Level 1: A company has sales data of January and February stored in two separate NumPy arrays. Combine both months into a single array using the appropriate NumPy function.
Level 2: A school stores marks of Class A and Class B in two arrays. Combine them so that each class appears in a separate row.
Level 3: An online store stores product IDs in three different arrays received from three warehouses. Merge all product IDs into one array and print the final dataset.
Level 4: Create two 2×3 arrays representing rainfall data collected from two weather stations. Join them horizontally and calculate the total rainfall of each row using the appropriate axis.
Level 5: A hospital stores patient temperature readings collected in the morning and evening in two arrays. Join both datasets vertically and calculate the average temperature of all patients.
Level 6: Create two 2×2 arrays representing the Red and Green color channels of an image. Combine them using np.dstack() and print the resulting 3D array.
Level 7: A company wants to divide its yearly sales data into four quarters. Create a NumPy array of 12 monthly sales values and split it into four equal parts.
Level 8: Create a 4×4 matrix and split it into two equal column groups using np.hsplit(). Print both parts separately.
Level 9: Create a 6×4 matrix representing student marks. Split the matrix into three equal row groups using np.vsplit().
Level 10: Create two arrays containing employee salaries. Join them, then use Boolean Indexing to display only salaries greater than ₹50,000.
Level 11: A supermarket has product prices from two branches. Merge both arrays, sort the prices in ascending order, and display only unique prices.
Level 12: Create two arrays of daily temperatures. Join them and calculate:
- Maximum temperature
- Minimum temperature
- Average temperature
- Standard deviation
Level 13: Create two arrays of student marks. Join them and display only students who scored 80 or above using Boolean Indexing.
Level 14: A company has monthly profits for two years stored separately. Combine both arrays, reshape the result into a 4×6 matrix, and calculate the yearly total using the correct axis.
Level 15: Create two matrices representing sales from Online and Offline stores. Stack them vertically and calculate the total sales of each store using axis operations.
Level 16: A sensor records data every hour. Create an array of 24 readings, split it into Morning, Afternoon, Evening, and Night, then calculate the average reading of each time period.
Level 17: Create two arrays representing customer IDs from two different databases. Join both arrays, remove duplicate IDs using np.unique(), and display the total number of unique customers.
Level 18: A cricket academy stores runs scored in Practice Match 1 and Practice Match 2. Join both datasets, sort them in descending order, and display only players who scored more than 50 runs.
Level 19: Create a 5×4 matrix of sales data.
Perform the following operations:
- Display the second column
- Display the last two rows
- Split the matrix vertically
- Join the split parts again
- Verify that the original matrix is restored
Level 20 (Challenge Project): A company receives monthly sales reports from North, South, East, and West regions.
Write a complete NumPy program that:
- Creates four monthly sales arrays
- Joins all regions into one dataset
- Reshapes the data into a meaningful matrix
- Calculates total sales for each region
- Calculates average monthly sales
- Displays only sales greater than ₹50,000 using Boolean Indexing
- Sorts all sales values
- Finds unique sales values
- Splits the final dataset into two equal halves for Training and Testing
- Prints every step with proper headings
