NumPy and scientific computing ecosystem - Mini Project: Build & Apply

Exploring NumPy and the Scientific Computing Ecosystem

📖 Scenario: You are working as a data analyst for a small weather station. You have collected temperature data for a week and want to analyze it using NumPy and related scientific computing tools.

🎯 Goal: Build a simple NumPy-based program to store temperature data, configure a threshold, filter temperatures above the threshold, and display the filtered results.

📋 What You'll Learn

Create a NumPy array with exact temperature values

Define a threshold variable for filtering

Use NumPy boolean indexing to filter temperatures

Print the filtered temperature array

💡 Why This Matters

🌍 Real World

Analyzing temperature data helps weather stations report hot days and prepare forecasts.

💼 Career

Data analysts and scientists use NumPy to efficiently process and analyze numerical data like temperatures.

1

DATA SETUP: Create a NumPy array with temperature data

Import NumPy as np and create a NumPy array called temperatures with these exact values: 23.5, 25.0, 19.8, 22.1, 26.3, 24.7, 20.0.

NumPy

import numpy as np
# Create the NumPy array called temperatures with the given values
# Your code here

Need a hint?

Use np.array([...]) to create the array with the exact temperature values.

2

CONFIGURATION: Define a temperature threshold

Create a variable called threshold and set it to 24.0 to use for filtering temperatures.

NumPy

import numpy as np

temperatures = np.array([23.5, 25.0, 19.8, 22.1, 26.3, 24.7, 20.0])
# Define the threshold variable with value 24.0
# Your code here

Need a hint?

Just assign the number 24.0 to the variable threshold.

3

CORE LOGIC: Filter temperatures above the threshold

Create a new NumPy array called hot_days that contains only the temperatures from temperatures that are greater than threshold using NumPy boolean indexing.

NumPy

import numpy as np

temperatures = np.array([23.5, 25.0, 19.8, 22.1, 26.3, 24.7, 20.0])

threshold = 24.0
# Filter temperatures greater than threshold into hot_days
# Your code here

Need a hint?

Use temperatures > threshold inside the brackets to select values above the threshold.

4

OUTPUT: Print the filtered temperatures

Print the hot_days array to display the temperatures above the threshold.

NumPy

import numpy as np

temperatures = np.array([23.5, 25.0, 19.8, 22.1, 26.3, 24.7, 20.0])

threshold = 24.0

hot_days = temperatures[temperatures > threshold]
# Print the hot_days array
# Your code here

Need a hint?

Use print(hot_days) to show the filtered temperatures.