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import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.dates as mdates
# Load the data from the CSV file
file_path = '/home/daniel/Git/Emissions-Monetisation-Calculator/proposals/versions/latest/scc-proposals.csv'
df = pd.read_csv(file_path)
# Convert 'date' to datetime objects
df['date'] = pd.to_datetime(df['date'], errors='coerce')
# Convert 'usd_proposed_value' to numeric, handling errors
df['usd_proposed_value'] = pd.to_numeric(df['usd_proposed_value'], errors='coerce')
# Filter out invalid rows.
df_filtered = df.dropna(subset=['date', 'usd_proposed_value']).copy()
# Sort by date
df_filtered = df_filtered.sort_values('date')
# Create a dictionary for shortened organization names
org_name_map = {
"International Foundation for Valuing Impacts": "IFVI",
"Environmental Protection Agency": "EPA",
"University of California, Davis": "UC Davis",
"International Monetary Fund": "IMF",
"New York State Agencies": "NY State",
"Biden Administration Interagency Working Group": "Biden Admin",
"Trump Administration": "Trump Admin",
"Climate Leadership Council": "CLC",
"Obama Administration Interagency Working Group": "Obama Admin",
"UK Government Economic Service": "UK Gov",
"Stern Review": "Stern",
"Government of Canada": "Canada Gov",
"Yale University": "Yale",
"Resources for the Future": "RFF",
"University College London": "UCL",
"Cambridge University": "Cambridge"
}
# Apply shortened names to the DataFrame
df_filtered['short_org'] = df_filtered['organization_name'].map(org_name_map)
# Create the line plot
plt.figure(figsize=(14, 8))
# Plot with the short names.
sns.lineplot(x='date',
y='usd_proposed_value',
hue='short_org',
data=df_filtered,
marker="o", # Add markers
markersize=8, # Enlarge markers
linewidth=1
)
plt.xlabel('Date')
plt.ylabel('Proposed Social Cost of Carbon (USD/ton)')
plt.title('Social Cost of Carbon Proposals Over Time by Organization')
plt.legend(title='Organization', loc='upper left', bbox_to_anchor=(1, 1))
# Format x-axis date display
date_fmt = mdates.DateFormatter('%Y-%b')
plt.gca().xaxis.set_major_formatter(date_fmt)
plt.gca().xaxis.set_major_locator(mdates.YearLocator())
plt.xticks(rotation=45, ha='right')
plt.tight_layout(rect=[0, 0, .9, 1])
# Annotate only the latest point for each organization with larger text.
for org in df_filtered['organization_name'].unique():
org_df = df_filtered[df_filtered['organization_name'] == org]
short_org = df_filtered['short_org'].loc[df_filtered['organization_name'] == org].iloc[0]
# Get the latest point
latest_point = org_df.iloc[-1]
# Annotate the latest point
plt.annotate(f"{short_org}",
xy=(latest_point['date'], latest_point['usd_proposed_value']),
xytext=(10, 0), # Adjust text position as needed
textcoords='offset points',
arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0.2'),
fontsize=10 # Increase the fontsize of the label
)
plt.grid(True, axis='y', linestyle='--', alpha=0.7)
plt.show() |