Merge pull request #3 from ONEcampaign/analysis

Analysis
ONEcampaign · Oct 25, 2024 · 44b25ae · 44b25ae
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diff --git a/output/README.md b/output/README.md
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+# Data for the analysis
+
+- `climate_finance_commitments.csv` contains the climate finance commitments data for all climate finance providers between 2010 and 2022.
+- `fossil_fuel_subsidies.csv` contains the fossil fuel subsidies data for all countries between 2010 and 2022.
+- `chart_1_data.csv` contains the data for the main chart in the page which includes climate finance commitments and fossil fuel subsidies aggregated for all high income countries between 2010 and 2022.
+- `chart_2_data.csv` contains the data for the second chart in the page which includes climate finance commitments and fossil fuel subsidies for specified countries between 2010 and 2022.
+
+
+
diff --git a/output/chart_1_data.csv b/output/chart_1_data.csv
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+year,climate_finance_commitments,fossil_fuel_subsidies,currency
+2010,21893467055.0,186469445295.0,US$ current
+2011,17292535537.0,208527172264.0,US$ current
+2012,23028812044.515625,222499879183.0,US$ current
+2013,26145721949.65625,236866523516.0,US$ current
+2014,29770939758.585938,223349743589.0,US$ current
+2015,31128861542.765625,170384642882.0,US$ current
+2016,31782042354.366943,141416390209.0,US$ current
+2017,31966082393.685547,152602715341.0,US$ current
+2018,34202588128.68457,161218056759.0,US$ current
+2019,36693368107.251465,160628302990.0,US$ current
+2020,48760260386.07495,140457603910.0,US$ current
+2021,45661184763.576675,214559259725.0,US$ current
+2022,59094904942.00781,489322710016.0,US$ current
diff --git a/output/chart_2_data.csv b/output/chart_2_data.csv
@@ -0,0 +1,92 @@
+year,iso3_code,climate_finance_commitments,fossil_fuel_subsidies,currency,country_name
+2010,BEL,191748248.0,2832889000.0,US$ current,Belgium
+2011,BEL,245891915.0,2862083000.0,US$ current,Belgium
+2012,BEL,102789892.0,2686533000.0,US$ current,Belgium
+2013,BEL,356799880.0,3018475000.0,US$ current,Belgium
+2014,BEL,495135712.0,2561219000.0,US$ current,Belgium
+2015,BEL,365394632.0,2455802000.0,US$ current,Belgium
+2016,BEL,302240624.0,2583381000.0,US$ current,Belgium
+2017,BEL,396248620.0,2961450000.0,US$ current,Belgium
+2018,BEL,360204336.0,3351782000.0,US$ current,Belgium
+2019,BEL,464960384.0,3548509000.0,US$ current,Belgium
+2020,BEL,459253120.0,3921943000.0,US$ current,Belgium
+2021,BEL,445219520.0,3893607000.0,US$ current,Belgium
+2022,BEL,422986816.0,7746767000.0,US$ current,Belgium
+2010,CAN,95947242.0,4032754000.0,US$ current,Canada
+2011,CAN,549096155.0,2684936000.0,US$ current,Canada
+2012,CAN,784711872.0,2889677000.0,US$ current,Canada
+2013,CAN,478913328.0,3032590000.0,US$ current,Canada
+2014,CAN,284722656.0,3125070000.0,US$ current,Canada
+2015,CAN,483636592.0,1980783000.0,US$ current,Canada
+2016,CAN,894131312.0,2224104000.0,US$ current,Canada
+2017,CAN,555750832.0,2462430000.0,US$ current,Canada
+2018,CAN,837664896.0,2225794000.0,US$ current,Canada
+2019,CAN,883300448.0,1931571000.0,US$ current,Canada
+2020,CAN,748377660.0,3569831000.0,US$ current,Canada
+2021,CAN,904346928.0,3232945000.0,US$ current,Canada
+2022,CAN,1947453728.0,4428242000.0,US$ current,Canada
+2010,DEU,3476314014.0,15752273000.0,US$ current,Germany
+2011,DEU,4599120782.0,15032699000.0,US$ current,Germany
+2012,DEU,3673832720.0,13731155000.0,US$ current,Germany
+2013,DEU,4460819488.0,13264537000.0,US$ current,Germany
+2014,DEU,7796556000.0,13723932000.0,US$ current,Germany
+2015,DEU,5629361952.0,9808038000.0,US$ current,Germany
+2016,DEU,7378621056.0,10200632000.0,US$ current,Germany
+2017,DEU,7343885120.0,9574237000.0,US$ current,Germany
+2018,DEU,8840272384.0,8891961000.0,US$ current,Germany
+2019,DEU,8692651136.0,8755082000.0,US$ current,Germany
+2020,DEU,8971566592.0,9475677000.0,US$ current,Germany
+2021,DEU,10556995968.0,7889734000.0,US$ current,Germany
+2022,DEU,13488980864.0,21401121000.0,US$ current,Germany
+2010,FRA,3196592520.0,3605735000.0,US$ current,France
+2011,FRA,1972004463.0,5918445000.0,US$ current,France
+2012,FRA,4751460233.0,6048392000.0,US$ current,France
+2013,FRA,2765924512.0,6129320000.0,US$ current,France
+2014,FRA,3339349984.0,5914809000.0,US$ current,France
+2015,FRA,4020407616.0,5590883000.0,US$ current,France
+2016,FRA,3384218624.0,6227395000.0,US$ current,France
+2017,FRA,5211286592.0,7346174000.0,US$ current,France
+2018,FRA,2763740224.0,10303833000.0,US$ current,France
+2019,FRA,6060038784.0,10000982000.0,US$ current,France
+2020,FRA,7904167648.0,7603994000.0,US$ current,France
+2021,FRA,8503958344.0,10515700000.0,US$ current,France
+2022,FRA,9574580480.0,29207327804.0,US$ current,France
+2010,GBR,1095766650.0,10092086000.0,US$ current,United Kingdom
+2011,GBR,390532198.0,8489527000.0,US$ current,United Kingdom
+2012,GBR,779734688.0,10408687000.0,US$ current,United Kingdom
+2013,GBR,1671452000.0,27156066000.0,US$ current,United Kingdom
+2014,GBR,2114828768.0,25158376000.0,US$ current,United Kingdom
+2015,GBR,3417216128.0,20759857000.0,US$ current,United Kingdom
+2016,GBR,2188959872.0,15147421000.0,US$ current,United Kingdom
+2017,GBR,2229768384.0,15090442000.0,US$ current,United Kingdom
+2018,GBR,2114432032.0,15693130000.0,US$ current,United Kingdom
+2019,GBR,2621597248.0,15326139000.0,US$ current,United Kingdom
+2020,GBR,3203645232.0,13698526000.0,US$ current,United Kingdom
+2021,GBR,2261871744.0,18920555000.0,US$ current,United Kingdom
+2022,GBR,2602125696.0,50207640853.0,US$ current,United Kingdom
+2010,ITA,39707138.0,11081615000.0,US$ current,Italy
+2011,ITA,96114132.0,11340242000.0,US$ current,Italy
+2012,ITA,90192558.5,12670792000.0,US$ current,Italy
+2013,ITA,230325168.0,13570932000.0,US$ current,Italy
+2014,ITA,254199420.0,13026661000.0,US$ current,Italy
+2015,ITA,404235392.0,10006324000.0,US$ current,Italy
+2016,ITA,222025076.0,9745937000.0,US$ current,Italy
+2017,ITA,620194784.0,10018464000.0,US$ current,Italy
+2018,ITA,438735928.0,11144166000.0,US$ current,Italy
+2019,ITA,503053184.0,11277775000.0,US$ current,Italy
+2020,ITA,894737984.0,10791698000.0,US$ current,Italy
+2021,ITA,900554816.0,16136249000.0,US$ current,Italy
+2022,ITA,583962288.0,50112338000.0,US$ current,Italy
+2010,USA,827844730.0,15053324000.0,US$ current,United States
+2011,USA,1121416380.0,11614896000.0,US$ current,United States
+2012,USA,1498282048.0,11727048000.0,US$ current,United States
+2013,USA,2536570256.0,11287730000.0,US$ current,United States
+2014,USA,2142482464.0,11465460000.0,US$ current,United States
+2015,USA,1761292048.0,10868947000.0,US$ current,United States
+2016,USA,2972719872.0,9276425000.0,US$ current,United States
+2017,USA,2191163712.0,9300564000.0,US$ current,United States
+2018,USA,1518862528.0,10452906000.0,US$ current,United States
+2019,USA,2081827072.0,11573847000.0,US$ current,United States
+2020,USA,2329149312.0,10308295000.0,US$ current,United States
+2021,USA,2450758720.0,12206586000.0,US$ current,United States
+2022,USA,5870904064.0,14032668000.0,US$ current,United States
diff --git a/scripts/analysis.py b/scripts/analysis.py
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+"""Module to run the analysis and create the charts"""
+
+import pandas as pd
+
+from scripts.config import Paths
+from scripts.logger import logger
+from scripts.utils import convert_entities
+
+CF = pd.read_csv(
+    Paths.output / "climate_finance_commitments.csv"
+)  # climate finance data
+FFS = pd.read_csv(
+    Paths.output / "fossil_fuel_subsidies.csv"
+)  # fossil fuel subsidies data
+
+small_multiple_countries = ["USA", "FRA", "DEU", "GBR", "ITA", "CAN", "BEL"]
+
+
+def cf_high_income_agg() -> pd.DataFrame:
+    """Aggregate the climate finance commitments for high-income countries"""
+
+    return (
+        CF.loc[lambda d: d.income_level == "High income"]
+        .groupby(["year"])
+        .agg({"value": "sum"})
+        .reset_index()
+    )
+
+
+def ffs_high_income_agg() -> pd.DataFrame:
+    """Aggregate the fossil fuel subsidies for high-income countries"""
+
+    return (
+        FFS.loc[lambda d: d.income_level == "High income"]
+        .groupby(["year"])
+        .agg({"value": "sum"})
+        .reset_index()
+    )
+
+
+def chart_1_data() -> pd.DataFrame:
+    """create data for chart 1: Aggregate Climate finance commitments vs fossil fuel subsidies for high income countries"""
+
+    cf = cf_high_income_agg().rename(columns={"value": "climate_finance_commitments"})
+    ffs = ffs_high_income_agg().rename(columns={"value": "fossil_fuel_subsidies"})
+
+    return cf.merge(ffs, on="year", how="outer").assign(currency="US$ current")
+
+
+def chart_2_data():
+    """create data for chart 2: small multiple of Climate finance commitments vs fossil fuel subsidies for specified countries"""
+
+    cf = (
+        CF.loc[
+            lambda d: d.iso3_code.isin(small_multiple_countries),
+            ["year", "iso3_code", "value"],
+        ]
+        .rename(columns={"value": "climate_finance_commitments"})
+        .reset_index(drop=True)
+    )
+
+    ffs = (
+        FFS.loc[
+            lambda d: d.iso3_code.isin(small_multiple_countries),
+            ["year", "iso3_code", "value"],
+        ]
+        .rename(columns={"value": "fossil_fuel_subsidies"})
+        .reset_index(drop=True)
+    )
+
+    return (
+        pd.merge(cf, ffs, how="outer")
+        .sort_values(["iso3_code", "year"])
+        .assign(
+            currency="US$ current",
+            country_name=lambda d: convert_entities(
+                d.iso3_code, from_type="ISO3", to_type="name_short"
+            ),
+        )
+        .reset_index(drop=True)
+    )
+
+
+if __name__ == "__main__":
+    chart_1_data().to_csv(Paths.output / "chart_1_data.csv", index=False)
+    logger.info("Chart 1 data successfully created and saved.")
+
+    chart_2_data().to_csv(Paths.output / "chart_2_data.csv", index=False)
+    logger.info("Chart 2 data successfully created and saved.")
+
+    logger.info("Analysis completed.")
diff --git a/scripts/charts.py b/scripts/charts.py
@@ -0,0 +1,94 @@
+"""Module to format the data for flourish charts"""
+
+import pandas as pd
+
+from scripts.config import Paths
+
+chart_1_data = pd.read_csv(Paths.output / "chart_1_data.csv")
+chart_2_data = pd.read_csv(Paths.output / "chart_2_data.csv")
+
+
+def _add_max(df, column):
+    """Add the value for latest year for each country to the data"""
+
+    max_dict = df.loc[df.groupby(["country_name"])["year"].idxmax()].set_index(
+        "country_name"
+    )[column]
+
+    return df.assign(max_value=df.country_name.map(max_dict))
+
+
+def format_to_billion(value, decimals=1):
+    """format value to billion"""
+
+    value_in_billion = value / 1e9
+
+    # if decimals is more than 0
+    # if the remainer is 0, then format to integer
+    if decimals > 0 and value_in_billion % 1 == 0:
+        return f"${value_in_billion:.0f} billion"
+
+    return f"${value_in_billion:.{decimals}f} billion"
+
+
+def create_chart_2() -> pd.DataFrame:
+    """Create data for chart 2"""
+
+    _ffs = (
+        chart_2_data.loc[:, ["year", "country_name", "fossil_fuel_subsidies"]]
+        .pipe(_add_max, "fossil_fuel_subsidies")
+        .assign(fossil_fuel_subsidies=lambda d: d.fossil_fuel_subsidies * -1)
+        .assign(max_value=lambda d: d.max_value.apply(format_to_billion))
+        .pivot(
+            index=["country_name", "year"],
+            columns="max_value",
+            values="fossil_fuel_subsidies",
+        )
+        .reset_index()
+        .assign(indicator="fossil_fuel_subsidies")
+    )
+
+    _cf = (
+        chart_2_data.loc[:, ["year", "country_name", "climate_finance_commitments"]]
+        .pipe(_add_max, "climate_finance_commitments")
+        .assign(max_value=lambda d: d.max_value.apply(format_to_billion))
+        .pivot(
+            index=["country_name", "year"],
+            columns="max_value",
+            values="climate_finance_commitments",
+        )
+        .reset_index()
+        .assign(indicator="climate_finance_commitments")
+    )
+
+    return pd.concat([_ffs, _cf], ignore_index=True)
+
+
+def create_chart_1() -> pd.DataFrame:
+    """Create data for chart 1"""
+
+    return (
+        chart_1_data.drop(columns="currency")
+        .rename(
+            columns={
+                "climate_finance_commitments": "climate finance commitments",
+                "fossil_fuel_subsidies": "fossil fuel subsidies",
+            }
+        )
+        .melt(id_vars="year")
+        .assign(
+            max_value=lambda d: d.variable.map(
+                d.loc[d.groupby(["variable"])["year"].idxmax()].set_index("variable")[
+                    "value"
+                ]
+            )
+        )
+        .assign(max_value=lambda d: d.max_value.apply(format_to_billion, decimals=0))
+        .assign(
+            value=lambda d: d.value.where(
+                d.variable != "fossil fuel subsidies", -d.value
+            )
+        )
+        .pivot(index=["year", "variable"], columns="max_value", values="value")
+        .reset_index()
+    )