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from __future__ import annotations
from typing import TYPE_CHECKING
from pandas import (
DataFrame,
concat,
)
if TYPE_CHECKING:
from pandas._typing import AxisInt
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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