Structured arrays vs DataFrames in NumPy - Practice Questions

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of accessing structured array fields

What is the output of this code snippet that uses a NumPy structured array?

NumPy

import numpy as np

arr = np.array([(1, 2.5, 'A'), (2, 3.5, 'B')], dtype=[('x', 'i4'), ('y', 'f4'), ('z', 'U1')])
print(arr['y'])

A [1 2]

B [2.5 3.5]

C ['A' 'B']

D [1. 2.]

💡 Hint

Remember that accessing a field by name returns all values in that field.

The structured array has fields 'x', 'y', and 'z'. Accessing arr['y'] returns the array of floats [2.5, 3.5].

❓ data_output

intermediate

DataFrame column selection output

Given this pandas DataFrame, what is the output of selecting the 'age' column?

NumPy

import pandas as pd

df = pd.DataFrame({'name': ['Alice', 'Bob'], 'age': [25, 30]})
print(df['age'])

A

name    Alice
age        25
Name: 0, dtype: object

B ['Alice', 'Bob']

C [25, 30]

D

0    25
1    30
Name: age, dtype: int64

💡 Hint

Selecting a column returns a Series with index and name.

df['age'] returns a pandas Series with index 0 and 1, values 25 and 30, and name 'age'.

🧠 Conceptual

advanced

Difference in data mutability between structured arrays and DataFrames

Which statement correctly describes a key difference in mutability between NumPy structured arrays and pandas DataFrames?

A DataFrames allow adding and removing columns easily, while structured arrays have fixed fields and cannot add or remove columns.

B Structured arrays allow changing the data type of a field after creation, but DataFrames do not.

C Both structured arrays and DataFrames allow dynamic resizing of rows but not columns.

D Structured arrays support missing data natively, but DataFrames do not.

💡 Hint

Think about how flexible the structure is after creation.

NumPy structured arrays have fixed fields defined at creation and cannot add or remove columns. Pandas DataFrames can add or remove columns dynamically.

🔧 Debug

advanced

Identify the error in structured array field assignment

What error will this code produce and why?

NumPy

import numpy as np

arr = np.array([(1, 2.5), (2, 3.5)], dtype=[('a', 'i4'), ('b', 'f4')])
arr['a'] = [10, 20, 30]

A No error, assignment succeeds

B TypeError: unsupported operand type(s) for assignment

C ValueError: could not broadcast input array from shape (3,) into shape (2,)

D IndexError: index 3 is out of bounds for axis 0 with size 2

💡 Hint

Check the length of the array being assigned compared to the structured array size.

The structured array has 2 elements, but the assignment tries to assign 3 values, causing a ValueError due to shape mismatch.

🚀 Application

expert

Choosing the best data structure for mixed data types with missing values

You have a dataset with numeric, string, and missing values. You want to perform data analysis and easily handle missing data. Which data structure is best and why?

A Pandas DataFrame, because it supports mixed types and has built-in missing data handling.

B NumPy ndarray with object dtype, because it is faster and supports missing values.

C NumPy structured array, because it supports mixed types and missing values natively.

D Python list of tuples, because it is simple and supports mixed types and missing values.

💡 Hint

Consider ease of handling missing data and mixed types in analysis.

Pandas DataFrames are designed to handle mixed data types and missing values efficiently with many built-in functions.

Practice

1. What is a key difference between a numpy structured array and a pandas DataFrame?

easy

A. Structured arrays automatically handle missing data, DataFrames do not.

B. Structured arrays can only store numbers, DataFrames can only store text.

C. DataFrames do not support named columns, structured arrays do.

D. Structured arrays have fixed data types per column, while DataFrames allow mixed types and more flexible operations.

Solution

1. Step 1: Understand data type handling in structured arrays

   Structured arrays in numpy require fixed data types for each named column, meaning each column's type is set and consistent.

2. Step 2: Compare with DataFrame flexibility

   DataFrames from pandas allow columns to have different data types and provide many flexible operations like handling missing data and complex indexing.

3. Final Answer:

   Structured arrays have fixed data types per column, while DataFrames allow mixed types and more flexible operations. -> Option D

4. Quick Check:

   Data type flexibility = D [OK]

Hint: Remember: structured arrays fix types, DataFrames are more flexible [OK]

Common Mistakes:

• Thinking structured arrays can handle missing data like DataFrames
• Assuming DataFrames cannot have mixed data types
• Believing structured arrays only store numbers

2. Which of the following is the correct way to create a numpy structured array with fields 'name' (string) and 'age' (integer)?

easy

A. np.array([('Alice', 25), ('Bob', 30)], dtype=[('name', 'int'), ('age', 'str')])

B. np.array([{'Name': 'Alice', 'age': 25}, {'Name': 'Bob', 'age': 30}])

C. np.array([('Alice', 25), ('Bob', 30)], dtype=[('name', 'U10'), ('age', 'i4')])

D. np.array([['Alice'], ['Bob']], dtype=[('name', 'U10'), ('age', 'i4')])

Solution

1. Step 1: Check dtype specification for structured arrays

   The dtype must be a list of tuples with field names and valid numpy data types, e.g., 'U10' for string and 'i4' for 4-byte integer.

2. Step 2: Verify the data matches the dtype

   np.array([('Alice', 25), ('Bob', 30)], dtype=[('name', 'U10'), ('age', 'i4')]) uses tuples matching the dtype fields correctly. np.array([{'name': 'Alice', 'age': 25}, {'name': 'Bob', 'age': 30}]) uses dicts which numpy does not accept directly for structured arrays. np.array([('Alice', 25), ('Bob', 30)], dtype=[('name', 'int'), ('age', 'str')]) swaps types incorrectly. np.array([['Alice', 25], ['Bob', 30]], dtype=[('name', 'U10'), ('age', 'i4')]) uses lists instead of tuples, which is invalid here.

3. Final Answer:

   np.array([('Alice', 25), ('Bob', 30)], dtype=[('name', 'U10'), ('age', 'i4')]) -> Option C

4. Quick Check:

   Correct dtype and tuple data = A [OK]

Hint: Use tuples and correct dtype list for structured arrays [OK]

Common Mistakes:

• Using dicts instead of tuples for structured array data
• Mixing up data types in dtype list
• Using lists instead of tuples for records

3. Given the code below, what will be the output?

import numpy as np
import pandas as pd

arr = np.array([(1, 'A'), (2, 'B')], dtype=[('id', 'i4'), ('label', 'U1')])
df = pd.DataFrame(arr)
print(df['label'][1])

medium

A. B

B. A

C. 1

D. Error: KeyError

Solution

1. Step 1: Understand conversion from structured array to DataFrame

   Creating a DataFrame from a structured array converts named fields into columns with the same names.

2. Step 2: Access the 'label' column and index 1

   df['label'] is a Series with values ['A', 'B']. Index 1 corresponds to 'B'.

3. Final Answer:

   B -> Option A

4. Quick Check:

   DataFrame column access = B [OK]

Hint: Structured array fields become DataFrame columns [OK]

Common Mistakes:

• Confusing index 0 and 1 values
• Expecting error due to structured array
• Mixing up field names and indices

4. What is wrong with this code snippet that tries to convert a pandas DataFrame to a numpy structured array?

import pandas as pd
import numpy as np

df = pd.DataFrame({'name': ['Tom', 'Jerry'], 'age': [5, 7]})
arr = np.array(df, dtype=[('name', 'U10'), ('age', 'i4')])
print(arr)

medium

A. The dtype should use 'S10' instead of 'U10' for strings.

B. The dtype argument is ignored; conversion does not create a structured array as expected.

C. The DataFrame must be converted to a list of tuples before creating the structured array.

D. There is no error; the code works correctly.

Solution

1. Step 1: Check how numpy.array handles DataFrame input with dtype

   Passing a DataFrame directly to np.array with dtype does not convert it into a structured array; dtype is ignored and a 2D array of objects is created.

2. Step 2: Identify correct conversion method

   To get a structured array, convert DataFrame to records (e.g., df.to_records()) before calling np.array.

3. Final Answer:

   The dtype argument is ignored; conversion does not create a structured array as expected. -> Option B

4. Quick Check:

   Direct np.array(df, dtype=...) ignores dtype [OK]

Hint: Convert DataFrame to records before numpy structured array [OK]

Common Mistakes:

• Assuming dtype works directly on DataFrame in np.array
• Not converting DataFrame to records first
• Confusing string dtype codes

5. You have a numpy structured array with fields 'city' (string) and 'temperature' (float). You want to convert it to a pandas DataFrame, filter rows where temperature > 20, then convert back to a structured array with the same fields. Which code snippet correctly does this?

hard

A. df = pd.DataFrame(arr); filtered = df.query('temperature > 20'); result = np.array(filtered.to_records(index=False), dtype=arr.dtype)

B. df = pd.DataFrame(arr); filtered = df[df.temperature > 20]; result = np.array(filtered, dtype=arr.dtype)

C. df = pd.DataFrame(arr); filtered = df[df['temperature'] > 20]; result = np.array(filtered.to_records())

D. df = pd.DataFrame(arr); filtered = df[df['temperature'] > 20]; result = np.array(filtered.to_dict())

Solution

1. Step 1: Convert structured array to DataFrame

   Creating a DataFrame from the structured array is straightforward: df = pd.DataFrame(arr).

2. Step 2: Filter rows where temperature > 20

   Using df.query('temperature > 20') or df[df['temperature'] > 20] both work, but query is concise and clear.

3. Step 3: Convert filtered DataFrame back to structured array with original dtype

   Use filtered.to_records(index=False) to get a structured array-like record array, then convert to numpy array with original dtype to keep field types consistent.

4. Final Answer:

   df = pd.DataFrame(arr); filtered = df.query('temperature > 20'); result = np.array(filtered.to_records(index=False), dtype=arr.dtype) -> Option A

5. Quick Check:

   Filter with query + to_records + dtype = A [OK]

Hint: Use to_records() and specify dtype when converting back [OK]

Common Mistakes:

• Not using to_records() before np.array conversion
• Forgetting to specify dtype on conversion back
• Using to_dict() which is incorrect here