refactor: 🔄 remove outdated assets and simplify data processing

- Deleted outdated assets for OWID energy and CO2 data retrieval.
- Simplified World Bank WDI data processing by removing unnecessary pivot & renaming steps.
- Removed `slugify` dependency for column name cleaning.

datadex/indicators/assets.py

@@ -4,7 +4,6 @@
 import dagster as dg
 import httpx
 import polars as pl
-from slugify import slugify
 
 
 @dg.asset()
@@ -49,32 +48,12 @@ def world_bank_wdi() -> pl.DataFrame:
     df = pl.read_csv("/tmp/WDICSV.csv")
 
     # Reshape the dataframe
-    df = df.melt(
-        id_vars=["Country Name", "Country Code", "Indicator Name", "Indicator Code"],
+    df = df.unpivot(
+        index=["Country Name", "Country Code", "Indicator Name", "Indicator Code"],
         value_name="Indicator Value",
         variable_name="Year",
     )
 
-    # Make one column per Indicator Name
-    df = df.pivot(
-        index=["Country Name", "Country Code", "Year"],
-        values="Indicator Value",
-        on="Indicator Value",
-    )
-
-    # Cast to floats
-    df = df.select(
-        [
-            pl.col("Country Name"),
-            pl.col("Country Code"),
-            pl.col("Year").cast(pl.Int32),
-            *[pl.col(col).cast(pl.Float32) for col in df.columns[3:]],
-        ]
-    )
-
-    # Clean column names
-    df = df.rename(
-        lambda column_name: slugify(column_name.replace("%", "percent"), separator="_")
-    )
+    df = df.with_columns(pl.col("Year").cast(pl.Int32))
 
     return df