diff --git a/demo.py b/demo.py
deleted file mode 100644
index 24f009e8b..000000000
--- a/demo.py
+++ /dev/null
@@ -1,33 +0,0 @@
-# ruff: noqa
-# type: ignore
-from typing import Any
-import polars as pl
-# import modin.pandas as mpd
-
-import narwhals as nw
-
-
-def func(df_raw):
-    df = nw.DataFrame(df_raw)
-    res = df.with_columns(
-        d=nw.col("a") + 1,
-        e=nw.col("a") + nw.col("b"),
-    )
-    res = res.group_by(["a"]).agg(
-        nw.col("b").sum(),
-        d=nw.col("c").sum(),
-        # e=nw.len(),
-    )
-    return nw.to_native(res)
-
-
-import pandas as pd
-
-df = pd.DataFrame({"a": [1, 1, 3], "b": [4, 5, 6], "c": [7, 8, 9]})
-print(func(df))
-# df = mpd.DataFrame({"a": [1, 1, 3], "b": [4, 5, 6], "c": [7, 8, 9]})
-# print(func(df))
-df = pl.DataFrame({"a": [1, 1, 3], "b": [4, 5, 6], "c": [7, 8, 9]})
-print(func(df))
-df = pl.LazyFrame({"a": [1, 1, 3], "b": [4, 5, 6], "c": [7, 8, 9]})
-print(func(df).collect())
diff --git a/f.py b/f.py
deleted file mode 100644
index a2f4835b4..000000000
--- a/f.py
+++ /dev/null
@@ -1,55 +0,0 @@
-# ruff: noqa
-# type: ignore
-import pandas as pd
-import polars as pl
-
-import narwhals as nw
-
-
-def my_agnostic_function(
-    suppliers_native,
-    parts_native,
-):
-    suppliers = nw.LazyFrame(suppliers_native)
-    parts = nw.LazyFrame(parts_native)
-
-    result = (
-        suppliers.join(parts, left_on="city", right_on="city")
-        .filter(nw.col("weight") > 10)
-        .group_by("s")
-        .agg(
-            weight_mean=nw.col("weight").mean(),
-            weight_max=nw.col("weight").max(),
-        )
-    )
-    return nw.to_native(result)
-
-
-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),
-    ).collect()
-)
diff --git a/t.py b/t.py
deleted file mode 100644
index 7ab0e9efc..000000000
--- a/t.py
+++ /dev/null
@@ -1,131 +0,0 @@
-# ruff: noqa
-# type: ignore
-import polars
-import pandas as pd
-import polars as pl
-
-import narwhals as nw
-
-df_raw = pd.DataFrame({"a": [1, 3, 2], "b": [4, 4, 6], "z": [7.0, 8, 9]})
-df = nw.LazyFrame(df_raw)
-df_raw_2 = pd.DataFrame({"a": [1, 3], "c": [7, 9]})
-df2 = nw.LazyFrame(df_raw_2)
-
-result = df.sort("a", "b")
-print(nw.to_native(result))
-
-result = df.filter(nw.col("a") > 1)
-print(nw.to_native(result))
-
-result = df.with_columns(
-    c=nw.col("a") + nw.col("b"),
-    d=nw.col("a") - nw.col("a").mean(),
-)
-print(nw.to_native(result))
-result = df.with_columns(nw.all() * 2)
-print(nw.to_native(result))
-
-result = df.with_columns(horizonal_sum=nw.sum_horizontal(nw.col("a"), nw.col("b")))
-print(nw.to_native(result))
-result = df.with_columns(horizonal_sum=nw.sum_horizontal("a", nw.col("b")))
-print(nw.to_native(result))
-
-
-result = df.select(nw.all().sum())
-print(nw.to_native(result))
-result = df.select(nw.col("a", "b") * 2)
-print(nw.to_native(result))
-
-# # TODO!
-# # result = (
-# #     df.collect()
-# #     .group_by("b")
-# #     .agg(
-# #         nw.all().sum(),
-# #     )
-# # )
-# # print(nw.to_native(result))
-
-result = (
-    df.collect()
-    .group_by("b")
-    .agg(
-        nw.col("a").sum(),
-        simple=nw.col("a").sum(),
-        complex=(nw.col("a") + 1).sum(),
-        other=nw.sum("a"),
-    )
-)
-print(nw.to_native(result))
-print("multiple simple")
-result = (
-    df.collect()
-    .group_by("b")
-    .agg(
-        nw.col("a", "z").sum(),
-    )
-)
-print(nw.to_native(result))
-
-result = df.join(df2, left_on="a", right_on="a")
-print(nw.to_native(result))
-
-
-result = df.rename({"a": "a_new", "b": "b_new"})
-print(nw.to_native(result))
-
-result = df.collect().to_dict()
-print(result)
-print(polars.from_pandas(nw.to_native(df)).to_dict())
-
-result = df.collect().to_dict(as_series=False)
-print("this")
-print(result)
-print("that")
-print(polars.from_pandas(nw.to_native(df)).to_dict(as_series=False))
-
-agg = (nw.col("b") - nw.col("z").mean()).mean()
-print(nw.to_native(df.with_columns(d=agg)))
-result = df.group_by("a").agg(agg)
-print(nw.to_native(result))
-
-print(nw.col("a") + nw.col("b"))
-print(nw.col("a", "b").sum())
-
-result = df.select(nw.col("a", "b").sum())
-print(nw.to_native(result))
-
-print(df.schema)
-print(df.schema["a"].is_numeric())
-
-df_raw = pd.DataFrame(
-    {
-        "a": [1, 3, 2],
-        "b": [4.0, 4, 6],
-        "c": ["a", "b", "c"],
-        "d": [True, False, True],
-    }
-)
-df = nw.DataFrame(df_raw)
-print(df.schema)
-print(df.schema["a"].is_numeric())
-print(df.schema["b"].is_numeric())
-print(df.schema["c"].is_numeric())
-print(df.schema["d"].is_numeric())
-
-result = df.with_columns(nw.col("a").cast(nw.Float32))
-print(nw.to_native(result))
-print(result._dataframe._dataframe.dtypes)
-
-print(df.schema)
-result = df.select([col for (col, dtype) in df.schema.items() if dtype == nw.Float64])
-print(nw.to_native(result))
-print(result._dataframe._dataframe.dtypes)
-
-result = df.select("a", "b").select(nw.all() + nw.col("a"))
-print(nw.to_native(result))
-
-df = nw.DataFrame(df_raw, features=["eager"])
-print(df["a"].mean())
-df = nw.DataFrame(pl.from_pandas(df_raw), features=["eager"])
-print(df["a"].mean())