Creating structured arrays in NumPy - Quick Revision & Summary

Recall & Review

beginner

What is a structured array in NumPy?

A structured array is a special type of NumPy array that allows you to store different data types in each element, similar to a table with columns of different types.

beginner

How do you define the data type for a structured array in NumPy?

You define a structured array's data type using a list of tuples, where each tuple contains a field name and its data type, for example: [('name', 'U10'), ('age', 'i4')] means a string field 'name' and an integer field 'age'.

beginner

Show how to create a structured array with fields 'name' (string) and 'age' (integer) with two entries.

Use numpy.array with dtype: np.array([('Alice', 25), ('Bob', 30)], dtype=[('name', 'U10'), ('age', 'i4')])

beginner

How do you access the 'age' field from a structured array named 'data'?

You access it by using data['age'], which returns an array of all ages in the structured array.

beginner

Why are structured arrays useful in data science?

They let you store and manipulate tabular data with different types efficiently, similar to a spreadsheet or database table, but with NumPy's speed and functionality.

What does the dtype [('name', 'U10'), ('age', 'i4')] specify in a structured array?

A A string field 'name' with max length 10 and an integer field 'age'

B Two integer fields named 'name' and 'age'

C A float field 'name' and a string field 'age'

D An array of 10 strings and 4 integers

The dtype specifies a Unicode string field 'name' with max length 10 and a 4-byte integer field 'age'.

How do you create a structured array with fields 'x' and 'y' both as floats?

A np.array([(1, 2)], dtype=[('x', 'i4'), ('y', 'i4')])

B np.array([1.0, 2.0], dtype='float')

C np.array([(1.0, 2.0)], dtype=[('x', 'f8'), ('y', 'f8')])

D np.array([{'x':1.0, 'y':2.0}])

Option C correctly creates a structured array with float fields 'x' and 'y'.

How can you access the 'name' field of a structured array called 'arr'?

A arr['name']

B arr.name

C arr[0]['name']

D arr.get('name')

You access fields in structured arrays using arr['field_name'] syntax.

What type of data can a structured array hold?

A Only integers

B Multiple data types in different fields

C Only floats

D Only strings

Structured arrays can hold different data types in each field.

Which of these is a benefit of using structured arrays?

A Only works with numeric data

B Faster than plain Python lists for strings only

C Automatically sorts data

D Efficient storage of tabular data with mixed types

Structured arrays efficiently store tabular data with mixed data types.

Explain how to create a structured array with fields for 'city' (string) and 'population' (integer).

Think about how to specify the dtype and how to pass data as tuples.

Describe how you would access and print all values of a specific field in a structured array.

Remember how to get a column from a table.