import pandas as pd import matplotlib.pyplot as plt import sys

2) Define the helper function at the top level

def find_col(df, keywords): candidates = [] for col in df.columns: low = str(col).lower() if any(k in low for k in keywords): candidates.append(col) return candidates[0] if candidates else None

def main(): # 1) Load the two CSVs ghana_path = "data_de_Ghana.csv" ivory_path = "data_de_Coast.csv"

# DataFrames are loaded inside main() as they are part of execution
ghana_df = pd.read_csv(ghana_path)
ivory_df = pd.read_csv(ivory_path)

# 2) Detect columns
year_col_ghana = find_col(ghana_df, ["year"])
area_col_ghana = find_col(ghana_df, ["area", "harvest", "harvested"])

year_col_ivory = find_col(ivory_df, ["year"])
area_col_ivory = find_col(ivory_df, ["area", "harvest", "harvested"])

# Error checking
if year_col_ghana is None or area_col_ghana is None:
    raise ValueError("Could not detect 'Year' or 'Area Harvested' columns in data_de_Ghana.csv.")
if year_col_ivory is None or area_col_ivory is None:
    raise ValueError("Could not detect 'Year' or 'Area Harvested' columns in data_de_Coast.csv.")

# 3) Clean and convert to numeric
ghana_df[year_col_ghana] = pd.to_numeric(ghana_df[year_col_ghana], errors="coerce")
ghana_df[area_col_ghana] = pd.to_numeric(ghana_df[area_col_ghana], errors="coerce")

ivory_df[year_col_ivory] = pd.to_numeric(ivory_df[year_col_ivory], errors="coerce")
ivory_df[area_col_ivory] = pd.to_numeric(ivory_df[area_col_ivory], errors="coerce")

# 4) Drop rows with missing essential values
ghana_clean = ghana_df.dropna(subset=[year_col_ghana, area_col_ghana])
ivory_clean = ivory_df.dropna(subset=[year_col_ivory, area_col_ivory])

# 5) Optional: ensure year as int for nicer plots (Using .loc for best practice)
ghana_clean.loc[:, year_col_ghana] = ghana_clean[year_col_ghana].astype(int)
ivory_clean.loc[:, year_col_ivory] = ivory_clean[year_col_ivory].astype(int)

# 6) Optional: align years to common set for meaningful comparison
ghana_years = set(ghana_clean[year_col_ghana].tolist())
ivory_years = set(ivory_clean[year_col_ivory].tolist())
common_years = sorted(list(ghana_years & ivory_years))

if not common_years:
    common_years = sorted(list(ghana_years | ivory_years))

ghana_plot = ghana_clean[ghana_clean[year_col_ghana].isin(common_years)]
ivory_plot = ivory_clean[ivory_clean[year_col_ivory].isin(common_years)]

# 7) Create the figure with 2x2 panels
plt.figure(figsize=(14, 10))
plt.suptitle("Ghana vs Ivory Coast Comparative Analysis of Data")

# Panel 1: Ghana — Yield by Year (scatter)
ax1 = plt.subplot(2, 2, 1)
ax1.scatter(ghana_plot[year_col_ghana], ghana_plot[area_col_ghana], color="green", alpha=0.9, s=40)
ax1.set_title("Ghana — Yield by Year")
ax1.set_xlabel("Year")
ax1.set_ylabel("Yield")
ax1.grid(True)

# Panel 2: Ivory Coast — Yield by Year (scatter)
ax2 = plt.subplot(2, 2, 2)
ax2.scatter(ivory_plot[year_col_ivory], ivory_plot[area_col_ivory], color="orange", alpha=0.9, s=40)
ax2.set_title("Ivory Coast — Yield by Year")
ax2.set_xlabel("Year")
ax2.set_ylabel("Yield")
ax2.grid(True)

# Panel 3: Ghana — Area Harvested by Year (bar)
ax3 = plt.subplot(2, 2, 3)
ax3.bar(ghana_plot[year_col_ghana], ghana_plot[area_col_ghana], color="green", edgecolor="black")
ax3.set_title("Ghana — Area Harvested by Year")
ax3.set_xlabel("Year")
ax3.set_ylabel("Area Harvested (units)")
ax3.grid(True, axis="y", linestyle="--", alpha=0.7)

# Panel 4: Ivory Coast — Area Harvested by Year (bar)
ax4 = plt.subplot(2, 2, 4)
ax4.bar(ivory_plot[year_col_ivory], ivory_plot[area_col_ivory], color="orange", edgecolor="black")
ax4.set_title("Ivory Coast — Area Harvested by Year")
ax4.set_xlabel("Year")
ax4.set_ylabel("Area Harvested (units)")
ax4.grid(True, axis="y", linestyle="--", alpha=0.7)

plt.tight_layout()
plt.show()

if name=="main": sys.exit(main())