net negative flows

output/README.md

@@ -33,6 +33,14 @@ It is created by the script [net_flows.py](../scripts/analysis/net_flows.py).
 All of the files above have an additional version ending in `_china_as_counterpart_type`, which
 separates inflows and outflows from China as a counterpart type, from the rest of the data.
 
+### Net negative flows
+- [`net_negative_flows_country.parquet`](net_negative_flows_country.parquet): Data on net
+negative flows (inflows - outflows < 0) by country (for all counterparts total). Presented yearly, including continent and income level.
+It is created by the script [negative_net_flows.py](../scripts/analysis/negative_net_flows.py).
+- [`net_negative_flows_group.parquet`](net_negative_flows_country.parquet): Data on net
+  negative flows (inflows - outflows < 0) by country groupings (for all counterparts total). Presented yearly. 
+  It is created by the script [negative_net_flows.py](../scripts/analysis/negative_net_flows.py).
+
 ### Projections
 (forthcoming)
 

scripts/analysis/common.py

@@ -143,3 +143,33 @@ def create_groupings(data: pd.DataFrame) -> pd.DataFrame:
     data_grouped = data_grouped.loc[lambda d: d.country.isin(GROUPS)]
 
     return data_grouped
+
+
+def reorder_countries(df: pd.DataFrame, counterpart_type: bool = False) -> pd.DataFrame:
+    """Reorder countries by continent and income level"""
+
+    df["order"] = df["country"].map(GROUPS).fillna(99)
+
+    counterpart_order = {
+        "Bilateral": 1,
+        "Multilateral": 2,
+        "Private": 3,
+        "China": 4,
+    }
+
+    if counterpart_type:
+        df["order_counterpart"] = (
+            df["counterpart_type"].map(counterpart_order).fillna(99)
+        )
+
+    df = (
+        df.sort_values(
+            ["order", "country", "year", "order_counterpart"]
+            if counterpart_type
+            else ["order", "country", "year"]
+        )
+        .drop(columns=["order", "order_counterpart"] if counterpart_type else ["order"])
+        .reset_index(drop=True)
+    )
+
+    return df

scripts/analysis/debt_service.py

@@ -6,8 +6,8 @@
     exclude_outlier_countries,
     create_grouping_totals,
     create_world_total,
-    GROUPS,
     add_china_as_counterpart_type,
+    reorder_countries,
 )
 from scripts.config import Paths
 from scripts.data.outflows import get_debt_service_data
@@ -47,36 +47,6 @@ def add_africa_total(df: pd.DataFrame) -> pd.DataFrame:
     return pd.concat([df, africa], ignore_index=True)
 
 
-def reorder_countries(df: pd.DataFrame, counterpart_type: bool = False) -> pd.DataFrame:
-    """Reorder countries by continent and income level"""
-
-    df["order"] = df["country"].map(GROUPS).fillna(99)
-
-    counterpart_order = {
-        "Bilateral": 1,
-        "Multilateral": 2,
-        "Private": 3,
-        "China": 4,
-    }
-
-    if counterpart_type:
-        df["order_counterpart"] = (
-            df["counterpart_type"].map(counterpart_order).fillna(99)
-        )
-
-    df = (
-        df.sort_values(
-            ["order", "country", "year", "order_counterpart"]
-            if counterpart_type
-            else ["order", "country", "year"]
-        )
-        .drop(columns=["order", "order_counterpart"] if counterpart_type else ["order"])
-        .reset_index(drop=True)
-    )
-
-    return df
-
-
 def pivot_flourish_columns(df: pd.DataFrame) -> pd.DataFrame:
     """Pivot the DataFrame to have the counterpart_type as columns"""
     return df.pivot(

scripts/analysis/negative_net_flows.py

@@ -4,6 +4,7 @@
     convert_to_net_flows,
     summarise_by_country,
     create_groupings,
+    reorder_countries,
 )
 from scripts.analysis.net_flows import get_all_flows, exclude_outlier_countries
 from scripts.analysis.population_tools import add_population_under18
@@ -46,20 +47,10 @@ def count_negative_flows_by_year(data: pd.DataFrame) -> pd.DataFrame:
     return data
 
 
-def negative_flows_list(data: pd.DataFrame, latest_only: bool = True) -> pd.DataFrame:
+def negative_flows_only(data: pd.DataFrame) -> pd.DataFrame:
     """produce a list of countries with negative flows"""
 
-    data = data.query("value < 0")
-
-    if latest_only:
-        data = data.query("year == 2022")
-
-    data = data.drop_duplicates(subset=["year", "country"]).reset_index(drop=True)
-
-    return data.sort_values(
-        ["year", "income_level", "continent", "value"],
-        ascending=[False, True, True, True],
-    )
+    return data.query("value < 0")
 
 
 def output_pipeline(constant: bool = False, limit_to_2022: bool = True) -> None:
@@ -68,12 +59,20 @@ def output_pipeline(constant: bool = False, limit_to_2022: bool = True) -> None:
         .pipe(exclude_outlier_countries)
         .pipe(convert_to_net_flows)
         .pipe(summarise_by_country)
+        .pipe(negative_flows_only)
     )
 
-    df_grouped = create_groupings(df)
+    df_grouped = create_groupings(df).pipe(reorder_countries)
 
-    return df_grouped
+    # Save data
+    df.reset_index(drop=True).to_parquet(
+        Paths.output / "net_negative_flows_country.parquet"
+    )
+
+    df_grouped.reset_index(drop=True).to_parquet(
+        Paths.output / "net_negative_flows_group.parquet"
+    )
 
 
 if __name__ == "__main__":
-    df = output_pipeline()
+    output_pipeline()

scripts/analysis/net_flow_projections.py

@@ -76,6 +76,9 @@ def projected_negative_list(net_negative_df: pd.DataFrame) -> pd.DataFrame:
     return data.filter(["year", "country", "continent", "income_level", "net_flows"])
 
 
+def projections_pipline() -> pd.DataFrame: ...
+
+
 if __name__ == "__main__":
     df = get_all_flows().pipe(exclude_outlier_countries)
     inflows_df = calculate_average_inflows(df).query("year == 2022")