NumPy Structured Arrays: Exercises, Practice and Solutions
This resource offers a total of 100 NumPy Structured Arrays problems for practice. It includes 20 main exercises, each accompanied by solutions, detailed explanations, and four related problems.
The following exercises explore NumPy structured arrays, focusing on creation, modification, filtering, sorting, and advanced indexing. They help in managing heterogeneous data efficiently, making structured arrays a powerful tool for handling tabular datasets within NumPy.
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1. Structured Array Creation
Write a NumPy program that creates a structured array with fields for 'name' (string), 'age' (integer), and 'height' (float). Populate it with sample data for five individuals.
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2. Access 'Name' Field
Write a NumPy program that accesses and prints all values of the 'Name' field from the structured array created in exercise.
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3. Update Age Field
Write a NumPy program that updates the 'age' field for the first individual in the structured array created in exercise 1 to 30.
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4. Add New Record
Write a NumPy program that adds a new record to the structured array created array with fields for 'name' (string), 'age' (integer), and 'height' (float) with the fields: 'name': ' Nela Suna ', 'age': 25, 'height': 5.9.
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5. Filter Records by Age
Write a NumPy program to filter and print all records where the 'age' field is greater than 25 from the structured array created with fields for 'name' (string), 'age' (integer), and 'height' (float).
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6. Sort by Height
Write a NumPy program that sorts the structured array created with fields for 'name' (string), 'age' (integer), and 'height' (float), by the 'height' field in ascending order and prints the sorted array.
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7. Average Age Calculation
Write a NumPy program to calculate and print the average age of individuals in the structured array created with fields for 'name' (string), 'age' (integer), and 'height' (float).
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8. Delete Record by Name
Write a NumPy program to delete the record where 'name' is a specific name from the structured array created with fields for 'name' (string), 'age' (integer), and 'height' (float).
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9. Increment Age Field
Write a NumPy program to increase the 'age' field by 1 for all individuals in the structured array created with fields for 'name' (string), 'age' (integer), and 'height' (float).
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10. Combine Structured Arrays
Write a NumPy program that combines two structured arrays with the same dtype into one.
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11. Split Array Based on Condition
Write a NumPy program to split a structured array created with fields for 'name' (string), 'age' (integer), and 'height' (float) based on a condition (e.g., age > 25).
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12. Convert Height Field to Regular Array
Write a NumPy program to convert the 'height' field of the structured array created with fields for 'name' (string), 'age' (integer), and 'height' (float) to a regular NumPy array.
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13. Rename Height Field to Stature
Write a NumPy program to rename the 'height' field to 'stature' in the structured array with fields for 'name' (string), 'age' (integer), and 'height' (float).
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14. Load Structured Array from CSV
Write a NumPy program that loads data from a CSV file into a structured array. Assume the CSV file has columns corresponding to 'name', 'age', and 'height'.
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15. Save Structured Array to File
Write a NumPy program to save a structured array created with fields for 'name' (string), 'age' (integer), and 'height' (float) to a file using NumPy's save function.
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16. Iterate Over Records and Print
Write a NumPy program that iterates over each record in the structured array created with fields for 'name' (string), 'age' (integer), and 'height' (float) and prints the 'name' and 'height' fields.
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17. Structured Array with Nested Fields
Write a NumPy program that creates a structured array with nested fields, such as 'person' (which has sub-fields 'name' and 'age') and 'score' (integer).
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18. Advanced Indexing on Structured Array
Write a NumPy program that uses advanced indexing to select records from the structured array created with fields for 'name' (string), 'age' (integer), and 'height' (float) where both 'age' is greater than 25 and 'height' is less than 6.0.
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19. Concatenate 'name' and 'age' to New Field
Write a NumPy program that creates a new field 'name_age' that concatenates the 'name' and 'age' fields from the structured array created with fields for 'name' (string), 'age' (integer), and 'height' (float).
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20. Broadcast Value across 'Height' Field
Write a NumPy program to broadcast a value (e.g., set all 'height' values to 6.0) across the 'height' field in the structured array created with fields for 'name' (string), 'age' (integer), and 'height' (float).
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