code: def test_from_dict_with_scalars_mixed() -> None:
      # a bit of everything
      mixed_dtype_data: dict[str, Any] = {
          "a": 0,
          "b": 8,
          "c": 9.5,
          "d": None,
          "e": True,
          "f": False,
          "g": time(0, 1, 2),
          "h": date(2023, 3, 14),
          "i": timedelta(seconds=3601),
          "j": datetime(2111, 11, 11, 11, 11, 11, 11),
          "k": "「趣味でヒーローをやっている者だ」",
      }
      # note: deliberately set this value large; if all dtypes are
      # on the fast-path it'll only take ~0.03secs. if it becomes
      # even remotely noticeable that will indicate a regression.
      # TODO: This is now slow (~0.15 seconds). Needs to be looked into.
      n_range = 1_000_000
      index_and_data: dict[str, Any] = {"idx": range(n_range)}
      index_and_data.update(mixed_dtype_data.items())
      df8 = pl.DataFrame(
          data=index_and_data,
          schema={
              "idx": pl.Int32,
              "a": pl.UInt16,
              "b": pl.UInt32,
              "c": pl.Float64,
              "d": pl.Float32,
              "e": pl.Boolean,
              "f": pl.Boolean,
              "g": pl.Time,
              "h": pl.Date,
              "i": pl.Duration,
              "j": pl.Datetime,
              "k": pl.String,
          },
      )
      dfx = df8.select(pl.exclude("idx"))
  
      assert len(df8) == n_range
      assert dfx[:5].rows() == dfx[5:10].rows()
      assert dfx[-10:-5].rows() == dfx[-5:].rows()
      assert dfx.row(n_range // 2, named=True) == mixed_dtype_data
  
file_path: polars/py-polars/tests/unit/dataframe/test_from_dict.py
imports: from __future__ import annotations
  from datetime import date, datetime, time, timedelta
  from typing import Any
  import numpy
  import pytest
  import polars
  from polars.testing import assert_frame_equal
label_desc: Use image node_id 3 for calling a global function with example usage: test_from_dict_with_scalars_mixed() without return types