Analyzing Temperature and Humidity Data with NumPy

This example demonstrates the use of NumPy in Python for analyzing temperature and humidity data collected over 12 months. The objective is to calculate the correlation coefficient between temperature and humidity to understand the relationship between these environmental factors.

Introduction

Backstory

Imagine you're a scientist working in a research facility that specializes in analyzing environmental data. Your team has collected temperature and humidity data from various locations around the world to study climate patterns. You're interested in finding out if there's a correlation between temperature and humidity in a specific region to better understand local climate behaviors.

Motivation

You want to use data analysis techniques to explore the relationship between temperature and humidity in the selected region. This analysis could help you make predictions about future climate trends and inform local communities about potential changes in their environment.

Statement

import numpy as np

# Simulated data for temperature (in Celsius) and humidity (as a percentage)
# over 12 months
temperature_data = np.array(
    [25.5, 27.3, 28.6, 30.2, 31.8, 32.5, 31.2, 29.8, 28.0, 26.4, 24.9, 23.7]
)
humidity_data = np.array(
    [65.2, 63.7, 60.9, 57.8, 55.2, 54.1, 55.6, 58.3, 61.0, 63.2, 64.7, 66.5]
)

# Calculate the correlation coefficient using NumPy's corrcoef function
correlation_matrix = np.corrcoef(temperature_data, humidity_data)

# The correlation coefficient is the value at position (0, 1) or (1, 0)
# in the correlation matrix
correlation_coefficient = correlation_matrix[0, 1]

log.info(
    f"Correlation Coefficient between Temperature and Humidity: {correlation_coefficient}"
)

Explanation

Creating NumPy Arrays: We create NumPy arrays to store temperature and humidity data for 12 months.
Calculating Correlation Coefficient: We use NumPy's corrcoef function to calculate the correlation matrix between temperature and humidity. The correlation coefficient is extracted from the matrix.

Conclusion

This NumPy-based analysis of climate data allows scientists and researchers to identify potential correlations between temperature and humidity. By leveraging NumPy's powerful array and mathematical functions, you can efficiently analyze and draw insights from environmental data, contributing to a better understanding of climate patterns and their potential impacts.