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Introduction to Python with Google Colab
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Introduction to Python with Google Colab

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@OptimisticCopernicium6348

Questions and Answers

What happens to the size of a sparse matrix when additional zero elements are added?

  • It increases in size proportionally to the number of added zeros.
  • It remains unchanged regardless of the number of added zeros. (correct)
  • It changes based on the type of sparse matrix used.
  • It becomes more efficient in storage.

    • Which of the following is NOT a type of sparse matrix mentioned?

  • Dense Matrix (correct)
  • Compressed Sparse Row (CSR)
  • Compressed Sparse Column
  • Dictionary of Keys

    • What is the index of the first element in a NumPy array?

  • Depends on the array length
  • 1
  • -1
  • 0 (correct)

    • What represents a sparse matrix that was created from the larger matrix in the example?

    <p>It only contains non-zero elements and their positions.</p> Signup and view all the answers

    Which method of selecting elements in a NumPy array correctly references the second row, second column?

    <p>matrix[1,1]</p> Signup and view all the answers

    What advantage does a sparse matrix have in terms of storage?

    <p>It reduces memory usage by storing only significant values.</p> Signup and view all the answers

    What is a primary purpose of the 'kernels' in a Google Colab notebook?

    <p>To execute code blocks in the notebook.</p> Signup and view all the answers

    Which of the following operations can be performed easily with NumPy arrays?

    <p>Selection of elements based on conditions.</p> Signup and view all the answers

    Which of the following statements about Python modules is correct?

    <p>The module name is derived from the file name with .py removed.</p> Signup and view all the answers

    Which command correctly imports a module in Python using the recommended method?

    <p>import module1 as mod1</p> Signup and view all the answers

    Why is it important to choose the right type of sparse matrix?

    <p>Each type has different implications and performance characteristics.</p> Signup and view all the answers

    In NumPy, what is the outcome of executing 'np.array([1, 2, 3])'?

    <p>It creates a one-dimensional row vector.</p> Signup and view all the answers

    What would be the output of 'print(vector_row)' if vector_row is defined as 'np.array([1, 2, 3])'?

    <p>[1 2 3]</p> Signup and view all the answers

    Which command is used to create a column vector in NumPy?

    <p>np.array([[1], [2], [3]])</p> Signup and view all the answers

    What does the combination 'Shift + Tab' perform in a code cell?

    <p>It brings up documentation for the function being typed.</p> Signup and view all the answers

    What is the primary data structure used in NumPy?

    <p>Multidimensional array</p> Signup and view all the answers

    What will be the result of executing matrix[:,1:2] on the defined matrix?

    <p>array([[2], [5], [8]])</p> Signup and view all the answers

    What value is returned by matrix.size for the given matrix?

    <p>12</p> Signup and view all the answers

    What does the ndim attribute of a NumPy array represent?

    <p>The number of dimensions of the array</p> Signup and view all the answers

    Using the vectorized_add_1000, what would be the output if applied to the array [[1, 2, 3], [4, 5, 6], [7, 8, 9]]?

    <p>array([[1001, 1002, 1003], [1004, 1005, 1006], [1007, 1008, 1009]])</p> Signup and view all the answers

    Which of the following correctly describes how to select all elements of a vector in NumPy?

    <p>vector[:]</p> Signup and view all the answers

    What will the output be for vector[-1] when vector is array([1, 2, 3, 4, 5, 6])?

    <p>6</p> Signup and view all the answers

    What is the shape of the matrix created by np.array([[1, 2, 3, 4],[5, 6, 7, 8],[9, 10, 11, 12]])?

    <p>(3, 4)</p> Signup and view all the answers

    Which of the following statements about NumPy's vectorize is true?

    <p>It converts a function to apply to all elements of an array.</p> Signup and view all the answers

    What does the argument -1 represent when using the reshape function in NumPy?

    <p>As many columns as needed</p> Signup and view all the answers

    Which command correctly finds the inverse of a matrix A using NumPy?

    <p>np.linalg.inv(A)</p> Signup and view all the answers

    What will be the output of the command matrix.reshape(12) for a matrix of size 4x3?

    <p>A 1D array with 12 elements</p> Signup and view all the answers

    To calculate the eigenvalues of matrix A using NumPy, which of the following commands is correct?

    <p>np.linalg.eig(A)</p> Signup and view all the answers

    If you want to create a 2x2 matrix of ones with a border of zeros, which approach should you use?

    <p>Use np.pad(np.ones((2,2)), pad_width=1, mode='constant')</p> Signup and view all the answers

    What will be the result of applying the operation matrix + 1000 to the following matrix: [[1, 2, 3], [4, 5, 6], [7, 8, 9]]?

    <p>array([[1001, 1002, 1003], [1004, 1005, 1006], [1007, 1008, 1009]])</p> Signup and view all the answers

    What is the output of np.max(matrix) for the given matrix [[1, 2, 3], [4, 5, 6], [7, 8, 9]]?

    <p>9</p> Signup and view all the answers

    When using np.max(matrix, axis=0), what does the output represent?

    <p>The maximum value in each column of the matrix</p> Signup and view all the answers

    Which of the following functions would you use to calculate the variance of the elements in a matrix?

    <p>np.var()</p> Signup and view all the answers

    Which statement is true about reshaping a matrix using np.reshape()?

    <p>The total number of elements in the new shape must equal the original shape.</p> Signup and view all the answers

    If you want to find the mean of the first row in the matrix [[1, 2, 3], [4, 5, 6], [7, 8, 9]], which code should you use?

    <p>np.mean(matrix[0])</p> Signup and view all the answers

    What is the result of np.max(matrix, axis=1) using the matrix [[1, 2, 3], [4, 5, 6], [7, 8, 9]]?

    <p>array([3, 6, 9])</p> Signup and view all the answers

    Which operation would you use to find the standard deviation of a matrix in NumPy?

    <p>np.std()</p> Signup and view all the answers

    Study Notes

    Introduction to Python with Google Colab

    • Python is a high-level, interpreted programming language suitable for various applications.
    • Google Colab is an online platform that allows users to write and execute Python code via Jupyter notebooks.

    Google Colab Interface

    • Kernels: The computational engine that executes code blocks.
    • Cells: Containers for code or text, including markdown and code cells.
      • Key Shortcuts:
        • Shift + Enter: Execute the current cell and move to the next.
        • Shift + Tab: View documentation for the selected function.

    Modules in Python

    • A module is a file containing functions and definitions, similar to #include in C/C++.
    • Import modules to access additional functions:
      • Recommended method: import module1 as mod1.

    Basics of NumPy

    • NumPy is essential for machine learning, enabling efficient operations with vectors, matrices, and arrays.

    Creating Vectors

    • Vectors can be created as one-dimensional arrays using NumPy:
      • Row Vector: vector_row = np.array([1, 2, 3])
      • Column Vector: vector_column = np.array([[1], [2], [3]])

    Creating Matrices

    • Use NumPy to create two-dimensional arrays (matrices).
    • Larger matrices can illustrate advantages of sparse representations. For example, a sparse matrix minimizes storage of zero elements using sparse.csr_matrix().

    Selecting Elements in Arrays

    • NumPy arrays are zero-indexed.
    • Selection example:
      • Third element of vector: vector[2]
      • Element at second row, second column in a matrix: matrix[1, 1]
    • Various slicing techniques allow selection of groups of elements.

    Describing Matrix Properties

    • Use attributes shape, size, and ndim to describe a matrix:
      • Shape: matrix.shape indicates rows and columns.
      • Size: matrix.size gives total elements.
      • Dimensions: matrix.ndim returns the number of matrix dimensions.

    Applying Functions to Arrays

    • Functions can be applied to multiple elements using np.vectorize() or broadcasting techniques, e.g., matrix + 1000.

    Finding Maximum and Minimum Values

    • Use np.max() and np.min() to find max/min values in arrays.
    • Operations can be specified along certain axes with parameters like axis=0 or axis=1.

    Descriptive Statistics

    • Calculate mean, variance, and standard deviation using np.mean(), np.var(), and np.std(), respectively.

    Reshaping Arrays

    • Reshape matrices while maintaining data with matrix.reshape(), ensuring the total number of elements remains unchanged.
    • Use -1 to auto-calculate dimensions.

    Introduction to Linear Algebra with NumPy

    • Calculate eigenvalues and eigenvectors using np.linalg.eig().
    • Compute matrix inverses with np.linalg.inv().

    Post Lab Exercises

    • Implement programs to:
      • Check if all elements in an array are non-zero.
      • Create a 2x2 matrix of ones with a border of zeros.
      • Multiply a randomly generated 4x3 matrix by a 3x5 matrix.
      • Solve a system of equations using np.linalg.solve().

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    Description

    This quiz covers the basics of Python programming using Google Colab. You'll learn about the interface, modules, and how to work with NumPy for efficient operations. Test your understanding of essential Python concepts and tools in this interactive quiz.

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