diff --git a/demo.py b/demo.py
deleted file mode 100644
index 62ac4f46c..000000000
--- a/demo.py
+++ /dev/null
@@ -1,31 +0,0 @@
-# ruff: noqa
-import polars as pl
-from great_tables.data import sp500
-
-from puffin import to_original_object
-from puffin import to_polars_api
-
-# Define the start and end dates for the data range
-start_date = "2010-06-07"
-end_date = "2010-06-14"
-
-# Filter sp500 using Pandas to dates between `start_date` and `end_date`
-# sp500_mini = sp500[(sp500["date"] >= start_date) & (sp500["date"] <= end_date)]
-
-
-def dataframe_agnostic_filter(df_raw, start_date, end_date):
-    # opt-in to Polars API
-    df, pl = to_polars_api(df_raw, version="0.20")
-
-    # Use (supported subset of) Polars API
-    df = df.filter(
-        pl.col("date") >= start_date,
-        pl.col("date") <= end_date,
-    )
-
-    # Return underlying dataframe (same class passed by user)
-    return to_original_object(df)
-
-
-sp500_mini = dataframe_agnostic_filter(sp500, start_date, end_date)
-print(pl.from_pandas(sp500_mini))
diff --git a/f.py b/f.py
deleted file mode 100644
index 75cec5bbd..000000000
--- a/f.py
+++ /dev/null
@@ -1,59 +0,0 @@
-# ruff: noqa
-from typing import TypeVar
-import pandas as pd
-import polars as pl
-
-from puffin import to_polars_api, to_original_object
-
-AnyDataFrame = TypeVar("AnyDataFrame")
-
-
-def my_agnostic_function(
-    suppliers_native: AnyDataFrame,
-    parts_native: AnyDataFrame,
-) -> AnyDataFrame:
-    suppliers, pl = to_polars_api(suppliers_native, version="0.20")
-    parts, _ = to_polars_api(parts_native, version="0.20")
-    result = (
-        suppliers.join(parts, left_on="city", right_on="city")
-        .filter(
-            pl.col("color").is_in(["Red", "Green"]),
-            pl.col("weight") > 14,
-        )
-        .group_by("s", "p")
-        .agg(
-            weight_mean=pl.col("weight").mean(),
-            weight_max=pl.col("weight").max(),
-        )
-    )
-    return to_original_object(result.collect())
-
-
-suppliers = {
-    "s": ["S1", "S2", "S3", "S4", "S5"],
-    "sname": ["Smith", "Jones", "Blake", "Clark", "Adams"],
-    "status": [20, 10, 30, 20, 30],
-    "city": ["London", "Paris", "Paris", "London", "Athens"],
-}
-parts = {
-    "p": ["P1", "P2", "P3", "P4", "P5", "P6"],
-    "pname": ["Nut", "Bolt", "Screw", "Screw", "Cam", "Cog"],
-    "color": ["Red", "Green", "Blue", "Red", "Blue", "Red"],
-    "weight": [12.0, 17.0, 17.0, 14.0, 12.0, 19.0],
-    "city": ["London", "Paris", "Oslo", "London", "Paris", "London"],
-}
-
-print("pandas output:")
-print(
-    my_agnostic_function(
-        pd.DataFrame(suppliers),
-        pd.DataFrame(parts),
-    )
-)
-print("\nPolars output:")
-print(
-    my_agnostic_function(
-        pl.LazyFrame(suppliers),
-        pl.LazyFrame(parts),
-    )
-)
diff --git a/t.py b/t.py
deleted file mode 100644
index 9b2445797..000000000
--- a/t.py
+++ /dev/null
@@ -1,70 +0,0 @@
-# ruff: noqa
-import polars
-import pandas as pd
-
-import puffin
-
-df_raw = pd.DataFrame({"a": [1, 3, 2], "b": [4, 4, 6]})
-df, pl = puffin.to_polars_api(df_raw, version="0.20")
-df_raw_2 = pd.DataFrame({"a": [1, 3], "c": [7, 9]})
-df2, pl = puffin.to_polars_api(df_raw_2, version="0.20")
-
-result = df.sort("a", "b")
-print(result.dataframe)
-
-result = df.filter(pl.col("a") > 1)
-print(result.dataframe)
-
-result = df.with_columns(
-    c=pl.col("a") + pl.col("b"),
-    d=pl.col("a") - pl.col("a").mean(),
-)
-print(result.dataframe)
-result = df.with_columns(pl.all() * 2)
-print(result.dataframe)
-
-result = df.with_columns(horizonal_sum=pl.sum_horizontal(pl.col("a"), pl.col("b")))
-print(result.dataframe)
-result = df.with_columns(horizonal_sum=pl.sum_horizontal("a", pl.col("b")))
-print(result.dataframe)
-
-
-result = df.select(pl.all().sum())
-print(result.dataframe)
-result = df.select(pl.col("a", "b") * 2)
-print(result.dataframe)
-
-# TODO!
-# result = (
-#     df.collect()
-#     .group_by("b")
-#     .agg(
-#         pl.all().sum(),
-#     )
-# )
-# print(result.dataframe)
-
-result = (
-    df.collect()
-    .group_by("b")
-    .agg(
-        pl.col("a").sum(),
-        simple=pl.col("a").sum(),
-        complex=(pl.col("a") + 1).sum(),
-        other=pl.sum("a"),
-    )
-)
-print(result.dataframe)
-
-result = df.join(df2, left_on="a", right_on="a")
-print(result.dataframe)
-
-
-print(puffin.containers.is_pandas(df_raw))
-# print(puffin.containers.is_pandas(polars.from_pandas(df_raw)))
-print(puffin.containers.is_polars(df_raw))
-# print(puffin.containers.is_polars(polars.from_pandas(df_raw)))
-
-
-result = df.rename({"a": "a_new", "b": "b_new"})
-print(result.dataframe)