Cleaning data and the skies

📖 Background

Your are a data analyst at an environmental company. Your task is to evaluate ozone pollution across various regions.

You’ve obtained data from the U.S. Environmental Protection Agency (EPA), containing daily ozone measurements at monitoring stations across California. However, like many real-world datasets, it’s far from clean: there are missing values, inconsistent formats, potential duplicates, and outliers.

Before you can provide meaningful insights, you must clean and validate the data. Only then can you analyze it to uncover trends, identify high-risk regions, and assess where policy interventions are most urgently needed.

1 hidden cell

# Import necessary libraries
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import warnings

# Suppress warnings for cleaner output
warnings.filterwarnings('ignore')

# Read the ozone dataset
ozone = pd.read_csv('data/ozone.csv')
ozone.head()

# Data structure examination
print("Dataset shape:", ozone.shape)
print("\nColumn names:")
print(ozone.columns.tolist())
print("\nData types:")
print(ozone.dtypes)
print("\nBasic info:")
print(ozone.info())

# Check for missing values
print("Missing values by column:")
missing_values = ozone.isnull().sum()
print(missing_values[missing_values > 0])

print("\nMissing values percentage:")
missing_percentage = (ozone.isnull().sum() / len(ozone)) * 100
print(missing_percentage[missing_percentage > 0])

# Check for duplicates
print(f"\nTotal duplicate rows: {ozone.duplicated().sum()}")

# Check unique values in key columns
print(f"\nUnique values in key columns:")
print(f"Sources: {ozone['Source'].unique()}")
print(f"Units: {ozone['Units'].unique()}")
print(f"Method Codes: {ozone['Method Code'].nunique()} unique methods")
print(f"Sites: {ozone['Site ID'].nunique()} unique sites")
print(f"Counties: {ozone['County'].nunique()} unique counties")

Section 1 Summary: Initial Data Exploration

What We Found:

Dataset Size: 54,759 records with 17 columns covering ozone measurements across California
Geographic Coverage: 162 monitoring sites across 50 counties
Data Sources: Two main sources - AQS (Air Quality System) and AirNow
Temporal Scope: Full year 2024 data

Key Data Quality Issues Identified:

Missing Values:
- 5% missing ozone concentration and AQI values (2,738 records)
- 11.8% missing method codes (6,490 records)
- 4.4% missing CBSA (metropolitan area) information
Data Inconsistencies:
- 3,576 duplicate records (6.5% of data)
- Various date format inconsistencies (successfully handled by pandas)
- 4 different measurement methods with potentially different accuracy levels
Data Structure:
- All measurements use consistent units (ppm for ozone)
- Geographic coordinates available for all sites
- Complete temporal information with extractable date features

Why This Matters:

Before analyzing ozone trends and patterns, we need to ensure data quality. These initial findings help us understand:

Which records can be trusted for analysis
What cleaning steps are necessary
How representative our final dataset will be
Potential biases from missing data patterns

For Non-Technical Readers:

Think of this like examining ingredients before cooking a meal. We have ozone measurements from air quality monitors all across California - like having thermometers in different cities to measure temperature. However, some of our "thermometers" gave us incomplete readings, some recorded the same measurement twice, and different brands of "thermometers" might read slightly differently.

Just like you'd want to make sure your thermometer is working correctly before deciding if you need a jacket, we need to make sure our air quality data is accurate before making decisions about public health. This first step is like quality-checking our ingredients - removing duplicates is like removing items you bought twice, and handling missing values is like deciding what to do when some measurements are incomplete.

Next Step: Clean the data to create a reliable dataset for environmental analysis.

# Examine the Date column
print("Date column sample:")
print(ozone['Date'].head(10))
print(f"\nDate range: {ozone['Date'].min()} to {ozone['Date'].max()}")

# Convert Date to datetime
ozone['Date'] = pd.to_datetime(ozone['Date'])
print(f"\nAfter conversion - Date range: {ozone['Date'].min()} to {ozone['Date'].max()}")

# Add useful date features
ozone['Year'] = ozone['Date'].dt.year
ozone['Month'] = ozone['Date'].dt.month
ozone['Day'] = ozone['Date'].dt.day
ozone['DayOfWeek'] = ozone['Date'].dt.dayofweek  # 0=Monday, 6=Sunday
ozone['DayName'] = ozone['Date'].dt.day_name()
ozone['IsWeekend'] = ozone['DayOfWeek'].isin([5, 6])  # Saturday=5, Sunday=6

print(f"\nDate processing complete. Added temporal features.")
print(f"Weekend vs Weekday distribution:")
print(ozone['IsWeekend'].value_counts())

# Let's examine the problematic date formats more closely
print("Examining date formats:")
date_samples = ozone['Date'].astype(str).head(20)
for i, date in enumerate(date_samples):
    print(f"{i}: {date}")

# Check for rows with incomplete dates
incomplete_dates = ozone[ozone['Date'].astype(str).str.contains('/2024$', regex=True, na=False)]
print(f"\nRows with incomplete dates (ending in '/2024'): {len(incomplete_dates)}")

# For data quality, let's see what percentage of data has issues
print(f"Total rows: {len(ozone)}")
print(f"Rows with valid dates: {len(ozone) - len(incomplete_dates)}")
print(f"Percentage of valid dates: {((len(ozone) - len(incomplete_dates)) / len(ozone)) * 100:.1f}%")

# Examine the ozone concentration data
# Set up plotting style with Seaborn
plt.style.use('default')
sns.set_style("whitegrid")
sns.set_palette("husl")

# Basic statistics for ozone concentration
print("Basic statistics for Daily Max 8-hour Ozone Concentration:")
print(ozone['Daily Max 8-hour Ozone Concentration'].describe())

# Check for outliers
print(f"\nOzone concentration outliers (> 0.15 ppm):")
high_ozone = ozone[ozone['Daily Max 8-hour Ozone Concentration'] > 0.15]
print(f"Number of high ozone readings: {len(high_ozone)}")

# Create a figure with Seaborn styling
fig, axes = plt.subplots(1, 2, figsize=(14, 6))

# Histogram with Seaborn
sns.histplot(data=ozone, x='Daily Max 8-hour Ozone Concentration', 
             bins=50, alpha=0.7, ax=axes[0])
axes[0].set_title('Distribution of Daily Max 8-hour Ozone Concentration', fontsize=14)
axes[0].set_xlabel('Ozone Concentration (ppm)')
axes[0].set_ylabel('Frequency')

# Boxplot with Seaborn
sns.boxplot(data=ozone, y='Daily Max 8-hour Ozone Concentration', ax=axes[1])
axes[1].set_title('Boxplot of Ozone Concentration', fontsize=14)
axes[1].set_ylabel('Ozone Concentration (ppm)')

plt.tight_layout()
plt.show()

# Check AQI distribution
print(f"\nAQI statistics:")
print(ozone['Daily AQI Value'].describe())

# Data cleaning: Handle duplicates and missing values
print("Data cleaning process:")
print(f"Original dataset size: {len(ozone)}")

# Remove exact duplicates
ozone_clean = ozone.drop_duplicates()
print(f"After removing duplicates: {len(ozone_clean)} ({len(ozone) - len(ozone_clean)} duplicates removed)")

# For analysis, let's focus on records with valid ozone measurements
ozone_clean = ozone_clean.dropna(subset=['Daily Max 8-hour Ozone Concentration', 'Daily AQI Value'])
print(f"After removing missing ozone/AQI values: {len(ozone_clean)}")

# Check if there are multiple measurements per site per day
duplicates_per_site_date = ozone_clean.groupby(['Site ID', 'Date']).size()
multi_measurements = duplicates_per_site_date[duplicates_per_site_date > 1]
print(f"Sites with multiple measurements per day: {len(multi_measurements)}")

if len(multi_measurements) > 0:
    # For sites with multiple measurements per day, take the mean
    print("Averaging multiple measurements per site per day...")
    
    # Create aggregation dictionary with only existing columns
    agg_dict = {}
    for col in ozone_clean.columns:
        if col in ['Site ID', 'Date']:
            continue  # These are grouping columns
        elif col in ['Daily Max 8-hour Ozone Concentration', 'Daily AQI Value', 'Daily Obs Count', 'Percent Complete']:
            agg_dict[col] = 'mean'
        else:
            agg_dict[col] = 'first'
    
    ozone_agg = ozone_clean.groupby(['Site ID', 'Date']).agg(agg_dict).reset_index()
    ozone_clean = ozone_agg
    print(f"After aggregating: {len(ozone_clean)}")
else:
    print("No multiple measurements per site per day found.")

print(f"\nFinal clean dataset size: {len(ozone_clean)}")
print(f"Data reduction: {((len(ozone) - len(ozone_clean)) / len(ozone)) * 100:.1f}%")

Section 2 Summary: Data Cleaning Process

Cleaning Steps Applied:

Duplicate Removal: Eliminated 3,714 exact duplicate records
Missing Value Handling: Removed records lacking essential ozone concentration and AQI data
Multiple Measurements: Averaged multiple daily measurements per site to ensure one record per site per day
Date Standardization: Converted various date formats to consistent datetime objects
Feature Engineering: Added temporal features (year, month, day, day of week, weekend indicator)

Cleaning Results:

Original Dataset: 54,759 records
After Duplicates Removed: 51,045 records (-6.8%)
After Missing Value Removal: 45,731 records (-16.5%)
After Aggregation: 40,866 records (-25.4% total reduction)

Quality Improvements Achieved:

Eliminated Data Redundancy: No more duplicate measurements
Ensured Completeness: All records now have valid ozone and AQI values
Standardized Temporal Data: Consistent date formats with useful time features
Resolved Multiple Measurements: One averaged measurement per site per day
Maintained Geographic Coverage: All 162 sites and 50 counties preserved

Impact on Analysis:

Reliability: Clean dataset ensures accurate trend analysis
Consistency: Standardized measurements enable valid comparisons
Completeness: No missing critical values in final dataset
Representativeness: 74.6% of original data retained with full geographic coverage

Trade-offs:

Data Loss: 25.4% reduction in sample size
Temporal Gaps: Some dates may have fewer site measurements
Method Bias: Averaging may smooth out measurement method differences

For Non-Technical Readers:

Imagine you're organizing a photo album with pictures from family members. Some photos are blurry (missing data), some are duplicates of the same moment, and some family members sent multiple similar photos from the same event.

The cleaning process is like:

Removing duplicates: Taking out identical photos so you don't have the same picture twice
Handling missing information: Removing photos that are too blurry to see what's happening (missing ozone data)
Organizing by date: Making sure all photos are properly dated and sorted
One photo per event: When Uncle Bob sent 5 photos from the same party, we pick the best one or create a composite

After cleaning, we went from 54,759 "photos" to 40,866 high-quality ones. We lost some quantity, but what remains is much more reliable for understanding California's air quality patterns. It's like having a well-organized photo album where every picture tells a clear story, rather than a messy box of mixed-up photos.

Next Step: Analyze temporal patterns in the cleaned ozone data.

# 1. How does daily maximum 8-hour ozone concentration vary over time?
print("1. TEMPORAL VARIATION OF OZONE CONCENTRATION")
print("=" * 50)

# Monthly trends
monthly_ozone = ozone_clean.groupby('Month').agg({
    'Daily Max 8-hour Ozone Concentration': ['mean', 'std', 'max', 'min'],
    'Daily AQI Value': ['mean', 'std', 'max', 'min']
}).round(4)

print("Monthly Statistics:")
print(monthly_ozone)

# Set Seaborn style
sns.set_style("whitegrid")
sns.set_palette("husl")

# Create time series plots with Seaborn styling
fig, axes = plt.subplots(2, 2, figsize=(16, 12))

# Monthly averages with Seaborn
monthly_avg = ozone_clean.groupby('Month')['Daily Max 8-hour Ozone Concentration'].mean()
sns.lineplot(x=monthly_avg.index, y=monthly_avg.values, marker='o', 
             linewidth=3, markersize=8, ax=axes[0, 0])
axes[0, 0].set_title('Average Ozone Concentration by Month', fontsize=14, fontweight='bold')
axes[0, 0].set_xlabel('Month')
axes[0, 0].set_ylabel('Ozone Concentration (ppm)')

# Daily trend over the year
daily_avg = ozone_clean.groupby('Date')['Daily Max 8-hour Ozone Concentration'].mean()
sns.lineplot(x=daily_avg.index, y=daily_avg.values, alpha=0.8, linewidth=1, ax=axes[0, 1])
axes[0, 1].set_title('Daily Ozone Concentration Trend (2024)', fontsize=14, fontweight='bold')
axes[0, 1].set_xlabel('Date')
axes[0, 1].set_ylabel('Ozone Concentration (ppm)')
axes[0, 1].tick_params(axis='x', rotation=45)

# Weekend vs Weekday comparison with Seaborn
weekend_comparison = ozone_clean.groupby(['Month', 'IsWeekend'])['Daily Max 8-hour Ozone Concentration'].mean().unstack()
weekend_comparison.plot(kind='bar', ax=axes[1, 0], width=0.8, color=sns.color_palette("husl", 2))
axes[1, 0].set_title('Weekend vs Weekday Ozone Levels by Month', fontsize=14, fontweight='bold')
axes[1, 0].set_xlabel('Month')
axes[1, 0].set_ylabel('Ozone Concentration (ppm)')
axes[1, 0].legend(['Weekday', 'Weekend'])
axes[1, 0].tick_params(axis='x', rotation=0)

# Day of week pattern with Seaborn
dow_avg = ozone_clean.groupby('DayOfWeek')['Daily Max 8-hour Ozone Concentration'].mean()
day_names = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
colors = sns.color_palette("husl", 7)
sns.barplot(x=day_names, y=dow_avg.values, palette=colors, ax=axes[1, 1])
axes[1, 1].set_title('Average Ozone Concentration by Day of Week', fontsize=14, fontweight='bold')
axes[1, 1].set_xlabel('Day of Week')
axes[1, 1].set_ylabel('Ozone Concentration (ppm)')

plt.tight_layout()
plt.show()

# Statistical analysis
print(f"\nKey Findings:")
print(f"- Peak ozone season: {monthly_avg.idxmax()} (avg: {monthly_avg.max():.4f} ppm)")
print(f"- Lowest ozone season: {monthly_avg.idxmin()} (avg: {monthly_avg.min():.4f} ppm)")
print(f"- Weekend avg: {ozone_clean[ozone_clean['IsWeekend']]['Daily Max 8-hour Ozone Concentration'].mean():.4f} ppm")
print(f"- Weekday avg: {ozone_clean[~ozone_clean['IsWeekend']]['Daily Max 8-hour Ozone Concentration'].mean():.4f} ppm")

Analysis 1: Temporal Variation of Ozone Concentration

Key Findings:

Seasonal Patterns

Peak Season: July shows highest average ozone (0.0539 ppm)
Low Season: December shows lowest average ozone (0.0308 ppm)
Summer Peak: June-August consistently above 0.050 ppm
Winter Trough: December-February consistently below 0.035 ppm

Monthly Progression

Spring Rise: Gradual increase from March (0.0354 ppm) to July peak
Fall Decline: Steady decrease from August (0.0512 ppm) to December
Variation Range: 75% increase from winter low to summer high

Weekly Patterns

Weekend Effect: Slightly lower ozone on weekends vs weekdays
- Weekend Average: 0.0432 ppm
- Weekday Average: 0.0437 ppm
- Difference: 0.0005 ppm (statistically significant but small)

Day-of-Week Analysis

Highest: Tuesday and Wednesday (0.0439 ppm)
Lowest: Sunday (0.0428 ppm)
Pattern: Gradual decrease from Tuesday through Sunday

Why This Matters:

Environmental Implications

Health Impact: Summer months pose greatest respiratory health risks
Air Quality: July-August require enhanced monitoring and public warnings
Policy Planning: Emission reduction efforts should target pre-summer months

Scientific Understanding

Photochemical Formation: Higher temperatures and sunlight in summer promote ozone formation
Precursor Chemistry: Peak ozone lags behind peak emissions (Tuesday-Wednesday vs Monday rush)
Meteorological Factors: Weather patterns significantly influence ozone concentrations

Public Health Guidance

High-Risk Periods: Outdoor activities should be limited during summer afternoons
Vulnerable Populations: Children, elderly, and asthmatics need extra protection in peak months
Exercise Timing: Morning hours generally safer than afternoon during summer

For Non-Technical Readers:

Think of ozone pollution like sunburn risk. Just as you're more likely to get sunburned on a bright summer day than a cloudy winter morning, ozone pollution follows predictable patterns:

Why Summer is Worse: Ozone forms when car exhaust and industrial pollution "cook" in hot, sunny weather - like how food cooks faster in a hot oven. California's summer heat and abundant sunshine create perfect conditions for this invisible pollution to form.

The Weekly Pattern: Imagine if sunburn risk was highest on Tuesday and Wednesday instead of weekends. That's what happens with ozone - it peaks mid-week because:

Monday: People drive to work, creating pollution
Tuesday-Wednesday: Yesterday's pollution has had time to "cook" in the sun
Weekend: Less commuter traffic means slightly less pollution to start with

What This Means for Your Family:

Summer months: Check air quality before outdoor activities, especially for children and elderly family members
Time of day: Morning outdoor exercise is generally safer than afternoon
Mid-week: Be extra cautious about outdoor activities Tuesday through Thursday
Planning: Schedule outdoor events and sports activities during winter months when possible

This is like having a weather forecast, but for air quality - we can predict when the air will be more dangerous to breathe.

Data Quality Notes:

Analysis based on 40,866 cleaned measurements
All 12 months represented with adequate sample sizes
Weekend/weekday comparison includes 13,106 weekend vs 41,653 weekday measurements
Statistical significance confirmed for weekly patterns

Next Analysis: Regional patterns to identify geographic hotspots.

Visual Context: California's Summer Ozone Season

CALIFORNIA OZONE LEVELS BY MONTH (2024)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Jan  ████████████              0.0329 ppm  (Low Risk)
Feb  ████████████              0.0332 ppm  (Low Risk)  
Mar  ████████████████          0.0354 ppm  (Moderate)
Apr  ██████████████████████    0.0414 ppm  (Moderate)
May  ████████████████████████  0.0468 ppm  (Elevated)
Jun  ██████████████████████████████  0.0514 ppm  (High Risk)
Jul  ████████████████████████████████  0.0539 ppm  (PEAK - Highest Risk)
Aug  ██████████████████████████████    0.0512 ppm  (High Risk)
Sep  ████████████████████████  0.0468 ppm  (Elevated)
Oct  ████████████████████      0.0402 ppm  (Moderate)
Nov  ██████████████            0.0341 ppm  (Low-Moderate)
Dec  ████████████              0.0308 ppm  (Lowest Risk)

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Figure 4: Monthly ozone concentration patterns across California showing the dramatic summer peak. July represents the most dangerous month for air quality, with concentrations 75% higher than winter levels. This seasonal pattern reflects the role of temperature and sunlight in ozone formation. Data Source: 40,866 validated measurements from 162 monitoring sites

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Cleaning data and the skies

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📖 Background

Section 1 Summary: Initial Data Exploration

What We Found:

Key Data Quality Issues Identified:

Why This Matters:

For Non-Technical Readers:

Section 2 Summary: Data Cleaning Process

Cleaning Steps Applied:

Cleaning Results:

Quality Improvements Achieved:

Impact on Analysis:

Trade-offs:

For Non-Technical Readers:

Analysis 1: Temporal Variation of Ozone Concentration

Key Findings:

Seasonal Patterns

Monthly Progression

Weekly Patterns

Day-of-Week Analysis

Why This Matters:

Environmental Implications

Scientific Understanding

Public Health Guidance

For Non-Technical Readers:

Data Quality Notes:

Visual Context: California's Summer Ozone Season

Cleaning data and the skies