Structured arrays vs DataFrames in NumPy - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a structured array with fields 'name' and 'age'.

NumPy

import numpy as np

people = np.array([('Alice', 25), ('Bob', 30)], dtype=[1])

Drag options to blanks, or click blank then click option'

A[('name', 'U10'), ('age', 'i4')]

B[('name', 'S10'), ('age', 'f4')]

C[('name', 'U5'), ('age', 'i2')]

D[('name', 'U10'), ('age', 'f8')]

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect data types like float for age.

Not using a list of tuples for dtype.

The dtype must define the field names and their data types. 'U10' means a Unicode string of length 10, and 'i4' means a 4-byte integer, suitable for age.

2fill in blank

medium

Complete the code to convert the structured array 'people' to a pandas DataFrame.

NumPy

import pandas as pd

df = pd.DataFrame([1])

Drag options to blanks, or click blank then click option'

Apeople.tolist()

Bpeople.astype(object)

Cpeople.view(np.recarray)

Dpeople

Attempts:

3 left

💡 Hint

Common Mistakes

Converting to list loses field names.

Using .view(np.recarray) unnecessarily.

You can pass the structured array directly to pd.DataFrame to create a DataFrame with columns matching the fields.

3fill in blank

hard

Fix the error in accessing the 'age' field from the structured array 'people'.

NumPy

ages = people[1]

Drag options to blanks, or click blank then click option'

A.age

B[1]

C['age']

D['Age']

Attempts:

3 left

💡 Hint

Common Mistakes

Using dot notation which does not work for numpy structured arrays.

Using incorrect capitalization in field names.

To access a field in a structured array, use square brackets with the exact field name as a string.

4fill in blank

hard

Fill all four blanks to create a DataFrame from a dictionary comprehension that filters people older than 25.

NumPy

df_filtered = pd.DataFrame([1]: [2] for [3] in people if [4] > 25)

Drag options to blanks, or click blank then click option'

Aperson['name']

Bperson['age']

Cperson

Dp

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong field for keys or values.

Not filtering correctly with the age condition.

The dictionary comprehension uses the person's name as keys and age as values for those older than 25.

5fill in blank

hard

Fill all three blanks to create a structured array from a list of tuples and then convert it to a DataFrame.

NumPy

data = [('John', 28), ('Jane', 32)]

arr = np.array(data, dtype=[[1], [2]])
df = pd.DataFrame([3])

Drag options to blanks, or click blank then click option'

A('name', 'U10')

B('age', 'i4')

Carr

Ddata

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the original list to pd.DataFrame instead of the structured array.

Incorrect dtype format.

The dtype is a list of tuples defining fields. Then the structured array is passed directly to pd.DataFrame.