EU and NATO countries imports of Russian energy (oil, gas, coal) 2021 to 2025

Model: DeepSeek V4.1 Flash Checked on:

I have gathered the data. Here is the summary of EU and NATO countries' imports of Russian energy (oil, gas, coal) from 2021 onwards.

Key findings

The data come from Eurostat's energy-imports statistics, which report volumes imported by country of origin = Russia for each EU member state (and a few non-EU European countries). Gas is measured in million m³, oil and coal in thousand tonnes. The annual series run through 2024; for 2025 I used the monthly datasets (annual 2025 is not yet published).

EU27 imports of Russian fossil fuels12345

Year Natural gas (million m³) Oil & petroleum products (kt) Coal / solid fossil fuels (kt)
2021 153,226 173,229 50,986
2022 83,824 132,022 28,091
2023 33,403 21,404 838
2024 37,843 15,308 241
2025* ~28,198 ~11,707 n/a

*2025 derived from monthly data (coal monthly by origin is not published). The collapse is dramatic: by 2024 Russian gas imports were –75% vs 2021, oil –91%, and coal –99.5%, with a further decline in 20256. The trend is illustrated in the charts generated in 6 (EU27 trend) and 7 (EU27 levels and top importers 2021 vs 2024).

Which countries still import Russian energy (2024)76

NATO vs EU — and the Türkiye exception

Because Eurostat covers EU27 plus only a handful of non‑EU European countries, the NATO aggregate below excludes the United States, United Kingdom and Canada (not reported by Eurostat). Among covered members, Türkiye stands out as the only large NATO importer that did not cut Russian purchases:

2021 2022 2023 2024 2025*
NATO (covered members) – gas 180,128 105,729 55,158 59,764 ~49,358
NATO – oil 184,155 152,220 47,834 49,169 ~43,759
NATO – coal 63,711 48,003 29,235 25,396 n/a
Türkiye – gas 26,343 21,575 21,340 21,572 ~21,160
Türkiye – oil 10,961 19,968 26,423 33,859 ~32,052
Türkiye – coal 14,083 20,734 28,630 25,221 n/a

While EU27 imports of all three fuels collapsed, Türkiye's imports of Russian oil roughly tripled (2021→2024) and its coal imports rose ~80%, keeping the NATO aggregate (excluding US/UK/Canada) well above the EU27 level6.

Caveats

  1. Eurostat dataset: Natural gas imports - annual data
  2. Eurostat dataset: Oil and petroleum products imports - annual data
  3. Eurostat dataset: Solid fossil fuels imports
  4. Eurostat dataset: Oil and petroleum products imports - monthly data
  5. Eurostat dataset: Natural gas imports - monthly data
  6. Dataset: nrg_ti_gasm Dataset: nrg_ti_gas

    Dataset filters for nrg_ti_gasm

    FilterValue(s)
    partnerRU
    geoDE, BE, CZ, EU27_2020
    unitMIO_M3
    siecG3000
    sinceTimePeriod2021-01

    Dataset filters for nrg_ti_gas

    FilterValue(s)
    partnerRU
    geoDE, BE, CZ, EU27_2020
    unitMIO_M3
    siecG3000
    sinceTimePeriod2021

    Program output

    MONTHLY dataset (17) freq values: ['M']
MONTHLY rows for DE: 66
    freq   siec partner    unit geo     time     value
191    M  G3000      RU  MIO_M3  DE  2021-01  5231.082
35     M  G3000      RU  MIO_M3  DE  2021-02  4632.046
18     M  G3000      RU  MIO_M3  DE  2021-03  5055.982
49     M  G3000      RU  MIO_M3  DE  2021-04  4885.920
50     M  G3000      RU  MIO_M3  DE  2021-05  5026.483
246    M  G3000      RU  MIO_M3  DE  2021-06  4763.817
10     M  G3000      RU  MIO_M3  DE  2021-07  3269.762
14     M  G3000      RU  MIO_M3  DE  2021-08  5005.853
219    M  G3000      RU  MIO_M3  DE  2021-09  4869.683
181    M  G3000      RU  MIO_M3  DE  2021-10  5012.221
224    M  G3000      RU  MIO_M3  DE  2021-11  5017.635
253    M  G3000      RU  MIO_M3  DE  2021-12  5192.534
243    M  G3000      RU  MIO_M3  DE  2022-01  4771.900
54     M  G3000      RU  MIO_M3  DE  2022-02  4488.998
136    M  G3000      RU  MIO_M3  DE  2022-03  4852.934
182    M  G3000      RU  MIO_M3  DE  2022-04  4819.831
227    M  G3000      RU  MIO_M3  DE  2022-05  5000.845
242    M  G3000      RU  MIO_M3  DE  2022-06  3048.891
101    M  G3000      RU  MIO_M3  DE  2022-07  1196.241
229    M  G3000      RU  MIO_M3  DE  2022-08   952.901
247    M  G3000      RU  MIO_M3  DE  2022-09     0.000
164    M  G3000      RU  MIO_M3  DE  2022-10     0.070
236    M  G3000      RU  MIO_M3  DE  2022-11     0.000
170    M  G3000      RU  MIO_M3  DE  2022-12     0.000
135    M  G3000      RU  MIO_M3  DE  2023-01     0.000
228    M  G3000      RU  MIO_M3  DE  2023-02     0.000
67     M  G3000      RU  MIO_M3  DE  2023-03     0.000
140    M  G3000      RU  MIO_M3  DE  2023-04     0.000
155    M  G3000      RU  MIO_M3  DE  2023-05     0.000
180    M  G3000      RU  MIO_M3  DE  2023-06     0.000

ANNUAL dataset (16) freq values: ['A']
   freq   siec partner    unit geo  time      value
4     A  G3000      RU  MIO_M3  DE  2022  25941.144
10    A  G3000      RU  MIO_M3  DE  2024      0.000
13    A  G3000      RU  MIO_M3  DE  2021  55443.282
15    A  G3000      RU  MIO_M3  DE  2023      0.000

ANNUAL BE/CZ:
   freq   siec partner    unit geo  time     value
0     A  G3000      RU  MIO_M3  CZ  2024  1791.029
1     A  G3000      RU  MIO_M3  CZ  2023   551.475
2     A  G3000      RU  MIO_M3  CZ  2022  6837.935
3     A  G3000      RU  MIO_M3  BE  2023  4018.800
5     A  G3000      RU  MIO_M3  BE  2021  2404.900
6     A  G3000      RU  MIO_M3  CZ  2021  8718.897
8     A  G3000      RU  MIO_M3  BE  2022  2820.800
14    A  G3000      RU  MIO_M3  BE  2024  3444.700

    Code

    
import pandas as pd
pd.set_option('display.width', 200)
mon = pd.read_csv(DATASET_FILES[0])
ann = pd.read_csv(DATASET_FILES[1])
print("MONTHLY dataset (17) freq values:", mon['freq'].unique().tolist())
print("MONTHLY rows for DE:", mon[mon.geo=='DE'].shape[0])
print(mon[(mon.geo=='DE')].sort_values('time').head(30).to_string())
print("\nANNUAL dataset (16) freq values:", ann['freq'].unique().tolist())
print(ann[ann.geo=='DE'].to_string())
print("\nANNUAL BE/CZ:")
print(ann[ann.geo.isin(['BE','CZ'])].to_string())
  7. Dataset: nrg_ti_gas Dataset: nrg_ti_sff Dataset: nrg_ti_oil

    Images

    eu27_ru_energy.svg
    top_importers.svg

    Dataset filters for nrg_ti_gas

    FilterValue(s)
    partnerRU
    geoAT, BE, BG, HR, CY, CZ, DK, EE, FI, FR, DE, EL, HU, IE, IT, LV, LT, LU, MT, NL, PL, PT, RO, SK, SI, ES, SE, AL, IS, ME, MK, NO, TR, EU27_2020
    unitMIO_M3, TJ_GCV
    siecG3000
    sinceTimePeriod2021

    Dataset filters for nrg_ti_sff

    FilterValue(s)
    partnerRU
    geoAT, BE, BG, HR, CY, CZ, DK, EE, FI, FR, DE, EL, HU, IE, IT, LV, LT, LU, MT, NL, PL, PT, RO, SK, SI, ES, SE, AL, IS, ME, MK, NO, TR, EU27_2020
    unitTHS_T
    siecC0000X0350-0370
    sinceTimePeriod2021

    Dataset filters for nrg_ti_oil

    FilterValue(s)
    partnerRU
    geoAT, BE, BG, HR, CY, CZ, DK, EE, FI, FR, DE, EL, HU, IE, IT, LV, LT, LU, MT, NL, PL, PT, RO, SK, SI, ES, SE, AL, IS, ME, MK, NO, TR, EU27_2020
    unitTHS_T
    siecO4000
    sinceTimePeriod2021

    Program output

    EU27 aggregate (as reported) Russian imports:
      Gas (MIO m3)  Gas (TJ GCV)  Oil (kt)  Coal (kt)
year                                                 
2021      153226.0     5907965.0  173229.0    50986.0
2022       83824.0     3262546.0  132022.0    28091.0
2023       33403.0     1325877.0   21404.0      838.0
2024       37843.0     1495248.0   15308.0      241.0

EU27 = sum of 27 member states? Gas check:
reported: [153226.0, 83824.0, 33403.0, 37843.0]  sum MS: [153226.0, 83824.0, 33403.0, 37843.0]

--- By country, GAS (MIO m3) ---
year     2021     2022     2023     2024
geo                                     
AT        0.0      0.0      0.0      0.0
BE     2405.0   2821.0   4019.0   3445.0
BG     2627.0   1205.0      0.0      0.0
HR        0.0      0.0      0.0      0.0
CY        0.0      0.0      0.0      0.0
CZ     8719.0   6838.0    551.0   1791.0
DK        0.0      0.0      0.0      0.0
EE       58.0      0.0      0.0      0.0
FI     1927.0    673.0    181.0    141.0
FR    10756.0   8149.0   6010.0   7406.0
DE    55443.0  25941.0      0.0      0.0
EL     2604.0   1001.0   2111.0   3340.0
HU     7105.0   7671.0   6413.0   4064.0
IE        0.0      0.0      0.0      0.0
IT    29171.0  13988.0   2931.0   5696.0
LV     1187.0    190.0      0.0      0.0
LT      887.0    279.0      0.0      0.0
LU      142.0      0.0      2.0      0.0
MT        0.0      0.0      0.0      0.0
NL     9177.0   3910.0   1358.0   2127.0
PL    10468.0   2986.0      0.0      0.0
PT      780.0    299.0    367.0    246.0
RO     2766.0    365.0      0.0      0.0
SK     3536.0   2388.0   2877.0   3089.0
SI      129.0     72.0      0.0      0.0
ES     3220.0   4983.0   6466.0   6436.0
SE      120.0     65.0    118.0     62.0
AL        0.0      0.0      0.0      0.0
IS        0.0      NaN      0.0      NaN
ME        0.0      0.0      0.0      0.0
MK      432.0    287.0    356.0    331.0
NO      128.0     44.0     58.0     19.0
TR    26343.0  21575.0  21340.0  21572.0

--- By country, OIL (thousand tonnes) ---
year     2021     2022     2023     2024
geo                                     
AT      642.0    123.0      0.0      0.0
BE    11771.0   9017.0    210.0      0.0
BG      662.0    805.0    584.0    233.0
HR      266.0    345.0    369.0    296.0
CY        5.0    190.0      0.0      0.0
CZ     3429.0   4169.0   4308.0   2715.0
DK     1696.0     88.0      0.0      0.0
EE      803.0    440.0     12.0     17.0
FI     8651.0   3306.0     21.0     12.0
FR    11510.0   8759.0     57.0      0.0
DE    34592.0  28304.0    893.0      0.0
EL     6647.0   5947.0    754.0      0.0
HU     4060.0   5564.0   4813.0   4988.0
IE      349.0    120.0      0.0      0.0
IT     8374.0  14175.0    260.0      0.0
LV      448.0    360.0    419.0    331.0
LT     7025.0   1687.0     83.0     37.0
LU        0.0      0.0      0.0      0.0
MT      111.0     37.0      0.0      0.0
NL    32197.0  20168.0   1804.0   1579.0
PL    20432.0  15109.0   1998.0   1083.0
PT      800.0    302.0      0.0      0.0
RO     3745.0   3780.0    195.0      0.0
SK     5477.0   5261.0   4561.0   4018.0
SI      110.0    450.0     63.0      0.0
ES     6764.0   2658.0      0.0      0.0
SE     2663.0    857.0      0.0      0.0
AL       74.0     14.0      5.0      NaN
IS        0.0      0.0      0.0      0.0
ME        4.0      1.0      0.0      0.0
MK        5.0     10.0      2.0      1.0
NO      988.0    674.0      0.0      0.0
TR    10961.0  19968.0  26423.0  33859.0

--- By country, COAL (thousand tonnes) ---
year     2021     2022     2023     2024
geo                                     
AT      369.0    337.0    234.0     67.0
BE     1261.0    782.0      0.0      4.0
BG      802.0    526.0      0.0     24.0
HR      527.0    342.0      0.0      0.0
CY       65.0     58.0      0.0      0.0
CZ      210.0     59.0      0.0      0.0
DK      693.0    262.0      0.0      0.0
EE        4.0      2.0      0.0      0.0
FI      970.0    612.0      0.0      0.0
FR     2806.0   1354.0     14.0      4.0
DE    20455.0  12983.0    576.0    138.0
EL      255.0     10.0      0.0      0.0
HU      213.0     56.0      0.0      0.0
IE     1261.0    528.0      0.0      0.0
IT     4992.0   3959.0      7.0      0.0
LV       15.0     18.0      0.0      0.0
LT      225.0    214.0      0.0      0.0
LU        4.0      3.0      0.0      5.0
MT        0.0      0.0      0.0      0.0
NL     3355.0    970.0      0.0      0.0
PL     8247.0   2515.0      0.0      0.0
PT        0.0      0.0      0.0      0.0
RO     1024.0    541.0      6.0      0.0
SK      881.0    265.0      0.0      0.0
SI        0.0      0.0      0.0      0.0
ES     1687.0   1600.0      0.0      0.0
SE      665.0     94.0      0.0      0.0
AL       70.0      2.0      1.0      0.0
IS        0.0      0.0      0.0      0.0
ME        0.0      0.0      0.0      0.0
MK       32.0      3.0      0.0      0.0
NO      234.0     97.0      0.0      0.0
TR    14083.0  20734.0  28630.0  25221.0

charts written

    Code

    
import pandas as pd, numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
pd.set_option('display.width', 220)

NAME = {'AL':'Albania','AT':'Austria','BE':'Belgium','BG':'Bulgaria','CY':'Cyprus','CZ':'Czechia',
'DE':'Germany','DK':'Denmark','EE':'Estonia','EL':'Greece','ES':'Spain','FI':'Finland','FR':'France',
'HR':'Croatia','HU':'Hungary','IE':'Ireland','IS':'Iceland','IT':'Italy','LT':'Lithuania','LU':'Luxembourg',
'LV':'Latvia','ME':'Montenegro','MK':'North Macedonia','MT':'Malta','NL':'Netherlands','NO':'Norway',
'PL':'Poland','PT':'Portugal','RO':'Romania','SE':'Sweden','SI':'Slovenia','SK':'Slovakia','TR':'Turkiye',
'EU27_2020':'EU27 (aggregate)'}
EU27 = ['AT','BE','BG','HR','CY','CZ','DK','EE','FI','FR','DE','EL','HU','IE','IT','LV','LT','LU','MT','NL','PL','PT','RO','SK','SI','ES','SE']
NATOnonEU = ['AL','IS','ME','MK','NO','TR']

def load(i, unit):
    df = pd.read_csv(DATASET_FILES[i]); df=df[df['unit']==unit].copy()
    df['year']=df['time'].astype(int); return df

gas_v=load(0,'MIO_M3'); gas_e=load(0,'TJ_GCV'); coal=load(1,'THS_T'); oil=load(2,'THS_T')
def pv(df): return df.pivot_table(index='geo',columns='year',values='value',aggfunc='sum')

gv,ge,co,oi = pv(gas_v),pv(gas_e),pv(coal),pv(oil)

print("EU27 aggregate (as reported) Russian imports:")
summ = pd.DataFrame({'Gas (MIO m3)':gv.loc['EU27_2020'],'Gas (TJ GCV)':ge.loc['EU27_2020'],
                     'Oil (kt)':oi.loc['EU27_2020'],'Coal (kt)':co.loc['EU27_2020']})
print(summ.round(0).to_string())

# cross-check: sum of member states
print("\nEU27 = sum of 27 member states? Gas check:")
print("reported:", gv.loc['EU27_2020'].round(0).tolist(), " sum MS:", gv.reindex(EU27).sum().round(0).tolist())

print("\n--- By country, GAS (MIO m3) ---")
print(gv.reindex(EU27+NATOnonEU).round(0).to_string())
print("\n--- By country, OIL (thousand tonnes) ---")
print(oi.reindex(EU27+NATOnonEU).round(0).to_string())
print("\n--- By country, COAL (thousand tonnes) ---")
print(co.reindex(EU27+NATOnonEU).round(0).to_string())

# save for reuse
gv.to_csv('gas_v.csv'); ge.to_csv('gas_e.csv'); co.to_csv('coal.csv'); oi.to_csv('oil.csv')

# ---------- Chart 1: EU27 totals by fuel ----------
years=[2021,2022,2023,2024]
fig,ax=plt.subplots(1,3,figsize=(15,4.2))
ax[0].bar(years,gv.loc['EU27_2020',years],color='#c0392b'); ax[0].set_title('Natural gas (million m³)')
ax[1].bar(years,oi.loc['EU27_2020',years],color='#2c3e50'); ax[1].set_title('Oil & petroleum products (kt)')
ax[2].bar(years,co.loc['EU27_2020',years],color='#7f8c8d'); ax[2].set_title('Solid fossil fuels / coal (kt)')
for a in ax:
    a.set_xlabel('year'); a.set_xticks(years)
    for x,y in zip(years,a.patches if hasattr(a,'patches') else []):
        a.annotate(f'{y.get_height():,.0f}',(y.get_x()+y.get_width()/2,y.get_height()),ha='center',va='bottom',fontsize=8)
fig.suptitle('EU27 imports of Russian energy, 2021-2024',fontsize=13)
fig.tight_layout(); fig.savefig('eu27_ru_energy.svg'); plt.close(fig)

# ---------- Chart 2: top countries 2021 vs 2024 per fuel ----------
fig,axs=plt.subplots(1,3,figsize=(16,4.6))
for ax,(df,title,col) in zip(axs,[(gv,'Natural gas (MIO m³)','#c0392b'),(oi,'Oil (kt)','#2c3e50'),(co,'Coal (kt)','#7f8c8d')]):
    d=df.reindex(EU27+NATOnonEU)
    top=d[2021].sort_values(ascending=False).head(10).index
    x=np.arange(len(top)); w=0.4
    ax.bar(x-w/2,d.loc[top,2021],w,label='2021',color=col)
    ax.bar(x+w/2,d.loc[top,2024],w,label='2024',color=col,alpha=0.45)
    ax.set_xticks(x); ax.set_xticklabels([NAME[g] for g in top],rotation=45,ha='right',fontsize=8)
    ax.set_title(title); ax.legend()
fig.suptitle('Top importers of Russian energy: 2021 vs 2024',fontsize=13)
fig.tight_layout(); fig.savefig('top_importers.svg'); plt.close(fig)
print("\ncharts written")
  8. Dataset: nrg_ti_gas Dataset: nrg_ti_sff Dataset: nrg_ti_oil Dataset: nrg_ti_oilm Dataset: nrg_ti_gasm

    Images

    eu27_trend_2025.svg
    eu27_vs_turkiye.svg

    Dataset filters for nrg_ti_gas

    FilterValue(s)
    partnerRU
    geoAT, BE, BG, HR, CY, CZ, DK, EE, FI, FR, DE, EL, HU, IE, IT, LV, LT, LU, MT, NL, PL, PT, RO, SK, SI, ES, SE, AL, IS, ME, MK, NO, TR, EU27_2020
    unitMIO_M3
    siecG3000
    sinceTimePeriod2021

    Dataset filters for nrg_ti_sff

    FilterValue(s)
    partnerRU
    geoAT, BE, BG, HR, CY, CZ, DK, EE, FI, FR, DE, EL, HU, IE, IT, LV, LT, LU, MT, NL, PL, PT, RO, SK, SI, ES, SE, AL, IS, ME, MK, NO, TR, EU27_2020
    unitTHS_T
    siecC0000X0350-0370
    sinceTimePeriod2021

    Dataset filters for nrg_ti_oil

    FilterValue(s)
    partnerRU
    geoAT, BE, BG, HR, CY, CZ, DK, EE, FI, FR, DE, EL, HU, IE, IT, LV, LT, LU, MT, NL, PL, PT, RO, SK, SI, ES, SE, AL, IS, ME, MK, NO, TR, EU27_2020
    unitTHS_T
    siecO4000
    sinceTimePeriod2021

    Dataset filters for nrg_ti_oilm

    FilterValue(s)
    partnerRU
    geoAT, BE, BG, HR, CY, CZ, DK, EE, FI, FR, DE, EL, HU, IE, IT, LV, LT, LU, MT, NL, PL, PT, RO, SK, SI, ES, SE, AL, IS, ME, MK, NO, TR, EU27_2020
    unitTHS_T
    siecO4000
    sinceTimePeriod2021-01

    Dataset filters for nrg_ti_gasm

    FilterValue(s)
    partnerRU
    geoAT, BE, BG, HR, CY, CZ, DK, EE, FI, FR, DE, EL, HU, IE, IT, LV, LT, LU, MT, NL, PL, PT, RO, SK, SI, ES, SE, AL, IS, ME, MK, NO, TR, EU27_2020
    unitMIO_M3
    siecG3000
    sinceTimePeriod2021-01

    Program output

    ### EU27 imports of Russian fossil fuels (annual 2021-2024; 2025 from monthly) ###
      Gas MIO m3    Oil kt  Coal kt
2021    153226.0  173229.0  50986.0
2022     83824.0  132022.0  28091.0
2023     33403.0   21404.0    838.0
2024     37843.0   15308.0    241.0
2025     28198.0   11707.0      NaN

### NATO (members present in Eurostat data; UK/US/CA not covered) ###
      Gas MIO m3    Oil kt  Coal kt
2021    180128.0  184155.0  63711.0
2022    105729.0  152220.0  48003.0
2023     55158.0   47834.0  29235.0
2024     59764.0   49169.0  25396.0
2025     49358.0   43759.0      NaN

### Turkiye (non-EU NATO) ###
      Gas MIO m3   Oil kt  Coal kt
2021     26343.0  10961.0  14083.0
2022     21575.0  19968.0  20734.0
2023     21340.0  26423.0  28630.0
2024     21572.0  33859.0  25221.0
2025     21160.0  32052.0      NaN

### 2025 (monthly-derived) by country, Oil kt ###
year          2025
geo               
TR         32052.0
EU27_2020  11707.0
SK          4923.0
HU          3762.0
NL          1716.0
CZ           527.0
HR           423.0
PL           295.0
FR            29.0
FI            15.0
DE             8.0
LT             7.0
LV             3.0
AL             0.0
DK             0.0
EE             0.0
AT             0.0
IE             0.0
EL             0.0
ES             0.0
BE             0.0
CY             0.0
BG             0.0
LU             0.0
IT             0.0
MT             0.0
MK             0.0
ME             0.0
NO             0.0
RO             0.0
PT             0.0
SI             0.0
SE             0.0

### 2025 (monthly-derived) by country, Gas MIO m3 ###
year          2025
geo               
EU27_2020  28198.0
TR         21160.0
BG         15467.0
BE          5936.0
ES          3792.0
NL          2503.0
PT           294.0
IT           207.0
AL             0.0
EE             0.0
DK             0.0
EL             0.0
FI             0.0
CZ             0.0
DE             0.0
AT             0.0
CY             0.0
IE             0.0
HU             0.0
HR             0.0
FR             0.0
LV             0.0
ME             0.0
LU             0.0
LT             0.0
MT             0.0
MK             0.0
PL             0.0
NO             0.0
RO             0.0
SE             0.0
SI             0.0
SK             0.0

charts written

    Code

    
import pandas as pd, numpy as np
import matplotlib; matplotlib.use('Agg'); import matplotlib.pyplot as plt
pd.set_option('display.width',220)
NAME={'AL':'Albania','AT':'Austria','BE':'Belgium','BG':'Bulgaria','CY':'Cyprus','CZ':'Czechia','DE':'Germany',
'DK':'Denmark','EE':'Estonia','EL':'Greece','ES':'Spain','FI':'Finland','FR':'France','HR':'Croatia','HU':'Hungary',
'IE':'Ireland','IS':'Iceland','IT':'Italy','LT':'Lithuania','LU':'Luxembourg','LV':'Latvia','ME':'Montenegro',
'MK':'N.Macedonia','MT':'Malta','NL':'Netherlands','NO':'Norway','PL':'Poland','PT':'Portugal','RO':'Romania',
'SE':'Sweden','SI':'Slovenia','SK':'Slovakia','TR':'Turkiye','EU27_2020':'EU27'}
EU27=['AT','BE','BG','HR','CY','CZ','DK','EE','FI','FR','DE','EL','HU','IE','IT','LV','LT','LU','MT','NL','PL','PT','RO','SK','SI','ES','SE']
NATO_avail=['BE','BG','HR','CZ','DK','EE','FI','FR','DE','EL','HU','IT','LV','LT','LU','NL','PL','PT','RO','SK','SI','ES','SE','AL','IS','ME','MK','NO','TR']

def loadann(i): 
    d=pd.read_csv(DATASET_FILES[i]); d['year']=d['time'].astype(int); return d
def pv(d): return d.pivot_table(index='geo',columns='year',values='value',aggfunc='sum')

gv=pv(loadann(0)); co=pv(loadann(1)); oi=pv(loadann(2))
oil_m=pd.read_csv(DATASET_FILES[3]); oil_m['year']=oil_m['time'].str[:4].astype(int)
gas_m=pd.read_csv(DATASET_FILES[4]); gas_m['year']=gas_m['time'].str[:4].astype(int)
oi25=oil_m.groupby(['geo','year'])['value'].sum().unstack('year')
gv25=gas_m.groupby(['geo','year'])['value'].sum().unstack('year')

print("### EU27 imports of Russian fossil fuels (annual 2021-2024; 2025 from monthly) ###")
tbl=pd.DataFrame({
 'Gas MIO m3':list(gv.loc['EU27_2020',[2021,2022,2023,2024]])+[gv25.loc['EU27_2020',2025]],
 'Oil kt':list(oi.loc['EU27_2020',[2021,2022,2023,2024]])+[oi25.loc['EU27_2020',2025]],
 'Coal kt':list(co.loc['EU27_2020',[2021,2022,2023,2024]])+[np.nan]},
 index=[2021,2022,2023,2024,2025])
print(tbl.round(0).to_string())

print("\n### NATO (members present in Eurostat data; UK/US/CA not covered) ###")
nat=pd.DataFrame({
 'Gas MIO m3':list(gv.reindex(NATO_avail).sum()[ [2021,2022,2023,2024] ])+[gv25.reindex(NATO_avail).sum()[2025]],
 'Oil kt':list(oi.reindex(NATO_avail).sum()[[2021,2022,2023,2024]])+[oi25.reindex(NATO_avail).sum()[2025]],
 'Coal kt':list(co.reindex(NATO_avail).sum()[[2021,2022,2023,2024]])+[np.nan]},
 index=[2021,2022,2023,2024,2025])
print(nat.round(0).to_string())

print("\n### Turkiye (non-EU NATO) ###")
tr=pd.DataFrame({'Gas MIO m3':list(gv.loc['TR',[2021,2022,2023,2024]])+[gv25.loc['TR',2025]],
 'Oil kt':list(oi.loc['TR',[2021,2022,2023,2024]])+[oi25.loc['TR',2025]],
 'Coal kt':list(co.loc['TR',[2021,2022,2023,2024]])+[np.nan]}, index=[2021,2022,2023,2024,2025])
print(tr.round(0).to_string())

print("\n### 2025 (monthly-derived) by country, Oil kt ###")
print(oi25[[2025]].dropna().round(0).sort_values(2025,ascending=False).to_string())
print("\n### 2025 (monthly-derived) by country, Gas MIO m3 ###")
print(gv25[[2025]].dropna().round(0).sort_values(2025,ascending=False).to_string())

# ---- Chart: EU27 trend 2021-2025 ----
years=[2021,2022,2023,2024,2025]
fig,ax=plt.subplots(1,3,figsize=(15,4.3))
ax[0].plot(years,tbl['Gas MIO m3'],'o-',color='#c0392b'); ax[0].set_title('Natural gas (million m³)')
ax[1].plot(years,tbl['Oil kt'],'o-',color='#2c3e50'); ax[1].set_title('Oil & petroleum products (kt)')
ax[2].plot([2021,2022,2023,2024],tbl['Coal kt'].iloc[:4],'o-',color='#7f8c8d'); ax[2].set_title('Coal (kt)')
for a in ax:
    a.set_xticks(years); a.set_xlabel('year'); a.grid(alpha=.3)
    a.axvspan(2024.5,2025.5,color='orange',alpha=.12)
    for x,y in zip(years,a.get_lines()[0].get_xdata() if False else []): pass
fig.suptitle('EU27 imports of Russian energy, 2021–2025 (2025 = monthly-based; coal 2025 n/a)',fontsize=12)
fig.tight_layout(); fig.savefig('eu27_trend_2025.svg'); plt.close(fig)

# ---- Chart: Turkiye vs EU27, oil & gas 2021-2025 ----
fig,ax=plt.subplots(1,2,figsize=(12,4.3))
ax[0].plot(years,tr['Gas MIO m3'],'o-',label='Turkiye',color='#e67e22')
ax[0].plot(years,tbl['Gas MIO m3'],'o-',label='EU27',color='#c0392b'); ax[0].set_title('Natural gas (million m³)'); ax[0].legend()
ax[1].plot(years,tr['Oil kt'],'o-',label='Turkiye',color='#e67e22')
ax[1].plot(years,tbl['Oil kt'],'o-',label='EU27',color='#2c3e50'); ax[1].set_title('Oil (kt)'); ax[1].legend()
for a in ax: a.set_xticks(years); a.set_xlabel('year'); a.grid(alpha=.3)
fig.suptitle('Russian energy imports: EU27 vs Turkiye')
fig.tight_layout(); fig.savefig('eu27_vs_turkiye.svg'); plt.close(fig)
print("\ncharts written")