Explore UNFCCC emissions data¶
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import os
from pathlib import Path
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
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# data directory
data_dir = Path(os.path.abspath("../data/processed/"))
data_file = data_dir / "unfccc-ghg-without-lulucf-1990-2021.csv"
# load data
df = pd.read_csv(data_file)
1. Who are the top 10 emitters for 2021¶
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df.loc[df['year'] == 2021].sort_values(by='emissions_gt',ascending=False)[0:10]
Out[3]:
| party | year | emissions_gt | |
|---|---|---|---|
| 1407 | United States of America | 2021 | 6.340228 |
| 383 | European Union (Convention) | 2021 | 3.468394 |
| 1119 | Russian Federation | 2021 | 2.156599 |
| 671 | Japan | 2021 | 1.168094 |
| 479 | Germany | 2021 | 0.760358 |
| 191 | Canada | 2021 | 0.670428 |
| 1311 | Türkiye | 2021 | 0.564390 |
| 1375 | United Kingdom of Great Britain and Northern I... | 2021 | 0.429489 |
| 447 | France | 2021 | 0.420061 |
| 639 | Italy | 2021 | 0.417591 |
In [4]:
top10 = list(df.loc[df['year'] == 2021].sort_values(by='emissions_gt',ascending=False)[0:10]['party'])
In [5]:
for party in top10:
data = df.loc[df['party'] == party]
plt.plot(data['year'], data['emissions_gt'], label=party)
plt.xlabel('Year')
plt.ylabel('Emissions (gigatonne)')
plt.legend(title='Country', bbox_to_anchor=(1.05, 1), loc='upper left')
Out[5]:
<matplotlib.legend.Legend at 0x137b26600>
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