| 12345678910111213141516171819202122232425262728293031 |
- def _check_mixed_float(df, dtype=None):
- # float16 are most likely to be upcasted to float32
- dtypes = dict(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 = dict(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"]
|
