tools’]`. It then prints the original data series, which displays the values in the series. Next, the code appends a new Pandas Series to the existing `df` series using the `append()` function. The new series contains the values `[‘python’, 100]`. The appended series is assigned to the variable `new`. Finally, the code prints the modified data series `new`, which includes the original values as well as the appended values. The output displays the combined series with values `[54, 27.9, ‘sqa’, 33.33, ‘tools’, ‘python’, 100]`. The dtype is ‘object’ because the series contains mixed data types.

16). What is the output of the following code?

				
					import pandas as pd
df = pd.Series([10, 25, 69, 74, 33, 54, 21])
print(df)
print("Subset of the above Data Series:")
new = df[df < 40]
print(new)

a)
0 10
1 25
2 69
3 74
4 33
5 54
6 21
dtype: int64
Subset of the above Data Series:
0 10
1 25
4 33
6 21
dtype: int64

b)
10
25
69
74
33
54
21
Subset of the above Data Series:
10
25
33
21

c)
10
25
69
74
33
54
21
Subset of the above Data Series:
10
25
33

d)
0 10
1 25
2 69
3 74
4 33
5 54
6 21
dtype: int64
Subset of the above Data Series:
33
21

Correct answer is: a)
0 10
1 25
2 69
3 74
4 33
5 54
6 21
dtype: int64
Subset of the above Data Series:
0 10
1 25
4 33
6 21
dtype: int64
Explanation: The code begins by importing the Pandas library and creating a Series called `df` with the values [10, 25, 69, 74, 33, 54, 21]. This shows the index and corresponding values of the `df` Series. Next, the statement `print(“Subset of the above Data Series:”)` is executed, which simply prints the given text. Then, a new Series called `new` is created by selecting the elements from `df` where the value is less than 40 (`df[df < 40]`). The resulting `new` Series contains the values [10, 25, 33, 21].

17). What is the output of the following code?

				
					import pandas as pd
df = pd.Series([5, 4, 8, 6])
print(df)
print("Mean of the given series: ", df.mean())

a)
0 5
1 4
2 8
3 6
dtype: int64
Mean of the given series: 5.75

b)
5 0
4 1
8 2
6 3
dtype: int64
Mean of the given series: 5.75

c)
0 5
1 4
2 8
3 6
dtype: int64
Mean of the given series: 5.5

d)
5 0
4 1
8 2
6 3
dtype: int64
Mean of the given series: 5.5

Correct answer is: a)
0 5
1 4
2 8
3 6
dtype: int64
Mean of the given series: 5.75
Explanation: The code first creates a Pandas Series object `df` with the values [5, 4, 8, 6]. When printed, the output shows the series with index labels and corresponding values. The mean of the series is calculated using the `mean()` function, which returns the average value of the series. In this case, the mean is 5.75.

18). What is the output of the following code?

				
					import pandas as pd
df = pd.Series([2, 4, 6, 8, 10])
print(df)
print("Standard deviation of the given series: ", df.std())

a)
0 2
1 4
2 6
3 8
4 10
dtype: int64
Standard deviation of the given series: 2.8284271247461903

b)
2 0
4 1
6 2
8 3
10 4
dtype: int64
Standard deviation of the given series: 3.1622776601683795

c)
2 0
4 0
6 0
8 0
10 0
dtype: int64
Standard deviation of the given series: 4.47213595499958

d)
0 2
1 4
2 6
3 8
4 10
dtype: int64
Standard deviation of the given series: 3.1622776601683795

Correct answer is: d)
0 2
1 4
2 6
3 8
4 10
dtype: int64
Standard deviation of the given series: 3.1622776601683795
Explanation: The code creates a Pandas Series `df` with values [2, 4, 6, 8, 10]. When printing `df`, it displays the series with index labels and corresponding values. The `df.std()` calculates the standard deviation of the given series, which is 3.1622776601683795. This value is printed along with the string “Standard deviation of the given series: “.

19). What is the output of the following code?

				
					import pandas as pd
df1 = pd.Series([4, 7, 3, 10])
df2 = pd.Series([9, 4, 3])
print("First series: \n", df1)
print("Second series: \n", df2)
result = df1[~df1.isin(df2)]
print(result)

a) 7, 10
b) 9, 4, 3
c) 3, 7, 10
d) 4, 7, 3, 10

Correct answer is: a) 7, 10
Explanation: The given code first creates two Series objects, `df1` and `df2`, with values [4, 7, 3, 10] and [9, 4, 3] respectively. Then, it prints the contents of both Series using the `print()` function. Next, the code assigns to the variable `result` the values from `df1` that are not present in `df2` using the `~` operator and the `isin()` method) In this case, `~df1.isin(df2)` checks which values in `df1` are not found in `df2`. The resulting Boolean Series is then used to filter `df1`, storing only the values that are not present in `df2` in the variable `result`. Finally, the code prints the contents of `result`.

20). What is the output of the following code?

				
					import pandas as pd
df = pd.Series([54, 38, 67, 87])
print(df)
print("Maximum value: ", df.max())

a)
0 54
1 38
2 67
3 87
dtype: int64
Maximum value: 87

b)
54 0
38 1
67 2
87 3
dtype: int64
Maximum value: 87

c)
0 54
1 38
2 67
3 87
dtype: int64
Maximum value: 3

d)
54
Maximum value: 87

Correct answer is: a)
0 54
1 38
2 67
3 87
dtype: int64
Maximum value: 87
Explanation: The given code first creates a Pandas Series object named `df` with values [54, 38, 67, 87]. When the `print(df)` statement is executed, it displays the Series object with the index and corresponding values. This shows the index (0, 1, 2, 3) and the corresponding values (54, 38, 67, 87) in the Series. Next, the `print(“Maximum value: “, df.max())` statement is executed) It calculates the maximum value in the Series using the `max()` function and displays it along with the specified text.

21). What is the output of the following code?

				
					import pandas as pd
df = pd.Series([54, 38, 67, 87])
print("Minimum value: ", df.min())

a) Minimum value: 54
b) Minimum value: 38
c) Minimum value: 67
d) Minimum value: 87

Correct answer is: b) Minimum value: 38
Explanation: The code creates a Pandas Series called `df` with the values [54, 38, 67, 87]. The `min()` function is then applied to the Series, which returns the minimum value from the Series. In this case, the minimum value is 38. The print statement displays the output as “Minimum value: 38”. Therefore, option b is the correct answer.

22). What is the output of the following code?

				
					import pandas as pd
df = pd.Series([3, 0, 3, 2, 2, 0, 3, 3, 2])
result = df.value_counts()
print(result)

a) 0 2
2 3
3 4
dtype: int64
b) 0 3
2 3
3 3
dtype: int64
c) 0 2
2 2
3 3
dtype: int64
d) 0 3
2 2
3 4
dtype: int64

Correct answer is: a) 0 2
2 3
3 4
dtype: int64
Explanation: The code creates a Pandas Series `df` with a list of values. The `value_counts()` function is then applied to the Series to count the occurrences of each unique value. The resulting output is a new Series object `result` that contains the counts of each unique value in the original Series. In this case, the unique values in `df` are [3, 0, 2]. The counts of these values are 4 for 3, 2 for 0, and 3 for 2.

23). What is the output of the following code?

				
					import pandas as pd

df = pd.Series([3, 0, 3, 2, 2, 0, 3, 3, 2])
positions = [1, 4, 7]
result = df.take(positions)
print("Extract items at given positions of the said series:")
print(result)

a) 0, 2, 3
b) 0, 2, 2
c) 0, 2, 3, 3
d) 0, 3, 3

Correct answer is: a) 0, 2, 3
Explanation: In the given code, the `take()` function is used to extract items at the specified positions from the `df` Series. The positions `[1, 4, 7]` correspond to the elements at index 1, 4, and 7 in the Series. Therefore, the output of `print(result)` will be `0, 2, 3`.

24). What is the output of the following code?

				
					import pandas as pd
df = pd.Series(['sqatools', 'python', 'data', 'science'])
result = df.map(lambda x: x[0].upper() + x[1:-1] + x[-1].upper())
print("First and last character of each word to upper case:")
print(result)

a) `[‘SqaToolS’, ‘PythoN’, ‘DatA’, ‘SciencE’]`
d) `[‘SqatoolS’, ‘Python’, ‘DatA’, ‘SciencE’]`
c) `[‘SqaTools’, ‘PythoN’, ‘Data’, ‘Science’]`
d) `[‘sqatoolS’, ‘PythoN’, ‘Data’, ‘SciencE’]`

Correct answer is: a) `[‘SqaToolS’, ‘PythoN’, ‘DatA’, ‘SciencE’]`
Explanation: The lambda function applied to each element of the Series `df` extracts the first character of each word and converts it to uppercase using `x[0].upper()`. It then appends the middle characters `x[1:-1]` as they are, and finally converts the last character to uppercase using `x[-1].upper()`. The resulting Series `result` contains the modified strings with the first and last character of each word in uppercase. The output of the code will be `[‘SqaToolS’, ‘PythoN’, ‘DatA’, ‘SciencE’]`.

25). What is the output of the following code?

				
					import pandas as pd
df = pd.Series(['virat', 'rohit', 'pant', 'shikhar'])
result = df.map(lambda x: len(x))
print("Number of characters in each word of series:")
print(result)

a) 5, 5, 4, 7
b) 1, 1, 1, 1
c) 4, 5, 4, 7
d) 0, 0, 0, 0

Correct answer is: a) 5, 5, 4, 7
Explanation: The code creates a Pandas Ser

Python Pandas MCQ : Set 3

Python Pandas MCQ

1). What is the purpose of the `value_counts()` function in Pandas?

a) To calculate the cumulative sum of a column
b) To count the occurrences of each unique value in a column
c) To sort a DataFrame based on a specific column
d) To remove duplicate rows from a DataFrame

Correct answer is: b) To count the occurrences of each unique value in a column
Explanation: The `value_counts()` function in Pandas is used to count the occurrences of each unique value in a column.

2). How can you calculate the difference between two dates in Pandas?

a) df.diff_dates()
b) df.difference()
c) df.calculate_difference()
d) df.subtract_dates()

Correct answer is: a) df.diff_dates()
Explanation: The `diff_dates()` function is used to calculate the difference between two dates in Pandas.

3). What is the purpose of the `fillna()` function in Pandas?

a) To remove missing values from a DataFrame
b) To replace missing values with a specified value
c) To interpolate missing values in a DataFrame
d) To drop rows with missing values

Correct answer is: b) To replace missing values with a specified value
Explanation: The `fillna()` function is used to replace missing values in a DataFrame with a specified value in Pandas.

4). How can you calculate the median of each column in a DataFrame in Pandas?

a) df.median()
b) df.calculate_median()
c) df.column_median()
d) df.median_column()

Correct answer is: a) df.median()
Explanation: The `median()` function in Pandas is used to calculate the median of each column in a DataFrame.

5). What is the purpose of the `rename()` function in Pandas?

a) To calculate the mean of each column in a DataFrame
b) To remove duplicate rows from a DataFrame
c) To change the index labels of a DataFrame
d) To sort a DataFrame based on a specific column

Correct answer is: c) To change the index labels of a DataFrame
Explanation: The `rename()` function in Pandas is used to change the index labels of a DataFrame.

6). How can you calculate the mode of each column in a DataFrame in Pandas?

a) df.mode()
b) df.calculate_mode()
c) df.column_mode()
d) df.mode_column()

Correct answer is: a) df.mode()
Explanation: Each column in a DataFrame’s mode is determined using the Pandas’mode()’ function.

7). What is the purpose of the `cumprod()` function in Pandas?

a) To calculate the cumulative sum of a column
b) To calculate the cumulative product of a column
c) To calculate the cumulative mean of a column
d) To calculate the cumulative median of a column

Correct answer is: b) To calculate the cumulative product of a column
Explanation: The `cumprod()` function in Pandas is used to calculate the cumulative product of a column.

8). How can you calculate the maximum value of each column in a DataFrame in Pandas?

a) df.max()
b) df.maximum()
c) df.calculate_max()
d) df.column_max()

Correct answer is: a) df.max()
Explanation: The `max()` function in Pandas is used to calculate the maximum value of each column in a DataFrame.

9). What is the purpose of the `cummin()` function in Pandas?

a) To calculate the cumulative sum of a column
b) To calculate the cumulative minimum of a column
c) To calculate the cumulative mean of a column
d) To calculate the cumulative median of a column

Correct answer is: b) To calculate the cumulative minimum of a column
Explanation: The `cummin()` function in Pandas is used to calculate the cumulative minimum of a column.

10). How can you calculate the minimum value of each column in a DataFrame in Pandas?

a) df.min()
b) df.minimum()
c) df.calculate_min()
d) df.column_min()

Correct answer is: a) df.min()
Explanation: The `min()` function in Pandas is used to calculate the minimum value of each column in a DataFrame.

11). How can you select multiple columns from a DataFrame in Pandas?

a) df.select_columns()
b) df.columns()
c) df.get_columns()
d) df[[‘column1’, ‘column2’]]

Correct answer is: d) df[[‘column1’, ‘column2’]]
Explanation: To select multiple columns from a DataFrame in Pandas, you can use the double square bracket notation.

12). How can you calculate the standard deviation of each column in a DataFrame in Pandas?

a) df.std()
b) df.standard_deviation()
c) df.calculate_std()
d) df.column_std()

Correct answer is: a) df.std()
Explanation: The `std()` function in Pandas is used to calculate the standard deviation of each column in a DataFrame.

13). What is the purpose of the `agg()` function in Pandas?

a) To aggregate data based on a specific column
b) To determine a column’s average value
c) To remove rows with empty values
d) To sort a DataFrame based on a specific column

Correct answer is: a) To aggregate data based on a specific column
Explanation: The `agg()` function in Pandas is used to aggregate data based on a specific column using a specified function or a dictionary of functions.

14). How can you calculate the skewness of each column in a DataFrame in Pandas?

a) df.skew()
b) df.calculate_skewness()
c) df.column_skewness()
d) df.skewness()

Correct answer is: a) df.skew()
Explanation: The `skew()` function in Pandas is used to calculate the skewness of each column in a DataFrame.

15). How can you calculate the kurtosis of each column in a DataFrame in Pandas?

a) df.kurtosis()
b) df.calculate_kurtosis()
c) df.column_kurtosis()
d) df.kurt()

Correct answer is: a) df.kurtosis()
Explanation: The `kurtosis()` function in Pandas is used to calculate the kurtosis of each column in a DataFrame.

16). What is the purpose of the `iterrows()` function in Pandas?

a) To iterate over the rows of a DataFrame
b) To iterate over the columns of a DataFrame
c) To iterate over the unique values of a column
d) To iterate over the index labels of a DataFrame

Correct answer is: a) To iterate over the rows of a DataFrame
Explanation: The `iterrows()` function in Pandas is used to iterate over the rows of a DataFrame.

17). How can you calculate the covariance between two columns in a DataFrame in Pandas?

a) df.calculate_covariance()
b) df.column_covariance()
c) df.cov()
d) df.covariance()

Correct answer is: c) df.cov()
Explanation: The `cov()` function in Pandas is used to calculate the covariance between two columns in a DataFrame.

18). What is the purpose of the `pivot()` function in Pandas?

a) To transpose the rows and columns of a DataFrame
b) To calculate the average value of a column in a DataFrame
c) To create a summary table based on a DataFrame’s columns
d) To reshape the structure of a DataFrame

Correct answer is: d) To reshape the structure of a DataFrame
Explanation: The `pivot()` function in Pandas is used to reshape the structure of a DataFrame based on the values of a column.

19). How can you calculate the percentile of each column in a DataFrame in Pandas?

a) df.percentile()
b) df.calculate_percentile()
c) df.column_percentile()
d) df.quantile()

Correct answer is: d) df.quantile()
Explanation: The `quantile()` function in Pandas is used to calculate the percentile of each column in a DataFrame.

20). What is the purpose of the `rolling()` function in Pandas?

a) To calculate rolling statistics on a column
b) To remove duplicate rows from a DataFrame
c) To interpolate missing values in a DataFrame
d) To calculate the cumulative sum of a column

Correct answer is: a) To calculate rolling statistics on a column
Explanation: The `rolling()` function in Pandas is used to calculate rolling statistics, such as the rolling mean or rolling sum, on a column.

21). How can you calculate the exponential moving average of a column in a DataFrame in Pandas?

a) df.calculate_ema()
b) df.ema()
c) df.exp_moving_average()
d) df.ewm()

Correct answer is: d) df.ewm()
Explanation: The `ewm()` function in Pandas is used to calculate the exponential moving average of a column in a DataFrame.

22). What is the purpose of the `stack()` function in Pandas?

a) To stack multiple DataFrames vertically
b) To stack multiple DataFrames horizontally
c) To stack multiple columns into a single column
d) To stack multiple rows into a single row

Correct answer is: c) To stack multiple columns into a single column
Explanation: The `stack()` function in Pandas is used to stack multiple columns into a single column.

23). How can you calculate the weighted average of a column in a DataFrame in Pandas?

a) df.calculate_weighted_average()
b) df.weighted_avg()
c) df.column_weighted_average()
d) df.dot_product()

Correct answer is: b) df.weighted_avg()
Explanation: There is no built-in weighted average function in Pandas. However, you can calculate the weighted average by multiplying the values of a column by their corresponding weights and then dividing the sum by the sum of the weights.

24). What is the purpose of the `squeeze()` function in Pandas?

a) To remove a single-dimensional axis from a DataFrame
b) To compress the data in a DataFrame to reduce memory usage
c) To remove duplicate rows from a DataFrame
d) To sort a DataFrame based on a specific column

Correct answer is: a) To remove a single-dimensional axis from a DataFrame
Explanation: The `squeeze()` function in Pandas is used to remove a single-dimensional axis from a DataFrame, resulting in a Series if applicable.

25). What is the purpose of the melt() function in Pandas?

a) To merge multiple DataFrames based on a common column
b) To transpose the rows and columns of a DataFrame
c) To reshape a DataFrame from wide to long format
d) To calculate the median of each column in a DataFrame

Correct answer is: c) To reshape a DataFrame from wide to long format
Explanation: The melt() function in Pandas is used to reshape a DataFrame from wide to long format by unpivoting the data based on specified columns.

Python Pandas MCQ : Set 2

Python Pandas MCQ

1). What is the purpose of the `iloc` attribute in Pandas?

a) To access rows and columns of a DataFrame by their index location
b) To access rows and columns of a DataFrame by their label
c) To access rows and columns of a DataFrame by their data type
d) To access rows and columns of a DataFrame randomly

Correct answer is: a) To access rows and columns of a DataFrame by their index location
Explanation: The `iloc` attribute in Pandas is used to access rows and columns of a DataFrame by their index location (integer position).

2). How can you calculate the correlation between columns in a DataFrame in Pandas?

a) df.corr()
b) df.correlation()
c) df.calculate_correlation()
d) df.stats.correlation()

Correct answer is: a) df.corr()
Explanation: The correlation between columns in a DataFrame is determined using the ‘corr()’ function in the Pandas programming language.

3). What is the purpose of the `dtypes` attribute in Pandas?

a) To calculate descriptive statistics of a DataFrame
b) To access the data types of each column in a DataFrame
c) To convert the data types of a DataFrame
d) To filter rows based on a condition

Correct answer is: b) To access the data types of each column in a DataFrame
Explanation: The `dtypes` attribute in Pandas is used to access the data types of each column in a DataFrame.

4). How can you apply a filter to a DataFrame based on multiple conditions in Pandas?

a) df.filter(condition1, condition2)
b) df.where(condition1, condition2)
c) df.loc[condition1, condition2]
d) df.apply(condition1, condition2)

Correct answer is: c) df.loc[condition1, condition2]
Explanation: You can apply a filter to a DataFrame based on multiple conditions using the `df.loc[condition1, condition2]` syntax in Pandas.

5). What is the purpose of the `to_datetime()` function in Pandas?

a) To convert a DataFrame to a datetime format
b) To convert a string to a datetime object
c) To calculate the difference between two datetime objects
d) To extract specific components from a datetime object

Correct answer is: b) To convert a string to a datetime object
Explanation: The `to_datetime()` function in Pandas is used to convert a string to a datetime object, enabling date and time operations.

6). How can you resample time series data in Pandas?

a) df.resample()
b) df.time_resample()
c) df.sample_time()
d) df.time_sample()

Correct answer is: a) df.resample()
Explanation: The `resample()` function in Pandas is used to resample time series data, such as converting daily data to monthly data)

7). What is the purpose of the `shift()` function in Pandas?

a) To shift the index labels of a DataFrame
b) To shift the values in a DataFrame by a specified number of periods
c) To shift the columns of a DataFrame
d) To shift the rows of a DataFrame

Correct answer is: b) To shift the values in a DataFrame by a specified number of periods
Explanation: To move the values in a DataFrame by a defined number of periods, use Pandas’ “shift()” function.

8). How can you apply a function to groups in a DataFrame in Pandas?

a) df.groupby().apply()
b) df.group().apply()
c) df.group_by().apply()
d) df.groupby().function()

Correct answer is: a) df.groupby().apply()
Explanation: You can apply a function to groups in a DataFrame using the `groupby().apply()` syntax in Pandas.

9). What is the purpose of the `duplicated()` function in Pandas?

a) To remove duplicate rows from a DataFrame
b) To identify duplicate values in a DataFrame
c) To drop rows with missing values
d) To sort a DataFrame based on a specific column

Correct answer is: b) To identify duplicate values in a DataFrame
Explanation: The `duplicated()` function in Pandas is used to identify duplicate values in a DataFrame.

10). How can you change the data type of a column in a DataFrame in Pandas?

a) df.change_dtype(column_name, new_type)
b) df.column_name = new_type
c) df.astype(column_name, new_type)
d) df.change_type(column_name, new_type)

Correct answer is: c) df.astype(column_name, new_type)
Explanation: The `astype()` function is used to change the data type of a column in a DataFrame in Pandas.

11). What is the purpose of the `cut()` function in Pandas?

a) To divide a continuous variable into discrete bins
b) To calculate the cumulative sum of a column
c) To transform a categorical variable into numerical codes
d) To create a histogram of a column

Correct answer is: a) To divide a continuous variable into discrete bins
Explanation: The `cut()` function in Pandas is used to divide a continuous variable into discrete bins or intervals.

12). How can you create a new column based on existing columns in a DataFrame in Pandas?

a) df.add_column()
b) df.create_column()
c) df.new_column()
d) df[column_name] = expression

Correct answer is: d) df[column_name] = expression
Explanation: You can create a new column based on existing columns in a DataFrame by assigning a new column name and an expression to it.

13). What is the purpose of the `transform()` function in Pandas?

a) To apply a function to each element in a DataFrame
b) To calculate summary statistics for each group in a DataFrame
c) To transform a column by replacing values based on a condition
d) To reshape the structure of a DataFrame

Correct answer is: b) To calculate summary statistics for each group in a DataFrame
Explanation: The `transform()` function in Pandas is used to calculate summary statistics for each group in a DataFrame.

14). How can you drop duplicate rows from a DataFrame in Pandas?

a) df.drop_duplicates()
b) df.remove_duplicates()
c) df.delete_duplicates()
d) df.drop_rows_duplicates()

Correct answer is: a) df.drop_duplicates()
Explanation: The `drop_duplicates()` method is used to drop duplicate rows from a DataFrame in Pandas.

15). What is the purpose of the `isnull()` function in Pandas?

a) To check if values in a DataFrame are null or missing
b) To remove null or missing values from a DataFrame
c) To replace null or missing values with a specified value
d) To interpolate null or missing values in a DataFrame

Correct answer is: a) To check if values in a DataFrame are null or missing
Explanation: The `isnull()` function in Pandas is used to check if values in a DataFrame are null or missing.

16). How can you calculate the cumulative sum of a column in a DataFrame in Pandas?

a) df.cumulative_sum()
b) df.cumsum()
c) df.sum_cumulative()
d) df.calculate_cumulative()

Correct answer is: b) df.cumsum()
Explanation: The `cumsum()` function is used to calculate the cumulative sum of a column in a DataFrame in Pandas.

17). What is the purpose of the `nunique()` function in Pandas?

a) To count the number of unique values in each column
b) To remove duplicate rows from a DataFrame
c) To calculate the mean of each column
d) To sort a DataFrame based on a specific column

Correct answer is: a) To count the number of unique values in each column
Explanation: The `nunique()` function in Pandas is used to count the number of unique values in each column of a DataFrame.

18). How can you extract specific components from a datetime column in a DataFrame in Pandas?

a) df.extract()
b) df.components()
c) df.dt.component()
d) df.get_component()

Correct answer is: c) df.dt.component()
Explanation: You can extract specific components from a datetime column in a DataFrame using the `dt.component()` syntax in Pandas.

19). What is the purpose of the `sample()` function in Pandas?

a) To randomly shuffle the rows of a DataFrame
b) To select a random sample of rows from a DataFrame
c) To calculate the mean of each column
d) To sort a DataFrame based on a specific column

Correct answer is: b) To select a random sample of rows from a DataFrame
Explanation: The `sample()` function in Pandas is used to select a random sample of rows from a DataFrame.

20). How can you calculate the mean of each column in a DataFrame in Pandas?

a) df.mean()
b) df.calculate_mean()
c) df.column_mean()
d) df.mean_column()

Correct answer is: a) df.mean()
Explanation: The `mean()` function in Pandas is used to calculate the mean of each column in a DataFrame.

21). What is the purpose of the unique() function in Pandas?

a) To calculate the unique values in a column
b) To remove duplicate rows from a DataFrame
c) To count the occurrences of each unique value in a column
d) To sort a DataFrame based on a specific column

Correct answer is: a) To calculate the unique values in a column
Explanation: The unique() function in Pandas is used to calculate the unique values in a column.

22). How can you calculate the sum of each column in a DataFrame in Pandas?

a) df.calculate_sum()
b) df.sum()
c) df.column_sum()
d) df.total()

Correct answer is: b) df.sum()
Explanation: The sum() function in Pandas is used to calculate the sum of each column in a DataFrame.

23). What is the purpose of the dropna() function in Pandas?

a) To calculate descriptive statistics of a DataFrame
b) To remove missing values from a DataFrame
c) To replace missing values with a specified value
d) To interpolate missing values in a DataFrame

Correct answer is: b) To remove missing values from a DataFrame
Explanation: The dropna() function in Pandas is used to remove missing values from a DataFrame.

24). How can you calculate the correlation matrix of a DataFrame in Pandas?

a) df.calculate_correlation()
b) df.correlation_matrix()
c) df.corr()
d) df.matrix_correlation()

Correct answer is: c) df.corr()
Explanation: The corr() function in Pandas is used to calculate the correlation matrix of a DataFrame.

25). How can you calculate the cumulative maximum of each column in a DataFrame in Pandas?

a) df.cumulative_max()
b) df.max_cumulative()
c) df.cummax()
d) df.calculate_max()

Correct answer is: c) df.cummax()
Explanation: The cummax() function in Pandas is used to calculate the cumulative maximum of each column in a DataFrame.

Python Pandas MCQ : Set 1

Python Pandas MCQ

1). What is Pandas?

a) A Python package for data analysis and manipulation
b) A Python framework for web development
c) A Python module for machine learning
d) A Python library for graphical plotting

Correct answer is: a) A Python package for data analysis and manipulation
Explanation: Pandas is a popular Python package used for data analysis and manipulation. It offers data structures and procedures to handle structured data effectively.

2). Which of the following data structures is commonly used in Pandas?

a) Arrays
b) Lists
c) DataFrames
d) Tuples

Correct answer is: c) DataFrames
Explanation: The primary data structure in Pandas is the DataFrame, which is a two-dimensional labeled data structure capable of holding data of different types.

3). How can you install Pandas in Python?

a) Using pip: pip install pandas
b) Using conda: conda install pandas
c) Both a and b
d) Pandas is pre-installed with Python

Correct answer is: c) Both a and b
Explanation: Pandas can be installed using either pip or conda package managers.

4). Which of the following statements is true about Series in Pandas?

a) It is a one-dimensional labeled array
b) It is a two-dimensional labeled array
c) It is a three-dimensional labeled array
d) It is a multi-dimensional labeled array

Correct answer is: a) It is a one-dimensional labeled array
Explanation: A Series is a labelled one-dimensional array that can hold any kind of data.

5). How can you access the first n rows of a DataFrame in Pandas?

a) df.head(n)
b) df.tail(n)
c) df.first(n)
d) df.last(n)

Correct answer is: a) df.head(n)
Explanation: The `head(n)` method in Pandas allows you to access the first n rows of a DataFrame.

6). What is the default index type for a DataFrame in Pandas?

a) Integer index
b) String index
c) Date index
d) Floating-point index

Correct answer is: a) Integer index
Explanation: By default, Pandas assigns an integer index to each row in a DataFrame.

7). How can you drop a column from a DataFrame in Pandas?

a) df.drop(column_name)
b) df.drop(columns=column_name)
c) df.remove(column_name)
d) df.delete(column_name)

Correct answer is: b) df.drop(columns=column_name)
Explanation: The `drop(columns=column_name)` method is used to drop a column from a DataFrame in Pandas.

8). What is the function to calculate the summary statistics of a DataFrame in Pandas?

a) df.summary()
b) df.describe()
c) df.stats()
d) df.statistics()

Correct answer is: b) df.describe()
Explanation: The `describe()` function in Pandas provides summary statistics of a DataFrame, including count, mean, standard deviation, minimum, maximum, and quartiles.

9). How can you filter rows in a DataFrame based on a condition in Pandas?

a) df.filter(condition)
b) df.where(condition)
c) df.select(condition)
d) df.loc[condition]

Correct answer is: d) df.loc[condition]
Explanation: You can filter rows in a DataFrame based on a condition using the `df.loc[condition]` syntax in Pandas.

10). What is the purpose of the `fillna()` function in Pandas?

a) To remove missing values from a DataFrame
b) To replace missing values with a specified value
c) To interpolate missing values in a DataFrame
d) To drop rows with missing values

Correct answer is: b) To replace missing values with a specified value
Explanation: The `fillna()` function is used to replace missing values in a DataFrame with a specified value in Pandas.

11). How can you sort a DataFrame by a specific column in Pandas?

a) df.sort(column_name)
b) df.sort_by(column_name)
c) df.sort_values(by=column_name)
d) df.order_by(column_name)

Correct answer is: c) df.sort_values(by=column_name)
Explanation: The `sort_values(by=column_name)` method is used to sort a DataFrame by a specific column in Pandas.

12). What is the purpose of the `groupby()` function in Pandas?

a) To filter rows based on a condition
b) To combine rows based on a specific column
c) To calculate summary statistics for each group
d) To sort the DataFrame in ascending order

Correct answer is: b) To combine rows based on a specific column
Explanation: The `groupby()` function in Pandas is used to combine rows based on a specific column, creating groups of related data)

13). How can you apply a function to each element in a DataFrame in Pandas?

a) df.apply()
b) df.map()
c) df.transform()
d) df.iterate()

Correct answer is: a) df.apply()
Explanation: The `apply()` function in Pandas is used to apply a function to each element in a DataFrame.

14). What is the purpose of the `merge()` function in Pandas?

a) To remove duplicate rows from a DataFrame
b) To combine two DataFrames based on a common column
c) To calculate the correlation between columns in a DataFrame
d) To reshape the structure of a DataFrame

Correct answer is: b) To combine two DataFrames based on a common column
Explanation: The `merge()` function in Pandas is used to combine two DataFrames based on a common column, similar to the SQL join operation.

15). What is the purpose of the `pivot_table()` function in Pandas?

Correct answer is: c) To create a summary table based on a DataFrame’s columns
Explanation: The `pivot_table()` function in Pandas is used to create a summary table based on a DataFrame’s columns, similar to Excel’s PivotTable functionality.

16). How can you save a DataFrame to a CSV file in Pandas?

a) df.to_csv(filename)
b) df.save_csv(filename)
c) df.write_csv(filename)
d) df.export_csv(filename)

Correct answer is: a) df.to_csv(filename)
Explanation: The `to_csv(filename)` method is used to save a DataFrame to a CSV file in Pandas.

17). What is the purpose of the `read_csv()` function in Pandas?

a) To read data from a CSV file into a DataFrame
b) To export a DataFrame to a CSV file
c) To calculate summary statistics for a DataFrame
d) To sort a DataFrame based on a specific column

Correct answer is: a) To read data from a CSV file into a DataFrame
Explanation: The `read_csv()` function in Pandas is used to read data from a CSV file and create a DataFrame.

18). How can you handle missing values in a DataFrame in Pandas?

a) df.dropna()
b) df.fillna(value)
c) df.interpolate()
d) All of the above

Correct answer is: d) All of the above
Explanation: You can handle missing values in a DataFrame in Pandas by dropping them using `dropna()`, filling them with a specified value using `fillna(value)`, or interpolating them using `interpolate()`.

19). What is the purpose of the `datetime` module in Pandas?

a) To perform date and time calculations
b) To format dates and times in a DataFrame
c) To parse date and time strings into a DataFrame
d) To figure out how much time has passed between two dates

Correct answer is: a) To perform date and time calculations
Explanation: The `datetime` module in Pandas provides functions and classes to work with dates and times, perform calculations, and handle time-related operations.

20). How can you rename the columns of a DataFrame in Pandas?

a) df.rename(columns=new_columns)
b) df.columns = new_columns
c) df.relabel(columns=new_columns)
d) df.change_columns(new_columns)

Correct answer is: a) df.rename(columns=new_columns)
Explanation: The `rename(columns=new_columns)` method is used to rename the columns of a DataFrame in Pandas.

21). What is the purpose of the pivot() function in Pandas?

Correct answer is: d) To reshape the structure of a DataFrame
Explanation: The pivot() function in Pandas is used to reshape the structure of a DataFrame based on the values of a column.

22). How can you convert a DataFrame into a NumPy array in Pandas?

a) df.to_array()
b) df.to_numpy()
c) df.convert_array()
d) df.as_numpy()

Correct answer is: b) df.to_numpy()
Explanation: The to_numpy() method is used to convert a DataFrame into a NumPy array in Pandas.

23). What is the purpose of the corr() function in Pandas?

a) To calculate the correlation between columns in a DataFrame
b) To calculate the covariance between columns in a DataFrame
c) To calculate the cumulative sum of a column
d) To calculate the mean of each column

Correct answer is: a) To calculate the correlation between columns in a DataFrame
Explanation: The corr() function in Pandas is used to calculate the correlation between columns in a DataFrame.

24). How can you calculate the cross-tabulation between two columns in a DataFrame in Pandas?

a) df.cross_tab()
b) df.crosstab()
c) df.tabulate()
d) df.pivot_table()

Correct answer is: b) df.crosstab()
Explanation: The crosstab() function is used to calculate the cross-tabulation between two columns in a DataFrame in Pandas.

25). What is the purpose of the rank() function in Pandas?

a) To calculate the rank of values within a column
b) To remove duplicate rows from a DataFrame
c) To determine each column’s mean
d) To sort a DataFrame based on a specific column

Correct answer is: a) To calculate the rank of values within a column
Explanation: The rank() function in Pandas is used to calculate the rank of values within a column.

Python OOPS MCQ : Set 4

Python OOPS MCQ

1). What is the output of the following code?

				
					class Rectangle:
    def __init__(self, length, width):
        self.length = length
        self.width = width
    
    def calculate_area(self):
        return self.length * self.width
    
    def calculate_perimeter(self):
        return 2 * (self.length + self.width)

# Create an object of the Rectangle class
rectangle = Rectangle(5, 13)
area = rectangle.calculate_area()
perimeter = rectangle.calculate_perimeter()
print("Area:", area)
print("Perimeter:", perimeter)

a) Area: 65, Perimeter: 54
b) Area: 65, Perimeter: 36
c) Area: 18, Perimeter: 26
d) Area: 18, Perimeter: 36

Correct answer is: b) Area: 65, Perimeter: 36
Explanation: The code defines a `Rectangle` class with an `__init__` method to initialize the `length` and `width` attributes. It also has two additional methods, `calculate_area()` and `calculate_perimeter()`, which calculate the area and perimeter of the rectangle, respectively. In the code, an object `rectangle` is created using the `Rectangle` class with `length=5` and `width=13`. The `calculate_area()` method is called on the `rectangle` object and the result is assigned to the `area` variable. Similarly, the `calculate_perimeter()` method is called and the result is assigned to the `perimeter` variable. Finally, the values of `area` and `perimeter` are printed to the console.

2). What is the output of the following code?

				
					import math
class Circle:
    def __init__(self, radius):
        self.radius = radius
    
    def calculate_area(self):
        return math.pi * self.radius**2
    
    def calculate_circumference(self):
        return 2 * math.pi * self.radius

# Create an object of the Circle class
circle = Circle(10)
area = circle.calculate_area()
circumference = circle.calculate_circumference()
print("Area:", area)
print("Circumference:", circumference)

a) Area: 314.1592653589793 Circumference: 62.83185307179586
b) Area: 314.1592653589793 Circumference: 31.41592653589793
c) Area: 628.3185307179587 Circumference: 31.41592653589793
d) Area: 628.3185307179587 Circumference: 62.83185307179586

Correct answer is: a) Area: 314.1592653589793 Circumference: 62.83185307179586
Explanation: In the given code, a Circle class is defined with an `__init__` method to initialize the radius attribute. It also has two methods, `calculate_area` and `calculate_circumference`, to calculate the area and circumference of the circle, respectively. An object `circle` is created with a radius of 10. The `calculate_area` method is called on the `circle` object, which returns the area of the circle using the formula `math.pi * self.radius**2`. Similarly, the `calculate_circumference` method is called, which returns the circumference of the circle using the formula `2 * math.pi * self.radius`. Finally, the calculated area and circumference are printed using the `print` function.

3). What is the output of the following code?

				
					class Person:
    def __init__(self, name, age):
        self.name = name
        self.age = age
    
    def print_details(self):
        print("Name:", self.name)
        print("Age:", self.age)

# Create an object of the Person class
person = Person("John Snow", 28)
person.print_details()

a) Name: John Snow, Age: 28
b) Name: John Snow, Age: None
c) Name: None, Age: 28
d) Error: missing arguments for __init__ method

Correct answer is: a) Name: John Snow, Age: 28
Explanation: In the given code, a class `Person` is defined with an `__init__` method to initialize the name and age attributes. The `print_details` method is used to print the name and age of the person. An object `person` is created with the name “John Snow” and age 28. Then, the `print_details` method is called on the `person` object, which prints the name as “John Snow” and the age as 28.

4). What is the output of the following code?

				
					class Person:
    def __init__(self, name, age):
        self.name = name
        self.age = age
    
    def print_details(self):
        print("Name:", self.name)
        print("Age:", self.age)
        
class Student(Person):
    def __init__(self, name, age, student_id, grades):
        super().__init__(name, age)
        self.student_id = student_id
        self.grades = grades
    
    def print_details(self):
        super().print_details()
        print("Student ID:", self.student_id)

# Create an object of the Student class
student = Student("Jade Smith", 24, "A12345", ['A','A+'])
student.print_details()

a) Name: Jade Smith, Age: 24, Student ID: A12345
b) Name: Jade Smith, Age: 24
c) Name: Jade Smith, Age: 24, Student ID: A12345, Grades: [‘A’,’A+’]
d) This code will raise an error.

Correct answer is: a) Name: Jade Smith, Age: 24, Student ID: A12345
Explanation: The code defines two classes, `Person` and `Student`, where `Student` is a subclass of `Person`. The `Person` class has an `__init__` method that initializes the `name` and `age` attributes, and a `print_details` method that prints the name and age. The `Student` class inherits from `Person` and adds the `student_id` and `grades` attributes. It also overrides the `print_details` method to include printing the student ID. In the code, an object `student` is created with the name “Jade Smith”, age 24, student ID “A12345”, and grades [‘A’,’A+’]. When the `print_details` method is called on the `student` object, it first calls the `print_details` method of the superclass `Person` using `super().print_details()`. This prints the name and age. Then, it prints the student ID using `print(“Student ID:”, self.student_id)`.

5). What is the output of the following code?

				
					class Animal:
    def __init__(self, name, color):
        self.name = name
        self.color = color
    
    def print_details(self):
        print("Name:", self.name)
        print("Color:", self.color)

class Cat(Animal):
    def __init__(self, name, color, breed, weight):
        super().__init__(name, color)
        self.breed = breed
        self.weight = weight
    
    def print_details(self):
        super().print_details()
        print("Breed:", self.breed)
        print("Weight:", self.weight)

# Create an object of the Cat class
cat = Cat("Whiskers", "Gray", "Persian", 15)
cat.print_details()

a) Name: Whiskers
Color: Gray
Breed: Persian
Weight: 15
b) Name: Whiskers
Breed: Persian
Weight: 15
c) Name: Whiskers
Color: Gray
Breed: Persian
d) Name: Whiskers
Color: Gray
Breed: Persian
Weight: 15
Animal object

Correct answer is: a) Name: Whiskers
Color: Gray
Breed: Persian
Weight: 15
Explanation: The given code defines two classes, `Animal` and `Cat`. The `Cat` class is a subclass of the `Animal` class and overrides the `print_details()` method. In the `__init__()` method of the `Cat` class, the `super()` function is used to call the `__init__()` method of the superclass (`Animal`) and initialize the `name` and `color` attributes. The `print_details()` method of the `Cat` class first calls the `print_details()` method of the superclass using `super().print_details()`. This prints the name and color from the `Animal` class. Then, it prints the breed and weight specific to the `Cat` class.

6). What is the output of the following code?

				
					class BankAccount:
    def __init__(self, account_number, balance):
        self.account_number = account_number
        self.balance = balance
    
    def deposit(self, amount):
        self.balance += amount
        print("Deposit of", amount, "successful. New balance:", self.balance)
    
    def withdraw(self, amount):
        if self.balance >= amount:
            self.balance -= amount
            print("Withdrawal of", amount, "successful. New balance:", self.balance)
        else:
            print("Insufficient funds. Withdrawal denied.")

# Create an object of the BankAccount class
account = BankAccount("1234567890", 1000)
account.deposit(500)
account.withdraw(2000)

a) Deposit of 500 successful. New balance: 1500. Insufficient funds. Withdrawal denied.
b) Deposit of 1500 successful. New balance: 2500. Insufficient funds. Withdrawal denied.
c) Deposit of 1500 successful. New balance: 1500. Withdrawal of 2000 successful. New balance: -500.
d) Deposit of 500 successful. New balance: 1500. Withdrawal of 2000 successful. New balance: -500.

Correct answer is: a) Deposit of 500 successful. New balance: 1500. Insufficient funds. Withdrawal denied.
Explanation: The code defines a `BankAccount` class with an `__init__` method, `deposit` method, and `withdraw` method. An object `account` is created using the `BankAccount` class with an initial balance of 1000. `account.deposit(500)` is called, which increases the balance by 500 and prints “Deposit of 500 successful. New balance: 1500”. `account.withdraw(2000)` is called, which checks if the balance is sufficient. Since the balance is less than the requested withdrawal amount, it prints “Insufficient funds. Withdrawal denied.”

7). What is the output of the following code?

				
					class Car:
    def __init__(self, make, model, year):
        self.make = make
        self.model = model
        self.year = year
    
    def print_details(self):
        print("Make:", self.make)
        print("Model:", self.model)
        print("Year:", self.year)

# Create an object of the Car class
car = Car("Mahindra", "Thar", 2021)
car.print_details()

a) Make: Mahindra
Model: Thar
Year: 2021
b) Mahindra
Thar
2021
c) Car
d) Error: Missing argument in the `__init__` method

Correct answer is:
a) Make: Mahindra
Model: Thar
Year: 2021
Explanation: The code defines a `Car` class with an `__init__` method and a `print_details` method. The `__init__` method is used to initialize the attributes `make`, `model`, and `year` of the `Car` object. The `print_details` method prints the values of these attributes. In the code, an object `car` of the `Car` class is created with the arguments “Mahindra”, “Thar”, and 2021. Then, the `print_details` method is called on the `car` object.

8). What is the output of the following code?

				
					class Car:
    def __init__(self, make, model, year):
        self.make = make
        self.model = model
        self.year = year
    
    def print_details(self):
        print("Make:", self.make)
        print("Model:", self.model)
        print("Year:", self.year)
        
class ElectricCar(Car):
    def __init__(self, make, model, year, battery_size, range_per_charge):
        super().__init__(make, model, year)
        self.battery_size = battery_size
        self.range_per_charge = range_per_charge
    
    def calculate_range(self):
        return self.battery_size * self.range_per_charge

# Create an object of the ElectricCar class
electric_car = ElectricCar("Tesla", "Model S", 2022, 75, 5)
range = electric_car.calculate_range()
print("Range:", range)

a) Range: 375
b) Make: Tesla, Model: Model S, Year: 2022, Range: 375
c) Range: 150
d) Make: Tesla, Model: Model S, Year: 2022, Range: 150

Correct answer is:
a) Range: 375
Explanation: The code defines two classes, `Car` and `ElectricCar`, where `ElectricCar` is a subclass of `Car`. The `ElectricCar` class has an additional method `calculate_range()` that multiplies the battery size (`75`) with the range per charge (`5`) to calculate the range. In the code, an object `electric_car` of the `ElectricCar` class is created with the parameters “Tesla”, “Model S”, 2022, 75, and 5. The `calculate_range()` method is then called on `electric_car` and the result is stored in the variable `range`. Finally, the value of `range` is printed as “Range: 375”.

9). What is the output of the following code?

				
					class StudentRecord:
    def __init__(self, name, age, grades):
        self.name = name
        self.age = age
        self.grades = grades
    
    def calculate_average_grade(self):
        total_grades = sum(self.grades)
        average_grade = total_grades / len(self.grades)
        return average_grade
    
    def print_details(self):
        print("Name:", self.name)
        print("Age:", self.age)
        print("Average Grade:", self.calculate_average_grade())

# Create an object of the StudentRecord class
student = StudentRecord("John show", 20, [80, 90, 85, 95])
student.print_details()

a) Name: John show, Age: 20, Average Grade: 87.5
b) Name: John show, Age: 20, Average Grade: 90.0
c) Name: John show, Age: 20, Average Grade: 82.5
d) Name: John show, Age: 20, Average Grade: 92.5

Correct answer is: a) Name: John show, Age: 20, Average Grade: 87.5
Explanation: The code defines a class `StudentRecord` with an `__init__` method, a `calculate_average_grade` method, and a `print_details` method. It then creates an object `student` of the `StudentRecord` class with the name “John show”, age 20, and grades [80, 90, 85, 95]. The `print_details` method is called on the `student` object, which prints the name, age, and average grade. The average grade is calculated by summing the grades and dividing by the number of grades, resulting in 87.5. Therefore, the output of the code will be “Name: John show, Age: 20, Average Grade: 87.5”.

10). What is the output of the following code?

				
					class Course:
    def __init__(self, name, teacher):
        self.name = name
        self.teacher = teacher
        self.students = []
    
    def add_student(self, student):
        self.students.append(student)
    
    def remove_student(self, student):
        if student in self.students:
            self.students.remove(student)
    
    def print_details(self):
        print("Course Name:", self.name)
        print("Teacher:", self.teacher)
        print("Students:")
        for student in self.students:
            print("- ", student)

# Create an object of the Course class
course = Course("Python", "Mr. Dipesh Yadav")
course.add_student("John Walker")
course.add_student("Jade Smith")
course.print_details()
course.remove_student("Jade Smith")
course.print_details()

a) Course Name: Python
Teacher: Mr. Dipesh Yadav
Students:
– John Walker
– Jade Smith
Course Name: Python
Teacher: Mr. Dipesh Yadav
Students:
– John Walker
b) Course Name: Python
Teacher: Mr. Dipesh Yadav
Students:
– John Walker
– Jade Smith
Course Name: Python
Teacher: Mr. Dipesh Yadav
Students:
– John Walker
– Jade Smith
c) Course Name: Python
Teacher: Mr. Dipesh Yadav
Students:
– Jade Smith
Course Name: Python
Teacher: Mr. Dipesh Yadav
Students:
– John Walker
– Jade Smith
d) Course Name: Python
Teacher: Mr. Dipesh Yadav
Students:
– John Walker
– Jade Smith
Course Name: Python
Teacher: Mr. Dipesh Yadav
Students:

Correct answer is: a) Course Name: Python
Teacher: Mr. Dipesh Yadav
Students:
– John Walker
– Jade Smith
Course Name: Python
Teacher: Mr. Dipesh Yadav
Students:
– John Walker
Explanation: The code defines a class `Course` with methods to add and remove students, and print the details of the course. The function generates a Python-themed object of the “Course” class with the teacher “Mr. Dipesh Yadav” as its name. Two students, “John Walker” and “Jade Smith”, are added to the course using the `add_student` method. The `print_details` method is called, which prints the course name, teacher, and the list of students. After that, the `remove_student` method is called to remove “Jade Smith” from the course. Finally, the `print_details` method is called again to print the updated course details. The correct output is option b) as it shows the initial course details with both students and the updated course details after removing “Jade Smith” from the list of students.

11). What is the output of the following code?

				
					class Shape:
    def calculate_area(self):
        pass

class Square(Shape):
    def __init__(self, side_length):
        self.side_length = side_length
    
    def calculate_area(self):
        return self.side_length ** 2

class Triangle(Shape):
    def __init__(self, base, height):
        self.base = base
        self.height = height
    
    def calculate_area(self):
        return 0.5 * self.base * self.height

# Create objects of the Square and Triangle classes
square = Square(5)
triangle = Triangle(4, 6)
print("Square Area:", square.calculate_area())
print("Triangle Area:", triangle.calculate_area())

a) Square Area: 25, Triangle Area: 12
b) Square Area: 20, Triangle Area: 24
c) Square Area: 25, Triangle Area: 24
d) Square Area: 20, Triangle Area: 12

Correct answer is: a) Square Area: 25, Triangle Area: 12
Explanation: The code defines three classes: `Shape`, `Square`, and `Triangle`. The `Shape` class is the base class with a method `calculate_area()` that is not implemented. The `Square` class is a subclass of `Shape` and overrides the `calculate_area()` method to calculate the area of a square using the formula `side_length ** 2`. The `Triangle` class is also a subclass of `Shape` and overrides the `calculate_area()` method to calculate the area of a triangle using the formula `0.5 * base * height`. In the code, an object `square` of the `Square` class is created with a side length of 5, and an object `triangle` of the `Triangle` class is created with a base of 4 and a height of 6.

12). What is the output of the following code?

				
					class Employee:
    def __init__(self, name, salary):
        self.name = name
        self.salary = salary
    
    def print_details(self):
        print("Name:", self.name)
        print("Salary:", self.salary)

# Create an object of the Employee class
employee = Employee("John Walker", 50000)
employee.print_details()

a) Name: John Walker Salary: 50000
b) Name: None Salary: None
c) Name: John Walker Salary: None
d) Name: None Salary: 50000

Correct answer is: a) Name: John Walker Salary: 50000
Explanation: In the given code, an `Employee` class is defined with an `__init__` method and a `print_details` method. The `__init__` method initializes the `name` and `salary` attributes of the class. The `print_details` method prints the values of `name` and `salary` using the `print` function. After defining the class, an object of the `Employee` class is created with the name “John Walker” and a salary of 50000. The `print_details` method is then called on the `employee` object, which prints the name and salary as “John Walker” and 50000, respectively.

13). What is the output of the following code?

				
					class Employee:
    def __init__(self, name, salary):
        self.name = name
        self.salary = salary
    
    def print_details(self):
        print("Name:", self.name)
        print("Salary:", self.salary)

class Manager(Employee):
    def __init__(self, name, salary, department, bonus):
        super().__init__(name, salary)
        self.department = department
        self.bonus = bonus
    
    def calculate_total_compensation(self):
        total_compensation = self.salary + self.bonus
        return total_compensation

# Create an object of the Manager class
manager = Manager("John Walker", 60000, "Sales", 5000)
total_compensation = manager.calculate_total_compensation()
print("Total Compensation:", total_compensation)

a) Total Compensation: 65000
b) Total Compensation: 110000
c) Total Compensation: 60000
d) Total Compensation: 5000

Correct answer is: a) Total Compensation: 65000
Explanation: The code defines two classes: `Employee` and `Manager`. The `Manager` class is a subclass of the `Employee` class and inherits its attributes and methods. In the `Manager` class, the `__init__` method is overridden to include additional attributes `department` and `bonus`. In the code, an object `manager` of the `Manager` class is created with the following arguments: (“John Walker”, 60000, “Sales”, 5000). The `calculate_total_compensation` method is called on the `manager` object, which calculates the total compensation by adding the salary and bonus. The calculated `total_compensation` is then printed. Since the salary is 60000 and the bonus is 5000, the total compensation is 65000.

14). What is the output of the following code?

				
					class Customer:
    def __init__(self, name, balance):
        self.name = name
        self.balance = balance
    
    def deposit(self, amount):
        self.balance += amount
    
    def withdraw(self, amount):
        if amount <= self.balance:
            self.balance -= amount
        else:
            print("Insufficient balance")

# Create an object of the Customer class
customer = Customer("Jason Roy", 1000)
customer.deposit(500)
customer.withdraw(200)
print("Balance:", customer.balance)

a) “Insufficient balance”
b) 800
c) 1300
d) 1200

Correct answer is: c) 1300
Explanation: The code creates an object of the `Customer` class with the name “Jason Roy” and an initial balance of 1000. The `deposit()` method is then called, adding 500 to the balance. Next, the `withdraw()` method is called with an amount of 200. Since the amount is less than or equal to the balance, 200 is subtracted from the balance. Finally, the current balance of the customer object is printed, resulting in an output of “Balance: 1300”.

15). What is the output of the following code?

				
					class Customer:
    def __init__(self, name, balance):
        self.name = name
        self.balance = balance
    
    def deposit(self, amount):
        self.balance += amount
    
    def withdraw(self, amount):
        if amount <= self.balance:
            self.balance -= amount
        else:
            print("Insufficient balance")

class VIPCustomer(Customer):
    def __init__(self, name, balance, credit_limit, discount_rate):
        super().__init__(name, balance)
        self.credit_limit = credit_limit
        self.discount_rate = discount_rate
    
    def calculate_available_credit(self):
        available_credit = self.credit_limit - self.balance
        return available_credit

# Create an object of the VIPCustomer class
vip_customer = VIPCustomer("John Doe", 5000, 10000, 0.1)
available_credit = vip_customer.calculate_available_credit()
print("Available Credit:", available_credit)

a) Available Credit: 5000
b) Available Credit: 6000
c) Available Credit: 9000
d) Available Credit: 10000

Correct answer is: a) Available Credit: 5000
Explanation: The code defines two classes, `Customer` and `VIPCustomer`, where `VIPCustomer` is a subclass of `Customer`. The `VIPCustomer` class inherits the `__init__` method from the `Customer` class using the `super()` function. An object `vip_customer` is created using the `VIPCustomer` class, with the name “John Doe”, an initial balance of 5000, a credit limit of 10000, and a discount rate of 0.1. The `calculate_available_credit` method is called on the `vip_customer` object, which calculates the available credit by subtracting the balance from the credit limit. In this case, the available credit is 5000.

16). What is the output of the following code?

				
					class Phone:
    def __init__(self, brand, model, storage):
        self.brand = brand
        self.model = model
        self.storage = storage
    
    def make_call(self, number):
        print(f"Making a call to {number}")
    
    def send_text_message(self, number, message):
        print(f"Sending a text message to {number}: {message}")
    
    def check_storage_capacity(self):
        print(f"Storage capacity: {self.storage}GB")

# Create an object of the Phone class
phone = Phone("Apple", "iPhone 14", 256)
phone.make_call("1234567890")
phone.send_text_message("1234567890", "Hello!")
phone.check_storage_capacity()

a) Making a call to 1234567890
Sending a text message to 1234567890: Hello!
Storage capacity: 256GB
b) Making a call to 1234567890
Sending a text message to 1234567890: Hello!
c) Sending a text message to 1234567890: Hello!
Storage capacity: 256GB
d) Making a call to 1234567890

Correct answer is: a) Making a call to 1234567890
Sending a text message to 1234567890: Hello!
Storage capacity: 256GB
Explanation: The code creates an object of the `Phone` class with the brand “Apple”, model “iPhone 14”, and storage capacity of 256GB. The `make_call` method is then called with the number “1234567890”, which prints the message “Making a call to 1234567890”. Next, the `send_text_message` method is called with the number “1234567890” and the message “Hello!”, which prints the message “Sending a text message to 1234567890: Hello!”. Finally, the `check_storage_capacity` method is called, which prints the message “Storage capacity: 256GB”. Therefore, the correct output is option a) Making a call to 1234567890, Sending a text message to 1234567890: Hello!, Storage capacity: 256GB.

17). What is the output of the following code?

				
					class Laptop:
    def __init__(self, brand, model, storage):
        self.brand = brand
        self.model = model
        self.storage = storage
    
    def start_up(self):
        print("Starting up the laptop")
        print("Model: ", self.model)
    
    def shut_down(self):
        print("Shutting down the laptop")
    
    def check_storage_capacity(self):
        print(f"Storage capacity: {self.storage}GB")

# Create an object of the Laptop class
laptop = Laptop("Dell", "XPS 13", 1000)
laptop.start_up()
laptop.shut_down()
laptop.check_storage_capacity()

a) Starting up the laptop
Model: XPS 13
Shutting down the laptop
Storage capacity: 1000GB
b) Starting up the laptop
Model: Dell
Shutting down the laptop
Storage capacity: XPS 13GB
c) Starting up the laptop
Model: XPS 13
Shutting down the laptop
Storage capacity: XPS 13GB
d) Starting up the laptop
Model: Dell
Shutting down the laptop
Storage capacity: 1000GB

Correct answer is: a) Starting up the laptop
Model: XPS 13
Shutting down the laptop
Storage capacity: 1000GB
Explanation: The code creates an object of the `Laptop` class with the brand “Dell”, model “XPS 13”, and storage capacity of 1000GB. When `laptop.start_up()` is called, it prints “Starting up the laptop” and then “Model: XPS 13” because `self.model` refers to the model attribute of the `laptop` object, which is “XPS 13”. After that, `laptop.shut_down()` is called, which prints “Shutting down the laptop”. Finally, `laptop.check_storage_capacity()` is called, which prints “Storage capacity: 1000GB” because `self.storage` refers to the storage attribute of the `laptop` object, which is 1000.

18). What is the output of the following code?

				
					class Book:
    def __init__(self, title, author, pages):
        self.title = title
        self.author = author
        self.pages = pages
    
    def get_title(self):
        return self.title
    
    def get_author(self):
        return self.author
    
    def get_pages(self):
        return self.pages

# Create an object of the Book class
book = Book("Harry Potter", "J.K. Rowling", 300)
print("Title:", book.get_title())
print("Author:", book.get_author())
print("Pages:", book.get_pages())

a) Title: Harry Potter
Author: J.K. Rowling
Pages: 300
b) Harry Potter
J.K. Rowling
300
c) Title: “Harry Potter”
Author: “J.K. Rowling”
Pages: 300
d) “Harry Potter”
“J.K. Rowling”
300

Correct answer is: a) Title: Harry Potter
Author: J.K. Rowling
Pages: 300
Explanation: The code defines a class named `Book` with an initializer method (`__init__`) and three getter methods (`get_title`, `get_author`, `get_pages`). It then creates an instance of the `Book` class with the title “Harry Potter,” author “J.K. Rowling,” and 300 pages. The `print` statements display the output using the getter methods to retrieve the values from the `book` object. T

19). What is the output of the following code?

				
					class Book:
    def __init__(self, title, author, pages):
        self.title = title
        self.author = author
        self.pages = pages
    
    def get_title(self):
        return self.title
    
    def get_author(self):
        return self.author
    
    def get_pages(self):
        return self.pages
    
class EBook(Book):
    def __init__(self, title, author, pages, file_size, format):
        super().__init__(title, author, pages)
        self.file_size = file_size
        self.format = format
    
    def open_book(self):
        print("Opening the e-book")
    
    def close_book(self):
        print("Closing the e-book")

# Create an object of the EBook class
ebook = EBook("Harry Potter", "J.K. Rowling", 300, "10MB", "PDF")
print("Title:", ebook.get_title())
print("Author:", ebook.get_author())
print("Pages:", ebook.get_pages())
print("File Size:", ebook.file_size)
print("Format:", ebook.format)
ebook.open_book()
ebook.close_book()

a) Title: Harry Potter
Author: J.K. Rowling
Pages: 300
File Size: 10MB
Format: PDF
Opening the e-book
Closing the e-book
b) Title: Harry Potter
Author: J.K. Rowling
Pages: 300
File Size: 10MB
Format: PDF
c) Opening the e-book
Closing the e-book
d) An error will occur due to incorrect method calls.

Correct answer is: a) Title: Harry Potter
Author: J.K. Rowling
Pages: 300
File Size: 10MB
Format: PDF
Opening the e-book
Closing the e-book
Explanation: The code defines two classes, `Book` and `EBook`, where `EBook` is a subclass of `Book`. The `EBook` class inherits the `__init__` method from the `Book` class using the `super()` function. It also has additional methods `open_book()` and `close_book()`. In the code, an object `ebook` is created with the title “Harry Potter”, author “J.K. Rowling”, 300 pages, a file size of “10MB”, and format “PDF”. The `print` statements then retrieve and display the title, author, pages, file size, and format of the `ebook` object. Finally, the `open_book()` and `close_book()` methods are called.

20). What is the output of the following code?

				
					class ShoppingCart:
    def __init__(self):
        self.items = []
        self.total_cost = 0
    
    def add_item(self, item, cost):
        self.items.append(item)
        self.total_cost += cost
    
    def remove_item(self, item, cost):
        if item in self.items:
            self.items.remove(item)
            self.total_cost -= cost
    
    def calculate_total_cost(self):
        return self.total_cost

# Create an object of the ShoppingCart class
cart = ShoppingCart()
cart.add_item("Shirt", 250)
cart.add_item("Pants", 500)
cart.add_item("Shoes", 1000)
cart.remove_item("Shirt", 250)
total_cost = cart.calculate_total_cost()
print("Items in cart:", cart.items)
print("Total Cost:", total_cost)

a) Items in cart: [‘Shirt’, ‘Pants’, ‘Shoes’], Total Cost: 1450
b) Items in cart: [‘Pants’, ‘Shoes’], Total Cost: 1250
c) Items in cart: [‘Pants’, ‘Shoes’], Total Cost: 1500
d) Items in cart: [‘Shirt’, ‘Pants’, ‘Shoes’], Total Cost: 1000

Correct answer is: b) Items in cart: [‘Pants’, ‘Shoes’], Total Cost: 1500
Explanation: The `ShoppingCart` class is defined with an `__init__` method to initialize the `items` list and `total_cost` to 0. The `add_item` method adds an item to the `items` list and increases the `total_cost` by the specified cost. The `remove_item` method removes an item from the `items` list and decreases the `total_cost` by the specified cost. The `calculate_total_cost` method returns the total cost.

21). What is the output of the following code?

				
					class Animal:
    def __init__(self, name, color):
        self.name = name
        self.color = color
    
    def print_details(self):
        print("Name:", self.name)
        print("Color:", self.color)

# Create an object of the Animal class
animal = Animal("Lion", "Golden")
animal.print_details()

a) Name: Lion
Color: Golden
b) Name: Tiger
Color: Yellow
c) Name: Elephant
Color: Gray
d) Name: Giraffe
Color: Brown

Correct answer is: a) Name: Lion, Color: Golden
Explanation: The code defines a class `Animal` with an `__init__` method and a `print_details` method. The `__init__` method initializes the attributes `name` and `color` of the object, while the `print_details` method prints the values of these attributes. In the code, an object `animal` of the `Animal` class is created with the name “Lion” and the color “Golden”. The `print_details` method is then called on the `animal` object.

22). What is the output of the following code?

				
					class Animal:
    def __init__(self, name, color):
        self.name = name
        self.color = color
    
    def print_details(self):
        print("Name:", self.name)
        print("Color:", self.color)
        
class Dog(Animal):
    def __init__(self, name, color, breed, weight):
        super().__init__(name, color)
        self.breed = breed
        self.weight = weight
    
    def print_details(self):
        super().print_details()
        print("Breed:", self.breed)
        print("Weight:", self.weight)

# Create an object of the Dog class
dog = Dog("Marco", "Golden", "Golden Retriever", 50)
dog.print_details()

a) Name: Marco, Color: Golden, Breed: Golden Retriever, Weight: 50
b) Name: Golden, Color: Marco, Breed: Golden Retriever, Weight: 50
c) Name: Marco, Color: Golden, Breed: Golden Retriever, Weight: 50, Color: Marco
d) Name: Golden, Color: Marco, Breed: Golden Retriever, Weight: 50, Color: Golden

Correct answer is: a) Name: Marco, Color: Golden, Breed: Golden Retriever, Weight: 50
Explanation: The code defines two classes, `Animal` and `Dog`. The `Dog` class is a subclass of `Animal` and inherits its `__init__` and `print_details` methods. The `Dog` class also has its own `__init__` method, which takes additional parameters for `breed` and `weight`. In the code, an object `dog` of the `Dog` class is created with the arguments “Marco”, “Golden”, “Golden Retriever”, and 50. When the `print_details` method is called on this `dog` object, it first calls the `print_details` method of the superclass `Animal` using `super().print_details()`. This prints the name and color inherited from the `Animal` class. After that, it prints the additional attributes specific to the `Dog` class, which are the breed and weight.