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10 | 10 |
|
11 | 11 | from pandas._config import using_string_dtype
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12 | 12 |
|
| 13 | +from pandas.compat import HAS_PYARROW |
13 | 14 | from pandas.compat.pyarrow import (
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14 | 15 | pa_version_under12p0,
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15 | 16 | pa_version_under19p0,
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@@ -45,15 +46,20 @@ def cls(dtype):
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45 | 46 | return dtype.construct_array_type()
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46 | 47 |
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47 | 48 |
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48 |
| -DTYPE_HIERARCHY = [ |
49 |
| - pd.StringDtype("python", na_value=np.nan), |
50 |
| - pd.StringDtype("pyarrow", na_value=np.nan), |
51 |
| - pd.StringDtype("python", na_value=pd.NA), |
52 |
| - pd.StringDtype("pyarrow", na_value=pd.NA), |
53 |
| -] |
54 |
| - |
55 |
| - |
56 | 49 | def string_dtype_highest_priority(dtype1, dtype2):
|
| 50 | + if HAS_PYARROW: |
| 51 | + DTYPE_HIERARCHY = [ |
| 52 | + pd.StringDtype("python", na_value=np.nan), |
| 53 | + pd.StringDtype("pyarrow", na_value=np.nan), |
| 54 | + pd.StringDtype("python", na_value=pd.NA), |
| 55 | + pd.StringDtype("pyarrow", na_value=pd.NA), |
| 56 | + ] |
| 57 | + else: |
| 58 | + DTYPE_HIERARCHY = [ |
| 59 | + pd.StringDtype("python", na_value=np.nan), |
| 60 | + pd.StringDtype("python", na_value=pd.NA), |
| 61 | + ] |
| 62 | + |
57 | 63 | h1 = DTYPE_HIERARCHY.index(dtype1)
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58 | 64 | h2 = DTYPE_HIERARCHY.index(dtype2)
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59 | 65 | return DTYPE_HIERARCHY[max(h1, h2)]
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|
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