NumPy Structured Arrays: Exercises, Practice and Solutions
NumPy Structured Arrays [ 20 exercises with solution]
The following exercises will help you become familiar with NumPy structured arrays, including creating, modifying, sorting, and filtering heterogeneous data.
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1. 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. 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. 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. 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. 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. 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. 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. 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. 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. Write a NumPy program that combines two structured arrays with the same dtype into one.
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11. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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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