Structured arrays vs DataFrames in NumPy - Hands-On Comparison

Structured arrays vs DataFrames

📖 Scenario: You work in a small shop that tracks sales data. You want to organize the sales information to analyze it easily. You will use two common ways to store data in Python: NumPy structured arrays and Pandas DataFrames.This project will help you see how to create and use both data structures with the same data.

🎯 Goal: Create a NumPy structured array and a Pandas DataFrame with the same sales data. Then, select and print the sales records where the amount is greater than 50.

📋 What You'll Learn

Create a NumPy structured array with fields: 'product' (string), 'quantity' (integer), and 'price' (float).

Create a Pandas DataFrame with the same data.

Filter and select sales where 'quantity' is greater than 50 in both data structures.

Print the filtered results.

💡 Why This Matters

🌍 Real World

Organizing and filtering sales data helps businesses understand which products sell more and manage inventory better.

💼 Career

Data scientists and analysts often use NumPy and Pandas to clean and analyze data efficiently.

1

Create a NumPy structured array

Create a NumPy structured array called sales_array with these exact entries: ('apple', 30, 0.5), ('banana', 60, 0.3), ('orange', 80, 0.7). Use the data type with fields: 'product' as a string of length 10, 'quantity' as integer, and 'price' as float.

NumPy

# Create the NumPy structured array called sales_array
# Your code here

Need a hint?

Use np.array with a list of tuples and specify dtype as a list of tuples with field names and types.

2

Create a Pandas DataFrame

Import pandas as pd. Create a DataFrame called sales_df with columns 'product', 'quantity', and 'price' using the same data as in sales_array.

NumPy

import numpy as np

sales_array = np.array([
    ('apple', 30, 0.5),
    ('banana', 60, 0.3),
    ('orange', 80, 0.7)
], dtype=[('product', 'U10'), ('quantity', 'i4'), ('price', 'f4')])

# Import pandas as pd
# Create the DataFrame sales_df with the same data
# Your code here

Need a hint?

Use pd.DataFrame and pass a dictionary with keys as column names and values as the fields from sales_array.

3

Filter sales with quantity greater than 50

Create a variable called filtered_array that selects rows from sales_array where quantity is greater than 50. Also create a variable called filtered_df that selects rows from sales_df where quantity is greater than 50.

NumPy

import numpy as np
import pandas as pd

sales_array = np.array([
    ('apple', 30, 0.5),
    ('banana', 60, 0.3),
    ('orange', 80, 0.7)
], dtype=[('product', 'U10'), ('quantity', 'i4'), ('price', 'f4')])

sales_df = pd.DataFrame({
    'product': sales_array['product'],
    'quantity': sales_array['quantity'],
    'price': sales_array['price']
})

# Filter sales_array where quantity > 50 into filtered_array
# Filter sales_df where quantity > 50 into filtered_df
# Your code here

Need a hint?

Use boolean indexing with sales_array['quantity'] > 50 and sales_df['quantity'] > 50 to filter.

4

Print the filtered results

Print the variables filtered_array and filtered_df to show the sales records where quantity is greater than 50.

NumPy

import numpy as np
import pandas as pd

sales_array = np.array([
    ('apple', 30, 0.5),
    ('banana', 60, 0.3),
    ('orange', 80, 0.7)
], dtype=[('product', 'U10'), ('quantity', 'i4'), ('price', 'f4')])

sales_df = pd.DataFrame({
    'product': sales_array['product'],
    'quantity': sales_array['quantity'],
    'price': sales_array['price']
})

filtered_array = sales_array[sales_array['quantity'] > 50]
filtered_df = sales_df[sales_df['quantity'] > 50]

# Print filtered_array and filtered_df
# Your code here

Need a hint?

Use print(filtered_array) and print(filtered_df) to show the filtered data.