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fix: pyarrow backend trimming timestamp to date #2875

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Merged
merged 1 commit into from
Jun 26, 2024
Merged

fix: pyarrow backend trimming timestamp to date #2875

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2 changes: 1 addition & 1 deletion awswrangler/_data_types.py
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Original file line number Diff line number Diff line change
Expand Up @@ -369,7 +369,7 @@ def athena2pandas(dtype: str, dtype_backend: str | None = None) -> str: # noqa:
if (dtype == "string") or dtype.startswith("char") or dtype.startswith("varchar"):
return "string" if dtype_backend != "pyarrow" else "string[pyarrow]"
if dtype in ("timestamp", "timestamp with time zone"):
return "datetime64" if dtype_backend != "pyarrow" else "date64[pyarrow]"
return "datetime64" if dtype_backend != "pyarrow" else "timestamp[ns][pyarrow]"
if dtype == "date":
return "date" if dtype_backend != "pyarrow" else "date32[pyarrow]"
if dtype.startswith("decimal"):
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29 changes: 15 additions & 14 deletions tests/_utils.py
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Expand Up @@ -334,31 +334,32 @@ def get_df_dtype_backend(dtype_backend: Literal["numpy_nullable", "pyarrow"] = "
"int32_nullable": [1, None, 3],
"int64_nullable": [1, None, 3],
"float_nullable": [0.0, None, 2.2],
# "bool_nullable": [True, None, False],
"bool_nullable": [True, None, False],
"string_nullable": ["Washington", None, "Seattle"],
# "date_nullable": [dt("2020-01-01"), None, dt("2020-01-02")],
# "timestamp_nullable": [ts("2020-01-01 00:00:00.0"), None, ts("2020-01-02 00:00:01.0")],
"date_nullable": [dt("2020-01-01"), None, dt("2020-01-02")],
"timestamp_nullable": [ts("2020-01-01 00:00:00.0"), None, ts("2020-01-02 00:00:01.0")],
}
)
if dtype_backend == "numpy_nullable":
df["int8_nullable"] = df["int8_nullable"].astype("Int8")
df["int16_nullable"] = df["int16_nullable"].astype("Int16")
df["int32_nullable"] = df["int32_nullable"].astype("Int32")
df["int64_nullable"] = df["int64_nullable"].astype("Int64")
df["float_nullable"] = df["float_nullable"].astype("Float64")
# df["bool_nullable"] = df["bool_nullable"].astype("boolean")
# df["date_nullable"] = df["date_nullable"].astype("string[python]")
df["string_nullable"] = df["string_nullable"].astype("string[python]")
df["int8_nullable"] = df["int8_nullable"].astype(pd.Int8Dtype())
df["int16_nullable"] = df["int16_nullable"].astype(pd.Int16Dtype())
df["int32_nullable"] = df["int32_nullable"].astype(pd.Int32Dtype())
df["int64_nullable"] = df["int64_nullable"].astype(pd.Int64Dtype())
df["float_nullable"] = df["float_nullable"].astype(pd.Float64Dtype())
df["bool_nullable"] = df["bool_nullable"].astype(pd.BooleanDtype())
df["string_nullable"] = df["string_nullable"].astype(pd.StringDtype())
df["timestamp_nullable"] = df["timestamp_nullable"].astype(pd.DatetimeTZDtype())
df["date_nullable"] = df["date_nullable"].astype(pd.StringDtype())
elif dtype_backend == "pyarrow":
df["int8_nullable"] = df["int8_nullable"].astype(pd.ArrowDtype(pa.int8()))
df["int16_nullable"] = df["int16_nullable"].astype(pd.ArrowDtype(pa.int16()))
df["int32_nullable"] = df["int32_nullable"].astype(pd.ArrowDtype(pa.int32()))
df["int64_nullable"] = df["int64_nullable"].astype(pd.ArrowDtype(pa.int64()))
df["float_nullable"] = df["float_nullable"].astype(pd.ArrowDtype(pa.float64()))
# df["bool_nullable"] = df["bool_nullable"].astype(pd.ArrowDtype(pa.bool_()))
# df["date_nullable"] = df["date_nullable"].astype(pd.ArrowDtype(pa.string()))
df["bool_nullable"] = df["bool_nullable"].astype(pd.ArrowDtype(pa.bool_()))
df["string_nullable"] = df["string_nullable"].astype(pd.ArrowDtype(pa.string()))
# df["timestamp_nullable"] = df["timestamp_nullable"].astype("date64[ms][pyarrow]")
df["date_nullable"] = df["date_nullable"].astype(pd.ArrowDtype(pa.date32()))
df["timestamp_nullable"] = df["timestamp_nullable"].astype(pd.ArrowDtype(pa.timestamp("ns")))
else:
raise ValueError(f"Unknown dtype_backend: {dtype_backend}")
return df
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2 changes: 1 addition & 1 deletion tests/unit/test_pandas_pyarrow_dtype_backend.py
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Expand Up @@ -105,7 +105,7 @@ def test_athena_csv_dtype_backend(
df["string_nullable"] = df["string_nullable"].astype("string[pyarrow]")

if ctas_approach or unload_approach:
df2["string_nullable"].replace("", pa.NA, inplace=True)
df2["string_nullable"] = df2["string_nullable"].replace("", pa.NA)

assert_pandas_equals(df, df2)

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