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- from __future__ import annotations
- from pandas._typing import AxisInt
- from pandas import (
- DataFrame,
- concat,
- )
- def _check_mixed_float(df, dtype=None):
- # float16 are most likely to be upcasted to float32
- dtypes = {"A": "float32", "B": "float32", "C": "float16", "D": "float64"}
- if isinstance(dtype, str):
- dtypes = {k: dtype for k, v in dtypes.items()}
- elif isinstance(dtype, dict):
- dtypes.update(dtype)
- if dtypes.get("A"):
- assert df.dtypes["A"] == dtypes["A"]
- if dtypes.get("B"):
- assert df.dtypes["B"] == dtypes["B"]
- if dtypes.get("C"):
- assert df.dtypes["C"] == dtypes["C"]
- if dtypes.get("D"):
- assert df.dtypes["D"] == dtypes["D"]
- def _check_mixed_int(df, dtype=None):
- dtypes = {"A": "int32", "B": "uint64", "C": "uint8", "D": "int64"}
- if isinstance(dtype, str):
- dtypes = {k: dtype for k, v in dtypes.items()}
- elif isinstance(dtype, dict):
- dtypes.update(dtype)
- if dtypes.get("A"):
- assert df.dtypes["A"] == dtypes["A"]
- if dtypes.get("B"):
- assert df.dtypes["B"] == dtypes["B"]
- if dtypes.get("C"):
- assert df.dtypes["C"] == dtypes["C"]
- if dtypes.get("D"):
- assert df.dtypes["D"] == dtypes["D"]
- def zip_frames(frames: list[DataFrame], axis: AxisInt = 1) -> DataFrame:
- """
- take a list of frames, zip them together under the
- assumption that these all have the first frames' index/columns.
- Returns
- -------
- new_frame : DataFrame
- """
- if axis == 1:
- columns = frames[0].columns
- zipped = [f.loc[:, c] for c in columns for f in frames]
- return concat(zipped, axis=1)
- else:
- index = frames[0].index
- zipped = [f.loc[i, :] for i in index for f in frames]
- return DataFrame(zipped)
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