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| # | ||
| # Copyright (c) 2012-2024 Snowflake Computing Inc. All rights reserved. | ||
| # | ||
|
|
||
| import modin.pandas as pd | ||
| import pandas as native_pd | ||
| import pytest | ||
|
|
||
| import snowflake.snowpark.modin.plugin # noqa: F401 | ||
| from tests.integ.modin.sql_counter import sql_count_checker | ||
| from tests.integ.modin.utils import ( | ||
| assert_snowpark_pandas_equal_to_pandas, | ||
| create_test_series, | ||
| eval_snowpark_pandas_result, | ||
| ) | ||
|
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| @pytest.mark.parametrize( | ||
| "data, table", | ||
| [ | ||
| ( | ||
| # Simple 1-element mapping | ||
| ["aaaaa", "bbbaaa", "cafdsaf;lh"], | ||
| str.maketrans("a", "b"), | ||
| ), | ||
| ( | ||
| # Mapping with mixed str, unicode code points, and Nones | ||
| ["aaaaa", "fjkdsajk", "cjghgjqk", "yubikey"], | ||
| str.maketrans( | ||
| {ord("a"): "A", ord("f"): None, "y": "z", "k": None, ord("j"): ""} | ||
| ), | ||
| ), | ||
| ( | ||
| # Mapping with special characters | ||
| [ | ||
| "Peña", | ||
| "Ordoñez", | ||
| "Raúl", | ||
| "Ibañez", | ||
| "François", | ||
| "øen", | ||
| "2πr = τ", | ||
| "München", | ||
| ], | ||
| str.maketrans( | ||
| { | ||
| "ñ": "n", | ||
| "ú": "u", | ||
| "ç": "c", | ||
| "ø": "o", | ||
| "τ": "t", | ||
| "π": "p", | ||
| "ü": "u", | ||
| } | ||
| ), | ||
| ), | ||
| ( | ||
| # Mapping with compound emojis. Each item in the series renders as a single emoji, | ||
| # but is actually 4 characters. Calling `len` on each element correctly returns 4. | ||
| # https://unicode.org/emoji/charts/emoji-zwj-sequences.html | ||
| # Inputs: | ||
| # - "head shaking horizontally" = 1F642 + 200D + 2194 + FE0F | ||
| # - "heart on fire" = 2764 + FE0F + 200D + 1F525 | ||
| # - "judge" = 1F9D1 + 200D + 2696 + FE0F | ||
| # Outputs: | ||
| # - "head shaking vertically" = 1F642 + 200D + 2195 + FE0F | ||
| # - "mending heart" = 2764 + FE0F + 200D + 1FA79 | ||
| # - "health worker" = 1F91D1 + 200D + 2695 + FE0F | ||
| ["🙂↔️", "❤️🔥", "🧑⚖️"], | ||
| { | ||
| 0x2194: 0x2195, | ||
| 0x1F525: 0x1FA79, | ||
| 0x2696: 0x2695, | ||
| }, | ||
| ), | ||
| ], | ||
| ) | ||
| @sql_count_checker(query_count=1) | ||
| def test_translate(data, table): | ||
| eval_snowpark_pandas_result( | ||
| *create_test_series(data), lambda ser: ser.str.translate(table) | ||
| ) | ||
|
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| @sql_count_checker(query_count=1) | ||
| def test_translate_without_maketrans(): | ||
| # pandas requires all table keys to be unicode ordinal values, and does not know how to handle | ||
| # string keys that were not converted to ordinals via `ord` or `str.maketrans`. Since Snowflake | ||
| # SQL uses strings in its mappings, we accept string keys as well as ordinals. | ||
| data = ["aaaaa", "fjkdsajk", "cjghgjqk", "yubikey"] | ||
| table = {ord("a"): "A", ord("f"): None, "y": "z", "k": None} | ||
| snow_ser = pd.Series(data) | ||
| assert_snowpark_pandas_equal_to_pandas( | ||
| snow_ser.str.translate(table), | ||
| native_pd.Series(data).str.translate(str.maketrans(table)), | ||
| ) | ||
| # Mappings for "y" and "k" are ignored if not passed through str.maketrans because they are | ||
| # not unicode ordinals | ||
| assert ( | ||
| not native_pd.Series(data) | ||
| .str.translate(table) | ||
| .equals(native_pd.Series(data).str.translate(str.maketrans(table))) | ||
| ) | ||
|
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| @pytest.mark.parametrize( | ||
| "table", | ||
| [ | ||
| {"😶🌫️": "a"}, # This emoji key is secretly 4 code points | ||
| {"aa": "a"}, # Key is 2 chars | ||
| # Mapping 1 char to multiple is valid in vanilla pandas, but we don't support this | ||
| {ord("a"): "😶🌫️"}, # This emoji value is secretly 4 code points | ||
| {ord("a"): "aa"}, # Value is 2 chars | ||
| ], | ||
| ) | ||
| @sql_count_checker(query_count=0) | ||
| def test_translate_invalid_mappings(table): | ||
| data = ["aaaaa", "fjkdsajk", "cjghgjqk", "yubikey"] | ||
| # native pandas silently treats all of these cases as no-ops. However, since Snowflake SQL uses | ||
| # strings as mappings instead of a dict construct, passing these arguments to the equivalent | ||
| # SQL argument would either cause an inscrutable error or unexpected changes to the output series. | ||
| snow_ser, native_ser = create_test_series(data) | ||
| native_ser.str.translate(table) | ||
| with pytest.raises(ValueError): | ||
| snow_ser.str.translate(table) |