# Numpy For Data Science - Real Time Exercises

### Let's start with the basics. Create Numpy arrays with the following specifications:

• A 1D array containing integers from 0 to 9.
• A 2D array (matrix) with dimensions 3x3, filled with random floating-point values.
• An identity matrix with size 5x5.
• A 1D array containing 10 equally spaced values between 0 and 1.

### Practice accessing specific elements and sections of Numpy arrays:

• Given a 2D array, extract the second column.
• Extract a sub-matrix from the original 3x3 matrix, including the second and third rows.
• Create a 2D array of shape (4, 4) and extract its diagonal elements.

### Perform basic arithmetic and element-wise operations on Numpy arrays:

• Create two 2D arrays of the same shape and perform element-wise addition.
• Multiply one of the arrays by a scalar value.
• Calculate the element-wise square root of an array.
• Calculate the dot product of two matrices.

### Explore aggregation and statistical functions using Numpy:

• Compute the mean, median, and standard deviation of an array.
• Find the index of the maximum and minimum values in an array.
• Sum the values along each row and column of a matrix.

### Practice broadcasting, a powerful feature of Numpy that allows operations on arrays of different shapes:

• Add a 1D array to a 2D array, broadcasting the values along each row.
• Multiply a 2D array by a scalar, broadcasting the scalar to all elements.

### Utilize boolean indexing to manipulate arrays based on conditions:

• Create a 1D array and filter out values greater than a certain threshold.
• Replace negative values in an array with zeros using boolean indexing.

### Explore reshaping and transposing arrays:

• Reshape a 1D array into a 2D array of a different shape.
• Transpose a 2D array (swap rows and columns).

### Apply Numpy's mathematical functions to arrays:

• Compute the exponential values of an array.
• Apply the sine function element-wise to an array.
• Calculate the element-wise logarithm of an array.

### Load and manipulate images using Numpy:

• Load an image using a library like Pillow and convert it into a Numpy array.
• Crop a specific region of the image using array slicing.
• Apply grayscale transformation to the image using array operations.

### Perform linear algebra operations using Numpy:

• Compute the eigenvalues and eigenvectors of a square matrix.
• Solve a system of linear equations using Numpy's linear algebra functions.

### Conclusion

Numpy is an essential tool for data scientists and analysts, providing the building blocks for efficient numerical computations and data manipulation.

These real-time exercises have provided hands-on experience with various aspects of Numpy, including array creation, indexing, operations, aggregation, broadcasting, boolean indexing, reshaping, and utilizing Numpy for basic image manipulation and linear algebra operations.

By mastering Numpy, you'll be better equipped to tackle complex data science tasks and enhance your analytical capabilities.