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