We’re announcing new community investments in Missouri.

Introduction

In Missouri, Google AI is investing in workforce development and energy programs. This tutorial will teach you how to use Python to analyze energy consumption data, a key skill for the next-generation workforce. You'll learn how to collect, clean, and visualize energy usage data using popular Python libraries. This hands-on approach will help you understand how AI and data science are being applied to real-world energy challenges.

Prerequisites

Before starting this tutorial, you should have:

• A basic understanding of Python programming concepts
• Python 3.6 or higher installed on your computer
• Basic knowledge of data analysis concepts
• Access to a computer with internet connection

Step-by-Step Instructions

Step 1: Install Required Python Libraries

First, you'll need to install the necessary Python packages for data analysis and visualization. Open your command prompt or terminal and run the following commands:

pip install pandas numpy matplotlib seaborn

Why this step? These libraries are essential for handling data (pandas), numerical operations (numpy), and creating visualizations (matplotlib/seaborn). They form the foundation of data science in Python.

Step 2: Create Your Python Project Structure

Create a new folder called energy_analysis on your computer. Inside this folder, create two files:

1. energy_data.py - for your main analysis code
2. sample_energy_data.csv - for your sample data

Why this step? Organizing your work in a structured way helps you maintain clean code and makes it easier to expand your analysis later.

Step 3: Generate Sample Energy Data

Open the sample_energy_data.csv file and add the following sample data:

date,location,consumption_kwh,temperature_f
2023-01-01,St. Louis,1200,32
2023-01-02,St. Louis,1100,28
2023-01-03,St. Louis,1300,35
2023-01-04,St. Louis,1400,40
2023-01-05,St. Louis,1500,45
2023-01-01,Kansas City,1100,30
2023-01-02,Kansas City,1000,25
2023-01-03,Kansas City,1200,32
2023-01-04,Kansas City,1300,38
2023-01-05,Kansas City,1400,42

Why this step? Sample data allows you to test your code without needing access to real energy consumption systems. This simulates the type of data Google AI might analyze in Missouri's energy programs.

Step 4: Import Libraries and Load Data

Open energy_data.py and add the following code:

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Load the energy data
energy_df = pd.read_csv('sample_energy_data.csv')

# Display first few rows of data
print(energy_df.head())

# Show basic information about the dataset
print(energy_df.info())

Why this step? Loading data and checking its structure is the first step in any data analysis project. This helps you understand what you're working with before performing any analysis.

Step 5: Clean and Prepare the Data

Add the following code to your energy_data.py file:

# Convert date column to datetime format
energy_df['date'] = pd.to_datetime(energy_df['date'])

# Check for missing values
print("Missing values:")
print(energy_df.isnull().sum())

# Display summary statistics
print("\nSummary statistics:")
print(energy_df.describe())

Why this step? Data cleaning is crucial in real-world projects. You need to ensure your data is in the correct format and check for any issues before analysis. This step helps you identify potential problems in your dataset.

Step 6: Create Energy Consumption Visualizations

Add the following code to create visualizations:

# Set up the plotting style
plt.style.use('seaborn-v0_8')

# Create a line plot of energy consumption over time
plt.figure(figsize=(12, 6))
sns.lineplot(data=energy_df, x='date', y='consumption_kwh', hue='location')
plt.title('Energy Consumption Over Time by Location')
plt.xlabel('Date')
plt.ylabel('Energy Consumption (kWh)')
plt.xticks(rotation=45)
plt.tight_layout()
plt.savefig('energy_consumption_trend.png')
plt.show()

Why this step? Visualizations help you understand patterns in energy consumption data. This type of analysis is valuable for energy program planning and optimization, similar to what Google AI might do in Missouri.

Step 7: Analyze Energy Usage Patterns

Add this code to calculate and display energy usage patterns:

# Calculate average consumption by location
avg_consumption = energy_df.groupby('location')['consumption_kwh'].mean()
print("Average energy consumption by location:")
print(avg_consumption)

# Calculate correlation between temperature and consumption
correlation = energy_df['temperature_f'].corr(energy_df['consumption_kwh'])
print(f"\nCorrelation between temperature and consumption: {correlation:.2f}")

Why this step? Understanding relationships between variables (like temperature and energy consumption) is key to energy efficiency programs. This analysis helps identify patterns that could inform energy conservation strategies.

Step 8: Save Your Analysis Results

Add this final code to save your findings:

# Save cleaned data to a new file
energy_df.to_csv('cleaned_energy_data.csv', index=False)

# Save summary statistics to a text file
with open('energy_analysis_summary.txt', 'w') as f:
    f.write('Energy Consumption Analysis Summary\n')
    f.write('=================================\n')
    f.write(f'Average consumption by location:\n{avg_consumption}\n\n')
    f.write(f'Correlation between temperature and consumption: {correlation:.2f}\n')
    f.write(f'Total records analyzed: {len(energy_df)}\n')

print("\nAnalysis complete! Files saved:")
print("- cleaned_energy_data.csv")
print("- energy_consumption_trend.png")
print("- energy_analysis_summary.txt")

Why this step? Saving your results ensures you can review and share your work. This is an important part of any data science workflow, especially when working on community projects like those in Missouri.

Summary

In this tutorial, you've learned how to analyze energy consumption data using Python. You've installed necessary libraries, loaded and cleaned sample data, created visualizations, and calculated key metrics. These skills are directly applicable to the energy programs Google AI is investing in Missouri. By understanding energy usage patterns, you're contributing to the development of more efficient energy systems that benefit communities. This hands-on approach gives you practical experience in data analysis that could help support workforce development initiatives in your area.