updating the conda env usage

Author: connorjmack

demo/keeling-curve/.gitignore

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+*.csv
+*.png
+__pycache__/

demo/keeling-curve/analyze.py

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+"""
+analyze.py — Compute summary statistics and trends from the Keeling Curve data.
+"""
+
+import pandas as pd
+
+INPUT_FILE = "co2_clean.csv"
+
+
+def analyze(input_path: str) -> None:
+    """Print key insights from the Keeling Curve data."""
+    df = pd.read_csv(input_path, parse_dates=["date"])
+
+    # --- Annual averages ---
+    annual = df.groupby("year")["co2"].mean()
+
+    print("=" * 50)
+    print("KEELING CURVE ANALYSIS")
+    print("=" * 50)
+
+    # Overall statistics
+    print(f"\nDate range: {df['date'].min().year} – {df['date'].max().year}")
+    print(f"Total measurements: {len(df)}")
+    print(f"CO₂ start: {annual.iloc[0]:.2f} ppm ({int(annual.index[0])})")
+    print(f"CO₂ latest: {annual.iloc[-1]:.2f} ppm ({int(annual.index[-1])})")
+    print(f"Total increase: {annual.iloc[-1] - annual.iloc[0]:.2f} ppm")
+
+    # --- Growth rate by decade ---
+    print("\n--- Average Annual Growth Rate by Decade ---")
+    df_annual = annual.reset_index()
+    df_annual.columns = ["year", "co2"]
+    df_annual["decade"] = (df_annual["year"] // 10) * 10
+    df_annual["co2_diff"] = df_annual["co2"].diff()
+
+    decade_growth = df_annual.groupby("decade")["co2_diff"].mean()
+    for decade, rate in decade_growth.items():
+        if pd.notna(rate):
+            print(f"  {int(decade)}s: +{rate:.2f} ppm/year")
+
+    # --- Seasonal amplitude ---
+    print("\n--- Seasonal Cycle ---")
+    monthly_avg = df.groupby("month")["co2"].mean()
+    amplitude = monthly_avg.max() - monthly_avg.min()
+    peak_month = monthly_avg.idxmax()
+    trough_month = monthly_avg.idxmin()
+
+    month_names = [
+        "", "Jan", "Feb", "Mar", "Apr", "May", "Jun",
+        "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"
+    ]
+
+    print(f"  Seasonal amplitude: {amplitude:.2f} ppm")
+    print(f"  Peak month: {month_names[int(peak_month)]} ({monthly_avg.max():.2f} ppm)")
+    print(f"  Trough month: {month_names[int(trough_month)]} ({monthly_avg.min():.2f} ppm)")
+
+
+if __name__ == "__main__":
+    analyze(INPUT_FILE)

demo/keeling-curve/clean_data.py

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+"""
+clean_data.py — Load, clean, and prepare the Keeling Curve data.
+"""
+
+import pandas as pd
+
+INPUT_FILE = "co2_monthly.csv"
+OUTPUT_FILE = "co2_clean.csv"
+
+
+def load_and_clean(input_path: str) -> pd.DataFrame:
+    """
+    Load the raw Scripps CSV and return a clean DataFrame.
+
+    The raw file has:
+    - Comment lines starting with "
+    - Missing values coded as -99.99
+    - Columns: Yr, Mn, Date_Excel, Date, CO2, seasonally_adjusted, fit, ...
+    """
+    # Read the CSV, skipping comment lines
+    df = pd.read_csv(
+        input_path,
+        comment='"',
+        header=0,
+        skipinitialspace=True,
+        na_values=["-99.99", -99.99],
+    )
+
+    # Strip whitespace from column names
+    df.columns = df.columns.str.strip()
+
+    # Keep only the columns we need
+    # The exact column names may vary — let's inspect and adapt
+    print(f"Raw columns: {list(df.columns)}")
+    print(f"Raw shape: {df.shape}")
+
+    # Rename columns to something consistent
+    # Typical columns: Yr, Mn, Date Excel, Date, CO2, seasonally adjusted, fit, ...
+    col_map = {}
+    for col in df.columns:
+        col_lower = col.lower().strip()
+        if col_lower in ("yr", "year"):
+            col_map[col] = "year"
+        elif col_lower in ("mn", "month"):
+            col_map[col] = "month"
+        elif col_lower == "co2":
+            col_map[col] = "co2"
+        elif "seasonally" in col_lower:
+            col_map[col] = "co2_adjusted"
+        elif col_lower == "fit":
+            col_map[col] = "co2_fit"
+
+    df = df.rename(columns=col_map)
+
+    # Keep only the columns we mapped
+    keep_cols = [c for c in ["year", "month", "co2", "co2_adjusted", "co2_fit"] if c in df.columns]
+    df = df[keep_cols].copy()
+
+    # Convert types
+    df["year"] = pd.to_numeric(df["year"], errors="coerce")
+    df["month"] = pd.to_numeric(df["month"], errors="coerce")
+    df["co2"] = pd.to_numeric(df["co2"], errors="coerce")
+
+    # Drop rows where year or co2 is missing
+    df = df.dropna(subset=["year", "co2"])
+
+    # Create a proper date column
+    df["date"] = pd.to_datetime(
+        df["year"].astype(int).astype(str) + "-" + df["month"].astype(int).astype(str) + "-01"
+    )
+
+    # Sort by date
+    df = df.sort_values("date").reset_index(drop=True)
+
+    print(f"Clean shape: {df.shape}")
+    print(f"Date range: {df['date'].min()} to {df['date'].max()}")
+    print(f"CO₂ range: {df['co2'].min():.2f} to {df['co2'].max():.2f} ppm")
+
+    return df
+
+
+if __name__ == "__main__":
+    df = load_and_clean(INPUT_FILE)
+    df.to_csv(OUTPUT_FILE, index=False)
+    print(f"\nSaved clean data to {OUTPUT_FILE}")
+    print(df.head(10))

demo/keeling-curve/fetch_data.py

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+"""
+fetch_data.py — Download the Keeling Curve dataset from Scripps CO2 Program.
+"""
+
+import requests
+import os
+
+DATA_URL = "https://scrippsco2.ucsd.edu/assets/data/atmospheric/stations/in_situ_co2/monthly/monthly_in_situ_co2_mlo.csv"
+OUTPUT_FILE = "co2_monthly.csv"
+
+
+def download_data(url: str, output_path: str) -> None:
+    """Download the raw CSV data from Scripps."""
+    print(f"Downloading data from {url}...")
+    response = requests.get(url, timeout=30)
+    response.raise_for_status()
+
+    with open(output_path, "w") as f:
+        f.write(response.text)
+
+    print(f"Saved to {output_path} ({len(response.text):,} bytes)")
+
+
+if __name__ == "__main__":
+    download_data(DATA_URL, OUTPUT_FILE)

demo/keeling-curve/requirements.txt

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+pandas
+matplotlib
+requests

demo/keeling-curve/visualize.py

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+"""
+visualize.py — Create plots of the Keeling Curve data.
+"""
+
+import pandas as pd
+import matplotlib.pyplot as plt
+import matplotlib.dates as mdates
+
+
+INPUT_FILE = "co2_clean.csv"
+
+
+def plot_full_curve(df: pd.DataFrame) -> None:
+    """Plot the complete Keeling Curve."""
+    fig, ax = plt.subplots(figsize=(12, 6))
+
+    ax.plot(df["date"], df["co2"], color="#2196F3", linewidth=0.8, alpha=0.8, label="Monthly average")
+
+    # Add a trend line using the adjusted/fit values if available
+    if "co2_fit" in df.columns:
+        ax.plot(df["date"], df["co2_fit"], color="#F44336", linewidth=1.5, label="Trend (fit)")
+
+    ax.set_xlabel("Year", fontsize=12)
+    ax.set_ylabel("CO₂ Concentration (ppm)", fontsize=12)
+    ax.set_title("The Keeling Curve — Atmospheric CO₂ at Mauna Loa", fontsize=14, fontweight="bold")
+    ax.legend(loc="upper left")
+    ax.grid(True, alpha=0.3)
+
+    # Format x-axis
+    ax.xaxis.set_major_locator(mdates.YearLocator(10))
+    ax.xaxis.set_major_formatter(mdates.DateFormatter("%Y"))
+
+    plt.tight_layout()
+    plt.savefig("keeling_curve_full.png", dpi=150)
+    print("Saved: keeling_curve_full.png")
+    plt.close()
+
+
+def plot_seasonal_cycle(df: pd.DataFrame) -> None:
+    """Plot the average seasonal CO₂ cycle."""
+    monthly_avg = df.groupby("month")["co2"].mean()
+
+    fig, ax = plt.subplots(figsize=(8, 5))
+    months = range(1, 13)
+    month_labels = ["Jan", "Feb", "Mar", "Apr", "May", "Jun",
+                    "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
+
+    ax.bar(months, monthly_avg.values, color="#4CAF50", alpha=0.7, edgecolor="white")
+    ax.set_xticks(months)
+    ax.set_xticklabels(month_labels)
+    ax.set_xlabel("Month", fontsize=12)
+    ax.set_ylabel("Average CO₂ (ppm)", fontsize=12)
+    ax.set_title("Average Seasonal CO₂ Cycle", fontsize=14, fontweight="bold")
+    ax.grid(True, axis="y", alpha=0.3)
+
+    plt.tight_layout()
+    plt.savefig("keeling_curve_seasonal.png", dpi=150)
+    print("Saved: keeling_curve_seasonal.png")
+    plt.close()
+
+
+def plot_decade_comparison(df: pd.DataFrame) -> None:
+    """Plot CO₂ trend for each decade."""
+    fig, ax = plt.subplots(figsize=(12, 6))
+
+    colors = plt.cm.viridis_r  # Color map: darker = more recent
+    decades = sorted(df["year"].apply(lambda y: int(y) // 10 * 10).unique())
+
+    for i, decade in enumerate(decades):
+        mask = (df["year"] >= decade) & (df["year"] < decade + 10)
+        subset = df[mask]
+        if len(subset) > 0:
+            color = colors(i / len(decades))
+            ax.plot(subset["month"], subset["co2"], alpha=0.3, color=color)
+
+    # Plot the mean for first and last decades
+    for decade, style, label in [(decades[0], "--", f"{decades[0]}s avg"),
+                                  (decades[-1], "-", f"{decades[-1]}s avg")]:
+        mask = (df["year"] >= decade) & (df["year"] < decade + 10)
+        subset = df[mask]
+        if len(subset) > 0:
+            monthly = subset.groupby("month")["co2"].mean()
+            ax.plot(monthly.index, monthly.values, style, linewidth=2.5, label=label)
+
+    ax.set_xticks(range(1, 13))
+    ax.set_xticklabels(["Jan", "Feb", "Mar", "Apr", "May", "Jun",
+                         "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"])
+    ax.set_xlabel("Month", fontsize=12)
+    ax.set_ylabel("CO₂ (ppm)", fontsize=12)
+    ax.set_title("CO₂ Seasonal Cycle by Decade", fontsize=14, fontweight="bold")
+    ax.legend()
+    ax.grid(True, alpha=0.3)
+
+    plt.tight_layout()
+    plt.savefig("keeling_curve_decades.png", dpi=150)
+    print("Saved: keeling_curve_decades.png")
+    plt.close()
+
+
+if __name__ == "__main__":
+    df = pd.read_csv(INPUT_FILE, parse_dates=["date"])
+    plot_full_curve(df)
+    plot_seasonal_cycle(df)
+    plot_decade_comparison(df)
+    print("\nAll plots saved!")

docs/index.md

@@ -1,6 +1,6 @@
 # Data Engineering Workshop
 
-**A hands-on workshop for master's students — from zero to data pipeline with AI-assisted coding.**
+**CSP data workshop for capstone development | Spring 2026**
 
 ---
 

mkdocs.yml

@@ -1,5 +1,5 @@
 site_name: Data Engineering Workshop
-site_description: A hands-on workshop for master's students — from zero to data pipeline with AI-assisted coding.
+site_description: CSP data workshop for capstone development | Spring 2026
 site_url: https://connorjmack.github.io/csp-data-workshop/
 repo_url: https://github.com/connorjmack/csp-data-workshop
 repo_name: connorjmack/csp-data-workshop