Initial Commit

Author: eyrei123

narwhals-python/README.md

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+This file contains the downloadable code from the RealPython tutorial: \[Writing DataFrame Agnostic Python Code With Narwhals](https://realpython.com/dataframe-agnostic-code-with-narwhals-python/)
+
+
+
+authors.parquet		- File of data for main sections of the tutorial.
+
+books.parquet           - File of data for the self study section of the tutorial.
+
+presidents.parquet      - File of data for the self study section of the tutorial.
+
+
+
+exercise\_solution.py    - File containing a possible solution to the self study exercise.
+
+universal\_processing.py - File containing functions used in the main sections of the tutorial.
+
+
+
+
+

narwhals-python/authors.parquet

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+import narwhals as nw
+from narwhals.typing import IntoFrameT
+
+
+def rowling_books(df: IntoFrameT, lf: IntoFrameT) -> IntoFrameT:
+    return (
+        nw.from_native(df)
+        .join(nw.from_native(lf).filter(nw.col("last_name").str.contains("Rowling")).collect(), on="author_id")
+        .select(["book_title", "year_published", "first_name", "last_name"])
+        .sort("year_published")
+        .to_pandas()
+    )

narwhals-python/books.parquet

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+import narwhals as nw
+from narwhals.typing import FrameT, IntoFrameT
+
+
+def universal_groupby_v1(df: IntoFrameT) -> IntoFrameT:
+    return (
+        nw.from_native(df)
+        .group_by("party_name")
+        .agg(nw.col("last_name").count())
+        .sort("party_name")
+        .to_native()
+    )
+
+@nw.narwhalify
+def universal_groupby_v2(df: FrameT) -> FrameT:
+    return (
+        df
+        .group_by("party_name")
+        .agg(nw.col("last_name").count())
+        .sort("party_name")
+    )
+
+
+def universal_groupby_v3(df: IntoFrameT) -> IntoFrameT:
+    return (
+        nw.from_native(df)
+        .group_by("party_name")
+        .agg(nw.col("last_name").count())
+        .sort("party_name")
+        .to_polars()
+    )
+
+
+def universal_pivot_v1(df: IntoFrameT) -> IntoFrameT:
+    return (
+         nw.from_native(df)
+         .pivot(
+             on="party_name",
+             index="century",
+             aggregate_function="count",
+             values="last_name"
+         ).to_native()
+    )
+
+
+def universal_pivot_v2(df: IntoFrameT) -> IntoFrameT:
+    if isinstance(nw.from_native(df), nw.LazyFrame):
+        df = nw.from_native(df).collect()
+    return (
+        nw.from_native(df)
+        .pivot(
+            on="party_name",
+            index="century",
+            aggregate_function="count",
+            values="last_name"
+        ).to_native()
+    )